EXPERIMENTAL, NUMERICAL, AND SOFT COMPUTING-BASED …
Transcript of EXPERIMENTAL, NUMERICAL, AND SOFT COMPUTING-BASED …
EXPERIMENTAL, NUMERICAL, AND SOFT
COMPUTING-BASED ANALYSIS OF THE VAPEX
PROCESS IN HEAVY OIL SYSTEMS
A Thesis
Submitted to the Faculty of Graduate Studies and Research
In Partial Fulfilment of the Requirements
For the Degree of
Doctor of Philosophy
in
Petroleum Systems Engineering
University of Regina
By
Mehdi Mohammadpoor
Regina, Saskatchewan
July, 2014
Copyright 2014: M. Mohammadpoor
UNIVERSITY OF REGINA
FACULTY OF GRADUATE STUDIES AND RESEARCH
SUPERVISORY AND EXAMINING COMMITTEE
Mehdi Mohammadpoor, candidate for the degree of Doctor of Philosophy in Petroleum Systems Engineering, has presented a thesis titled, Experimental, Numerical, and Soft Computing-Based Analysis of the Vapex Process in Heavy Oil Systems, in an oral examination held on July 11, 2014. The following committee members have found the thesis acceptable in form and content, and that the candidate demonstrated satisfactory knowledge of the subject material. External Examiner: *Dr. Japan Trivedi, University of Alberta
Supervisor: Dr. Farshid Torabi, Petroleum Systems Engineering
Committee Member: Dr. Fanhua Zeng, Petroleum Systems Engineering
Committee Member: Dr. Paitoon Tontiwachwuthikul, P Systems Engineering
Committee Member: **Dr. Ezeddin Shirif, Petroleum Systems Engineering
Committee Member: Dr. Nader Mobed, Department of Physics
Chair of Defense: Dr. Andrei Volodin, Department of Mathematics & Statistics *Via tele-conference **Not present at defense
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ABSTRACT
There are significant heavy oil and bitumen resources in Canada. Considering increasing
energy demands, these abundant resources are a potential energy source. Regardless,
looking for an economically viable and environmentally friendly heavy oil recovery
technique is essential for exploiting not just these resources, but all future heavy oil
resources.
The problems with highly viscous heavy oil reservoirs—excessive heat loss to the
surrounding formations, low permeability carbonate reservoirs, and the large amount of
CO2 emitted during these thermal processes—introduce economic and environmental
drawbacks for thermal methods. In fact, solvent-based heavy oil recovery methods have
recently gained attention due to the potential environmental and economic advantages
over the thermal processes.
In this research, an extensive experimental investigation was carried out to evaluate the
effect of solvent type and drainage height, as the key parameters of VAPEX in heavy oil
recovery. To accomplish this goal, two large, visual rectangular, sand-packed VAPEX
models with 24.5 cm and 47.5 cm heights were employed to run the experiments using
Plover Lake heavy oil (5650mPa.s) with a low permeability (6~9 D) sand pack. Propane,
methane, CO2, butane, propane/CO2 mixture, and propane/methane mixture were
considered as respective solvents for the experiments. Various parameters were
monitored and recorded during the course of experiments.
Moreover, separate experiments were carried out at the end of each VAPEX experiment
to measure the asphaltene precipitation at different locations of the VAPEX models. To
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observe the drainage height effect in more detail, a comprehensive image analysis was
completed during the solvent chamber evolution.
As a result, it was determined that drainage height has a significant impact on production
rate and heavy oil recovery. The results prove the complexity of the effect of drainage
height and the up-scaling issues with the VAPEX process. Furthermore, in terms of
solvents, propane showed the best recovery performance due to its favourable low vapour
pressure and high solubility. Ultimately, promising recovery performance after
introducing CO2 and methane as the carrier gases was observed.
Separate experiments were conducted to obtain adequate PVT data for the heavy oil and
solvents used in this study. A numerical simulation study was carried out to match
experimental results and investigate the effect of well spacing, permeability, and
diffusivity on the VAPEX process.
Finally, the data gathered from the experiments were combined with available data in the
literature and a soft computing approach was utilized to develop a model that predicts the
recovery performance of the VAPEX process. Several experimental studies together with
various analytical models have been proposed to simulate and describe the performance
of the VAPEX process. However, due to the complexity of the mechanisms associated
with the solvent injection process (i.e., diffusion and gravity drainage processes), such
models are incapable of accurately predicting the production rate during the VAPEX
process. In this research, artificial neural networks (ANN) technique was utilized to
tackle the limitations that analytical methods encounter where there is uncertainty, and
imprecision.
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ACKNOWLEDGMENTS
I would like to express my profound gratitude and appreciation to my supervisor, Dr.
Farshid Torabi, for his guidance, encouragement, suggestions, and support throughout the
course of this project. I learnt a lot from his great personality as well as his scientific
knowledge, creativity, and experience. I feel privileged to have had Dr. Torabi as my
supervisor during these years of study.
I am also grateful to Dr. Mobed, Dr. Tontiwachwuthikul, Dr. Zeng, and Dr. Shirif for
serving as members of my examination committee, and for their constructive suggestions.
In addition, I would like to acknowledge the financial support from the University of
Regina Faculty of Graduate Studies and Research (FGSR) and Natural Sciences and
Engineering Research Council (NSERC) Canada, and also thank Dr. Shirif for providing
lab space.
I am also very grateful to my friend, Mr. Abbasali Dehghan Tazerjani, for his help to
prepare and program the image analysis software. I would like to thank Mr. Ali Abedini
for his help and technical discussions.
Last, but not least, a heartfelt thank you to Asal, my wonderful wife, and to my family for
their patience and unrelenting support during this study.
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DEDICATION
This dissertation is dedicated to my beloved wife, Asal, my dearest parents, Elyas and
Kobra, and my dear brother and sisters.
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TABLE OF CONTENTS
ABSTRACT ......................................................................................................................... I
ACKNOWLEDGMENTS ................................................................................................ III
DEDICATION .................................................................................................................. IV
LIST OF TABLES ........................................................................................................... XII
LIST OF FIGURES ....................................................................................................... XVI
NOMENCLATURE ................................................................................................... XXVI
CHAPTER 1: INTRODUCTION ....................................................................................... 1
1.1 Background ......................................................................................................... 1
1.2 Vapour extraction (VAPEX)............................................................................... 5
1.3 Objectives ........................................................................................................... 8
1.4 Organization of the thesis ................................................................................... 9
CHAPTER 2: LITERATURE REVIEW .......................................................................... 11
2.1 Heavy oil recovery methods ............................................................................. 11
2.1.1 Waterflooding ............................................................................................... 11
2.1.2 Cold heavy oil production (CHOPS) ............................................................ 12
2.1.3 Gas EOR methods ......................................................................................... 14
2.1.4 Thermal EOR processes ................................................................................ 14
2.1.5 Chemical EOR processes .............................................................................. 16
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2.1.6 Emerging EOR technologies......................................................................... 17
2.2 Vapour extraction (VAPEX)............................................................................. 19
2.2.1 Solvent requirement ...................................................................................... 22
2.3 VAPEX mechanism .......................................................................................... 24
2.3.1 Molecular diffusion ....................................................................................... 27
2.3.2 Physical dispersion........................................................................................ 33
2.4 Asphaltene precipitation ................................................................................... 35
2.4.1 Asphaltene precipitation in VAPEX ............................................................. 39
2.5 Economic and environmental advantages ......................................................... 42
CHAPTER 3: EXPERIMENTAL SETUP, MATERIALS, AND PROCEDURE ........... 45
3.1 Experimental setup............................................................................................ 45
3.1.1 Solvent injection unit .................................................................................... 45
3.1.2 Physical models ............................................................................................ 49
3.1.3 Solvent and liquid production unit ................................................................ 57
3.1.4 Data acquisition unit ..................................................................................... 60
3.2 Materials ........................................................................................................... 65
3.2.1 Sand............................................................................................................... 65
3.2.2 Heavy oil ....................................................................................................... 65
3.2.3 Injection solvents and back pressure gas ...................................................... 67
3.3 Experimental procedure .................................................................................... 70
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3.3.1 Preparation .................................................................................................... 70
3.3.1.1 Sand packing........................................................................................ 70
3.3.1.2 Porosity measurement.......................................................................... 71
3.3.1.3 Oil saturation ....................................................................................... 73
3.3.1.4 Permeability measurement .................................................................. 76
3.3.2 VAPEX experiments ..................................................................................... 77
3.3.3 Residual oil saturation and asphaltene content measurement ....................... 79
3.3.3.1 Residual oil saturation measurement ................................................... 81
3.3.3.2 Asphaltene content measurement ........................................................ 81
3.3.4 Cleaning ........................................................................................................ 84
CHAPTER 4: EXPERIMENTAL RESULTS AND DISCUSSION ................................ 85
4.1 VAPEX performance ........................................................................................ 88
4.1.1 Effect of drainage height ............................................................................... 88
4.1.1.1 Recovery factor and produced oil rate ................................................. 88
4.1.1.1.1 Propane injection ............................................................................. 88
4.1.1.1.2 Methane injection ............................................................................ 91
4.1.1.1.3 CO2 injection ................................................................................... 94
4.1.1.1.4 Butane injection............................................................................... 94
4.1.1.1.5 Propane/CO2 injection ..................................................................... 99
4.1.1.1.6 Propane/methane injection ............................................................ 102
4.1.1.2 Solvent utilization factor (SUF) ........................................................ 105
4.1.1.2.1 Propane injection ........................................................................... 105
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4.1.1.2.2 Methane injection .......................................................................... 105
4.1.1.2.3 CO2 injection ................................................................................. 108
4.1.1.2.4 Butane injection............................................................................. 108
4.1.1.2.5 Propane/ CO2 injection .................................................................. 108
4.1.1.2.6 Propane/methane injection ............................................................ 112
4.1.1.3 Viscosity, density, molecular weight, and hydrocarbon components for
the produced oil................................................................................................... 112
4.1.1.3.1 Propane injection ........................................................................... 112
4.1.1.3.2 Methane injection .......................................................................... 117
4.1.1.3.3 CO2 injection ................................................................................. 121
4.1.1.3.4 Butaneinjection.............................................................................. 121
4.1.1.3.5 Propane/CO2 injection ................................................................... 128
4.1.1.3.6 Propane/methaneinjection ............................................................. 128
4.1.2 Effect of solvent type .................................................................................. 135
4.1.2.1 Small model ....................................................................................... 135
4.1.2.1.1 Recovery factor and produced oil rate .......................................... 135
4.1.2.1.2 Solvent utilization factor (SUF) .................................................... 138
4.1.2.1.3 Viscosity, density, molecular weight, and hydrocarbon components
for the produced oil ......................................................................................... 138
4.1.2.2 Large model ....................................................................................... 142
4.1.2.2.1 Recovery factor and produced oil rate .......................................... 142
4.1.2.2.2 Solvent utilization factor (SUF) .................................................... 145
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4.1.2.2.3 Viscosity, density, molecular weight, and hydrocarbon components
for the produced oil ......................................................................................... 145
4.2 Residual oil saturation..................................................................................... 149
4.3 Asphaltene precipitation ................................................................................. 152
4.3.1 Effect of drainage height ............................................................................. 155
4.3.1.1 Propane injection ............................................................................... 155
4.3.1.2 Methane injection .............................................................................. 157
4.3.1.3 CO2 injection ..................................................................................... 157
4.3.1.4 Butane injection ................................................................................. 157
4.3.1.5 Propane/CO2 injection ....................................................................... 161
4.3.1.6 Propane/methane injection ................................................................ 161
4.3.2 Effect of solvent type .................................................................................. 164
4.3.2.1 Small model ....................................................................................... 164
4.3.2.2 Large model ....................................................................................... 166
4.4 Image analysis (IA) ......................................................................................... 169
4.5 Effect of injection-production wells connection ............................................. 179
4.5.1 Small model ................................................................................................ 179
4.5.2 Large model ................................................................................................ 187
4.6 Scale-up: ......................................................................................................... 194
4.7 Dimensionless VAPEX number, Ns calculation: ............................................ 204
CHAPTER 5: PVT STUDIES AND NUMERICAL SIMULATION ........................... 206
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5.1 Viscosity and density measurement ................................................................ 206
5.2 Vapour pressure .............................................................................................. 206
5.3 Solubility measurement .................................................................................. 210
5.4 Solvent volume fraction in heavy oil for VAPEX tests .................................. 213
5.5 Numerical simulation ...................................................................................... 216
5.5.1 Model construction ..................................................................................... 216
5.5.2 Injection and production wells’ constraints ................................................ 221
5.5.3 History matching ......................................................................................... 221
5.5.4 Effect of well configurations ...................................................................... 230
5.5.5 Effect of permeability ................................................................................. 232
5.5.6 Effect of grid thickness ............................................................................... 232
5.5.7 Effect of time step ....................................................................................... 232
CHAPTER 6: SOFT COMPUTING APPROACH ........................................................ 237
6.1 Data handling procedures ............................................................................... 239
6.1.1 Data acquisition .......................................................................................... 239
6.1.2 Data normalization ...................................................................................... 240
6.2 Neural network development .......................................................................... 243
6.3 Sensitivity analysis.......................................................................................... 252
6.4 Comparison of results ..................................................................................... 256
CHAPTER 7: CONCLUSIONS AND RECOMMENDATIONS .................................. 264
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7.1 Conclusions ..................................................................................................... 264
7.2 Recommendations ........................................................................................... 268
REFERENCES ............................................................................................................... 269
Appendix A ..................................................................................................................... 291
Appendix B ..................................................................................................................... 301
Appendix C ..................................................................................................................... 310
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LIST OF TABLES
Table 2-1: Standard methods for asphaltene precipitation measurement (after Speight,
2004) ................................................................................................................................. 38
Table 3-1: DFM Specifications ......................................................................................... 48
Table 3-2: Physical models dimensions ............................................................................ 50
Table 3-3: List of experimental equipment ....................................................................... 64
Table 3-4: Compositional analysis result of the injection heavy oil with viscosity of 5650
mPa.s at 21 °C ................................................................................................................... 68
Table 4-1: Operating conditions of the VAPEX experiments .......................................... 87
Table 4-2: Compositional analysis result of the produced heavy oil after propane injection
in small model ................................................................................................................. 114
Table 4-3: Compositional analysis result of the produced heavy oil after propane injection
in large model ................................................................................................................. 115
Table 4-4: Compositional analysis result of the produced heavy oil after methane
injection in small model .................................................................................................. 118
Table 4-5: Compositional analysis result of the produced heavy oil after methane
injection in large model .................................................................................................. 119
Table 4-6: Compositional analysis result of the produced heavy oil after CO2 injection in
small model ..................................................................................................................... 122
Table 4-7: Compositional analysis result of the produced heavy oil after CO2 injection in
large model...................................................................................................................... 123
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Table 4-8: Compositional analysis result of the produced heavy oil after butane injection
in small model ................................................................................................................. 125
Table 4-9: Compositional analysis result of the produced heavy oil after butane injection
in large model ................................................................................................................. 126
Table 4-10: Compositional analysis result of the produced heavy oil after propane/ CO2
injection in small model .................................................................................................. 129
Table 4-11: Compositional analysis result of the produced heavy oil after propane/ CO2
injection in large model .................................................................................................. 130
Table 4-12: Compositional analysis result of the produced heavy oil after propane/
methane injection in small model ................................................................................... 132
Table 4-13: Compositional analysis result of the produced heavy oil after
propane/methane injection in large model ...................................................................... 133
Table 4-14: Produced oil properties for the small model ............................................... 140
Table 4-15: Produced oil properties for the large model ................................................ 147
Table 4-16: Produced oil properties ................................................................................ 185
Table 4-17: Produced oil properties ................................................................................ 192
Table 5-1: Vapor pressure of solvents used in this study at 21 °C ................................. 208
Table 5-2: Properties of small simulation model ............................................................ 217
Table 5-3: Properties of large simulation model ............................................................. 218
Table 6-1: Data range for various input and output parameters used in this study ........ 242
Table 6-2: Summary of the results for some selected training and testing trials ............ 249
Table 6-3: Error analysis for various techniques to predict drainage rate ...................... 263
Table A-1: Production method versus heavy oil resource (1) (after Clark, 2007) ......... 291
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Table A-2: Production method versus heavy oil resource (2) (after Clark, 2007) ......... 292
Table A-3: Production method versus heavy oil resource (3) (after Clark, 2007) ......... 293
Table A-4: Production method versus heavy oil resource (4) (after Clark, 2007) ......... 294
Table A-5: Technology versus production method (1) (after Clark, 2007) .................... 295
Table A-6: Technology versus production method (2) (after Clark, 2007) .................... 296
Table A-7: Technology versus production method (3) (after Clark, 2007) .................... 297
Table A-8: Technology versus production method (4) (after Clark, 2007) .................... 298
Table A-9: Technology versus production method (5) (after Clark, 2007) .................... 299
Table A-10: Technology versus production method (6) (after Clark, 2007) .................. 300
Table B-1: The experimental data on VAPEX experiments conducted by different
researchers....................................................................................................................... 301
Table B-2: The experimental data on VAPEX experiments conducted by different
researchers (Cont'd) ........................................................................................................ 302
Table B-3: The experimental data on VAPEX experiments conducted by different
researchers (Cont'd) ........................................................................................................ 303
Table B-4: The experimental data on VAPEX experiments conducted by different
researchers (Cont'd) ........................................................................................................ 304
Table B-5: The experimental data on VAPEX experiments conducted by different
researchers (Cont'd) ........................................................................................................ 305
Table B-6: The experimental data on VAPEX experiments conducted by different
researchers (Cont'd) ........................................................................................................ 306
Table B-7: The experimental data on VAPEX experiments conducted by different
researchers (Cont'd) ........................................................................................................ 307
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Table B-8: The experimental data on VAPEX experiments conducted by different
researchers (Cont'd) ........................................................................................................ 308
Table B-9: The experimental data on VAPEX experiments conducted by different
researchers (Cont'd) ........................................................................................................ 309
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LIST OF FIGURES
Figure 1-1: Canadian heavy oil deposits (from Canadian Association of Petroleum
Producers) ........................................................................................................................... 2
Figure 1-2: (a) Total Canadian crude oil and production (Canada’s National Energy
Board), (b) Oil production from EOR methods in US (reprinted after ASPO-USA) ......... 3
Figure 1-3: (a) VAPEX in typical layout of heavy oil reservoir, (b) Concept of VAPEX
(after Upreti et al., 2007)..................................................................................................... 7
Figure 2-1: Mechanisms of VAPEX process .................................................................... 25
Figure 2-3: Yield of asphaltene precipitation for various hydrocarbon solvents (after
Speight, 2007) ................................................................................................................... 37
Figure 2-4: Effect of asphaltene content on heavy oil viscosity (after Luo and Gu, 2005)
........................................................................................................................................... 41
Figure3-1: Digital flow meter (DFM) ............................................................................... 47
Figure 3-2: Plexiglas slabs, (a) Large model slab (b) Small model slab .......................... 51
Figure 3-3: Gaskets, (a) Large model gasket, (b) Small model gasket ............................. 52
Figure 3-4: Steel cover protectors, (a) Large model, (b) Small model ............................. 53
Figure 3-5: Physical models assembled on a steel frame mounted on steel stand ............ 54
Figure 3-6: The schematic of the large physical model and its sand pack cavity ............. 55
Figure: 3-7: The schematic of the small physical model and its sand pack cavity ........... 56
Figure 3-8: High pressure back-pressure regulator (BPR) ............................................... 58
Figure 3-9:Two-phase separators ...................................................................................... 59
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Figure 3-10: Wet test meters (WTM) ............................................................................... 61
Figure 3-11: Schematic diagram of the experimental set-up ............................................ 62
Figure 3-12: Experimental setup ....................................................................................... 63
Figure 3-13: Screen analysis for Ottawa sand #530 ......................................................... 66
Figure 3-14: Hydrocarbon composition of injected oil..................................................... 69
Figure 3-15: Sand-packed VAPEX models ...................................................................... 72
Figure 3-16: The schematic of the oil saturation set-up ................................................... 74
Figure 3-17: Oil saturated sand packs ............................................................................... 75
Figure 3-18: Sample locations, (a) Small model, (b) Large model .................................. 80
Figure 3-19: Schematic of the set up used to separate the oil from the sand .................... 82
Figure 3-20: Schematic of the set up used to measure the asphaltene content of the oil
samples .............................................................................................................................. 83
Figure 4-1: The recovery factor after propane injection in VAPEX models .................... 89
Figure 4-2: The produced oil rate after propane injection in VAPEX models ................. 90
Figure 4-3: The recovery factor after methane injection in VAPEX models ................... 92
Figure 4-4: The produced oil rate after methane injection in VAPEX models ................ 93
Figure 4-5: Recovery factor after CO2 injection in the VAPEX models ......................... 95
Figure 4-6: Produced oil rate after CO2 injection in the VAPEX models ........................ 96
Figure 4-7: Recovery factor after butane injection in the VAPEX models ...................... 97
Figure 4-8: Produced oil rate after butane injection in the VAPEX models .................... 98
Figure 4-9: Recovery factor after propane/CO2mixture injection in the VAPEX models
......................................................................................................................................... 100
Figure 4-10: Produced oil rate after first propane/CO2 injection in VAPEX models ..... 101
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Figure 4-11: Recovery factor after propane/methane mixture injection in the VAPEX
models ............................................................................................................................. 103
Figure 4-12: Produced oil rate after propane/methane mixture injection in the VAPEX
models ............................................................................................................................. 104
Figure 4-13: Solvent utilization factor (SUF) after propane injection in VAPEX models
......................................................................................................................................... 106
Figure 4-14: Solvent utilization factor (SUF) after methane injection in VAPEX models
......................................................................................................................................... 107
Figure 4-15: Solvent utilization factor (SUF) after CO2 injection in VAPEX models ... 109
Figure 4-16: Solvent utilization factor (SUF) after butane injection in VAPEX models 110
Figure 4-17: Solvent utilization factor (SUF) after propane/CO2 injection in VAPEX
models ............................................................................................................................. 111
Figure 4-18: Solvent utilization factor (SUF) after propane/methane injection in VAPEX
models ............................................................................................................................. 113
Figure 4-19: Compositional analysis of the produced oil after propane injection .......... 116
Figure 4-20: Compositional analysis of the produced oil after methane injection ......... 120
Figure 4-21: Compositional analysis of the produced oil after CO2 injection ................ 124
Figure 4-22: Compositional analysis of the produced oil after butane injection ............ 127
Figure 4-23: Compositional analysis of the produced oil after propane/CO2 injection .. 131
Figure 4-24: Compositional analysis of the produced oil after propane/methane injection
......................................................................................................................................... 134
Figure 4-25: Effect of the solvent type on recovery factor in small model .................... 136
Figure 4-26: Effect of the solvent type on produced oil rate in small model ................. 137
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Figure 4-27: Effect of solvent type on solvent utilization factor (SUF) for small model139
Figure 4-28: Effect of solvent type on hydrocarbon components in small model .......... 141
Figure 4-29: Effect of the solvent type on recovery factor in large model ..................... 143
Figure 4-30: Effect of the solvent type on produced oil rate in large model .................. 144
Figure 4-31: Effect of solvent type on the solvent utilization factor (SUF) for large model
......................................................................................................................................... 146
Figure 4-32: Effect of solvent type on hydrocarbon components in large model .......... 148
Figure 4-33: Residual oil saturation profile for various solvents in the small model ..... 150
Figure 4-34: Residual oil saturation profile for various solvents in the large model ..... 151
Figure 4-35: Asphaltene precipitate after conducting the asphaltene measurement tests
......................................................................................................................................... 153
Figure 4-36: Schematic of the locations of each heavy oil samples in the physical models
......................................................................................................................................... 154
Figure 4-37: Effect of drainage height on asphaltene precipitation at different locations in
the small and large models after propane injection ........................................................ 156
Figure 4-38: Effect of drainage height on asphaltene precipitation at different locations in
the small and large models after methane injection ........................................................ 158
Figure 4-39: Effect of drainage height on asphaltene precipitation at different locations in
the small and large models after CO2 injection .............................................................. 159
Figure 4-40: Effect of drainage height on asphaltene precipitation at different locations in
the small and large models after butane injection ........................................................... 160
Figure 4-41: Effect of drainage height on asphaltene precipitation at different locations in
small and large models after propane/CO2 injection ...................................................... 162
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Figure 4-42: Effect of drainage height on asphaltene precipitation at different locations in
small and large models after propane/methane injection ................................................ 163
Figure 4-43: Effect of solvent type on asphaltene precipitation at different locations in the
small model ..................................................................................................................... 165
Figure 4-44: Effect of solvent type on asphaltene precipitation at different locations in the
large model...................................................................................................................... 167
Figure 4-45: (a) Asphaltene precipitation close to the injection point, (b) Asphaltene
streaks on the sand pack at the end of experiments ........................................................ 168
Figure 4-46: The interface of the coded software for IA ................................................ 170
Figure 4-47: The procedure for conducting IA in the small model: (a) The coordinates of
the image are specified, (b) The interface curve is defined, and (c) The oil and solvent
zones are schematically reprinted by the software ......................................................... 171
Figure 4-48: The procedure for conducting IA in the large model: (a) The coordinates of
the image are specified, (b) The interface curve is defined, and (c) The oil and solvent
zones are schematically reprinted by the software ......................................................... 172
Figure 4-49: Solvent chamber evolution in small model after propane injection........... 174
Figure 4-50: Solvent chamber evolution in large model after propane injection ........... 175
Figure 4-51: Sweep efficiency of various solvents in the small model .......................... 177
Figure 4-52: Sweep efficiency of various solvents in the large model ........................... 178
Figure 4-53: Effect of connection establishment between the injection and production
wells on the recovery factor in the small model ............................................................. 181
Figure 4-54: Effect of connection establishment between the injection and production
wells on the produced oil rate in the small model .......................................................... 182
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Figure 4-55: Effect of connection establishment between the injection and production
wells on the asphaltene precipitation in the small model ............................................... 183
Figure 4-56: Solvent chamber evolution in small model after propane injection (first
injection scenario) ........................................................................................................... 186
Figure 4-57: Effect of connection establishment between the injection and production
wells on the recovery factor in the large model .............................................................. 188
Figure 4-58: Effect of connection establishment between the injection and production
wells on the produced oil rate in the large model ........................................................... 189
Figure 4-59: Effect of connection establishment between the injection and production
wells on the asphaltene precipitation in the large model ................................................ 191
Figure 4-60: Solvent chamber evolution in large model after propane injection (first
injection scenario) ........................................................................................................... 193
Figure 4-61: The results obtained for up-scaling the stabilized drainage rate based on the
proposed exponent by Butler (1994), (n=0.5). The dotted line is the drainage rate
prediction based on Butler’s model; the data points for different solvents are the
experimental results obtained in this study. .................................................................... 199
Figure 4-62: The results obtained for up-scaling the stabilized drainage rate based on the
proposed exponent by Yazdani (2007), (n=1.1). The dotted line is the drainage rate
predicted based on Yazdani’s model; the data points for different solvents are the
experimental results obtained in this study. .................................................................... 200
Figure 4-63: The results obtained for up-scaling the stabilized drainage rate based on the
proposed exponent by Yazdani (2007), (n=1.3). The dotted line is the drainage rate
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predicted based on Yazdani’s model; the data points for different solvents are the
experimental results obtained in this study. .................................................................... 201
Figure 4-64: The results obtained for up-scaling the stabilized drainage rate. The dotted
line is the drainage rate predicted based on n=1.2; the data points for different solvents
are the experimental results obtained in this study. ........................................................ 202
Figure 4-65: Linear regression for the results obtained for different solvents in the small
and large physical models ............................................................................................... 203
Figure 4-66: Effect of drainage height and solvent type on dimensionless VAPEX
number, Ns ....................................................................................................................... 205
Figure 5-1: Densities and viscosities of the heavy oil used in this study at various
temperatures and atmospheric pressure .......................................................................... 207
Figure 5-2: Two-phase envelopes for propane/CO2 and propane/methane mixtures ..... 209
Figure 5-3: Schematic of the experimental set-up used for solubility measurement tests
......................................................................................................................................... 211
Figure 5-4: Solubility of (a) propane, (b) methane, (c) CO2, and (d) butane at 21°C .... 212
Figure 5-5: Solvent volume fraction in the produced oil from the small model for various
solvents at 21°C .............................................................................................................. 214
Figure 5-6: Solvent volume fraction in the produced oil from the large model for various
solvents at 21°C .............................................................................................................. 215
Figure 5-7: (a) 2D view of the simulated model with the injection and production wells
for the small physical model, (b) 3D view of the simulated model with the injection and
production wells for the small physical model ............................................................... 219
XXIII
Figure 5-8: (a) 2D view of the simulated model with the injection and production wells
for the large physical model, (b) 3D view of the simulated model with the injection and
production wells for the large physical model ................................................................ 220
Figure 5-9: Experimental and simulation results for the recovery factor after injecting
propane in the small model ............................................................................................. 224
Figure 5-10: Experimental and simulation results for the recovery factor after injecting
propane in the large model .............................................................................................. 225
Figure 5-11: Experimental and simulation results for the recovery factor after injecting
butane in the small model ............................................................................................... 226
Figure 5-12: Experimental and simulation results for the recovery factor after injecting
(a)CO2 and (b) methane in the small model.................................................................... 227
Figure 5-13: Experimental and simulation results for the recovery factor after injecting
(a) propane/CO2 and (b) propane/methane mixtures in the small model ........................ 228
Figure 5-14: Chamber evolution after 26 h in (a) simulated small model, (b) laboratory
model............................................................................................................................... 229
Figure 5-15: Effect of well configuration on the recovery factor. For the first well
configuration, the injection well is located at the top of the model and 24 cm above the
production well; for the second well configuration, the injection well is located 16 cm
above the production well; for the third well configuration, the injection well is 4 cm
above the production well; for the forth well configuration, the injection well is 24 cm
above the production well; and for the fifth well configuration, the injection well is at the
right top corner of the model; the production well is at the left bottom corner. ............. 231
Figure 5-16: Effect of permeability on the recovery factor after injecting propane ....... 234
XXIV
Figure 5-17: Effect of grid thickness on the recovery factor after injecting propane ..... 235
Figure 5-18: Effect of time step change on recovery factor after injecting propane ...... 236
Figure 6-1: Schematic of an artificial neural network .................................................... 238
Figure 6-2: Data distribution for training and testing sets; stabilized drainage rate vs. (a)
height (cm), (b) injection pressure (kPa), (c) porosity (%), (d) permeability (D), and (e)
viscosity (mPa.s) ............................................................................................................. 241
Figure 6-3: An example of network training procedure; plot of: (a) predicted outputs by
network for training data sets, (b) predicted outputs by network for validation data sets,
(c) predicted outputs by network for testing data sets, and (d) predicted outputs by
network for the whole group of data sets chosen for training procedure ........................ 247
Figure 6-4: Schematic of the developed BP network; there are 20 neurons on the first
hidden layer and 15 neurons on the second hidden layer. The transfer functions used for
hidden layers were log sigmoid functions, and linear transfer function was used for output
layer................................................................................................................................. 250
Figure 6-5: Output of the developed network vs. the actual data after simulating the
model with training data sets .......................................................................................... 251
Figure 6-6: Output of the developed network vs. the actual data after simulating the
model with testing data sets ............................................................................................ 253
Figure 6-7: Relevancy (r) factor for various parameters to the production rate ............. 255
Figure 6-8: Plot of predicted stabilized drainage rate by eq. 6.15 versus actual data sets
for testing ........................................................................................................................ 257
Figure 6-9: Plot of predicted stabilized drainage rate by eq. 6.16 versus actual data sets
for testing ........................................................................................................................ 258
XXV
Figure 6-10: Plot of predicted stabilized drainage rate by eq. 4.15 versus actual data sets
for testing ........................................................................................................................ 259
Figure 6-11: Plot of predicted stabilized drainage rate by eq. 4.16 versus actual data sets
for testing ........................................................................................................................ 260
Figure 6-12: Plot of predicted stabilized drainage rate by eq. 4.17 versus actual data sets
for testing ........................................................................................................................ 261
Figure 6-13: Plot of predicted stabilized drainage rate by eq. 4.18 versus actual data sets
for testing ........................................................................................................................ 262
XXVI
NOMENCLATURE
Symbols Definitions
A Specific pore surface area, L2
Cmax Maximum solvent concentration
Cmin Minimum solvent concentration
Cp Coefficient of variance
Cs Solvent concentration
D Dispersion coefficient, L2t-1
Deff Effective diffusivity, L2t-1
Do Molecular diffusivity, L2t-1
Dp Particle size, L
Ds Diffusivity of solvent in bitumen, L2t-1
F Formation electrical diffusivity, L2t-1
H Drainage or model height, L
M Molecular weight of solvent, [g/mol]
Ns VAPEX number
P Pressure, ML-1
t-2
Pf Final Pressure, ML-1
t-2
Q Stabilized drainage rate per unit length of the horizontal well, L2t-1
R Universal gas constant, ML2t-2
N-1
T-1
T Temperature, T
XXVII
V Molal volume of solvent, L3N
-1M
-1
VA Molar volume of solute, L3N
-1
VB Molar volume of solvent, L3N
-1
Z Compressibility factor
d Diameter, L
g Gravitational acceleration, L2t-2
k Permeability, L2
m Mass, M
m1 Original mass of the heavy oil sample, M
m2 Mass of the dried particulate, M
n Moles of solvent, N
t Time, t
x Effective molecular weight of the solvent with respect to the diffusion
process
Greek symbols
γ Skewness
λ Mass transfer enhancement coefficient
μ Viscosity, ML-1
t-1
μmix Viscosity of mixture at solvent concentration, ML-1
t-1
τ Tortuosity
σ Surface tension, Mt-2
Porosity
Δρ Density difference between solvent and bitumen, ML-3
ΔSO Change in oil saturation
XXVIII
Abbreviations
ANN Artificial neural networks
APAD Average percent arithmetic deviation
BPR Back pressure regulator
CAT Computer assisted tomography
CERI Canadian Energy Research Institute
CHOPS Cold heavy oil production
CSS Cyclic steam stimulation
DFM Digital flowmeters
EOR Enhanced oil recovery
GOR Gas oil ratio
IA Image Analysis
MEOR Microbial enhanced oil recovery
MSE Mean square error
MW Molecular weight
NMR Nuclear magnetic resonance
PSD Particle Size Distribution
SAGD Steam assisted gravity drainage
SOR Solvent oil ratio
SRC Saskatchewan Research Council
SUF Solvent utilization factor
XXIX
VAPEX Vapour extraction
WTM Wet test meter
1
1. CHAPTER 1: INTRODUCTION
1.1 Background
Canada has significant crude oil resources, 50% of which are heavy oil and bitumen. The
application of numerous heavy oil recovery techniques has led to the recovery of small
portions of this oil. However, in many cases, more than 90% of the oil remains in place.
Improved profitability, technological advances, huge reserve size, low geological risk,
and low capital investment have drawn attention to heavy oil production from many
companies (Chugh et al., 2000). Figure 1-1 shows the distribution of Canadian heavy oil
reserves.
Increasing the capillary number and/or lowering the mobility ratio are the basic principles
of enhanced oil recovery (EOR) methods. EOR processes are mainly divided into four
categories: thermal, gas, chemical, and other. In addition, oil production from EOR
projects continues to supply an increasing percentage of the world’s oil. About 3% of the
worldwide production now comes from EOR processes, and this portion is increasing
each year. Figure 1-2(a) shows the amount of Canadian oil to be produced based on the
approved projects by major oil companies, while Figure 1-2(b) shows the oil production
in the U.S. using various EOR techniques. Based on these figures, the importance of
choosing the most feasible recovery technique is increasingly important to petroleum
engineers.
2
Figure 1-1: Canadian heavy oil deposits (from Canadian Association of Petroleum Producers)
3
(a)
Date (year)
1975 1980 1985 1990 1995 2000 2005 2010 2015
Enh
ance
d pr
oduc
tion
(bb
l/da
y)
0
200x103
400x103
600x103
800x103
1x106
Chemical
Gas injection
Thermal
Total EOR
(b)
Figure 1-2: (a) Total Canadian crude oil and production (Canada’s National Energy Board), (b) Oil
production from EOR methods in US (reprinted after ASPO-USA)
4
While the choice of injectants has widened considerably, petroleum engineers still must
choose an injection fluid and/or feasible recovery process to maximize the recovered oil
from the reservoir. Not surprising, screening criteria have evolved through the years to
help petroleum engineers make appropriate decisions. However, in recent years,
computer technology has improved the application of screening criteria through the use
of artificial intelligence techniques; yet, the reliability of such programs depends on input
data quantity and accuracy.
The continuing rise in demand, the decline in conventional domestic production, and the
belated development of alternatives to petroleum combine to increase the importance of
seeking new resources and methods for enhanced oil recovery. Enhanced oil recovery
could offset some of this dependence; though, the amount, cost, and timing of the EOR
contribution are highly uncertain.
Furthermore, selecting and implementing an EOR method requires several steps. Initially,
reservoir properties and formation fluid characteristics are used as a preliminary technical
screening guide for any possible EOR method. After the selection of candidate methods,
basic static tests are carried out. Then, more practical methods will be chosen and
subjected to flow studies in porous media where a semi-realistic environment is
introduced. Next, pilot projects demonstrate the viability of the selected method. Finally,
assuming success at the lower screening levels, a field-wide EOR project is implemented.
Of course, economic studies are conducted throughout all screening levels (Goodlet et al.,
1986).
5
1.2 Vapour extraction (VAPEX)
The vapour extraction (VAPEX) or vapour assisted petroleum extraction process is the
solvent analog of the steam-assisted gravity drainage process (SAGD), which reduces oil
viscosity by diluting the in-situ heavy oil or bitumen with the help of injected vapourized
solvents. The idea of injecting the solvent vapours to enhance oil recovery was first
proposed by Allen (1974) (Allen 1976;Chatzis and James, 2007).
Later, Butler and Mokrys (1989) introduced a brief discussion on the process during a
study of Athabasca and Suncor Coker feed bitumen. This process was later named
VAPEX in 1990 (Butler and Mokrys, 1990). In this process, the horizontal production
well is located near the bottom of the pay zone and the injection well is completed right
above the production well. A schematic of the process is shown in Figure 1-3.
First, the solvent is injected through the injector to form an initial vertical solvent vapour
chamber between the injector and the producer. The vapour chamber spreads, and the oil-
solvent interface becomes stabilized by gravity. The drainage is controlled by molecular
diffusion of solvent vapour into the bitumen (Butler and Mokrys, 1990). There is a phase
change during VAPEX when the solvent diffuses into the oil at the solvent-oil interface.
During this phase change, changes to temperature, pressure, and concentration occur at
the contact interface. This causes asphaltene deposition. The asphaltene precipitation has
a contentious effect on the VAPEX process. While the asphaltene precipitation may
improve the quality of heavy oil by reducing its viscosity, it may also alter the wettability
of rock and consequently plug pores.
6
Since the VAPEX process is a non-thermal process, compared to SAGD, it might be
considered as an energy efficient and environmentally friendly process. This is because it
does not require steam generation and water recycling. Therefore, it is significantly fuel
efficient. Also, because this is a non-thermal process, there will be no CO2 emissions,
and, in the case of the CO2-based VAPEX process, it will help sequester CO2
underground.
7
Figure 1-3: (a) VAPEX in typical layout of heavy oil reservoir, (b) Concept of VAPEX (after Upreti et al.,
2007)
(a)
8
1.3 Objectives
The main issue for implementing the VAPEX technique is a lack of knowledge about
upscale performance estimates. Other researchers have proven that current analytical
models under predict the VAPEX produced oil rate. Such assumptions, and the limited
operating conditions under which these analytical models are developed, make them
unsuitable to predict recovery performance (in most cases). Conversely, because of the
complex effect of diffusion and dispersion mechanisms on the process, specifically in
porous media like sand-packed models, there are still issues in upscaling the laboratory
results to the field scale.
This research focused on providing an extensive study of VAPEX process performance
by considering the injection of different solvents in large-scale physical models and by
combining the experimental results, numerical analysis, and soft computing tools to
model the production of heavy oil through the VAPEX process. Ultimately, the goal of
this study is to develop a tool that can accurately predict the production rate and
performance of the VAPEX process when applied to a field scale. More specifically, the
following tasks will be carried out:
1. Build and design an experimental set-up with two large visual physical models to
conduct VAPEX experiments.
2. Conduct several tests using different solvents in two different models with
different sizes to produce a wide range of data.
3. Investigate the effect of model size and solvent type on recovery performance.
4. Monitor asphaltene precipitation at different physical model locations using
different solvents.
9
5. Match the experimental results by numerically simulating the process and
comparing the performance of the simulated model with the experimental results,
thereby highlighting simulation issues.
6. Gather, categorize, and pre-process the obtained data to develop a soft-
computing-based model.
7. Train, validate, and test a soft-computing-based model to predict the recovery
performance after implementing the VAPEX technique.
1.4 Organization of the thesis
Chapter 1 provides background on heavy oil resources, heavy oil recovery methods, and
the VAPEX process. It also includes an introduction to the research objectives and
structure of the dissertation.
Chapter 2 includes an extensive literature review on the heavy oil recovery techniques
and VAPEX mechanisms. Moreover, it contains a complete literature review on the
feasibility of different experimental studies on VAPEX, which includes solvent selection,
diffusion and dispersion during VAPEX, asphaltene precipitation and environmental as
well as economic considerations for VAPEX.
Chapter 3 provides a detailed explanation of the experimental set-up and the equipment
and materials used. In addition, the experimental procedure is discussed fully.
Chapter 4 describes the experimental results. These results are analysed and discussed in
detail. Next, the effect of model size and solvent type is investigated. Furthermore, the
asphaltene precipitation experiments are shown and the results are explained for each
solvent.
10
Chapter 5 presents the results for the PVT experiments and measurements. Moreover,
these results were incorporated into a compositional simulator (i.e., CMG’s STARS
package (Computer Modelling Group Ltd., Inc.) to simulate the VAPEX process and
match the experiments.
Chapter 6 describes the soft computing approach utilized in this study to predict the
recovery performance after implementing VAPEX technique. The experimental results
alongside a wide range of data gathered from the literature were employed, and an
artificial neural network (ANN) was utilized to develop a model to predict the oil
drainage rate after conducting VAPEX. Furthermore, the validity of the developed model
was tested by comparing the results with available prediction techniques.
Finally, Chapter 7 summarizes the experimental, numerical, and soft computing results
for the VAPEX tests that have been conducted. This chapter includes the highlighted
results as conclusions. In the second section of this chapter, the recommendations for
future work are explained in detail.
11
2. CHAPTER 2: LITERATURE REVIEW
2.1 Heavy oil recovery methods
For the first EOR method to implement in a specific reservoir, the candidate reservoir and
the recovery mechanism of the EOR method should be studied in detail. In terms of
Canadian reservoirs, the first methods implemented are waterflooding, cold production,
or steam flooding. While chemical flooding and other emerging new technologies are
mostly coupled with the above mentioned methods, implementing these technologies is
highly dependent on economic profitability.
2.1.1 Waterflooding
For nearly 50 years, heavy oil waterfloods have operated in Saskatchewan and Alberta. If
a waterflood is located in an area near heavy oil cold production, it is often classified as
heavy oil waterflooding. When defining heavy oil, the emphasis is mostly on oil gravity;
however, there is another important controlling parameter: oil viscosity. In fact, problems
obtaining consistent heavy oil viscosity measurements and confusion about whether
available viscosity values were collected using dead oil, live oil, or something in between
means that researchers often pay less attention to viscosity terms (Miller, 1995, Miller,
2005 and Miller, 2006).
Furthermore, Forth et al. (1996) conducted a review of Golden Lake heavy oil field that
determined the areal sweep was very poor and the viscosity variation affected the
waterflooding performance. On the other hand, Smith (1992) studied the causes of the
successful waterfloods in Wainwright and the Wildmere areas around Lloydminster.
12
Smith found that induced fracture networks allow the formation to simultaneously filter
the input water, which is quite dirty and plugs the formation and has a negative impact on
filtrate disposal. Moreover, Adams (1982) noted that injected water channeling was so
severe that converted mature injectors sometimes became low water cut producers shortly
after conversion. Then, Turta et al. presented several ‘toe to heel’ waterflooding papers
(Turta et al., 2002, Turta et al., 2003 and Zhao and Turta, 2004) that show that no
permeability restrictions are present in the vertical direction. However, this assumption
limited the applicability of their proposed method. Additionally, Stephen et al. (1995)
studied the effect of well spacing reduction from 20 acres to 10 acres by infill drilling,
but they did not observe any significant recovery. Finally, Mai and Kantzas (2007)
performed a set of ambient temperature laboratory core floods and found that capillary
forces, which are often neglected due to the high oil viscosity, are in fact important even
in heavy oil systems.
2.1.2 Cold heavy oil production (CHOPS)
Cold production refers to the use of operating techniques and specialized pumping
equipment to aggressively produce heavy oil reservoirs without applying heat. Production
remained stable up to 1991, with a yearly output averaging 3.6 million m3; after cold
production became more common, the production tripled to 11 million m3/year in 2003
(Nakutnyy and Renouf, 2009). Sand production—a function of (1) the absence of clays
and cementation materials, (2) oil viscosity, (3) the producing water cut and GOR, and
(4) pressure drawdown rate—is the basis of cold production (Chugh et al., 2000).
Several researchers have investigated the appropriate candidates for cold production
(Dusseault and Geilikman, 1995; Chugh et al., 2000; Dusseault et al., 2000). According
13
to the studies conducted by these researchers, the appropriate reservoir for cold
production should have the following specifications:
High oil viscosity (2,000 ~ 30,000 cp) because the higher the oil viscosity, the greater
the drag force on a sand particle (Chugh et al., 2000).The IFT between gas and oil
should increase with a decrease in oil API. Dusseault and El-Sayed (2000) said that in
more viscous oils (μ > 15,000 cp), despite several attempts. CHOP has not yet been
economically successful.
Unconsolidated formations with less cement bands, which are better candidates for
cold production (Chugh, 2000 & Dusseault, 1995). According to Dusseault, most of
these reservoirs have porosity of 29% to 31%.
Low initial water production and preferably no bottom water (< 40% water cut)
(Chugh et al., 2000).
High initial reservoir pressure because the better the initial drawdowns, the more the
well will cleanup (i.e., will produce sand with oil) (Chugh et al., 2000). As such, the
reservoirs should be buried at depths of 300m to 600m (Dusseault and Geilikman,
1995).
Reservoir thickness of 8 to 15 m; however, the thinnest reported is ~4 m and the
thickest is ~30 m. The sand lithology varies from quartz arenites (>95% SiO2) to
arkoses or litharenites with ~15% feldspar grains, ~20% siliceous volcanic shards,
and ~5-8% lithic fragments (Dusseault et al., 2000).
While these specifications lead to better efficiency, cold production can be improved by
forming high permeability channels (wormholes) in the formation. In fact, Tremblay et
al. (1997; 1998) as well as Tremblay and Forshner (1998) studied wormhole growth
14
under solution gas drive. They visualized wormhole growth during oil flow through a
horizontal sand pack. The wormhole developed in the higher porosity region, which
indicates that the wormhole likely followed the weaker (higher porosity) sand.
2.1.3 Gas EOR methods
In these methods, the injectant can be dry gas, enriched gas (hydrocarbon miscible), CO2,
nitrogen or flue gas, or combinations of these injectants. These methods increase
capillary number. They are also called solvent flooding, miscible-gas flooding, or simply
gas flooding methods. N2 and flue gas are the cheapest possible injectants. In the
literature, successful projects use these cheap gases (Taber, 1988; Taber, 1990; Moritis,
1994; Babadagli et al., 2008; and Sahin et al., 2008). Moreover, for a miscible flood, the
main factor for screening criteria is average pressure, and this parameter is dependent on
depth (Taber, 1988 and Moritis, 1994). Ultimately, the advantages of carbon dioxide
flooding in comparison to N2 and flue gas are that CO2 is very soluble in oils at reservoir
pressure; it reduces the oil viscosity before miscibility is achieved between CO2 and
crude oil, and CO2 will stay dissolved in crude oil (Martin and Taber, 1992).
2.1.4 Thermal EOR processes
Thermal methods lower mobility ratio by decreasing oil viscosity. Since the effect of
temperature is especially pronounced for viscous crudes, these processes are normally
applied to heavy crudes. Thermal methods are divided into in-situ combustion, cyclic
steam stimulation (CSS), hot waterflooding, steam-assisted gravity drainage, and steam
flooding (Prats, 1982 and White and Moss, 1983).
Furthermore, in-situ combustion has been extensively field tested (Farouq, 1972; Farouq
and Meldau, 1979 and Chu, 1982). According to many, in-situ combustion is feasible
15
under a wide variety of field conditions, and, next to waterflooding, it could become the
most widely used recovery method. In fact, a well-designed fireflood could be expected
to recover 50 percent of the oil in place, and could make a profit, especially if
simultaneous or intermittent water injection with air is employed (Farouq and Meldau,
1979). Actually, Yannimaras introduced a new method to screen crude oils for
applicability of the air-injection/ in-situ combustion process (Yannimaras and Tiffin,
1995).
Steam flooding is usually used in reservoirs containing high viscosity crude oils that are
difficult to mobilize by methods other than thermal recovery. Good steamflooding
projects require thick, shallow deposits with high oil saturations and good permeabilities.
Advancements in steam injection applications have made it possible to apply the new
technology in previously unsuitable reservoirs. In addition, the introduction of steam
assisted gravity drainage (SAGD) has transformed the huge quantities of tar sand oil in
Alberta to proven oil reserves, moving Canada to second place in terms of oil reserves
worldwide behind only Saudi Arabia (Shin and Polikar, 2005). SAGD has different
applications that have been mentioned in the literature (Mendoza et al., 1999; Mendoza
and Herrera, 2001; Sedaee and Rashidi, 2006 and Bagci, 2006). SAGD is carried out at
very small pressure gradients, which helps stabilize the process, avoiding the high-
pressure gradients that can potentially lead to channeling and isolation of parts of the
reservoir. Furthermore, Shale barriers can present a challenge to SAGD (Alvarado and
Manrique, 2010). Kumar et al. (1995) simulated a cyclically steamed well in Cymric
field, San Joaquin Valley, California. Their results show that fluid flow from the well to
the reservoir is primarily through the hydraulic fracture induced by the injected steam.
16
CSS is also known as steam soak, or huff and puff. Wong et al. (2003) presented a field
review of the Pikes Peak steam project, showing key performance indicators of CSS and
steam drive in non-bottom water. He concluded that CSS has been conducted
successfully with economic steam/oil ratios (SORs) in areas with up to 4 m of bottom
water by injecting significantly larger steam slugs in what is termed a “drive, block, and
drain process” (Wong et al., 2003). Moreover, Williams et al. (2001) studied the effects
of discontinuous shales on multizone steamflood performance in the Kern River field.
Kern River is a shallow, heavy oil field. According to the literature, this field has been on
steam flooding since the mid-1960s (Bursell and Pittman, 1975; Belvins and Billingley,
1975; Oglesby et al., 1982; Restine, 1983 and Restine et al., 1987). Williams et al. (2001)
concluded that discontinuous shales allow significant oil drainage from upper to lower
sands, as well as fluid migration across zones, and small pattern-element or single-sand
models cannot adequately explain observed field behaviour in this type of reservoir.
2.1.5 Chemical EOR processes
Chemical processes are not widely used, especially when compared to thermal and gas
injection methods. In fact, replacing the trapped oil is approximately 10 times more
difficult than replacing continuous oil (Chatzis and Morrow, 1983); therefore, the chosen
chemical must be very efficient.
The chemical processes are divided into polymer, alkaline, and surfactant/polymer
methods. Regardless of category, chemical flooding methods require low to moderate oil
viscosities and moderate to high permeability; the latter is for favourable water injection
(Maerker and Gale, 1990 and Baviere et al., 1995). However, alkaline flooding consists
17
of injecting solutions of sodium hydroxide, sodium carbonate, and sodium silicate or
potassium hydroxide into the reservoir (Mayer et al., 1983 and Shutang et al., 1995).
Cooke et al. (1974) describe a new method for alkaline flooding. In the process, they
submitted that the alkaline water must be saline rather than fresh water. The use of saline
water causes the sand to become oil-wet in the presence of the alkaline water. High
salinity also leads to the formation of a water-in-oil type of emulsion that does not form
in the other processes. Furthermore, Dong (2008) investigated the effective viscosity of
water-in-oil emulsions in porous media experimentally using four different qualities of
water-in-oil emulsions flowing through sand packs of different permeabilities at different
injection flow rates. He found that the effective viscosity of an emulsion in a sand pack
decreased with increasing flow rate and that the relative variation was minimal.
Ultimately, the emulsions exhibited a higher effective viscosity in a higher permeability
sand pack than in a lower permeability sand pack.
2.1.6 Emerging EOR technologies
New methods for downhole dielectric heating using electromagnetic radiation,
microwaves and radiofrequency are now applicable in enhancing heavy oil recovery
(Emmons et al., 1986 and Islam et al., 1991). To that end, researchers have conducted
successful laboratory experiments using different heating techniques. The heating of
formation fluids and porous media can improve oleic phase mobility relative to the
aqueous and gas phases that enhance oil recovery (Fanchi, 1990 and Islam, 1999). In fact,
electromagnetic frequency can enhance the heavy oil recovery by more than 50%
(Ovalles et al., 2001 and Ovalles et al., 2002).
18
Beckman (1926) first proposed the concept of using microorganisms to enhance oil
recovery (MEOR). Then, Zobel studied this subject more closely in 1950 (Zobel, 1946
and 1947). Since then, different MEOR technologies have been developed to enhance the
oil recovery. For example, some microbial methods aid in paraffin removal while others
are designed to modify heavy oil. Likewise, other methods use microorganisms to
produce chemicals, such as surfactants, polymers, or solvents that are useful in oil
recovery processes, either in above-ground facilities or in situ. However, unfortunately,
MEOR has not gained credibility in the oil industry due to technical and economic
constraints (Maudgalya, 2007).
Another method, which is now becoming more common, is VAPEX. VAPEX is an
energy-efficient method of recovering high viscosity heavy oil and bitumen from
reservoirs. The process uses a solvent in the miscible displacement of bitumen or heavy
crude oil. VAPEX improves energy efficiency and reduces emissions and operating costs.
However, production rates with this process are lower than with traditional steam
processes. In the conventional VAPEX process, a mixture of vapourized solvent (propane
and/or butane) and a commercially available non-condensable gas (methane, natural gas)
is injected into the reservoir to reduce oil viscosity. While the VAPEX process became
less attractive with the increase of gas price, injecting CO2 will decrease solvent cost.
Moreover, CO2 is more soluble in heavy oils than methane. On the other hand, this can be
environmentally important because, nowadays, CO2 sequestration itself is an important
environmental issue.
Yazdani et al. (2005) did a scale-up for the VAPEX method and studied the effects of
drainage height and grain size on production rates in the VAPEX process. They found
19
that minor changes in heavy oil composition do not significantly affect the observed
drainage rates. They also observed that scaled-up, stabilized oil-drainage rates are much
higher than the predictions published in the literature. Thus, the VAPEX process may be
more widely applicable than previously thought.
In Appendix A, a summary of applicability of some EOR techniques found in the
literature is provided. Tables A-1 to A-4 estimate which production method applies to
each heavy oil resource. Tables A-5 to A-10estimate the potential impact of specific
technologies on various subsurfaces deeper than 50m, which constitute 90% of Canada’s
heavy oil resource production methods and 100% of the US’s and Venezuela’s resource
production methods. The potential impacts have been rated “high”, “medium”, “low”,
and “unknown” (Clark, 2007).
2.2 Vapour extraction (VAPEX)
The VAPEX process is the solvent analog of SAGD, which reduces oil viscosity by
diluting the in-situ bitumen with vapourized solvents. The idea of injecting solvent
vapours to enhance oil recovery was first proposed in 1974 by Allen (Allen, 1974, Allen,
1976 and James and Chatzis, 2007), in which, the Cyclic Steam Stimulation (CSS)
process was varied by alternating steam and solvent. The solvents used in his experiments
were butane and propane. Because of the low oil recovery, the idea was not field tested.
Later, Allen (1976) improved the idea by injecting a mixture of two gases: one gas as the
carrier gas and the other one as the solvent. Then, Butler and Mokrys (1989) introduced a
brief discussion on the process during a study on Athabasca and Suncor Coker feed
bitumen. They named it VAPEX in 1990 (Butler and Mokrys,1989 and Butler and
Mokrys, 1990). In this process, the horizontal production well is located near the bottom
20
of the pay zone and the injection well is completed right above the production well. Even
though this process is significantly slower than SAGD, using vapour to reduce oil
viscosity and increase operating temperature will make VAPEX economically
advantageous.
The low cost of the injected solvent, which can be recovered and recycled, the
applicability of this method in thin and low-porosity-good permeability reservoirs are the
key advantages of the VAPEX process (Yazdani, 2007). Indeed, the energy requirements
for VAPEX are less than thermal methods. Besides, the thermal recovery methods cannot
be implemented in the reservoirs with bottom aquifer (Das, 1998, James et al., 2007,
Rahnema et al., 2008 and Pourabdollah, 2013).
In VAPEX, diluted oil becomes less viscous along the boundary of the vapour chamber
and drains via gravity toward the production well, which is directly located below the
injection well. Of note, long horizontal wells are required to obtain reasonably high
production rates because gravity drainage is a slow recovery process (Jiang and Butler,
1996). Nevertheless, the vapour chamber forms around the injection well in the swept
zone by pore spaces filling with solvent vapour. The mixing of solvent and bitumen
occurs mainly by molecular diffusion and convective dispersion mechanisms that are
combined during the solvent and bitumen mixing process (Das and Butler, 1998).
However, in the mixing process, convective dispersion is more important than molecular
diffusion (Nghiem et al., 2001). Regardless, the solvent dispersion coefficient is an
important parameter governing the efficiency of bitumen dilution during the VAPEX
process. In short, to estimate the oil recovery after implementing VAPEX, an accurate
dispersion coefficient estimate is crucial. However, while there is not any proven
21
methodology to predict dispersion coefficient, Karmaker and Maini (2003) proposed a
new technique to extract the net dispersion coefficient using a 2-D magnetic resonance
imaging tool.
Yazdani and Maini (2005) did a scale-up for the VAPEX method and studied the effects
of drainage height and grain size on production rates in the VAPEX process. In their
research, it was found that minor changes in heavy oil composition do not significantly
affect the observed drainage rates. It was also observed that scaled-up, stabilized oil-
drainage rates are much higher than the predictions published in the literature. Thus, the
VAPEX process may be more widely applicable than previously thought.
Even though the most suitable solvents for the process are propane and ethane, a mixture
of butane, propane and ethane may suffice depending on reservoir pressure and
temperature (Karmaker and Maini, 2003). Regardless of solvent selection, the optimum
injection point is near the dew point where the vapour phase has maximum solubility and
there is maximum diffusivity in the liquid phase (Talbi and Maini, 2003).
Increases in the price of gas have hurt the feasibility of the conventional VAPEX method.
Currently, CO2, as a carrier gas, is a good alternative because of its low cost and higher
solubility than methane, meaning it will dilute better. Besides, from an environmental
point of view, CO2 sequestration through CO2 injection in heavy oil reservoirs can be one
of the most promising technologies for mitigating atmospheric CO2 concentration (Manik
et al., 2003 and Mohammadpoor et al., 2012). However, CO2 mixture-heavy oil systems
form multiple liquid phases that reduce the gravity drainage effectiveness by introducing
complex relative permeability effects (Talbi and Maini, 2003).
22
2.2.1 Solvent requirement
Several factors affect appropriate injection solvent selection criteria. These factors
include equilibrium pressure, molecular weight, density difference, solubility, diffusivity,
reservoir temperature, and pressure (Upreti et al., 2007). Specifically, when a low
molecular weight vapourized solvent is injected into the reservoir near its dew point, the
solubility of the vapourized solvent reaches its maximum near its dew point (Upreti et al.,
2007 and Das, 1995). This causes additional viscosity reduction by deasphalting.
Additionally, a higher density difference between a vapourized solvent and heavy oil
results in superior gravity drainage (Das and Butler, 1998).
As an injected solvent, propane is common in VAPEX studies. After all, Das and Butler
found propane and butane to be the most effective solvents for VAPEX (Das and Butler,
1994). They found that propane diffuses faster and produces higher production rates.
Moreover, in further investigations, Butler and Jiang (2000) found that a 50:50 mixture of
butane and propane has approximately the same performance of pure propane and is
better than a pure butane injection. Likewise, Kok et al. (2009) and Yildirim (2003)
utilized propane and butane solvents on light, medium, and heavy oil in a Hele-Shaw cell
at three different injection rates. In the experiments with heavy oil, butane had the highest
injection rate, even better than propane. However, with the other two rates, both solvents
showed almost identical performance. Ultimately, the results revealed that propane
provides better results than butane in almost all injection rates for light and medium oils.
Azin et al. (2005) found that in the reservoirs with relatively low viscosity, higher
injection rates create higher oil production. Alternatively, in the reservoirs with high
23
initial viscosity, the higher injection rate results in a system pressure increase that may
cause an early solvent breakthrough.
Another viable and economically sound option is implementing non-condensable and
inert gases as carrier gases. For instance, methane and CO2 are suitable carrier gases for
solvent injection during the VAPEX process (Talbi and Maini, 2003).That is, they found
that the carbon dioxide and propane mixture showed better results than the methane and
propane mixture at 600 psig. Thus, at higher pressures, where carrier gas concentration is
increased, oil production decreased using either of the above-mentioned mixtures.
Furthermore, Torabi et al. (2012) found that incorporatingCO2 into the solvent for a
VAPEX process is a viable option. For them, the non-condensable gas portion of the
solvent should be less than 60% of the total mixture. Through several simulation runs
with different solvent compositions, they found that replacing a portion of the methane in
the solvent with CO2 resulted in equal or greater recovery factors in most simulations.
Frauenfeld and Lillico (1999) patented the use of a mixture of hydrocarbon solvents to
increase the effectiveness of solvent-assisted heavy oil recovery processes. They utilized
mixtures of propane, ethane, and butane with paired-injector and producer-well systems
or single-well cyclic systems.
After monitoring the effects of non-condensable and inert gases on the production history
of the VAPEX process, Chatzis et al. (2006) found that the accumulation of non-
condensable gas near the boundary reduced the advance rate of the VAPEX chamber.
They also found that the position of the vapour front is proportional to the square root of
time.
24
From an environmental perspective, many promote the mixing of the solvent with a non-
condensable gas such as CO2. It is also economically profitable, as doing so reduces
expensive solvent inventory. Therefore, CO2 sequestration through CO2 injection in
heavy oil reservoirs is one of the most promising technologies for mitigating atmospheric
CO2 concentration (Manik et al., 2003 and Mohammadpoor et al., 2012). However, the
lower rate of solvent mass transfer into the heavy oil and less heavy oil dilution may be
possible disadvantages of mixing the diffusing solvent with non-condensable gas (James,
2009).
2.3 VAPEX mechanism
In the first step of the VAPEX, the solvent is injected through the injector to form an
initially vertical solvent vapour chamber between the injector and the producer. The
vapour chamber then spreads, and gravity stabilizes the oil-solvent interface. Here,
molecular diffusion of solvent vapour into the bitumen controls drainage (Butler and
Mokrys, 1990). Thus, in order to maximize solvent vapour contact with the reservoir, the
injection and production wells should be drilled horizontally. Figure 2-1 shows the
VAPEX mechanism schematic.
There are two types of gravity drainage flow during the VAPEX process: boundary
drainage and transition film drainage (Roopa and Dawe, 2007). In fact, Roopa and Dawe
(2007) found that the rate of film drainage that occurs in the three-phase flow processes
within the vapour chamber depends on the effects of temperature on viscosity, diffusion
coefficients, mass transfer, interfacial tension, and wettability.
25
Figure 2-1: Mechanisms of VAPEX process
Pay zone
Solvent vapour chamber
Horizontal injection well
Horizontal production well
Solvent/heavy oil interface
Draining of diluted oil Draining of diluted oil
26
Also worth mentioning, Yang and Gu (2005) found that capillary and gravity forces
control gravity drainage in porous media. They measured the interfacial tension between
the Lloydminster heavy oil and four solvents (methane, ethane, propane, and carbon
dioxide) at different pressures below their vapour pressures by applying the axisymmetric
drop shape analysis (ADSA) technique for the pendant drop case. They found that the
interfacial tension between heavy oil and a solvent is reduced linearly with pressure
(Yang and Gu, 2005). Furthermore, Cuthiell et al. (2006) investigated the effect of
capillary force, concluding that capillary mixing could significantly influence
diffusion/dispersion. In short, layering effects will increase mixing and drainage speed.
However, reservoir layer heterogeneity will typically be much greater than that in a
prepared sand pack, and this may significantly enhance VAPEX drainage rates. In their
study, Cuthiell et al. (2006) found that dispersive mixing was consistent with molecular
diffusion only in that there was little or no enhancement due to convective dispersion.
Absence of capillary pressure allows the vapour to achieve maximum vertical
propagation with the lowest amount of sideways leaching (Ayub, 2009). Because
capillary pressure tends to delay the gas production without affecting the overall
recovery, it produces a significant amount of asphaltene precipitation near the injection
well (Ayub and Tuhinuzzaman, 2007).
Along the same lines, Rostami et al. (2007) observed the dual effect of capillary force in
the VAPEX process. Capillary forces hinder solvent breakthrough, and the establishment
of well communication and chamber extensions is different from conventional cases. In
fact, cumulative oil production is increased because of solvent-oil relative permeability
alteration that occurs due to surface tension reduction (Rostami et al., 2007).
27
2.3.1 Molecular diffusion
Diffusion plays an important role in the VAPEX process. Indeed, solvent gas diffusion is
the main molecular reaction that accounts for gas absorption and, consequently, a
reduction in mixture viscosity (Upreti et al., 2007). Of note, during the molecular
diffusion, the gas first moves towards the oil–gas interface; then, the gas penetrates the
interface before penetrated gas diffuses in the oil body in the last stage (Pourabdollah et
al., 2013). Because of the importance of molecular diffusion, in order to determine the
amount of injection gas, the amount of heavy oil reserves that will undergo the viscosity
reduction, the time required for the viscosity reduction to take place, and the rate of oil
production, an accurate knowledge of gas solvent-heavy oil system diffusion is
necessary.
In that vein, two main methods experimentally determine the diffusion coefficients. The
first method is the direct method, in which different liquid samples undergo
compositional analysis at different times (Schmidtet al., 1982).
The second method is the indirect method, which includes change in volume, pressure,
solute volatilization rate, position of the gas liquid interface, and nuclear magnetic
resonance (Upreti et al., 2007). Actually, Renner (1988) proposed an in-situ method for
measuring molecular diffusion coefficients of CO2, methane, ethane, and propane. His
proposed correlation was a function of liquid viscosity, molecular gas weight, molar
volume of gas, as well as gas pressure and temperature. Likewise, Riazi (1996) proposed
a semi-analytical model for the estimation of mass transfer rates caused by diffusion
between a non-equilibrium gas and a liquid in a constant volume cell with a constant
temperature. In his method of measuring diffusion coefficients, no compositional
28
measurements are necessary. Furthermore, Grogan et al. (1988) found that CO2
diffusivity in both pure hydrocarbons and crude oil at reservoir conditions depends
primarily on solvent viscosity. Measurements are based on the direct observation of
interface motion caused by CO2 diffusion through oil or oil shielded by water. Then, they
determined the diffusion coefficients by fitting the mathematical models to the observed
interface motions.
Oballa et al. (1989) found that the diffusivity coefficient is strongly dependent on
concentration. In short, overall diffusivity reaches a maximum at an intermediate
concentration.
Another indirect method to calculate the diffusion coefficient is nuclear magnetic
resonance (NMR) (Afsahi and Kantzas, 2005; Salama and Kantzas, 2005; Wen and
Kantzas, 2005). Afsahi and Kantzas (2005) found that diffusivity of heptane into Cold
Lake bitumen in the presence of sand is approximately 10-6
to 10-7
cm2/s, which is within
the same order of magnitude of the solvent diffusivity into pure heavy oil and bitumen.
They also observed that diffusivity decreases as diffused solvent concentration into
bitumen increases over time before nearly stabilizing after a few hours of diffusion.
However, they concluded that although the diffusivity is a function of concentration, it is
constant in short time intervals.
In addition, Wen and Kantzas (2005) successfully implemented the nuclear magnetic
resonance (NMR) method to study solvent-heavy oil/bitumen mixture properties. They
used low-field NMR and x-ray computer-assisted tomography (CAT) scanning for
solvent diffusion measurements with heavy oil or bitumen systems. They found that
29
diffusion coefficients calculated from NMR data provided results that were reasonable
and similar to those obtained via CAT scan.
Hayduk and Cheng (1971) introduced a relationship between the diffusivity equation and
viscosity. Based on the experimental data, they concluded that a unique diffusivity-
solvent-viscosity relationship—one independent of temperature and solvent
composition—exists for each different diffusing substance. After plotting data on a log-
log paper, they found a linear relationship between viscosity and diffusivity. In fact, the
slope of the line appeared to depend on the diffusivity itself: the lower the diffusivity, the
higher the slope. They proposed the following relationship:
BAD ……………………………………………………………………...……… (2.1)
Constants A and B apply to each diffusing substance.
However, the above-mentioned equation does not consider the effect of porous media. In
the presence of a porous medium, “apparent diffusivity” accounts for the effect of porous
media on diffusivity. In 1963, Perkins and Johnson suggested porous media (both
unconsolidated packs and consolidated rocks) can, as networks of flow chambers, have
random size and flow conductivity that are connected by smaller-sized openings. Then,
they proposed the following equation to calculate the diffusion coefficient in such a
porous medium:
FD
D
o
1 ……………………………………………………………………….…….. (2.2)
30
where D is the apparent diffusivity, is the diffusivity, while F is the formation
electrical resistivity factor, and is the fractional porosity. Fatt (1958) also reported the
same diffusivity and formation factor relationship.
In addition, Grane et al. (1961) found that at sufficiently low flow rates, transverse and
longitudinal dispersion are equal and are determined by the coefficient of fluid molecular
diffusion and porous medium formation factor. However, at high flow rates in
consolidated media, transverse and longitudinal dispersion exist independent of fluid
properties and are proportional to flow velocity.
Researchers offer diverse correlations for calculating diffusivity coefficient. However,
each of these correlations is valid in its own range of assumptions.
Wilke and Change (1955) carried out experiments involving iodine and toluene diffusion
in a variety of hydrocarbon solvents; they proposed the following equation:
…………………………………………………………...… (2.3)
where D is in cm2/sec and the association parameter x is introduced to define the effective
molecular weight of the solvent with respect to the diffusion process. M is molecular
solvent weight, T is temperature in , while µ is viscosity in centipoises, and V is molar
volume of a solute at normal boiling point in cc/g.mole.
Moreover, Hiss and Cussler (1973) calculated the diffusion coefficients of n-hexane and
naphthalene in a series of hydrocarbon liquids with viscosities from to 5 kg.m−1
sec−1
(0.5 to 5000 cp) at 25°C. They used a Savart-plate-wave front-shearing
oD
6.0
2/18 )(
104.7V
TxMD
K
4105
31
interferometer that allows direct determinations at effectively infinite dilution. They
proposed the following equation to calculate the diffusivity:
3/2Da ……………...……………………….…………………………………….. (2.4)
Next, Hayduk et al. (1973) conducted experiments using the steady state capillary cell
method; they proposed the following equation to calculate diffusivity:
……………………………………………………………… (2.5)
where D is m2/s and µ is Pa.s.
Later, Hayduk and Minhas (1982) proposed correlations for solute diffusivity in aqueous
solutions:
…………………………………………......…. (2.6)
……………………………………………………………………..… (2.7)
where D is in cm2/sec, V is molar volume of solute at normal boiling point in cc/ mole, T
is temperature in , µ is viscosity in centipoises. For non-aqueous solutions:
………………………………………………...……. (2.8)
where D is in cm2/sec, VA and VB are molar volume of solute and solvent respectively at
normal boiling point in cc/ mole, T is temperature in , while µ is viscosity in
545.09100591.0 D
52.119.08 )292.0(1025.1 TVD
12.158.9
V
K
105.0
125.0
92.0
29.1
42.0
27.081055.1
A
B
A
B T
V
VD
K
32
centipoises, and σ is surface tension at the normal boiling point temperature in dyne/cm.
For paraffin solutions:
………………………………………………………..….. (2.9)
………………………………………………………………..…… (2.10)
where D is in cm2/sec, V is molar volume of solute at normal boiling point in cc/ mole, T
is temperature in , while µ is viscosity in centipoises.
Reid et al. (1987) proposed another correlation for calculating diffusivity (Yazdani,
2007):
…………………………………………………………………………. (2.11)
Das and Butler (1996) proposed the following equations. For propane as the solvent:
………………………………………………………….…… (2.12)
Moreover, for butane as the solvent:
…………………………………………………………….… (2.13)
where D is in m2/sec, and is viscosity in Pa.s.
Moreover, Upreti and Mehrotra (2002) found that the gas diffusivity increases with
temperature and pressure. They used a non-intrusive experimental method to calculate the
71.047.18103.13 VTD
791.02.10
V
K
d
RTD
3
46.0910306.1 D
46.0910131.4 D
33
diffusivity of CO2, methane, ethane, and nitrogen as a function of gas concentration in
bitumen. They provided the following correlation for the average diffusivity:
………………………………………………………… (2.14)
where D is in m2/sec, T is temperature in , and d0 and d1 are correlated coefficients.
2.3.2 Physical dispersion
The term dispersion refers to the additional mixing caused by concentration gradients or
uneven fluid flow when the fluids are flowing through the porous medium. Of note,
dispersions in the longitudinal and transverse fluid flow are not equal; hence, there are
two different types of dispersion: longitudinal and transverse (Perkins and Johnston,
1963).
Dispersion, or effective diffusion, is fluid mixing due to diffusion and convective motion.
During VAPEX, gravity drainage causes convective motion and an additional mixing that
results in dispersion. Heavy oil viscosity is reduced when the vapourized solvent diffuses
into the heavy oil. In addition, the diluted oil drains due to gravity.
Several factors in addition to molecular diffusion will improve the heavy oil recovery
during VAPEX. These factors include convection, increased interfacial area, and the
resulting continuous renewal of surface area exposed to the solvent in the porous media
caused by the oil drainage, increased gas solubility, and capillary phenomena at the
solvent-oil interface (Upreti et al., 2007).
)15.273(ln 10 TddD
C
34
Taylor (1953) showed that the distribution of concentration after introducing a soluble
substance into a fluid (flowing slowly through a small-bore tube) spreads under the
combined effect of molecular diffusion and velocity variation.
Likewise, Kapadia et al. (2006) developed a mathematical model to calculate the
dispersion coefficient of butane along with its solubility in Cold Lake bitumen. They
found that gas dispersion, as well as heavy oil and bitumen viscosity, were dependent on
composition.
More recently, El-Haj et al. (2009) conducted VAPEX experiments using three different
types of glass beads and Athabasca bitumen. They developed a mathematical model to
calculate the optimum interfacial mass fraction, and the dispersion coefficient of butane
in the medium. They found that the dispersion coefficients were three orders of
magnitude higher than the molecular diffusion. Other researchers have reported this
enhanced mass transfer as well (Dunn, 1989; Das and Butler, 1995; Boustani and Maini,
2001; Odenton et al., 2001 and Yazdani and Maini, 2005).
Furthermore, Ahmadloo et al. (2011) proposed a new correlation for effective diffusivity.
They performed VAPEX experiments with butane as the solvent and concluded that
capillary forces play an important role in the process. They represented this effect in their
correlation by introducing the term A, which stands for specific pore surface area. They
also included the drainage height as an important parameter:
………………………...……………… (2.15) 7956.06096.0555.171045.1 AhDeff
35
where Deff is in cm2/sec, h is drainage height in cm, A is specific pore surface area in m
2/g,
and is viscosity in mPa·s.
Finally, Abukhalifeh et al. (2013) studied the effect of drainage height on the
concentration-dependent dispersion coefficient of propane in heavy oil during the
VAPEX process. They found that the propane dispersion coefficient, the amount of
dissolved propane, and the oil production rate all increase with an increase in model
height.
2.4 Asphaltene precipitation
Asphaltenes are components of heavy oil with high molecular weights ranging from
1,000 to 2,000,000 g/mole. They are dark brown or black friable solids that do not have
any definite melting point. Asphaltenes have complex molecules, so much so that the
exact structure of their molecules is still unknown. In addition, they contain aromatic
rings and oxygen, nitrogen, and sulphur, as well as heavy metals such as vanadium,
nickel and iron. Canada’s heavy oil asphaltene components on average are: 45.25%
carbon, 52.45% hydrogen, 0.74% nitrogen, 0.68% oxygen, and 0.87% sulphur (Fredrich,
2005).
The liquids used to obtain asphaltenes from petroleum are non-polar solvents with low
boiling points. These include petroleum naphtha, petroleum ether, n-pentane, n-heptane,
and iso-butane (Speight, 2004). Furthermore, asphaltene constituents are insoluble in
methane, ethane, and propane.
The amount of asphaltene precipitation after using any of the mentioned separating
liquids depends on the solvent used, temperature, solvent concentration, and the time
36
during which the crude oil and the solvent are mixed together. Figure 2-2 shows the
asphaltene precipitation in weight% of the bitumen using different hydrocarbon solvents
(Speight, 2007).
As mentioned, the volume of solvent added to crude oil also affects asphaltene
precipitation. There are different standard methods for asphaltene precipitation. These
standard methods when using n-pentane and n-heptane as the solvent are listed in Table
2-1. During asphaltene separation, if insufficient amounts of liquid hydrocarbon are used,
resins appear within the asphaltene fraction. This isolates asphaltene from crude oil and
consequently creates an incorrect report about crude oil asphaltene content.
To more accurately calculate asphaltene levels, Speight (2004) listed the following
factors to measure precipitation amount and type:
1. Adding more than 30 mL of hydrocarbon per g of feedstock.
2. Using n-pentane or n-heptane.
3. Providing 8 to 10 hours of contact time.
4. Doing a precipitation sequence to eliminate absorbed resin.
The experimental studies in this research were conducted by injecting various solvents in
two low permeability large scale visual models to monitor the asphaltene precipitation
with higher accuracy. The specific well configuration used in this study and the large
dimensions of VAPEX physical help to monitor the VAPEX process and asphaltene
precipitation in more details.
37
Figure 2-2: Yield of asphaltene precipitation for various hydrocarbon solvents (after Speight, 2007)
38
Table 2-1: Standard methods for asphaltene precipitation measurement (after Speight, 2004)
Method Precipitant
Volume precipitant per g of
sample (mL)
ASTM D-893 n-pentane 10
ASTM D-2006 n-pentane 50
ASTM D-2007 n-pentane 10
ASTM D-3279 n-heptane 100
ASTM D-4124 n-heptane 100
IP 143 n-heptane 30
Syncrude method n-pentane 20
39
2.4.1 Asphaltene precipitation in VAPEX
A phase change occurs during the VAPEX process when the solvent diffuses into the oil
at the solvent oil interface. During this phase change, there will be change in temperature,
pressure, and concentration at the contact interface. This will result in asphaltene
deposition. The asphaltene precipitation has a contentious effect on the VAPEX process.
While the asphaltene precipitation may improve the quality of heavy oil by reducing the
viscosity, at the same time, it may alter the wettability of rock and consequently plug
some of the pores.
Mokrys and Butler (1993) conducted a deasphalting experiment in a pressure cylinder
packed with l mm glass beads. Cold Lake bitumen and Lloydminster heavy oil were
deasphalted. They used propane as a precipitant. They found that the viscosity of Cold
Lake bitumen was decreased by a factor of 300 and the viscosity of Lloydminster oil was
decreased by a factor of 50.
Nghiem et al. (2000) carried out phase behaviour calculations and a compositional
simulation of asphaltene precipitation for the VAPEX process with Lindbergh heavy oil.
They found that asphaltene precipitation occurs in the oil phase region that is adjacent to
the vapour chamber and spreads with the growth of the chamber. They observed that the
streaks of asphaltene precipitation in their simulation occur in the same way other
researchers reported. However, they observed wider streaks at the upper part of the
solvent chamber.
Luo and Gu (2005) performed experiments to determine the effect of asphaltene content
on heavy oil viscosity. They found that if the heavy oil asphaltene content is reduced
40
from 14.5% to zero, the sample heavy oil viscosity is reduced by 13.7 times. Their results
appear in Figure 2-3.
Moreover, Pourabdollah et al. (2011) carried out VAPEX experiments on sand packs
with Iranian bitumen to investigate the effect of vapour dew point and permeability on
the movement of asphaltene streaks. Their results demonstrated that when the solvent
pressure was less than its dew point, precipitated streaks irregularly remained on the
surface of glass beads. Furthermore, they observed that when the solvent pressure was at
the dew point, the precipitated streaks moved faster and their movement was in the
direction of live oil to the production well. Ultimately, they did not observe any
precipitation in a high permeable porous medium (Pourabdollah et al., 2010).
Pourabdollah et al. (2011) conducted VAPEX experiments in a modified sand pack
model where nanoclay particles were added to glass beads. They used montmorillonite as
nanoclay alongside glass beads and used propane as the solvent for VAPEX experiments.
The nanoclays acted as adsorbents in heavy oil to adsorb the asphaltene and decrease
bitumen viscosity. They observed that, in the nano-assisted sand pack, there was higher
asphaltene precipitation and a larger contact area that resulted in higher production rates.
41
Figure 2-3: Effect of asphaltene content on heavy oil viscosity (after Luo and Gu, 2005)
42
2.5 Economic and environmental advantages
The VAPEX process is a non-thermal process. This makes it more energy and
environmental efficient. Since no steam generation and water recycling are required, this
process is significantly fuel efficient. From an environmental point of view, and being
non thermal, there will be no CO2 emissions. In addition, in cases of CO2-based VAPEX
processes, it will also help sequester CO2.
In 1998, the Petroleum Recovery Institute (PRI) conducted a project of 16 participants
with nine research-performing organizations to evaluate the full project engineering and
commercial scale economics for the VAPEX process. They calculated the supply cost
economics for VAPEX oil production from the Athabasca oil sands, Cold Lake oil sands,
and Southeast Alberta heavy oil. Supply cost means the threshold price required for
satisfactory economics. Their results showed that VAPEX has economic and
environmental benefits. However, they emphasized that specific field conditions should
be studied in detail and that a pilot test should be carried out before implementing
VAPEX (Luhning et al., 2003).
Among all these advantages, the possibility of solvent loss and compression is a
drawback of the VAPEX method. On the other hand, the key drawback for this method is
lack of knowledge about the process specifically in real field-operating conditions. This
method is not well tested in the field and there is not much information available about
legitimate field design for facilities.
Luhning et al. (2003) performed an economic analysis based on the results of the history
matching experimental results. Their economic analysis was based on matching the
43
simulation results with the real field-operation costs as well as for estimations of real
field costs. The analysis was based on supply costs reported by Canadian Energy
Research Institute (CERI). However, they estimated such costs at an even higher rate
than the reported amounts for facility and VAPEX solvent costs. Although they
considered their estimation margins safe, the results showed economic profits and
attractiveness.
The environmental advantages of VAPEX process can be listed as follows (Luhning et
al., 2003):
1. CO2 sequestration: Because of solvent recycling, there will be pressure depletion
in the reservoir after oil production using VAPEX; this happens in most heavy oil
recovery methods. Moreover, CO2 can be a very good candidate to pressurize the
reservoir and maintain reservoir pressure as greenhouse gas sequestration takes
place. In fact, there was an amazing comparison regarding the amount of CO2 that
can be sequestered during a VAPEX process. They found that during an
Athabasca VAPEX-depleted reservoir, the amount of CO2 that can be
permanently sequestered is 0.5% to 2% of the total CO2 produced by all the cars
in Alberta.
2. Lower transportation costs: Due to the in-situ upgrading that takes place during
the VAPEX process, there will be less asphaltene in the produced oil. This means
less energy is needed for the transportation of heavy oil in pipelines as well as less
maintenance costs for the pipelines and facilities. This will consequently decrease
the future emissions of and lower facility bottlenecking in refineries.
44
3. Conservation of natural gas, water, and injection solvent: Since VAPEX is a non-
thermal process, steam is not generated so, there will not be any transformation of
water and clean natural gas to steam and greenhouse gas emissions. Alternatively,
because of the nature of the VAPEX process, solvent gas recovery will reduce the
costs and environmental effects.
4. Less surface and overlying reservoir disturbance: The number of surface facilities
required for the VAPEX process is significantly fewer than the facilities required
for thermal methods such as SAGD. This means less surface disturbance,
especially in areas where there are limitations on the land surface available for
production operations. However, since VAPEX is a low-pressure non-thermal
gravity drainage method, there will not be much pressure and temperature
disturbance to the overlying layers of the recovery zone. In the end, the residual
temperature and pressure of the reservoir after implementing the VAPEX method
is significantly unchanged.
45
3. CHAPTER 3: EXPERIMENTAL SETUP, MATERIALS,
AND PROCEDURE
The implementation of EOR techniques in any oil reservoir often carries enormous costs.
As such, a preliminary study is necessary to avoid the loss of natural resources and
unnecessary expenses. Therefore, running an experimental setup—a smaller
representative of the actual reservoir—will give a better understanding of the
mechanisms affecting a specific EOR technique. This will give the researchers the
opportunity to understand the key parameters affecting the process, to investigate the
uncertainties, and to design a more cost-effective pilot field test. To achieve this goal, a
comprehensive experimental study was designed and carried out in order to investigate
the applicability of injecting different solvents during the VAPEX process. To better
represent and simulate the actual conditions, two large VAPEX models were designed
and successfully used for these experiments.
3.1 Experimental setup
The experimental set up consists of four major units: a solvent injection unit, the VAPEX
physical models, a solvent and liquid production unit, and a data acquisition system. In
this section, each unit is explained in more detail.
3.1.1 Solvent injection unit
In this study, VAPEX experiments were conducted under constant pressure. In this case,
the solvent injection unit was composed of gas cylinders (propane, CO2, methane and
butane), gas pressure regulators, digital pressure gauges, solvent injection valves, and
46
digital flow meters calibrated specifically for each gas. The solvent was injected through
the pressure regulators to monitor the cylinder injection pressure through the digital flow
meters. Another pressure gauge records pressure at the point of injection to the model. In
addition, the flow rate and total solvent volume were recorded accurately with the digital
flow meters. Pure CO2, methane, propane, and butane gas (99%) cylinders were
purchased from Praxair and were used for the VAPEX tests. The injected gas was passed
through the AALBORG digital flow meters (DFM) (Figure 3-1) before entering the
packed models. Furthermore, four different DFMs were purchased from AALBORG, and
each one was calibrated specifically for each pure gas. The details about each DFM are
shown in Table 3-1. All these DFMs recorded the flow rate and total volume of injected
gas during the experiments, and they were all connected to the data acquisition unit for
continuous data recording.
47
Figure3-1: Digital flow meter (DFM)
48
Table 3-1: DFM specifications
No. Calibrated Gas Model Number Max. Pressure
(kPa)
Flow Rate
(mL/min)
1 Propane (C3H8) DFM26 6800 0-1000
2 Methane (CH4) DFM26 6800 0-1000
3 Carbon Dioxide (CO2) DFM26 6800 0-1000
4 Butane (C4H10) DFM27 680 0-2000
49
3.1.2 Physical models
The major components of the experimental set up were the VAPEX physical models.
Two 2-D rectangular VAPEX models with different sizes were used to carry out the
experiments. The dimensions of these models are shown in Table 3-2. It should be noted
that these physical models were used for another study by other researchers (Ahmadloo et
al., 2011). These visual slab models are made of Plexiglas plates with a stainless steel
frame. Figure 3-2 shows the Plexiglas slabs for the large and small models. To seal the
model pressure, gaskets were put between the slabs and the steel frame (Figure 3-3). In
addition, another steel protection cover was bolted to the Plexiglas plates to increase the
pressure tolerance of these models (Figure 3-4). These models are designed for a
maximum pressure of 1 MPa. The visual slabs limit the maximum operating pressure.
However, their transparency was necessary for visual observation of the solvent injection
process, specifically in terms of gas chamber evolution. The physical models were
assembled on a steel frame mounted on a steel stand with rotation capability for better
packing and cleaning purposes (Figure 3-5). Four injection/production ports were
designed on the top and bottom of the large model; there are two injection/production
points for the small model on the top and bottom sides. Figures 3-6 and 3-7 show the
schematic and dimensions of the physical models as well as the cavity space of each
model.
50
Table 3-2: Dimensions of physical models
Physical model Height (cm) Length (cm) Thickness (cm) Volume (cm3)
Small 24.5 20 5 2450
Large 47.5 38 5 9025
51
Figure 3-2: Plexiglas slabs, (a) Large model slab (b) Small model slab
52
Figure 3-3: Gaskets, (a) Large model gasket, (b) Small model gasket
53
Figure 3-4: Steel cover protectors, (a) Large model, (b) Small model
54
Figure 3-5: Physical models assembled on a steel frame mounted on steel stand
55
Figure 3-6: The schematic of the large physical model and its sand pack cavity
47
.5 c
m
38 cm
Injection
Production
Sand pack cavity
Physical Model
Steel cover protector
Plexiglas Slabs
56
Figure: 3-7: The schematic of the small physical model and its sand pack cavity
24.5
cm
20 cm
Injection
Production
Sand pack cavity
Physical Model
Steel cover protector
Plexiglas Slabs
57
3.1.3 Solvent and liquid production unit
The fluid production unit included production control valves, digital pressure gauges,
back-pressure regulators (BPR), nitrogen gas cylinders, separators, wet test meters
(WTM), and oil sample collectors. Digital pressure gauges were mounted at the
production points to monitor the outlet pressure. The BPRs (Figure 3-8) were used to
maintain the pre-specified pressure in each VAPEX model during the experiments. Two
more pressure gauges were mounted on each BPR to monitor the pressure of the gas line
connected to each BPR from the nitrogen gas cylinders. The produced oil and gas were
collected in two separators below each physical model. These separators were visual and
calibrated to record the volume of oil produced during the course of experiments. The
separators were made of Plexiglas with two stainless steel flanges on the top and bottom
to seal the vessel. The separators were bolted on a steel stand. These separators are shown
in Figure 3-9. In addition, there were two connection points on top of each separator and
one connection point on the bottom. The produced oil was collected from the BPR
through one of the top connection points. The other connection point is connected to
another valve toward the WTM in order to collect the free gas. This is the same for each
separator. The dead oil is collected from the bottom. The rate and total volume of the
produced gas are accurately measured with two WTMs.
58
Figure 3-8: High pressure back-pressure regulator (BPR)
Oil Inlet
Oil Outlet Gas Dome
59
Figure 3-9:Two-phase separators
60
The WTMs used for these experiments are shown in Figure 3-10. These WTMs were
Ritter TG05s. They were drum-type gas meters with a maximum pressure of 1 bar and
maximum flow rate of 60 L/h. They had refined stainless steel casings and polypropylene
measuring drums.
Furthermore, each WTM included a pulse generator, which was connected directly to the
data acquisition system through Rigomo software to record the flow rate and total volume
of the produced gas. Because of the large volume of the physical models, and as a result
the large amount of produced gas, conventional bubblers did not seem to be accurate due
the necessity to continuously refill the water cylinder. Ultimately, the oil sample
collectors were simply glass jars that collected the dead oil through the valves connected
to the bottom connection points of each separator.
3.1.4 Data acquisition unit
During the course of experiments, different parameters were recorded. This unit was
composed of a computer as well as special ports, converters, and pulse generators. The
rate and total volume of injected gas were recorded with DFMs. The data was stored
using DFM controller software offered by AALBORG. The rate and total volume of
produced gas were recorded with the WTM pulse generators. Then, the data were sent to
the computer and recorded with Ritter’s Rigomo software.
Figure 3-11 shows the experimental set up schematic, while Figure 3-12 shows the
experimental set up design, and Table 3-3 summarizes all the parts and equipment used
for building and designing the experimental setup.
61
Figure 3-10: Wet test meters (WTM)
62
Figure 3-11: Schematic diagram of the experimental set-up
63
Figure 3-12: Experimental setup
64
Table 3-3: List of experimental equipment
No. Device/ Model Number Manufacturer Quantity
1 Physical Models UOR Workshop 2
2 Separators UOR Workshop 2
3 Syringe Pump/ 1000D Teledyne isco 1
4 Vacuum Pump Fisher Scientific 1
5 Transfer Cylinder 3
6 Digital Flow Meter/ DFM26,
DFM27 Aalborg 4
7 Wet Test Meter/ TG05 Ritter 2
8 Digital Pressure Gauge/ Ashcroft 4
9 Digital Pressure Gauge/ Heise 1
10 High Pressure BPR Core Laboratories 2
11 Pressure Regulators Swagelok 5
12 Gas Cylinders Praxair 5
13 Vibrator/ ABU-38 Deca Vibrators Industries 1
14 Electric Balance/ Mettler TOLEDO 1
15 Heater/ Stirrer Fisher Scientific 1
16 Computer Dell 1
17 Light Source Underwriters Laboratories
Inc. 1
18 Digital Camera Canon 1
19 High Pressure Gas Sampler Swagelok 1
20 Two Way Valve Swagelok 10
21 Three Way Valve Swagelok 6
22 Ball Valve Swagelok 4
23 Check Valve Swagelok 4
24 1/8 “ Steel Tubing Swagelok 60 ft
25 1/4 “ Steel Tubing Swagelok 4 ft
26 1/8 “ Plastic Tubing Swagelok 30 ft
27 Buchner Flask PyrexPlus 1
28 Buchner Funnel Coorstek 2
29 Filter Paper/ No.2, No. 5 Whatman N/A
65
3.2 Materials
3.2.1 Sand
Ottawa sand #530 (Bell and Mackenzie Co. ltd., Canada) was used to pack the VAPEX
physical models. This is a white sand with a rounded grain shape and is 99.88% silicon
dioxide (SiO2). The specific gravity of the sand used for this study was 2.65 (γH2O=1.0).
Figure 3-13 shows the screen analysis for Ottawa sand #530. In each experiment,
approximately 4.3 kg of sand was used to pack the small model, while, for the large
model, approximately 16.5 kg of sand was used for packing.
3.2.2 Heavy oil
A heavy oil sample representing Saskatchewan heavy oil with a viscosity of 14271 mPa·s
at 21°C was used in this study. In order to reach the pre-specified dead oil viscosity for
the VAPEX experiments, kerosene was added to the oil sample. In order to get a
homogeneous oil sample, a mixture of kerosene and heavy oil was stirred for an hour,
and then the mixture was placed in an air bath that was heated for several hours.
Meanwhile, the mixture was stirred with a mixer. It was then cooled to 21°C before
viscosity was measured. This process was repeated if additional kerosene was needed to
get to the pre-specified viscosity. Then, the compositional analysis of the heavy oil
sample was obtained by using the simulated distillation method. The Saskatchewan
Research Council (SRC) lab undertook this analysis.
66
U. S. Sieve
0 25 50 75 100 125 150 175 200 225 250 275 300 325
wt
% R
etai
ned
0
10
20
30
40
Figure 3-13: Screen analysis for Ottawa sand #530
67
The results are shown in Table 3-4 and in Figure 3-14. It is obvious that there are no
hydrocarbon components under C9, and that the weight percent of C50+ is 10.54%. In
each experiment, approximately 1000 mL of heavy oil was used to saturate the small
model; for the large model, approximately 4000 mL of heavy oil was used for saturation.
3.2.3 Injection solvents and back pressure gas
Propane, butane, methane, nitrogen, and carbon dioxide gas cylinders were purchased
from Praxair Canada with the stated purity of 99.50%, 99.50%, 99.97%, 99.99%, and
99.99%. Propane, butane, methane, and carbon dioxide were injected as pure gases and as
mixture gases to be the solvent in the VAPEX experiments. The nitrogen gas was used
for the backpressure line to maintain the desired pressure using the BPRs for each test. It
was also used before starting the VAPEX experiments to conduct pressure leak tests.
68
Table 3-4: Compositional analysis result of the injection heavy oil with viscosity of 5650 mPa.s at 21°C
Carbon Number Mol.% Carbon Number Mol.%
C1 0.0 C31 1.20 C2 0.0 C32 1.16 C3 0.0 C33 0.80 C4 0.0 C34 0.76 C5 0.0 C35 0.97 C6 0.0 C36 1.02 C7 0.00 C37 0.61 C8 0.00 C38 0.57 C9 3.38 C39 0.95 C10 11.17 C40 0.96 C11 12.95 C41 0.53 C12 5.76 C42 0.58 C13 3.22 C43 0.80 C14 3.02 C44 0.75 C15 3.60 C45 0.50 C16 3.19 C46 0.49 C17 3.47 C47 0.51 C18 3.31 C48 0.50 C19 2.93 C49 0.39 C20 2.59 C50 0.38 C21 2.75 C51 0.42 C22 1.68 C52 0.41 C23 2.11 C53 0.38 C24 1.83 C54 0.33 C25 1.75 C55 0.31 C26 1.56 C56 0.31 C27 1.61 C57 0.29 C28 1.61 C58 0.31 C29 1.32 C59 0.30 C30 1.25 C60+ 6.44
502:WeightMolecular , mPa.s, 5650=oil 3
oil kg/m 971.53=
69
Figure 3-14: Hydrocarbon composition of injected oil
Carbon Number
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
C20
C21
C22
C23
C24
C25
C26
C27
C28
C29
C30
C31
C32
C33
C34
C35
C36
C37
C38
C39
C40
+
Mo
l. %
0
2
4
6
8
10
12
14
70
3.3 Experimental procedure
Each of the VAPEX experiments was performed in three major steps. The first step was
preparation. During preparation, the model was packed with sand, pressure leaks were
tested, and the model was then vacuumed and saturated with oil. The next step was
running the experiments, which included the continuous solvent injection, monitoring the
process, and recording the data. The last step was unpacking and cleaning the model.
These steps are explained in detail in the following sub-sections.
3.3.1 Preparation
3.3.1.1 Sand packing
As mentioned earlier, the physical models were bolted on a movable stand with rotation
capability. For the packing, the VAPEX models were set into horizontal position while
one of the slabs on each model was bolted. The cavities of the VAPEX models were
packed with dry Ottawa sand. Then, the gaskets, second Plexiglas slabs, and steel
protection covers were bolted in sequence, and the models were set back to the vertical
position. At this point, additional sand was added with a funnel through the top injection
ports to pack the empty spaces. In order to achieve more homogeneous packing, wet
packing and simultaneous shaking were conducted; water was used for wet packing due
to solution glazing behaviours of other solvents such as acetone on Plexiglas slabs. Next,
a syringe pump was used to inject the water in the models. The models were saturated
with water through the top injection points, and they were vibrated to get uniform
packing. For vibrating the models, an ABU38 pneumatic ball vibrator from Deca
Vibrator Industries Inc. was used. The vibrating was continued for 24 hours. Then,
pressurized air was injected for 24 hours to dry the sand and prepare the sand packs for
71
porosity measurement. After the air injection, the models were vibrated again for several
hours. At the same time, the models were rotated manually back and forth at 45° angles
to add sand to any void space at the top portion of the models. At this point, the
connections and required fittings, valves, and piping were connected to the top and
bottom ports of the physical models. Then, nitrogen was injected into the models at the
maximum allowable operating pressure of the VAPEX models to conduct the pressure
test and look for any possible leakage. In the last step, the physical models were
evacuated with a Fisher Scientific vacuum pump. For the large model, the evacuation was
conducted for 7 hours in 1-hour intervals, and, for the small model, it was done for 3
hours in 1-hour intervals. Pressure gauges were mounted to monitor the vacuum process.
Figure 3-15 shows the sand-packed VAPEX models.
3.3.1.2 Porosity measurement
After evacuating the VAPEX models, the solvent injection and production points and any
other connection points at the inlet and outlet ports were sealed tightly. The imbibition
method (Dong et al., 2006) was used to measure the porosity. By measuring the volume
of the water imbibed in the sand pack, the pore volume of the sand pack was measured.
The ratio of the pore volume to the total bulk volume was determined to be the sand
pack’s porosity.
72
Figure 3-15: Sand-packed VAPEX models
73
3.3.1.3 Oil saturation
In this study, initial water saturation was not considered. So, before oil injection, the sand
pack was dried with pressurized air. To get uniform oil saturation in the VAPEX models,
the oil was injected to the VAPEX models through the bottom connection points.
Therefore, oil was injected through two valves for the small model and four valves for the
large model. For this purpose, a high-pressure transfer cell was employed and connected
to a Teledyne 1000D syringe pump. To push the piston upward, tap water was injected to
the lower portion of the transfer cell. Doing so displaced the oil into the VAPEX models.
Because of the pressure constraints of the physical models, the injection rate was very
low, which made the oil saturation process very slow. It took about 2 to 3 days to saturate
the small model and about 6 to 7 days to saturate the large model.
The oil saturation set-up schematic is shown in Figure 3-16. During the saturation period,
the pressures of the physical models were monitored carefully to avoid over pressuring.
Figure 3-17 shows the saturated VAPEX models.
74
Figure 3-16: The schematic of the oil saturation set-up
Transfer Cell
Wat
er In
ject
ion
Oil
Inje
ctio
nSyringe Pump
75
Figure 3-17: Oil saturated sand packs
76
3.3.1.4 Permeability measurement
To calculate permeability, pressure drops at the injection and production points were
recorded. Then, the Darcy equation was used to measure the permeability. For this
purpose, the oil was injected at different flow rates, and, at each time, the stabilized
pressure drop was recorded to measure permeability. This procedure was repeated five
times and the average permeability value was measured for each test.
To confirm the value obtained by this method, the following equation proposed by
Carmen-Kozeny and modified by Panda and Lake (Faruk, 2007) was employed:
………………………………………………………….. (3.1)
where DP is the particle size, is porosity, is skewness, σ is variance, CP is the
coefficient of variance, and is tortuosity.
PPPP dDDfDD
3
0
3
1
………...……………………………...……….……. (3.2)
PPPP dDDfDD
2
0
2
….………………………………………….………….. (3.3)
P
PD
C
……………………………………………………….……...…………….. (3.4)
Kozeny-Carman-based models are the most common and oldest models used for
estimating permeability. These models treat porous media as bundles of capillary tubes of
equal length and constant cross section. Kozeny derived the equation to predict
permeability by solving the Navier-Stokes equation for all capillary tubes passing
222
22332
1172
13
p
ppp
C
CCDk
77
through a point (Krause, 2009).Carman (1937) modified the Kozeny equation to its new,
more recognized form. Kozeny-Carman-based models are mostly used by researchers to
estimate permeability (Krause, 2009).
3.3.2 VAPEX experiments
In this study, a total of eighteen VAPEX experiments were carried out. Different solvents
were injected in two VAPEX models with different drainage heights. Once the models
were saturated, the solvent injection line was connected to the top connection ports of the
VAPEX models. The solvent was injected at constant pressure from the gas cylinders to
DFMs and then to the VAPEX models at a pre-specified constant pressure. The flow
rates and total injected solvent volumes were recorded by the DFMs. For each VAPEX
test, the solvent was injected to the physical models at the operating pressure while the
production pressure was atmospheric pressure and the solvent and oil production was
monitored carefully. The pressure at the production point was implemented after which
the connection between the injection and production well was visually observed.
Once the oil was produced through the BPR, it was collected in the separators. By
reading from the calibrated visual separators, the cumulative produced oil was recorded
regularly during the course of the experiments. The produced gas was separated, and
then, from the top valves on each of the separators, the produced gas was passed through
the WTMs to measure its total volume. During the experiments, the produced oil samples
were collected at the production point in small oil containers. Next, the weight of the
produced oil was recorded with a high precession Mettler TOLEDO electric balance.
Then, the samples were kept for 7 days at atmospheric pressure, and the final weight of
each sample was recorded to find the dissolved solvent amount.
78
In addition, a Canon EOS T3i digital camera, in conjunction with a fluorescent light
source, was used to take digital images of the solvent chamber and its evolution at
different times during the tests. These images were further used for Image Analysis (IA)
purposes.
The compositional analysis of the heavy oil samples collected from the separators was
obtained by using the simulated distillation method. Furthermore, the density and
viscosity of the produced oil sample from each VAPEX model was measured for each
test.
A continuous presence of an operator was needed during the experiments for manually
recording some of the above-mentioned data. The solvent-leaching gravity-drainage
process is a very slow recovery process; therefore, this part of the experiments took about
7 to 21 days, depending on the size of the model and the type of solvent used for each
experiment.
The termination time for each test was considered the time at which a stabilized oil
production rate was monitored when the gas production rate was significantly high. At
this stage, solvent injection was shut down and the injection valves were closed. Next, the
models were depressurized and a blow down process was initiated. The production valves
were kept open until the pressure in the VAPEX models reached atmospheric pressure
and no more oil and solvent were produced.
79
3.3.3 Residual oil saturation and asphaltene content measurement
At this stage of the experiments, the connection lines at the injection and production ports
of the VAPEX models were opened, and the models were set to horizontal position. The
VAPEX models were then disassembled carefully. From the horizontal position, and
because of the special design of the VAPEX models, each slab could be taken apart from
the main steel frame separately while the other slab remained in place. Once the top slab
was removed, four different samples were collected from four different locations of each
VAPEX model. These sand pack samples were picked to locally cover different parts of
the sand packs. Sample 1 was collected near the injection point; sample 2 was collected
from the transition zone; sample 3 was collected in the oil zone between the transition
zone and the production point, while sample 4 was collected near the production point.
Figure 3-18 shows the different sand pack sample locations. Finally, the residual oil
saturation and asphaltene content of each sample were measured.
80
Figure 3-18: Sample locations, (a) Small model, (b) Large model
81
3.3.3.1 Residual oil saturation measurement
To measure the oil saturation in each sample, oil was separated from the sand. The
weight of each oil sample was measured individually, and, by knowing the oil density
and the specific gravity of the sand, the volume for each component was calculated.
The setup shown in Figure 3-19 was used to separate the oil from the sand. Toluene was
added to each sample. Then, the mixture was passed through Whatman No. 2-filter paper.
The filter paper was mounted on a Buchner funnel that was sealed on a Buchner Flask.
The jar was connected to vacuum pump, and, then, toluene was added gradually to the
sample until no oil was observed in the sand. The mixture of drained oil and toluene was
collected in the Buchner Flask. The collected mixture was kept in the air bath until the
toluene evaporated from the mixture. Finally, the weight of the remaining oil was
recorded to calculate residual oil saturation.
3.3.3.2 Asphaltene content measurement
The asphaltene content of each sample was measured using the standard ASTM D2007-
03 method. The precipitant used here was n-pentane. n-pentane was added to the oil
sample and stirred thoroughly. Then, the mixture was filtered through 0.2 μm Whatman
No. 5 filter paper as shown in Figure 3-20. The n-Pentane was added to the oil mixture on
the filter paper and was stirred continuously; this process was continued until clean liquid
drainage was monitored from the filter paper. Afterward, the asphaltene precipitant on the
filter paper was kept in the air bath for one day to dry completely. The final weight of the
asphaltene precipitate was recorded to measure the asphaltene content of each sample.
82
Figure 3-19: Schematic of the set up used to separate the oil from the sand
E-1
P-1
Mixture of Toluene
and Sand sample
Filtrate collects here
Rubber Bung
E-2
Filter Paper
Buchner
Funnel
Buchner
Flask
Rubber
Tubing Vacuum
Pump
Extractor
hood
Oil
sample/
toluene
mixture
83
Figure 3-20: Schematic of the set up used to measure the asphaltene content of the oil samples
E-1
P-1
Mixture of n-Pentane
and oil sample
Filtrate collects here
Rubber Bung
E-2
Filter Paper
Buchner
Funnel
Buchner
Flask
Rubber
Tubing
Vacuum
Pump
Extractor
hoodAsphaltene
precipitate
Electric
balance
Asphaltene
precipitate
84
3.3.4 Cleaning
After each test, the VAPEX models and all the connection points, lines, valves, and
fittings were disassembled and cleaned to be ready for the next set of experiments. The
lines and connection points to the VAPEX models were removed and the models were set
to the horizontal position. Then, the steel cover protectors, the Plexiglas slabs, and the
gaskets were removed. After taking the required samples, the sand was discarded from
the VAPEX models to a dumping container. Because of the fine grains and the residual
oil in place, the cleaning procedure was cumbersome, especially for the large model.
However, once the models were unpacked, they were set back to the vertical position.
Next, the second steel cover protectors, the Plexiglas slabs, and the gaskets were
removed. The steel frame and all the piping and valves were cleaned with toluene before
being dried with pressurized air. The Plexiglas slabs were cleaned with non-corrosive
(kerosene and conventional glass cleaners) solvents. The same procedure was used to
clean the separators.
The total time required for running each test, including preparation, experimental runs
and cleaning was at least one month for each test. In some cases, unexpected leakages or
equipment failure could extend this time to two months.
85
4. CHAPTER 4: EXPERIMENTAL RESULTS AND
DISCUSSION
A total of18 experiments were conducted using different solvents. Propane, CO2,
methane, butane, a mixture of propane/CO2 (70%/30%), and a mixture of
propane/methane (70%/30%) were considered as respective injection solvents to carry
out the VAPEX experiments. The experiments were carried out at temperature of 21°C
and a pressure of 110 to 850 kPa. Silica sand number 530 was used for packing the
models. The summary of operating conditions for the VAPEX experiments is provided in
Table 4-1.
At times, there were sudden oil production rate fluctuations. This can be due to pressure
disturbance during gas injection. However, in the case of propane injection (pure and
mixture), this was more severe; however, different researchers have also observed these
sudden production rate changes (Yazdani and Maini, 2005 and Ahmadloo et al., 2011). In
fact, Ahmadloo et al. (2011) suggested that the fluctuations could be due to the re-
imbibition of the oil phase in swept zones of porous media. Furthermore, re-imbibition is
more significant in larger models due to faster drainage. Along these lines, Yazdani and
Maini (2005) suggested that this fluctuation could be the result of asphaltene precipitation
near production points. In such instances, the production port blockage caused by
asphaltene deposition will lead to a surge of oil that may cause fluctuations. After
measuring the asphaltene content of the injected oil and different samples from different
86
physical model locations, noticeable asphaltene precipitation was observed specifically
near the production ports.
The produced oil from each model was analyzed to measure its density and viscosity.
Following each test, a produced oil compositional analysis was carried out. On the one
hand, propane injection in the small model had the highest ultimate oil recovery factor.
On the other hand, the tests with pure CO2 injection have the lowest recovery factor.
However, the propane/CO2 mixture injection shows significant results in both models.
Different parameters are investigated during the test in the small and large models using
different solvents. Recovery factor, flow rate, solvent utilization factor (SUF), viscosity,
density, molecular weight, and hydrocarbon content of produced oil are described in
more detail in the following sections. Then, the results for asphaltene content experiments
are demonstrated. In the last section, a comprehensive image analysis (IA) is carried out
for the tests in order to monitor the solvent chamber velocity and drainage height in more
detail.
87
Table 4-1: Operating conditions of the VAPEX experiments
Test
No.
Model
height
(cm)
Solvent
Porosity
(%)
Permeability
(D)
Pressure
(kPa)
Temperature
(°C)
Oil
Density
(kg/m3)
Oil
Viscosity
(mPa.s)
1 47.5 propane 38.7 7.90 700 21 971.53 5650
2 24.5 propane 36.9 5.32 700 21 971.53 5650
3 47.5 propane 39.3 6.51 700 21 971.53 5650
4 24.5 methane 40.7 5.12 850 21 971.53 5650
5 47.5 methane 41.8 5.88 850 21 971.53 5650
6 24.5 CO2 42.1 6.11 850 21 971.53 5650
7 47.5 CO2 42.6 6.70 850 21 971.53 5650
8 24.5 propane/ CO2 41.5 5.63 850 21 971.53 5650
9 47.5 propane/ CO2 42.2 5.79 850 21 971.53 5650
10 24.5 butane 42.4 9.63 140 21 971.53 5650
11 24.5 butane 42.1 8.69 110 21 971.53 5650
12 47.5 butane 42.3 9.08 110 21 971.53 5650
13 24.5 propane 42.2 8.78 700 21 971.53 5650
14 47.5 propane 43.1 9.12 700 21 971.53 5650
15 24.5 propane/CO2 41.8 8.64 850 21 971.53 5650
16 47.5 propane/CO2 42.4 8.87 850 21 971.53 5650
17 24.5 propane/methane 42.0 8.50 850 21 971.53 5650
18 47.5 propane/methane 42.1 9.23 850 21 971.53 5650
88
4.1 VAPEX performance
4.1.1 Effect of drainage height
In this section, different parameters between the two physical models are compared.
Small model results are graphed alongside large model results.
4.1.1.1 Recovery factor and produced oil rate
4.1.1.1.1 Propane injection
Figure 4-1 shows the recovery factor after injecting propane as the solvent in the physical
models. Here, the ultimate recovery factors in the small and large models were
approximately the same and about 75% of original oil in place. However, it should be
mentioned that two more tests were conducted prior to these tests, and it was found that
connection between injection and production wells would significantly affect the results.
This disparity will be discussed in more detail later in this chapter.
The produced oil flow rate is also shown in Figure 4-2. It was found that stabilized
drainage rate was higher in the large model due to greater drainage height and effect of
gravity drainage. As can be seen, the stabilized drainage rate for the small model was
about 0.22 mL/min and 0.50 mL/min for the large model. Some fluctuations were
observed for the drainage rate during the experiments, which was also reported by some
other researchers (Ahmadloo et al., 2012 and Yazdani, 2007). The possible causes for
these fluctuations are a pressure drop in the pressure regulator or the backpressure
regulator and asphaltene precipitation may also cause such fluctuations. After conducting
the asphaltene precipitation experiments, asphaltene precipitation at different locations
was observed.
89
Time (h)
0 20 40 60 80 100 120 140
Rec
ov
ery F
acto
r (%
OO
IP)
0
20
40
60
80
100
Small Model
Large Model
Figure 4-1: The recovery factor after propane injection in VAPEX models
90
Time (hr)
0 20 40 60 80 100 120 140
Pro
duced
Oil
Rat
e (m
L/m
in)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Large Model
Small Model
Figure 4-2: The produced oil rate after propane injection in VAPEX models
91
4.1.1.1.2 Methane injection
Figure 4-3 shows the recovery factor after injecting methane as the solvent in the physical
models. Of note, the ultimate recovery factors in the small and large model were 36%
and 32% of original oil in place, respectively. As the figure demonstrates, the recovery
factor increases steadily for the small and large models. In addition, the process was
significantly slower than in the case of propane injection, specifically for the small
model. Moreover, the first solvent breakthrough was observed later in comparison to the
propane injection, which is due to the low solubility of methane in the operating injection
pressure.
The produced oil flow rates in the small and large physical models are presented in
Figure 4-4. Furthermore, the flow rate is relatively higher for the larger model with
greater drainage height, while production rate fluctuations were observed with methane
injection, specifically in the small model. It was found that stabilized drainage rate in the
small model was about 0.027 mL/min, while the stabilized drainage rate in the large
model with greater drainage height was about 0.057 mL/min. Considering these results, it
was found that pure methane in the operating pressure of the experiments was not a good
choice for injection solvent.
92
Time (h)
0 100 200 300 400 500
Rec
ove
ry F
acto
r (%
OO
IP)
0
10
20
30
40
Small Model
Large Model
Figure 4-3: The recovery factor after methane injection in VAPEX models
93
Time (h)
0 50 100 150 200 250 300 350
Pro
duc
ed o
il ra
te (
mL
/min
)
0.00
0.02
0.04
0.06
0.08
0.10
Large Model
Small Model
Figure 4-4: The produced oil rate after methane injection in VAPEX models
94
4.1.1.1.3 CO2 injection
Figure 4-5 shows the recovery factor after injecting CO2 as the solvent in the physical
models. Here, the ultimate recovery factor increased steadily in both models, and it was
almost the same in both physical models and found to be about 36% of original oil in
place. In addition, the process was significantly slow, specifically for the small model.
The produced oil flow rates are also shown in Figure 4-6. As can be seen, the stabilized
flow rate was significantly higher for the large model with greater drainage height due to
the gravity drainage. The stabilized drainage rates were 0.012 mL/min and 0.028 mL/min
for the small and large models, respectively. As with the methane injection, the results
showed that pure CO2 injection is not a good choice as an injection solvent. The low
production for this case may be due to the low injection pressure, which was limited
because of VAPEX model specifications.
4.1.1.1.4 Butane injection
As shown in Figure 4-7, the ultimate recovery factors for both models were almost the
same, and they were approximately 57% of original oil in place. Moreover, the process
seemed faster in the small model compared to butane injection in large model. This
disparity may be the result of well configuration and the shorter distance between the
injection and production well in the small model. However, compared to propane
injection, the process was significantly slower during butane injection, which might be
due to the low vapour pressure of butane. The produced oil flow rate is also shown in
Figure 4-8. As expected, the produced oil flow rate was higher in the large model. The
stabilized drainage rate was about 0.32 mL/min in the large model and about 0.14
mL/min in the small model. As it will be explained later in this chapter, the slow rate of
the process resulted in asphaltene precipitation especially close to the injection wells.
95
Time (h)
0 100 200 300 400 500 600
Rec
ove
ry F
acto
r (%
OO
IP)
0
5
10
15
20
25
30
Small Model
Large Model
Figure 4-5: Recovery factor after CO2 injection in the VAPEX models
96
Time (h)
0 100 200 300 400 500
Pro
duc
ed o
il ra
te (
mL
/min
)
0.00
0.01
0.02
0.03
0.04
Large Model
Small Model
Figure 4-6: Produced oil rate after CO2 injection in the VAPEX models
97
Time (h)
0 20 40 60 80 100 120 140
Rec
over
y F
acto
r (%
OO
IP)
0
10
20
30
40
50
60
70
Small Model
Large Model
Figure 4-7: Recovery factor after butane injection in the VAPEX models
98
Time (h)
0 20 40 60 80 100 120 140
Pro
du
ced
Oil
Rat
e (m
L/m
in)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Large model
Small model
Figure 4-8: Produced oil rate after butane injection in the VAPEX models
99
4.1.1.1.5 Propane/CO2 injection
Figure 4-9 shows the recovery factor after injecting propane/CO2 as the solvent in the
physical models. Here, the ultimate recovery factors for both models were almost the
same, and they were about 54% of original oil in place. The performance of the VAPEX
process was significantly improved compared to pure CO2 injection, specifically for the
small model. The results proved the suitability of CO2 as a carrier gas for solvents such as
propane and butane in the VAPEX process.
As can be seen in Figure 4-10, the flow rate was significantly higher in the large model,
and it was found to be around 0.33 mL/min. The observed flow rate in the small model
was about 0.15 mL/min.
Because of the pressure constraints of the physical model, a mixture of 30% CO2 and
70% propane was injected as the solvent. The performance trend was close to the case of
pure propane injection. By increasing the volume percent of CO2, the vapour pressure of
the mixture will be increased, meaning it will exceed the maximum pressure tolerance of
the physical models used in these tests. However, new models can be employed to
perform a sensitivity analysis on the recovery performance of solvent mixtures with
different volume percentages of CO2.
100
Time (h)
0 20 40 60 80 100 120 140
Rec
ov
ery F
acto
r (%
OO
IP)
0
10
20
30
40
50
60
Small
Large
Figure 4-9: Recovery factor after propane/CO2mixture injection in the VAPEX models
101
Time (h)
0 20 40 60 80 100 120 140
Pro
du
ced
Oil
Rat
e (m
L/m
in)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Large model
Small model
Figure 4-10: Produced oil rate after first propane/CO2 injection in VAPEX models
102
4.1.1.1.6 Propane/methane injection
Figure 4-11 shows the recovery factor after injecting propane/methane mixture as the
solvent in the VAPEX physical models. In this case, the ultimate recovery factor was
higher in the small model, and it was about 48% of original oil in place. On the other
hand, the ultimate recovery factor achieved in the large model was about 40% of original
oil in place. The performance of the VAPEX process was significantly improved
compared to pure methane injection. The results proved the suitability of methane as a
carrier gas for solvents such as propane and butane in the VAPEX process.
As can be seen in Figure 4-12, the flow rate was significantly higher in the large model,
and it was found to be around 0.25 mL/min. The observed flow rate in the small model
was about 0.13 mL/min.
Because of the pressure constraints of the physical model, a mixture of 30% methane and
70% propane was injected as the solvent. By increasing the volume percent of methane,
the vapour pressure of the mixture would be increased; therefore, to monitor the
performance of the process with such a solvent new physical models with higher pressure
tolerance would be required.
103
Time (h)
0 20 40 60 80 100 120 140
Rec
ov
ery F
acto
r (%
OO
IP)
0
10
20
30
40
50
60
Small
Large
Figure 4-11: Recovery factor after propane/methane mixture injection in the VAPEX models
104
Time (h)
0 20 40 60 80 100 120 140 160
Pro
du
ced
Oil
Rat
e (m
L/m
in)
0.0
0.1
0.2
0.3
0.4
0.5
Large model
Small model
Figure 4-12: Produced oil rate after propane/methane mixture injection in the VAPEX models
105
4.1.1.2 Solvent utilization factor (SUF)
During the experiments, the amount of injected solvent was recorded by DFMs for
various solvents. The solvent utilization factor (SUF) at any time during the experiments
is the ratio of the net oil production to the total injected volume of solvent. This
parameter was calculated with equation (4.1). The results are shown in more detail in the
following sections.
)(
)(
mLsolventinjectedofvolumeTotal
mLproductionoilNetSUF ............................................................ (4.1)
4.1.1.2.1 Propane injection
As Figure 4-13 demonstrates, the SUF is higher in the large model compared to the
results obtained for the small model. In fact, the SUF increases gradually until the final
breakthrough of the gas, after which there would be a great amount of solvent production
with less oil produced. The results showed that up-scaling the VAPEX process did not
result in solvent loss although the distance between the injection and production wells
was significantly increased.
4.1.1.2.2 Methane injection
Figure 4-14 indicates that SUF is higher in the small model in the case of methane
injection. In effect, the SUF increases until the final breakthrough of the gas. At that time,
there is a sharp decrease in the SUF for both the small and large models. At this time, the
process is no longer efficient because there is little oil production with the amount of
solvent injected. The SUF curves can also be used as an indicator of the final shut-in time
for the VAPEX tests. However, some fluctuations may occur due to the sudden pressure
drops in the BPR lines, which may cause a solvent production increase.
106
Time (h)
0 20 40 60 80 100 120 140
SU
F (
mL
Oil
Pro
d./
mL
Sol.
Inj.
)
5.0x10-4
10-3
1.5x10-3
2.0x10-3
2.5x10-3
3.0x10-3
3.5x10-3
Small Model
Large Model
Figure 4-13: Solvent utilization factor (SUF) after propane injection in VAPEX models
107
Time (h)
50 100 150 200 250 300 350 400 450
SU
F (
mL
Oil
Pro
d./
mL
Sol.
Inj.
)
0
2.0x10-4
4.0x10-4
6.0x10-4
8.0x10-4
10-3
1.2x10-3
1.4x10-3
Small Model
Large Model
Figure 4-14: Solvent utilization factor (SUF) after methane injection in VAPEX models
108
4.1.1.2.3 CO2 injection
The SUF is also shown in Figure 4-15. As can be seen in this figure, the SUF is higher in
the small model. In fact, the SUF increases until the final breakthrough of the solvent.
Then, there would be a sharp decrease in the SUF for both the small and large models. At
this time, the process is no longer efficient, as there is little oil production with the
solvent injected. The low injection pressure of pure CO2 resulted in low efficiency of the
process in the small model with small drainage height.
4.1.1.2.4 Butane injection
The SUF after butane injection is shown in Figure 4-16. As can be seen in this figure, the
SUF is higher in the small model, specifically at the earlier stages of the experiments.
This can be due to the lower vapour pressure of butane and the longer distance of the
injection and production wells in the large model. In fact, the results show that up-scaling
the VAPEX process in the case of butane injection might result in some additional
solvent loss. However, the increase rate of SUF is higher in the large model compared to
the small model, and the total SUF is greater in the large physical model.
4.1.1.2.5 Propane/CO2 injection
Figure 4-17 indicates that SUF is higher in the large model compared to the small model
for the case of propane/CO2 mixture injection. As can be seen in this figure, the total SUF
obtained in the large model is about 0.0024 (mL oil prod./mL of gas inj.), while the total
SUF for the small model is about 0.0019 (mL oil prod./mL of gas inj.). In fact, the SUF
increases slightly until the final breakthrough of the gas in the large and small physical
models.
109
Time (h)
0 50 100 150 200 250 300 350 400 450 500
SU
F (
mL
Oil
Pro
d./
mL
So
l. In
j.)
0
10-4
2x10-4
3x10-4
4x10-4
5x10-4
Small Model
Large Model
Figure 4-15: Solvent utilization factor (SUF) after CO2 injection in VAPEX models
110
Time (h)
0 20 40 60 80 100 120 140 160
SU
F (
mL
Oil
Pro
d./
mL
Sol.
Inj.
)
2x10-4
4x10-4
6x10-4
8x10-4
10-3
1x10-3
1x10-3
2x10-3
2x10-3
2x10-3
Small Model
Large Model
Figure 4-16: Solvent utilization factor (SUF) after butane injection in VAPEX models
111
Time (h)
0 20 40 60 80 100 120 140 160
SU
F (
mL
Oil
Pro
d./
mL
Sol.
Inj.
)
4.0x10-4
6.0x10-4
8.0x10-4
10-3
1.2x10-3
1.4x10-3
1.6x10-3
1.8x10-3
2.0x10-3
2.2x10-3
2.4x10-3
Small Model
Large Model
Figure 4-17: Solvent utilization factor (SUF) after propane/CO2 injection in VAPEX models
112
4.1.1.2.6 Propane/methane injection
The SUF obtained in the small and large models after injecting propane/methane mixture
is shown in Figure 4-18. As can be seen in this figure, the SUF is almost the same at
earlier stages of the experiments for small and large models. However, the increase rate
of SUF is higher for the large model toward the end of the experiments, and the SUF
increases until the final breakthrough of gas for both models. The total SUF for the large
model is about 0.0022 mL oil prod./mL of gas inj. while the total SUF achieved in the
small model is about 0.0019 mL oil prod./mL of gas inj..
4.1.1.3 Viscosity, density, molecular weight, and hydrocarbon components for the
produced oil
In this section, the effect of solvent injection on viscosity, density, molecular weight, and
the hydrocarbon components of original injection oil in the small and large models are
demonstrated.
4.1.1.3.1 Propane injection
Tables 4-2 and 4-3 show the compositional analysis results of the produced heavy oil
after propane injection in the small and large models, respectively. Viscosity, molecular
weight, and produced oil density are also presented. Propane injection has significantly
diluted the original oil and reduced its viscosity. The density and the molecular weight
are also decreased due to the extraction of some of the components. Figure 4-19 shows
the compositional analysis of the produced oil for the small and large models. The
comparison of the hydrocarbon components with the original oil shows an increase in the
mol% of lighter hydrocarbons. However, the lighter components have increased more in
the small model than in the large model.
113
Time (h)
0 20 40 60 80 100 120 140 160
SU
F (
mL
Oil
Pro
d./
mL
So
l. I
nj.
)
0
2.0x10-4
4.0x10-4
6.0x10-4
8.0x10-4
10-3
1.2x10-3
1.4x10-3
1.6x10-3
Small Model
Large Model
Figure 4-18: Solvent utilization factor (SUF) after propane/methane injection in VAPEX models
114
Table 4-2: Compositional analysis result of the produced heavy oil after propane injection in small model
Carbon Number Mol.% Carbon Number Mol.%
C1 0.0 C31 1.10
C2 0.0 C32 0.96
C3 12.49 C33 0.72
C4 0.0 C34 0.81
C5 0.0 C35 0.83
C6 0.0 C36 0.80
C7 0.0 C37 0.63
C8 0.0 C38 0.53
C9 2.45 C39 0.78
C10 9.67 C40 0.81
C11 10.88 C41 0.45
C12 4.58 C42 0.46
C13 2.64 C43 0.70
C14 2.73 C44 0.79
C15 3.31 C45 0.41
C16 2.90 C46 0.40
C17 3.00 C47 0.47
C18 3.04 C48 0.37
C19 2.49 C49 0.36
C20 2.19 C50 0.39
C21 2.52 C51 0.39
C22 1.54 C52 0.37
C23 1.80 C53 0.35
C24 1.65 C54 0.29
C25 1.61 C55 0.29
C26 1.47 C56 0.29
C27 1.45 C57 0.31
C28 1.42 C58 0.27
C29 1.18 C59 0.27
C30 1.10 C60+ 6.31
509:WeightMolecular , mPa.s, 999=oil 3
oil kg/m 853.50=
115
Table 4-3: Compositional analysis result of the produced heavy oil after propane injection in large model
Carbon Number Mol.% Carbon Number Mol.%
C1 0.0 C31 1.03
C2 0.0 C32 1.05
C3 5.27 C33 0.84
C4 0.0 C34 0.82
C5 0.0 C35 0.80
C6 0.0 C36 0.72
C7 1.00 C37 0.66
C8 1.72 C38 0.72
C9 6.32 C39 0.64
C10 12.23 C40 0.64
C11 8.58 C41 0.56
C12 3.34 C42 0.57
C13 2.99 C43 0.58
C14 3.25 C44 0.56
C15 3.31 C45 0.55
C16 3.08 C46 0.50
C17 3.35 C47 0.40
C18 2.85 C48 0.43
C19 2.71 C49 0.40
C20 2.44 C50 0.35
C21 2.20 C51 0.35
C22 1.99 C52 0.35
C23 1.79 C53 0.30
C24 1.67 C54 0.29
C25 1.57 C55 0.30
C26 1.57 C56 0.28
C27 1.50 C57 0.25
C28 1.30 C58 0.26
C29 1.25 C59 0.27
C30 1.08 C60+ 6.17
501:WeightMolecular , mPa.s, 469=oil 3
oil kg/m 938.17=
116
Carbon number
C1
C2
C3
C4
C5
C6
C7
C8
C9
C1
0C
11
C1
2C
13
C1
4C
15
C1
6C
17
C1
8C
19
C2
0C
21
C2
2C
23
C2
4C
25
C2
6C
27
C2
8C
29
C3
0C
31
C3
2C
33
C3
4C
35
C3
6C
37
C3
8C
39
C4
0+
Mole
%
0
2
4
6
8
10
12
14
16
Injection oil
Produced oil after propane injection in small model
Produced oil after propane injection in large model
Figure 4-19: Compositional analysis of the produced oil after propane injection
117
Likewise, Luo et al. (2005) found that dissolution of propane can significantly reduce the
heavy oil viscosity in the VAPEX process. However, they observed the formation of a
multilayer solvent-heavy oil system, in which the top layer is a solvent-enriched, liquid
phase heavy oil with the dissolved solvent comprising the middle layer, while the bottom
layer mainly consists of heavy components.
4.1.1.3.2 Methane injection
Tables 4-4 and 4-5 show the compositional analysis results of the produced heavy oil
after methane injection in the small and large models, respectively. Viscosity, molecular
weight, and produced oil density are also shown. It can be seen that methane injection has
not significantly diluted the original oil. Moreover, viscosity reduction is much less than
during propane injection. This can be due to the low solubility of methane in heavy oil at
the operating pressure. Regardless, the density and the molecular weight change are not
promising either. However, more viscosity reduction was observed in the small model
and, consequently, more dilution occured. Figure 4-20 shows the compositional analysis
of the produced oil for the small and large models. Ultimately, the comparison of the
hydrocarbon components with the original oil does not show any significant change in
the hydrocarbon component of the produced oil for each model.
118
Table 4-4: Compositional analysis result of the produced heavy oil after methane injection in small model
Carbon Number Mol.% Carbon Number Mol.%
C1 0.0 C31 1.22
C2 0.0 C32 1.18
C3 0.0 C33 0.81
C4 0.0 C34 0.90
C5 0.0 C35 0.99
C6 0.0 C36 0.95
C7 0.0 C37 0.70
C8 0.0 C38 0.59
C9 4.02 C39 0.99
C10 10.26 C40 0.98
C11 11.53 C41 0.54
C12 5.50 C42 0.62
C13 3.15 C43 0.84
C14 3.13 C44 0.83
C15 3.80 C45 0.46
C16 3.25 C46 0.45
C17 3.50 C47 0.60
C18 3.55 C48 0.53
C19 2.81 C49 0.45
C20 2.63 C50 0.43
C21 2.93 C51 0.43
C22 1.77 C52 0.38
C23 1.97 C53 0.35
C24 1.86 C54 0.34
C25 1.95 C55 0.35
C26 1.59 C56 0.30
C27 1.64 C57 0.30
C28 1.64 C58 0.34
C29 1.34 C59 0.29
C30 1.27 C60+ 6.79
507:WeightMolecular , mPa.s, 4730=oil 3
oil kg/m 969.28=
119
Table 4-5: Compositional analysis result of the produced heavy oil after methane injection in large model
Carbon Number Mol.% Carbon Number Mol.%
C1 0.0 C31 1.25
C2 0.0 C32 0.78
C3 0.0 C33 1.28
C4 0.0 C34 0.82
C5 0.0 C35 1.00
C6 0.0 C36 1.05
C7 0.00 C37 0.63
C8 0.00 C38 0.67
C9 3.94 C39 0.98
C10 9.26 C40 0.90
C11 11.00 C41 0.65
C12 5.15 C42 0.53
C13 3.42 C43 0.91
C14 3.32 C44 0.88
C15 3.77 C45 0.47
C16 3.45 C46 0.45
C17 3.75 C47 0.53
C18 3.50 C48 0.51
C19 3.02 C49 0.45
C20 2.66 C50 0.43
C21 3.02 C51 0.44
C22 1.73 C52 0.42
C23 2.17 C53 0.35
C24 1.88 C54 0.35
C25 1.97 C55 0.32
C26 1.68 C56 0.35
C27 1.62 C57 0.34
C28 1.74 C58 0.28
C29 1.39 C59 0.31
C30 1.30 C60+ 6.90
509:WeightMolecular , mPa.s, 5520=oil 3
oil kg/m 970.11=
120
Carbon number
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
C20
C21
C22
C23
C24
C25
C26
C27
C28
C29
C30
C31
C32
C33
C34
C35
C36
C37
C38
C39
C40+
Mo
le %
0
2
4
6
8
10
12
14
16
Produced oil after methane injection in small model
Injection oil
Produced oil after methane injection in large model
Figure 4-20: Compositional analysis of the produced oil after methane injection
121
4.1.1.3.3 CO2 injection
Tables 4-6 and 4-7 show the compositional analysis results of the produced heavy oil
after CO2 injection in the small and large models, respectively. Viscosity, molecular
weight, and produced oil density are shown in the above-mentioned tables. It is apparent
that CO2 injection has not significantly diluted the original oil. In fact, viscosity reduction
is much less than what happened during propane injection. This can be due to the low
solubility of CO2 in heavy oil at the operating pressure of the experiments. However, the
viscosity reduction in the large model is more prominent than in the small model.
Furthermore, changes in density and molecular weight are not promising either. Figure 4-
21 shows the compositional analysis of the produced oil for the small and large models.
Here, a comparison of the hydrocarbon components with the original oil does not show
any significant change in the hydrocarbon component of the produced oil for each model.
4.1.1.3.4 Butane injection
Tables 4-8 and 4-9 show the compositional analysis results of the produced heavy oil
after butane injection in the small and large models, respectively. Viscosity, molecular
weight, and produced oil density are also presented in those tables. A significant viscosity
reduction was observed after injecting butane. However, viscosity reduction is less than
what happened during propane injection. On the other hand, the viscosity reduction in the
large model is more prominent than in the small model. Figure 4-22 shows the
compositional analysis of the produced oil for the small and large models. The
comparison of the hydrocarbon components with the original oil shows an increase in the
mol% of lighter hydrocarbons. However, the lighter components have increased more in
the small model than in the large model.
122
Table 4-6: Compositional analysis result of the produced heavy oil after CO2 injection in small model
Carbon Number Mol.% Carbon Number Mol.%
C1 0.0 C31 1.35
C2 0.0 C32 1.18
C3 0.0 C33 0.81
C4 0.0 C34 0.78
C5 0.0 C35 1.09
C6 0.0 C36 0.96
C7 0.0 C37 0.64
C8 0.0 C38 0.69
C9 4.02 C39 0.91
C10 10.38 C40 1.08
C11 10.69 C41 0.54
C12 5.11 C42 0.57
C13 3.35 C43 0.84
C14 3.25 C44 0.88
C15 3.84 C45 0.46
C16 3.41 C46 0.45
C17 3.81 C47 0.52
C18 3.37 C48 0.51
C19 3.20 C49 0.44
C20 2.63 C50 0.43
C21 2.80 C51 0.43
C22 1.90 C52 0.41
C23 2.15 C53 0.39
C24 1.86 C54 0.37
C25 1.82 C55 0.31
C26 1.72 C56 0.30
C27 1.78 C57 0.33
C28 1.55 C58 0.29
C29 1.43 C59 0.29
C30 1.14 C60+ 6.56
505:WeightMolecular , mPa.s, 5010=oil 3
oil kg/m 962.88=
123
Table 4-7: Compositional analysis result of the produced heavy oil after CO2 injection in large model
Carbon Number Mol.% Carbon Number Mol.%
C1 0.0 C31 1.22
C2 0.0 C32 1.18
C3 0.0 C33 0.81
C4 0.0 C34 0.82
C5 0.0 C35 1.05
C6 0.0 C36 0.99
C7 0.0 C37 0.67
C8 0.0 C38 0.62
C9 4.45 C39 1.01
C10 9.95 C40 0.97
C11 10.96 C41 0.53
C12 5.16 C42 0.57
C13 3.23 C43 0.84
C14 3.28 C44 0.81
C15 3.83 C45 0.52
C16 3.32 C46 0.44
C17 3.70 C47 0.52
C18 3.59 C48 0.51
C19 2.98 C49 0.44
C20 2.63 C50 0.43
C21 2.98 C51 0.43
C22 1.71 C52 0.41
C23 2.32 C53 0.34
C24 1.85 C54 0.33
C25 1.78 C55 0.35
C26 1.50 C56 0.34
C27 1.87 C57 0.30
C28 1.55 C58 0.28
C29 1.42 C59 0.28
C30 1.27 C60+ 6.66
504:WeightMolecular , mPa.s, 4910=oil 3
oil kg/m 961.80=
124
Carbon number
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
C20
C21
C22
C23
C24
C25
C26
C27
C28
C29
C30
C31
C32
C33
C34
C35
C36
C37
C38
C39
C40+
Mo
le %
0
2
4
6
8
10
12
14
16
Injection oil
Produced oil after CO2 injection in small model
Produced oil after CO2 injection in large model
Figure 4-21: Compositional analysis of the produced oil after CO2 injection
125
Table 4-8: Compositional analysis result of the produced heavy oil after butane injection in small model
Carbon Number Mol.% Carbon Number Mol.%
C1 0.0 C31 1.01
C2 0.0 C32 0.94
C3 0.0 C33 0.92
C4 0.0 C34 0.80
C5 0.0 C35 0.89
C6 0.0 C36 0.70
C7 0.91 C37 0.63
C8 1.30 C38 0.69
C9 6.10 C39 0.66
C10 11.78 C40 0.66
C11 8.55 C41 0.52
C12 3.56 C42 0.59
C13 3.05 C43 0.57
C14 3.29 C44 0.52
C15 3.24 C45 0.47
C16 3.24 C46 0.50
C17 3.30 C47 0.47
C18 3.00 C48 0.41
C19 2.65 C49 0.33
C20 2.40 C50 0.39
C21 2.34 C51 0.33
C22 2.12 C52 0.28
C23 1.76 C53 0.25
C24 1.64 C54 0.32
C25 1.68 C55 0.32
C26 1.62 C56 0.27
C27 1.46 C57 0.25
C28 1.34 C58 0.44
C29 1.23 C59 0.22
C30 1.18 C60+ 5.73
490:WeightMolecular , mPa.s, 2960=oil 3
oil kg/m 934.54=
126
Table 4-9: Compositional analysis result of the produced heavy oil after butane injection in large model
Carbon Number Mol.% Carbon Number Mol.%
C1 0.0 C31 1.24
C2 0.0 C32 1.20
C3 0.0 C33 0.83
C4 0.0 C34 0.79
C5 0.0 C35 1.01
C6 0.0 C36 1.01
C7 0.0 C37 0.68
C8 0.0 C38 0.59
C9 4.91 C39 0.94
C10 9.83 C40 0.87
C11 11.19 C41 0.54
C12 4.89 C42 0.53
C13 3.13 C43 0.57
C14 3.11 C44 0.85
C15 3.72 C45 0.83
C16 3.31 C46 0.45
C17 3.39 C47 0.53
C18 3.44 C48 0.54
C19 3.04 C49 0.53
C20 2.48 C50 0.44
C21 2.85 C51 0.44
C22 1.75 C52 0.42
C23 2.17 C53 0.39
C24 1.75 C54 0.38
C25 1.82 C55 0.35
C26 1.78 C56 0.35
C27 1.67 C57 0.31
C28 1.61 C58 0.32
C29 1.40 C59 0.32
C30 1.19 C60+ 7.33
522:WeightMolecular , mPa.s, 3220=oil 3
oil kg/m 965.92=
127
Carbon number
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
C20
C21
C22
C23
C24
C25
C26
C27
C28
C29
C30
C31
C32
C33
C34
C35
C36
C37
C38
C39
C40+
Mole
%
0
2
4
6
8
10
12
14
16
18
20
Injection oil
Produced oil after butane injection in small model
Produced oil after butane injection in large model
Figure 4-22: Compositional analysis of the produced oil after butane injection
128
4.1.1.3.5 Propane/CO2 injection
Tables 4-10 and 4-11 show the compositional analysis results of the produced heavy oil
after a mixture of propane/CO2 injection in the small and large models, respectively. On
the same tables, viscosity, molecular weight, and produced oil density are also shown. It
is apparent that proapne/CO2 injection has significantly diluted the original oil, and the
viscosity of the original oil is decreased drastically. In fact, viscosity reduction is much
more than what happened during pure CO2 injection. However, the viscosity reduction in
the large model is more prominent than in the small model. Viscosity of produced oil is
decreased to 1160 mPa.s and 1480 mPa.s in the large and small models, respectively.
Figure 4-23 shows the compositional analysis of the produced oil for the small and large
models.
4.1.1.3.6 Propane/methane injection
Tables 4-12 and 4-13 show the compositional analysis results of the produced heavy oil
after propane/methane injection in the small and large models, respectively. Viscosity,
molecular weight, and produced oil density of the produced oil after propane/methane
injection are also presented in those tables. It is apparent that propane/methane injection
has diluted the original oil and the viscosity of original oil is decreased from 5650 mPa.s
to 2080 and 2380 mPa.s in the large and small physical models, respectively. In fact,
viscosity reduction is less than what happened during propane injection, but the dilution
has significantly improved compared to pure methane injection. However, the viscosity
reduction in the large model is more significant than in the small model. Figure 4-24
shows the compositional analysis of the produced oil for the small and large models.
129
Table 4-10: Compositional analysis result of the produced heavy oil after propane/CO2 injection in small
model
Carbon Number Mol.% Carbon Number Mol.%
C1 0.0 C31 1.26
C2 0.0 C32 1.21
C3 0.0 C33 0.86
C4 0.0 C34 0.81
C5 0.0 C35 1.04
C6 0.0 C36 0.99
C7 0.0 C37 0.65
C8 0.0 C38 0.62
C9 0.0 C39 0.93
C10 9.73 C40 0.73
C11 13.17 C41 0.71
C12 6.79 C42 0.53
C13 3.58 C43 0.84
C14 3.15 C44 0.83
C15 3.69 C45 0.48
C16 3.30 C46 0.48
C17 3.42 C47 0.56
C18 3.51 C48 0.54
C19 3.01 C49 0.47
C20 2.61 C50 0.45
C21 2.86 C51 0.44
C22 1.74 C52 0.41
C23 2.17 C53 0.40
C24 1.86 C54 0.38
C25 1.85 C55 0.35
C26 1.70 C56 0.34
C27 1.66 C57 0.34
C28 1.65 C58 0.33
C29 1.41 C59 0.32
C30 1.24 C60+ 7.58
525:WeightMolecular , mPa.s, 1480=oil 3
oil kg/m 954.48=
130
Table 4-11: Compositional analysis result of the produced heavy oil after propane/CO2 injection in large
model
Carbon Number Mol.% Carbon Number Mol.%
C1 0.0 C31 1.07
C2 0.0 C32 0.96
C3 12.79 C33 0.40
C4 0.0 C34 0.38
C5 0.0 C35 0.87
C6 0.0 C36 0.33
C7 0.0 C37 0.32
C8 0.0 C38 0.27
C9 1.99 C39 0.26
C10 8.71 C40 0.18
C11 11.95 C41 0.18
C12 6.88 C42 0.18
C13 3.04 C43 0.13
C14 2.66 C44 0.13
C15 3.01 C45 0.13
C16 2.66 C46 0.13
C17 2.80 C47 0.12
C18 2.93 C48 0.08
C19 2.43 C49 0.07
C20 2.13 C50 0.07
C21 2.29 C51 0.07
C22 1.50 C52 0.07
C23 1.73 C53 0.07
C24 1.50 C54 0.06
C25 1.55 C55 0.05
C26 1.40 C56 0.05
C27 1.31 C57 0.05
C28 1.38 C58 0.05
C29 1.11 C59 0.05
C30 1.00 C60+ 9.45
611:WeightMolecular , mPa.s, 1160=oil 3
oil kg/m 944.46=
131
Carbon number
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
C20
C21
C22
C23
C24
C25
C26
C27
C28
C29
C30
C31
C32
C33
C34
C35
C36
C37
C38
C39
C40+
Mole
%
0
2
4
6
8
10
12
14
16
18
20
Injection oil
Produced oil after propane/CO2 injection in small model
Produced oil after propane/CO2 injection in large model
Figure 4-23: Compositional analysis of the produced oil after propane/CO2 injection
132
Table 4-12: Compositional analysis result of the produced heavy oil after propane/methane injection in
small model
Carbon Number Mol.% Carbon Number Mol.%
C1 0.0 C31 1.13
C2 0.0 C32 1.11
C3 2.71 C33 0.81
C4 0.0 C34 0.78
C5 0.0 C35 0.98
C6 0.0 C36 0.95
C7 0.0 C37 0.61
C8 1.74 C38 0.69
C9 1.42 C39 0.84
C10 9.67 C40 0.81
C11 11.85 C41 0.51
C12 7.31 C42 0.53
C13 3.15 C43 0.78
C14 2.75 C44 1.21
C15 3.25 C45 0.03
C16 3.23 C46 0.45
C17 3.19 C47 0.51
C18 3.11 C48 0.48
C19 2.94 C49 0.46
C20 2.38 C50 0.43
C21 2.74 C51 0.43
C22 1.52 C52 0.37
C23 2.11 C53 0.34
C24 1.69 C54 0.34
C25 1.72 C55 0.34
C26 1.56 C56 0.30
C27 1.61 C57 0.31
C28 1.55 C58 0.33
C29 1.32 C59 0.29
C30 1.18 C60+ 7.15
508:WeightMolecular , mPa.s, 2380=oil 3
oil kg/m 963.10=
133
Table 4-13: Compositional analysis result of the produced heavy oil after propane/methane injection in
large model
Carbon Number Mol.% Carbon Number Mol.%
C1 0.0 C31 1.11
C2 0.0 C32 1.09
C3 1.51 C33 0.79
C4 0.0 C34 0.75
C5 0.0 C35 0.96
C6 0.0 C36 0.92
C7 0.0 C37 0.59
C8 2.74 C38 0.66
C9 2.44 C39 0.82
C10 10.67 C40 0.79
C11 12.79 C41 0.48
C12 7.29 C42 0.50
C13 3.05 C43 0.76
C14 2.85 C44 1.19
C15 3.35 C45 0.00
C16 3.13 C46 0.43
C17 3.17 C47 0.50
C18 3.05 C48 0.47
C19 2.89 C49 0.46
C20 2.36 C50 0.42
C21 2.71 C51 0.42
C22 1.48 C52 0.37
C23 2.09 C53 0.33
C24 1.66 C54 0.33
C25 1.71 C55 0.34
C26 1.54 C56 0.29
C27 1.59 C57 0.29
C28 1.53 C58 0.33
C29 1.30 C59 0.28
C30 1.16 C60+ 6.26
504:WeightMolecular , mPa.s, 2080=oil 3
oil kg/m 961.10=
134
Carbon number
C1
C2
C3
C4
C5
C6
C7
C8
C9
C1
0C
11
C1
2C
13
C1
4C
15
C1
6C
17
C1
8C
19
C2
0C
21
C2
2C
23
C2
4C
25
C2
6C
27
C2
8C
29
C3
0C
31
C3
2C
33
C3
4C
35
C3
6C
37
C3
8C
39
C4
0+
Mo
le %
0
2
4
6
8
10
12
14
16
18
Injection oil
Produced oil after propane/ methane injection in small model
Produced oil after propane/ methane injection in large model
Figure 4-24: Compositional analysis of the produced oil after propane/methane injection
135
4.1.2 Effect of solvent type
In this section, the results obtained for each model after injecting different solvents are
graphed together to investigate the effect of solvent type on VAPEX process recovery
performance.
4.1.2.1 Small model
4.1.2.1.1 Recovery factor and produced oil rate
Figure 4-25 shows the effect of solvent type on recovery factor after utilizing the VAPEX
process in the small model. As can be seen, the recovery factor is significantly higher
during propane injection, and the ultimate recovery factor was found to be about 80% of
original oil in place. The second best solvent was found to be the mixture of propane and
CO2 with an ultimate recovery factor of about 60% of original oil in place. Butane
seemed to show high recovery performance, and the ultimate recovery factor achieved
after injecting butane was also about 60%, however the process was observed to be
slower compared to propane and propane/CO2 injection. On the other hand, injecting pure
methane and CO2 did not show promising results and the process was extremely slow.
Figure 4-26 shows the effect of solvent type on the produced oil rate. The same trend as
the recovery factor can be seen for different solvents. In short, the highest production rate
was observed for propane injection, while the lowest production rate was observed for
pure methane and pure CO2 injection.
136
Time (h)
0 100 200 400 420 440
Rec
ov
ery F
acto
r (%
OO
IP)
0
20
40
60
80
Propane
Propane/ CO2
CO2
Butane
Methane
Propane/ methane
Figure 4-25: Effect of the solvent type on recovery factor in small model
137
Time (h)
0 100 400 450
Pro
du
ced
Oil
Rat
e (m
L/m
in)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Propane
Propane/CO2
CO2
Butane
Methane
Propane/ methane
Figure 4-26: Effect of the solvent type on produced oil rate in small model
138
4.1.2.1.2 Solvent utilization factor (SUF)
Figure 4-27 shows the effect of solvent type on SUF in the small model. As expected, the
highest SUF was observed for the case of propane injection, which shows the efficiency
of the process after using propane as the injection solvent. The total SUF was about
2.3×10-3
(mL Oil Prod./mL Sol. Inj.) for propane injection. Taking into account the
importance of solvent inventory, these results confirm the suitability of propane as an
injection solvent.
4.1.2.1.3 Viscosity, density, molecular weight, and hydrocarbon components for the
produced oil
Table 4-14 shows the effect of solvent type on viscosity, density and molecular weight of
the produced oil. The highest viscosity reduction was achieved using propane as the
solvent. In fact, the viscosity of original oil was diluted from 5650 mPa.s to 999 mPa.s
after injecting propane, while the produced oil viscosity was found to be 1480, 2380 and
2960 mPa.s after injecting propane/CO2, propane/methane, and butane, respectively.
However, injecting pure methane and CO2 did not result in a noticeable heavy oil
dilution.
Figure 4-28 shows the effect of solvent type on the hydrocarbon components of the
produced oil. It was observed that the amount of lighter hydrocarbons in the produced oil
was highest for the propane injection. This shows that heavier hydrocarbons can be
extracted after injecting propane as the solvent. Comparing the results after injecting
methane and CO2 with the carbon number of the hydrocarbons in the original oil reveals
that no significant extraction occurred after injecting the above-mentioned gases as the
injection solvent.
139
Time (h)
0 100 200 400
SU
F (
mL
Oil
Pro
d./
mL
So
l. I
nj.
)
0
5.0x10-4
10-3
1.5x10-3
2.0x10-3
2.5x10-3
Propane
Propane/ CO2
CO2
Butane
Methane
Propane/ methane
Figure 4-27: Effect of solvent type on solvent utilization factor (SUF) for small model
140
Table 4-14: Produced oil properties for the small model
Solvent Viscosity (mPa.s) Density (kg/m3) Molecular weight
Propane 999 853.50 509
Methane 4730 969.28 507
CO2 5010 962.88 505
Butane 2960 934.54 490
Propane/CO2 1480 954.48 525
Propane/methane 2380 963.10 508
141
Carbon number
C1
C2
C3
C4
C5
C6
C7
C8
C9
C1
0C
11
C1
2C
13
C1
4C
15
C1
6C
17
C1
8C
19
C2
0C
21
C2
2C
23
C2
4C
25
C2
6C
27
C2
8C
29
C3
0C
31
C3
2C
33
C3
4C
35
C3
6C
37
C3
8C
39
C4
0+
Mole
%
0
2
4
6
8
10
12
14
16
18
20
Injection oil
Produced oil after propane injection in small model
Produced oil after methane injection in small model
Produced oil after CO2 injection in small model
Produced oil after propane/CO2 injection in small model
Produced oil after butane injection in small model
Produced oil after propane/methane injection in small model
Figure 4-28: Effect of solvent type on hydrocarbon components in small model
142
4.1.2.2 Large model
4.1.2.2.1 Recovery factor and produced oil rate
Figure 4-29 shows the effect of solvent type on recovery factor in the large model. The
same trend as the small model was observed in the large model after injecting propane as
the injection solvent. As can be seen, the recovery factor is significantly higher during
propane injection and the ultimate recovery factor was found to be about 80% of original
oil in place. The second best solvents were found to be the mixture of propane and CO2
and pure butane with ultimate recovery factors of about 60% of original oil in place.
However, injecting pure methane and CO2 did not show promising results and the process
was extremely slow.
Figure 4-30 shows the effect of solvent type on the produced oil rate. The same trend as
the recovery factor can be seen for different solvents. The highest stabilized drainage rate
was observed for propane injection, which was about 0.50 mL/min, while the lowest
production rate was observed for pure CO2 injection. The stabilized drainage rates were
0.33 mL/min, 0.25 mL/min and 0.32 mL/min for propane/CO2, propane/methane, and
butane injection.
143
Time (h)
0 100 200 300 400 500
Rec
ov
ery F
acto
r (%
OO
IP)
0
20
40
60
80
Propane
Propane/ CO2
CO2
Butane
Methane
Propane/ methane
Figure 4-29: Effect of the solvent type on recovery factor in large model
144
Time (h)
0 100 400 500
Pro
duced
Oil
Rat
e (m
L/m
in)
0.0
0.2
0.4
0.6
0.8
1.0
Propane
Propane/CO2
CO2
Butane
Methane
Propane/ methane
Figure 4-30: Effect of the solvent type on produced oil rate in large model
145
4.1.2.2.2 Solvent utilization factor (SUF)
Figure 4-31 shows the effect of solvent type on SUF in the large model. More or less the
same trend as the small model was observed in the large model for various solvents. As
expected, the highest SUF was observed for the case of propane injection, which shows
the efficiency of the process after using propane as the injection solvent. The total SUF
was about 2.9×10-3
(mL Oil Prod./mL Sol. Inj.) for propane injection. These results show
that up-scaling the process did not result in additional solvent loss, and it was even
observed that the VAPEX process was significantly improved.
4.1.2.2.3 Viscosity, density, molecular weight, and hydrocarbon components for the
produced oil
Table 4-15 shows the effect of solvent type on viscosity, density, and molecular weight of
the produced oil. The heavy oil dilution was more prominent in the large model, and the
viscosity of original oil was diluted from 5650 mPa.s to469 mPa.s after injecting propane,
while the produced oil viscosity was found to be 1160, 2080, and 3220 mPa.s after
injecting propane/CO2, propane/methane, and butane, respectively. However, injecting
pure methane and CO2 did not result in a noticeable heavy oil dilution.
Figure 4-32 shows the effect of solvent type on the hydrocarbon components of the
produced oil. The same behaviour as the small model was observed after utilizing various
solvents in the large physical model.
146
Time (h)
0 100 200 350 400 450
SU
F (
mL
Oil
Pro
d./
mL
Sol.
Inj.
)
0
5.0x10-4
10-3
1.5x10-3
2.0x10-3
2.5x10-3
3.0x10-3
3.5x10-3
Propane
Propane/ CO2
CO2
Butane
Methane
Propane/ methane
Figure 4-31: Effect of solvent type on the solvent utilization factor (SUF) for large model
147
Table 4-15: Produced oil properties for the large model
Solvent Viscosity (mPa.s) Density (kg/m3) Molecular weight
Propane 469 938.17 501
Methane 5520 970.11 509
CO2 4910 961.80 504
Butane 3220 965.92 522
Propane/CO2 1160 944.46 611
Propane/methane 2080 961.10 504
148
Carbon number
C1
C2
C3
C4
C5
C6
C7
C8
C9
C1
0C
11
C1
2C
13
C1
4C
15
C1
6C
17
C1
8C
19
C2
0C
21
C2
2C
23
C2
4C
25
C2
6C
27
C2
8C
29
C3
0C
31
C3
2C
33
C3
4C
35
C3
6C
37
C3
8C
39
C4
0+
Mole
%
0
2
4
6
8
10
12
14
16
18
20
Injection oil
Produced oil after propane injection in small model
Produced oil after methane injection in small model
Produced oil after CO2 injection in small model
Produced oil after propane/CO2 injection in small model
Produced oil after butane injection in small model
Produced oil after propane/methane injection in small model
Figure 4-32: Effect of solvent type on hydrocarbon components in large model
149
4.2 Residual oil saturation
The procedure for residual oil saturation measurements was explained in detail in Chapter
3. As explained, different samples were taken from different locations of the small and
large physical models. The saturation profiles are presented in Figures 4-33 and 4-34 for
the small and large models, respectively.
It was observed that residual oil saturations close to the injection well were very low for
all the solvents. However, the lowest residual oil saturation was obtained after injecting
propane for both the small and large models. The residual oil saturation for sample
location 1 was 4.3% and 5.1% for the small and large models, respectively. On the other
hand the highest residual oil saturation was observed at the bottom of the physical models
and close to production wells. The highest residual oil saturation was found to be 80.4%
in the small model and 88.9% in the large model for the case of CO2 injection.
The residual oil saturations were the lowest at the top of the models and close to the
injection points because the solvents were injected from the top injection point and the
diluted oil was drained downward by gravity and solvent flooding.
150
Residual oil saturation (%)
0 20 40 60 80 100
Hei
ght
(cm
)
0
5
10
15
20
25
Propane
Methane
CO2
Butane
Propane/CO2
Propane/methane
Injector
Producer
Figure 4-33: Residual oil saturation profile for various solvents in the small model
151
Residual oil saturation (%)
0 20 40 60 80 100
Hei
ght
(cm
)
0
5
10
15
20
25
30
35
40
45
50
Propane
Methane
CO2
Butane
Propane/CO2
Propane/methane
Injector
Producer
Figure 4-34: Residual oil saturation profile for various solvents in the large model
152
4.3 Asphaltene precipitation
The asphaltene content of each sample was measured using the standard ASTM D2007-
03 method. The precipitant used was n-Pentane. The experimental procedure was
explained in Chapter 3. Figure 4-35 shows some of the asphaltene precipitate on the filter
paper after the experiments. The mass of the dried particulate on the filter paper, m2, was
compared to the original mass of the heavy oil sample, m1, to determine the asphaltene
mass percent:
%100%1
2
m
mAsphaltenewt ...……………………………………………….…. (4.2)
In order to investigate the asphaltene precipitation in more detail, various samples were
taken after each experiment from various locations of the VAPEX models. Figure 4-36
shows the schematic of the locations of each heavy oil sample in the physical models. In
the next sections, the corresponding graphs for each sample are provided.
153
Figure 4-35: Asphaltene precipitate after conducting the asphaltene measurement tests
154
Figure 4-36: Schematic of the locations of each heavy oil samples in the physical models
155
4.3.1 Effect of drainage height
4.3.1.1 Propane injection
Figure 4-37 shows the asphaltene precipitation for different locations in the small and
large models following propane injection. As can be seen, the amount of asphaltene
precipitation is almost the same for both physical models. However, at the injection point,
the asphaltene content is slightly more in the large model; this might be due to the contact
time between the solvent and the heavy oil system. The contact time, which is an
effective parameter on asphaltene precipitation, is longer for the large model than for the
small model. Furthermore, more asphaltene precipitation occurs at the injection point and
at the solvent/heavy oil interfaces in comparison to the location close to the production
point. As can be seen, the asphaltene precipitation was about 40% and 31.5% close to the
injection wells for the large and small models, respectively. The minimum asphaltene
precipitation was observed to be about 23% at location #4, which was close to the
production wells. As discussed earlier in Chapter 2, there is a phase change during the
VAPEX process when the solvent diffuses into the oil at the solvent oil interface. During
this phase change, there will be change in temperature, pressure, and concentration at the
contact interface. This proves the difference regarding the amount of asphaltene
precipitation at the injection and production points. It was also found that the texture of
precipitated asphaltene on the filter paper changed at different locations. For instance,
asphaltene precipitants close to the injection points were brittle; however, precipitants
close to the production points were more ductile.
156
Asphaltene weight percent
0 10 20 30 40 50
Sam
ple
num
ber
s
Sample 4
Sample 3
Sample 2
Sample 1
Small model
Large model
Figure 4-37: Effect of drainage height on asphaltene precipitation at different locations in the small and
large models after propane injection
157
4.3.1.2 Methane injection
Figure 4-38 shows the asphaltene precipitation for different locations in the small and
large models after methane injection. The same trend as the propane injection was
observed; the amount of asphaltene precipitation is slightly more in the large model,
which might be due to the contact time between the solvent and the heavy oil system. The
highest asphaltene precipitation was found to be about 48% at location #1 in the large
model.
4.3.1.3 CO2 injection
Figure 4-39 shows the asphaltene precipitation for different locations in the small and
large models after CO2 injection. The trend for this solvent was slightly different from
what was observed for propane and methane. In fact, the amount of asphaltene
precipitation is slightly more in the large model at various locations except for location 2
in the small model. At this point, significant asphaltene precipitation of about 63% was
observed in the small model, which indicates the low recovery factor of that specific test.
4.3.1.4 Butane injection
Figure 4-40 shows the asphaltene precipitation for different locations in the small and
large models after butane injection. More asphaltene precipitation was observed in the
large model with higher drainage rate and longer distance between the injection and
production wells. The asphaltene precipitation was highest at location #1, and it was
38.7% and 31.3% for the large and small models, respectively.
158
Asphaltene weight percent
0 10 20 30 40 50 60
Sam
ple
num
ber
s
Sample 4
Sample 3
Sample 2
Sample 1
Small model
Large model
Figure 4-38: Effect of drainage height on asphaltene precipitation at different locations in the small and
large models after methane injection
159
Asphaltene weight percent
0 10 20 30 40 50 60 70
Sam
ple
num
ber
s
Sample 4
Sample 3
Sample 2
Sample 1
Small model
Large model
Figure 4-39: Effect of drainage height on asphaltene precipitation at different locations in the small and
large models after CO2 injection
160
Asphaltene weight percent
0 10 20 30 40 50
Sam
ple
num
ber
s
Sample 4
Sample 3
Sample 2
Sample 1
Small model
Large model
Figure 4-40: Effect of drainage height on asphaltene precipitation at different locations in the small and
large models after butane injection
161
4.3.1.5 Propane/CO2 injection
As can be seen in Figure 4-41, the same trend as the propane injection was observed in
the case of propane/CO2 mixture injection. The asphaltene precipitation was slightly
more in the large model. The highest asphaltene precipitation was about 38.6% at
location #1 for the large model. However, the highest asphaltene precipitation for the
small model was observed at the injection point, which was about 29.7%. The minimum
asphaltene precipitation was about 22% for both models and was observed at the
production points. These results confirm the in-situ upgrading of heavy oil by injecting a
mixture of propane and CO2.
4.3.1.6 Propane/methane injection
As can be seen in Figure 4-42, the same trend as the propane injection was observed in
the case of propane/methane mixture injection. The highest asphaltene precipitation was
about 39.1% at location #1 for the large model. The minimum asphaltene precipitation
was observed at location #4 it was about 22%.
It was also found that the asphaltene precipitants close to the injection wells were brittle;
however, precipitants close to the production wells were more ductile.
162
Asphaltene weight percent
0 10 20 30 40 50
Sam
ple
num
ber
s
Sample 4
Sample 3
Sample 2
Sample 1
Small model
Large model
Figure 4-41: Effect of drainage height on asphaltene precipitation at different locations in small and large
models after propane/CO2 injection
163
Asphaltene weight percent
0 10 20 30 40 50
Sam
ple
num
ber
s
Sample 4
Sample 3
Sample 2
Sample 1
Small model
Large model
Figure 4-42: Effect of drainage height on asphaltene precipitation at different locations in small and large
models after propane/methane injection
164
4.3.2 Effect of solvent type
In this section, the results obtained for each model after injecting different solvents are
graphed together to investigate the effect of solvent type on asphaltene precipitation in
each physical model.
4.3.2.1 Small model
Figure 4-43 shows the results of the asphaltene content measurement test after using
different solvents in the small model. As can be seen, the highest asphaltene precipitation
was achieved after injecting methane, and it was about 41.7%. Generally, injecting CO2
showed the least asphaltene precipitation and, consequently, the least heavy oil dilution.
The asphaltene precipitation after injecting CO2 at location #1 was about 22.5%. It should
be mentioned that the low injection pressure for CO2 could be a reason for this low
dilution. It was expected that the difference in asphaltene precipitation for butane and
propane injection would be more prominent; however, the slow process of butane
injection resulted in some excessive asphaltene precipitation. Comparing the results for
the propane injection with the mixture of propane/CO2, it can be seen that there will be
less asphaltene precipitation at different locations of the physical models. It was also
observed by Javaheri and Abedi (2013) that by adding CO2 to pure propane, less
asphaltene precipitation would be observed. Moreover, it was observed that adding
methane, would also results in less asphaltene precipitation compared to pure propane
injection.
165
Asphaltene weight percent
0 10 20 30 40 50 60 70
Sam
ple
num
ber
s
Sample 4
Sample 3
Sample 2
Sample 1
Propane
Methane
CO2
Butane
Propane/ CO2
Propane/ methane
Figure 4-43: Effect of solvent type on asphaltene precipitation at different locations in the small model
166
4.3.2.2 Large model
Figure 4-44 shows the results of the asphaltene content measurement test after using
different solvents in the large model. This time, the overall amount of asphaltene
precipitation was highest at location #1 for the case of methane injection, and it was
about 48%. This means that more dilution and in-situ upgrading of heavy oil was
achieved by injecting propane. The trend for various solvents was almost the same as
what was observed in the small model. The asphaltene precipitation for location #1 was
found to be 40%, 39.1%, 38.7%, and 38.6% for propane, propane/ methane, butane, and
propane/CO2, respectively.
Figure 4-45(a) shows the asphaltene precipitation close to the injection points in one of
the tests. Here, severe asphaltene precipitation was observed, and, as mentioned earlier,
the texture was more brittle. Asphaltene streaks were observed in most of the tests,
especially at the solvent/oil interface. In Figure 4-45(b) asphaltene streaks in the sand
pack can be seen after opening the Plexiglas plate at the end of one of the experiments.
167
Asphaltene weight percent
0 10 20 30 40 50 60
Sam
ple
num
ber
s
Sample 4
Sample 3
Sample 2
Sample 1
Propane
Methane
CO2
Butane
Propane/ CO2
Propane/ methane
Figure 4-44: Effect of solvent type on asphaltene precipitation at different locations in the large model
168
(a)
(b)
Figure 4-45: (a) Asphaltene precipitation close to the injection point, (b) Asphaltene streaks on the sand
pack at the end of experiments
169
4.4 Image analysis (IA)
As mentioned in Chapter 3, the physical models were designed so the solvent chamber
evolution could be monitored. To this end, a digital camera was used to take pictures of
the physical models at different times during the experiments. These images were used to
analyse the chamber evolution over time and also to measure the displacement efficiency
for each solvent in VAPEX models.
For this purpose, software with a graphical user interface was coded to analyse the
images from the small and large physical models. Software was specifically coded for
this purpose because of the limitations of commercial IA softwares. Such softwares need
a specific resolution and zooming for the test images. However, because of the
limitations in the laboratory with the large models, the angle and zooming of the images
changed for certain images.
The software was coded using C# programming with Microsoft Visual Studio 2012. The
interface of the coded software is shown in Figure 4-46.
The images are input to the software and the coordinates are fixed based on the model
dimensions. Then, an approximate interface of the solvent/heavy oil system can be
manually selected. Next, the software detects the interface based on the colour change
and determines the best polynomial for the detected interface. In addition, the swept zone
area and the drainage height will be shown at each time. Figure 4-47and 4-48 show these
steps for the small and large models respectively for a sample image.
170
Figure 4-46: The interface of the coded software for IA
171
(a)
(b)
(c)
Figure 4-47: The procedure for conducting IA in the small model: (a) The coordinates of the image are
specified, (b) The interface curve is defined, and (c) The oil and solvent zones are schematically reprinted
by the software
172
(a)
(b)
(c)
Figure 4-48: The procedure for conducting IA in the large model: (a) The coordinates of the image are
specified, (b) The interface curve is defined, and (c) The oil and solvent zones are schematically reprinted
by the software
173
Figures 4-49 and 4-50 show the post-propane-injection chamber evolution for the small
and large models, respectively. These pictures are processed with the developed IA
software. In these pictures, the solvent and oil zones appear distinctively during the
experiments. The untouched zone is shown with the darker colour, while the swept zone
is shown with light grey color. Of note, a similar shape is observed in both physical
models. The chamber forms and develops toward the sidewalls and then moves
downward with reduced available drainage height. It was also observed that the solvent
chamber descended slightly faster on the right wall because of the location of the
injection well, which was closer to the left wall until the end of the experiments when it
was fully developed and reached the bottom boundary of the physical models. As these
figures show, the solvent and oil interface is not a smooth straight line. Therefore, the
best curve was applied to model the solvent/oil interface. Then, the area of the swept
zone was calculated to be used to calculate the sweep efficiency for each solvent in the
small and large models. For this purpose, equation (4.3) was introduced, and it was
assumed that vertical sweep efficiency was equal to one.
dA EERF ……………………………………………………………..………….. (4.3)
where RF is the recovery factor, EA is the areal sweep efficiency, and Ed is the sweep
efficiency.
174
Figure 4-49: Solvent chamber evolution in small model after propane injection
175
Figure 4-50: Solvent chamber evolution in large model after propane injection
176
The results for the sweep efficiency are provided in Figures 4-51 and 4-52 for the small
and large models, respectively. It was found that the sweep efficiency in the small model
was very close to the large model for each solvent. However, from the solvent type point
of view, the highest sweep efficiency was achieved for the case of propane injection,
which was about 0.86. Injecting butane resulted in high sweep efficiency in both models,
and it was about 0.72. Additionally, injecting propane/CO2 and propane/methane
mixtures showed promising sweep efficiency. However, the sweep efficiency of
propane/CO2 mixture was slightly higher than the propane/methane mixture. On the other
hand, the sweep efficiency of pure CO2 injection was higher than methane injection. The
lowest sweep efficiency was achieved in the large model after injecting methane, which
was 0.38.
By monitoring the slight change of sweep efficiency for each solvent during the course of
the experiments, it was found that the sweep efficiency decreases after the solvent
breakthrough and during the first stages of chamber evolution.
177
Time (h)
0 20 40 60 80 100 200 250 300 350 400 450
Sw
eep E
ffic
iency
(E
d)
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Propane
Butane
CO2
Methane
Propane/CO2
Propane/methane
Figure 4-51: Sweep efficiency of various solvents in the small model
178
Time (h)
0 20 40 60 80 100 120 140 160 350 400 450 500
Sw
eep E
ffic
iency
(E
d)
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Propane
Butane
CO2
Methane
Propane/CO2
Propane/methane
Figure 4-52: Sweep efficiency of various solvents in the large model
179
4.5 Effect of injection-production wells connection
One of the key parameters to implement a successful VAPEX process is to control the
profiles of the vapour chamber, which will result in high areal sweep efficiency. To
achieve this goal, an optimum well configuration and connection between the injection
and production wells is desirable. After conducting experiments no. 2, 3, 8, and 9 with
propane and propane/CO2 mixture, some disparities with the results in literature were
observed. To further investigate these conflicting results, another injection scenario was
followed to observe the effect of connection between the injection and production wells.
Therefore, tests no. 13, 14, 15, and 16 were carried out as the second injection scenario.
In the following sections, the observations are presented for these tests. In the first
scenario (tests no. 2, 3, 8, and 9), the solvent was injected into the physical models at the
operating pressure while the production pressure was atmospheric pressure, and the
solvent and oil production was monitored carefully. Once, the solvent breakthrough was
monitored the production pressure was set to the operating pressure to eliminate the
pressure difference and start producing due to the gravity drainage. For the second
scenario (tests no. 13, 14, 15, and 16), the pressure at the production point was
implemented after that the connection between the injection and production well was
visually observed. It took more injection time and a larger amount of solvent was
produced before exerting the back-pressure at the production well.
4.5.1 Small model
Figure 4-53 shows the recovery factor after injecting propane and propane/CO2 for two
injection scenarios in the small VAPEX model. The first distinctive difference between
the results was that following the first injection scenario, the process was extremely slow.
180
However, the ultimate recovery factor was almost the same for both injection scenarios.
The ultimate recovery factor was about 75% of original oil in place after injecting
propane as the solvent in the small model, and the ultimate recovery factor after injecting
propane/CO2 was about 60% of original oil in place. The major conflict was observed
while comparing the drainage rates for the small and large models. As presented in
Figure 4-54, the drainage rate was found to be higher in the small model with smaller
drainage height. This disparity may be the result of well configuration and the poor
connection between the injection and production wells. Therefore, the solvent chamber
moved faster in the small physical model in comparison to the large model with a greater
drainage height. The stabilized drainage rate after propane injection was observed to be
about 0.12 mL/min for the first injection scenario and about 0.22 mL/min after
implementing the second injection scenario. For the case of propane/CO2 injection, the
stabilized drainage was found to be about 0.08 mL/min and 0.15 mL/min for the first and
second injection scenarios, respectively. Hence, it was observed that stabilized drainage
rate was increased approximately two times for the second injection scenario. After
measuring the asphaltene content of different samples from different locations of the
physical models, it was observed that less asphaltene deposition occurred after propane
and propane/CO2 injection in the small model following the second injection scenario.
Better connection between the injection and production wells decreased the recovery
process time significantly, which directly affected the asphaltene deposition in both the
small and large models. The results for the asphaltene content at different locations of the
small model are presented in Figure 4-55.
181
Time (h)
0 20 40 60 80 100 120
Rec
ov
ery F
acto
r (%
OO
IP)
0
20
40
60
80
100
Propane after first injection scenario
Propane after second injection scenario
Propane/CO2 after first injection scenario
Propane/CO2 after second injection scenario
Figure 4-53: Effect of connection establishment between the injection and production wells on the recovery
factor in the small model
182
Figure 4-54: Effect of connection establishment between the injection and production wells on the
produced oil rate in the small model
Time (h)
0 20 40 60 80 100 120
Pro
duce
d O
il R
ate
(mL
/min
)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Propane after first injection scenario
Propane after second injection scenario
Propane/ CO2 after first injection scenario
Propane/ CO2 after second injection scenario
183
Asphaltene weight percent
0 10 20 30 40 50 60
Sam
ple
num
ber
s
Sample 4
Sample 3
Sample 2
Sample 1
Propane after first injection scenario
Propane after second injection scenario
Propane/ CO2 after first injection scenario
Propane/ CO2 after second injection scenario
Figure 4-55: Effect of connection establishment between the injection and production wells on the
asphaltene precipitation in the small model
184
Fluid properties of the produced fluid were measured after each test to observe the effect
of different injection scenarios and the type of injection solvent on the produced fluid.
Table 4-16 showsviscosity, molecular weight, and oil density of the produced oil after
injecting propane and propane/CO2 for the two injection scenarios. The results show two
effective parameters as mentioned before: the type of the injection solvent used and the
connection establishment between the injection and production. Propane
injectionsignificantly diluted the original oil and reduced its viscosity in both injection
scenarios. However, the decrease in the viscosity is more noticeable in the second
injection scenario.For instance, for the case of propane injection, the first injection
scenario resulted in the reduction of original oil viscosity from 5650 mPa.s to 1235 mPa.s
while it decreased from 5650 mPa.s to 999 mPa.s for the second injection scenario.
Figure 4-56 shows the chamber evolution after implementing the first injection scenario.
The chamber forms and develops toward the sidewalls and then moves downward with
reduced available drainage height. It was also observed that the solvent chamber
descended faster on the left wall until the end of the experiments when it was fully
developed and reached the bottom boundary of the physical models. The chamber
evolution after implementing the second injection scenario in the small model was
presented in Figure 4-49 earlier in this chapter.
185
Table 4-16: Produced oil properties
Test
No.
Physical
Model
Solvent
Viscosity
(mPa.s)
Density
(kg/m3)
Molecular
weight
2 Small Propane 1235 957.51 493
8 Small Propane/CO2 1950 954.48 501
13 Small Propane 999 853.50 509
15 Small Propane/CO2 1480 944.46 505
186
Figure 4-56: Solvent chamber evolution in small model after propane injection (first injection scenario)
187
4.5.2 Large model
After following the second injection scenario to establish a more confident connection
between the injection and production wells, the results were different and higher
productions rates were observed in the larger model with greater drainage height. Figure
4-57 shows the recovery factor after injecting propane and propane/CO2as the solvent in
the large physical model. As can be seen in this figure, the effects of connection between
the injection and production wells were more noticeable in the large model. This can be
due to the specific well configuration used in this study and the longer distance between
the injection and production wells. The ultimate recovery factor of about 50% of original
oil in place was observed after injecting propane as the solvent for the first injection
scenario while utilizing the second injection scenario resulted in a recovery of 80% of
original oil in place and a significantly faster process. The same trend was observed when
a mixture of propane/CO2 was used as the solvent for the VAPEX process. In the case of
propane/CO2 injection, the ultimate recovery factor was found to increase from 42% of
original oil in place for the first injection scenario to 52% for the second injection
scenario. The stabilized drainage rate after propane injection was found to increase from
0.04 mL/min to 0.50 mL/min after injecting propane in the large model. The stronger
connection between the injection and production wells slightly improved the recovery
performance of the process in the small model, but it significantly boosted the process in
the large model. For the case of propane/CO2 injection, the stabilized drainage rate was
found to increase from 0.02 mL/min for the first injection scenario to 0.33 mL/min for
the second injection scenario. The results for produced oil drainage rate are presented in
Figure 4-58.
188
Time (h)
0 50 100 150 200 250 300 350 400 450
Rec
over
y F
acto
r (%
OO
IP)
0
20
40
60
80
Propane after first injection scenario
Propane after second injection scenario
Propane/CO2 after first injection scenario
Propane/CO2 after second injection scenario
Figure 4-57: Effect of connection establishment between the injection and production wells on the recovery
factor in the large model
189
Time (h)
0 100 200 500 600
Pro
duce
d O
il R
ate
(mL
/min
)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Propane after first injection scenario
Propane after second injection scenario
Propane/ CO2 after first injection scenario
Propane/ CO2 after second injection scenario
Figure 4-58: Effect of connection establishment between the injection and production wells on the
produced oil rate in the large model
190
The results for the asphaltene content at different locations of the large model are
presented in Figure 4-59. It was observed that less asphaltene deposition occurred after
propane and propane/CO2 injection in the large model following the second injection
scenario.
Viscosity, molecular weight, and oil density of the produced oil after injecting propane
and propane/CO2 for the two injection scenarios in the large VAPEX model are presented
in Table 4-17. Like other parameters that was discussed earlier, the connection between
the wells affected the produced oil properties more significantly in the large model
compared to the small model. For the case of propane injection, the first injection
scenario resulted in the reduction of original oil viscosity from 5650 mPa.s to 644 mPa.s,
while it decreased from 5650 mPa.s to 469 mPa.s for the second injection scenario.
The shape of the chamber after utilizing the second injection scenario in test no. 9 is
presented in Figure 4-50, while the shape of the chamber after following the first
injection scenario is presented in Figure 4-60.
191
Asphaltene weight percent
0 10 20 30 40 50
Sam
ple
num
ber
s
Sample 4
Sample 3
Sample 2
Sample 1
Propane after first injection scenario
Propane after second injection scenario
Propane/ CO2 after first injection scenario
Propane/ CO2 after second injection scenario
Figure 4-59: Effect of connection establishment between the injection and production wells on the
asphaltene precipitation in the large model
192
Table 4-17: Produced oil properties
Test
No.
Physical
Model
Solvent
Viscosity
(mPa.s)
Density
(kg/m3)
Molecular
weight
3 Large Propane 644 952.75 507
9 Large Propane/CO2 1500 953.71 506
14 Large Propane 469 938.17 469
16 Large Propane/CO2 1160 954.41 503
193
Figure 4-60: Solvent chamber evolution in large model after propane injection (first injection scenario)
194
4.6 Scale-up:
Butler and Mokrys (1989) carried out VAPEX experiments in Hele-Shaw cells and found
that there is square root functionality between the stabilized drainage rate and the
medium permeability, drainage height, and physical properties of oil and solvent. They
assumed that there is complete miscibility of solvent and bitumen, and they also
neglected the convection term and mechanical dispersion coefficients. Based on their
findings, they proposed equation (4.4) to predict the produced flow rate after
implementing the VAPEX process:
so HNSkgQ 22 ………………………………………………………………… (4.4)
In this equation, Ns is the VAPEX dimensionless number, which accounts for the oil-
solvent properties and is defined by equation (4.5):
max
min
1C
Cs
smix
sss dC
C
DCN
………………………………………………………… (4.5)
In these equations, Q is the stabilized drainage rate per unit length of the horizontal well,
k is permeability, g is acceleration due to gravity, φ is porosity, ΔSo is change in oil
saturation, Δρ is density difference between solvent and bitumen, Cs is solvent
concentration, Ds is diffusivity of solvent in bitumen, and μmix is the viscosity of the
mixture at solvent concentration.
Later, it was found by Das and Butler (1994, 1998) that the above equation
underestimates the production rate in porous media. To consider the effect of porous
195
media, they introduced the effective diffusion coefficient, Deff, and cementation factor, Ω.
Therefore, equations (4.4) and (4.5) were modified as follows:
so HNSkgQ 22 …………………………………………………….………… (4.6)
where
max
min
1C
Cs
smix
effs
s dCC
DCN
………………………………...……………………… (4.7)
and
seff DD ………………………………………………………………………… (4.8)
In equation (4.8), λ is the mass transfer enhancement coefficient.
Equation (4.6) can be rearranged as follows:
so NSgkHQ 2 …...…...……………………………………….………… (4.9)
The second term on the right hand side of equation (4.9), so NSg2 , is constant for a
specific oil-solvent system at constant pressure and temperature. Therefore, for two
different sand pack models with different drainage heights, the following equation can be
derived:
1
2
1
2
kH
kH
Q
Q…………………………………………………………………... (4.10)
196
Equation (4.10) can be used to upscale the drainage rate obtained in a model with smaller
drainage height to one with a larger drainage height. However, it was found later by
several researchers that this up-scaling equation still cannot predict the drainage rate
(Yazdani, 2007). Based on the results that were obtained during experiments with various
models with different drainage heights, Yazdani (2007) showed that this equation
underestimates the drainage rate. Equation (4.10) was modified by him and the following
equation was proposed:
1
2
1
2
1
2
k
k
H
H
Q
Qn
…………………………………………….....……………... (4.11)
The exponent n in equation (4.11) is in the range of 1.10 to 1.30, while this exponent is
0.50 in Butler’s equation. In order to find the correct value of exponent n, various values
of n = 0.50, 1.10, and 1.30 were used to predict the drainage rate. For this purpose,
equation (4.11) was rearranged to equation (4.12), and the two terms on each side of this
equation were measured for different solvents. To graphically present the results,
equations (4.13) and (4.14) were used; hence, the results are presented graphically in
Figures 4-61 to 4-63. The subscript, L stands for the large physical model, and the
subscript S stands for the small physical model.
3.13.1
SS
n
S
S
LL
n
L
L
kH
Q
kH
Q
………...……………..………………..………... (4.12)
3.1
LL
n
L
LL
kH
QR
………...……………..………………………………..……... (4.13)
197
3.1
SS
n
S
SS
kH
QR
………...…………………………..……...…………………... (4.14)
It can be seen in Figure 4-61 that Butler’s model significantly under-predicted the
drainage rate for all types of solvents used in this study. However, the results obtained
based on Yazdani’s model are more accurate, and the data points are closer to the
prediction line. The results in Figure 4-62 showing that exponent n = 1.1 still
underestimates the actual drainage rate, but as is presented in Figure 4-63, exponent n =
1.3 resulted in over estimating the experimental drainage rates. Therefore, exponent n =
1.2 was chosen, and the results obtained based on this value are graphed in Figure 4-64. It
was found that the experimental results match the prediction based on this new value, and
the best results were obtained with n = 1.2.
Considering the fact that there is a linear relationship between stabilized drainage rate, Q,
and 3.1kH n , and knowing the best value for exponent n is 1.2, the following equations
can be found for different solvents based on the results presented in Figure 4-65.
For propane:
3.12.10334.0 kHQ ................................................................................................ (4.15)
For propane/CO2 mixture:
3.12.10227.0 kHQ ................................................................................................ (4.16)
198
For butane:
3.12.10217.0 kHQ ................................................................................................ (4.17)
For propane/methane mixture:
3.12.10174.0 kHQ ............................................................................................... (4.18)
199
Rs (h×104)
0.0 0.1 0.2 0.3 0.4 0.5
RL (
h×104)
0.0
0.1
0.2
0.3
0.4
0.5
Propane
Methane
CO2
Propane/CO2
Butane
Propane/Methane
n=0.5
Figure 4-61: The results obtained for up-scaling the stabilized drainage rate based on the proposed
exponent by Butler (1994), (n=0.5). The dotted line is the drainage rate prediction based on Butler’s model;
the data points for different solvents are the experimental results obtained in this study.
200
Rs (h×104)
0.00 0.01 0.02 0.03 0.04 0.05
RL
(h×
10
4)
0.00
0.01
0.02
0.03
0.04
0.05
Propane
Methane
CO2
Propane/CO2
Butane
Propane/Methane
n=1.1
Figure 4-62: The results obtained for up-scaling the stabilized drainage rate based on the proposed
exponent by Yazdani (2007), (n=1.1). The dotted line is the drainage rate predicted based on Yazdani’s
model; the data points for different solvents are the experimental results obtained in this study.
201
Rs (h×104)
0.000 0.005 0.010 0.015 0.020 0.025 0.030
RL
(h×
10
4)
0.000
0.005
0.010
0.015
0.020
0.025
0.030
Propane
Methane
CO2
Propane/CO2
Butane
Propane/Methane
n=1.3
Figure 4-63: The results obtained for up-scaling the stabilized drainage rate based on the proposed
exponent by Yazdani (2007), (n=1.3). The dotted line is the drainage rate predicted based on Yazdani’s
model; the data points for different solvents are the experimental results obtained in this study.
202
Rs (h×104)
0.00 0.01 0.02 0.03 0.04 0.05
RL (
h×10
4)
0.00
0.01
0.02
0.03
0.04
0.05
Propane
Methane
CO2
Propane/CO2
Butane
Propane/Methane
n=1.2
Figure 4-64: The results obtained for up-scaling the stabilized drainage rate. The dotted line is the drainage
rate predicted based on n=1.2; the data points for different solvents are the experimental results obtained in
this study.
203
Figure 4-65: Linear regression for the results obtained for different solvents in the small and large physical
models
3.12.1 kH
60 80 100 120 140 160 180 200
Q(c
m2/h
)
1
2
3
4
5
6
7
Propane
Propane/CO2
Butane
Propane/Methane
204
4.7 Dimensionless VAPEX number, Ns calculation:
As described earlier in this chapter, Butler’s model includes a dimensionless number, Ns,
which is also called the VAPEX number. This number accounts for oil-solvent properties
and can be calculated by rearranging equations (4.6) and (4.7) to generate the following
equation:
HSkg
QdC
C
DCN
o
C
Cs
smix
sss
8
1 2max
min
…………………...………………..… (4.19)
The experimental results were used to calculate the VAPEX number using equation
(4.19). The results were graphed versus the drainage height to investigate the effect of
drainage height on VAPEX number. As can be seen in Figure 4-66, VAPEX number
increases with increasing drainage height. This was also observed by other researchers
(Ahmadloo 2012, Yazdani 2007), and it can be concluded that VAPEX number is
dependent on the drainage height and oil-solvent properties. However, it should be noted
that in equation 4.19 by changing the values for drainage height the other parameters are
changing too, which will result in the final value for Ns. Comparing different solvents
used for these experiments, the highest values for VAPEX number were achieved after
injecting propane, and VAPEX numbers for butane and propane/CO2 were also relatively
high and close to each other. However, the lowest values were obtained after injecting
pure methane and pure CO2.
205
H (m)
0.20 0.25 0.30 0.35 0.40 0.45 0.50
Ns
(dim
ensi
onle
ss)
0
5.0x10-5
10-4
1.5x10-4
2.0x10-4
2.5x10-4
Propane
Methane
CO2
Propane/CO2
Butane
Propane/Methane
Figure 4-66: Effect of drainage height and solvent type on dimensionless VAPEX number, Ns
206
5. CHAPTER 5: PVT STUDIES AND NUMERICAL
SIMULATION
5.1 Viscosity and density measurement
Viscosity and density of the heavy oil directly affect the amount of solvent dissolved in
the heavy oil; therefore, in order to tune the PVT model against the experimental data
viscosity and density of heavy oil used in these experiments were calculated at different
temperatures. The measurements are provided in Figure 5-1.
5.2 Vapour pressure
During solvent injection, vapour pressure is a key factor that significantly affects the
VAPEX performance. It has been found that the optimum VAPEX performance would be
obtained if the solvent is injected close to its vapour pressure. Therefore, CMG’s
WinpropTM
package (Computer Modelling Group Ltd., 2011) was utilized to calculate the
vapour pressure for various solvents used in this study. The results are provided in Table
5-1, and P-T two phase envelopes are presented in Figure 5-2.
207
Temperature, °C
15 20 25 30 35 40 45 50 55
Den
sity
, kg/m
3
950
955
960
965
970
975
Vis
cosi
ty, m
Pa.
s
0
1000
2000
3000
4000
5000
6000
Density
Viscosity
Figure 5-1: Densities and viscosities of the heavy oil used in this study at various temperatures and
atmospheric pressure
208
Table 5-1: Vapour pressure of solvents used in this study at 21 °C
Solvent Vapour pressure at T = 21°C (kPa)
CO2 5766.9
Methane NA
Propane 757.4
Butane 116.6
Propane/CO2 1156.5
Propane/methane 1204.1
209
Temperature (°C)
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100
Pre
ssure
(kP
a)
0
1000
2000
3000
4000
5000
6000
7000
P-T diagram for Propane (70%)/Methane (30%) mixture
P-T diagram for Propane (70%)/CO2 (30%) mixture
Experimental operating conditions
Figure 5-2: Two-phase envelopes for propane/CO2 and propane/methane mixtures
210
5.3 Solubility measurement
In this study, separate experiments were carried out to measure the solubility of pure
solvents used for the VAPEX experiments. The experiments to measure the solubility of
propane, butane, CO2, and methane were carried out at a temperature of 21°C and various
pressures for each solvent. The solubility measurement experimental set-up consists of a
stainless steel cylinder with a volume of 196.4 cm3, a solvent tank, a pressure regulator,
digital pressure gauges, DFMs, valves, and tubing. The schematic diagram of the
experimental set-up is presented in Figure 5-3. To carry out the solubility measurement
experiments, first, 20cm3 heavy oil was added into the cylinder. Then, the cylinder was
sealed with a cylinder cap and vacuumed with the vacuum pump. Secondly, the solvent
was introduced into the vacuumed cylinder through the digital flow meters until the
pressure inside the cylinder reached the operating pressure. A pressure regulator was set
on the solvent tank to maintain the pressure at the operating pressure. Therefore, solvent
was gradually injected into the cylinder, and the total volume was carefully recorded by
the digital flow meters for each solvent. The process was carried out for several days to
make sure that the operating pressure remained constant while no further solvent was
being injected. At the end of each test, the amount of free solvent was subtracted from the
total amount of injected solvent to obtain the dissolved amount of solvent in the heavy
oil. Results for various solvents at their respective operating pressures are presented in
Figure 5-4.
211
Figure 5-3: Schematic of the experimental set-up used for solubility measurement tests
Solvent
cylinder
Pressure
regulator
Digital
flow meter
Heavy oil/
solvent
cell
Digital
pressure
gauge
Vacuum
pump
212
Pressure (kPa)
0 100 200 300 400 500 600 700 800
Solu
bil
ity (
wt%
)
0
10
20
30
40
50
Propane
(a)
Pressure (kPa)
100 200 300 400 500 600 700 800 900
Solu
bil
ity (
wt%
)
0
2
4
6
8
10
Methane
(b)
Pressure (kPa)
100 200 300 400 500 600 700 800 900
Solu
bil
ity (
wt%
)
0
2
4
6
8
10
CO2
(c)
Pressure (kPa)
20 40 60 80 100 120 140 160
Solu
bil
ity (
wt%
)
0
5
10
15
20
25
30
35
Butane
(d)
Figure 5-4: Solubility of (a) propane, (b) methane, (c) CO2, and (d) butane at 21°C
213
5.4 Solvent volume fraction in heavy oil for VAPEX tests
As mentioned in Chapter 3, several samples were taken from the produced oil for both
the small and large models during the VAPEX experiments. The initial weights of the
samples were recorded, and, then, the samples were kept at room pressure and
temperature for 5-7 days. Then the final weight of each sample was recorded at the room
temperature and pressure. The difference between the weights of each sample was used to
estimate the dissolved volume of the solvent.
The results are presented in Figures 5-5 and 5-6 for the small and large models,
respectively. The highest solvent volume fraction was observed to be 0.35 in the case of
propane injection in the large model. The propane volume fraction was about 0.30 in the
small model. The lowest solvent fraction was observed for methane and CO2 in both
physical models.
In addition, it was observed that solvent volume fraction is higher in the large model with
greater drainage height due to excessive contact time and area between the solvent and
heavy oil. Muhammad (2012) and Yazdani (2007) reported the same trend for the effect
of drainage height on the solvent mass fraction.
214
Time (h)
0 100 200 400
Solv
ent
volu
me
frac
tion, C
s
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
Propane
Methane
CO2
Butane
Propane/CO2
Propane/methane
Figure 5-5: Solvent volume fraction in the produced oil from the small model for various solvents at 21°C
215
Time (h)
0 100 200 400 500
Solv
ent
volu
me
frac
tion, C
s
0.0
0.1
0.2
0.3
0.4
Propane
Methane
CO2
Butane
Propane/CO2
Propane/methane
Figure 5-6: Solvent volume fraction in the produced oil from the large model for various solvents at 21°C
216
5.5 Numerical simulation
In this section, the results of the simulation study of the VAPEX process are presented.
The main goal was to achieve the best history-match of the VAPEX experiments, which
were explained in the previous chapter. In this research, CMG’s STARSTM
package
(Computer Modelling Group Ltd., 2011) was utilized to carry out the numerical
simulation studies.
To construct the PVT model, CMG’s WinpropTM
package was utilized to tune the
equation of state based on the heavy oil-solvent system used in this study and the
experimental data presented earlier in this chapter. Then, the developed PVT model was
exported to STARSTM
to simulate the VAPEX experiments.
5.5.1 Model construction
Two lab-scale 3D simulation models were developed to simulate the experimental
conditions for this study. Each 3D model represents one of the VAPEX physical models,
which were used for the experimental studies. The detailed information about the
simulation models is provided in Tables 5-2 and 5-3 for the small and large models,
respectively. The injection wells were located at the top layer, and the production wells
were located at the bottom layer. Therefore, for the small models, grid numbers i =12 × j
= 1 to 5 × k = 1 were perforated for the injection well and grid numbers i =12 × j = 1 to
5 × k = 24 were perforated to represent the production well. For the case of the large
model, grid numbers i =22 × j = 1 to 5 × k = 1 were perforated for the injection well and
grid numbers i =22 × j = 1 to 5 × k = 47 were perforated to represent the production
well. The injection and production wells were perforated in a way to accurately represent
the experimental models (Figures 5-7 and 5-8).
217
Table 5-2: Properties of small simulation model
Grid type Cartesian
Number of grids in i-direction 20
Number of grids in j-direction 5
Number of grids in k-direction 24
Number of grid blocks 2400
Grid thickness (cm) 1
Porosity (%) 42
Permeability (i, j and k-directions) (D) 9
Temperature (°C) 21
Original oil in place (cm3) 1008
Solvent Propane
Oil saturation (%) 100
No. of injection wells 1
No. of production wells 1
218
Table 5-3: Properties of large simulation model
Grid type Cartesian
Number of grids in i-direction 40
Number of grids in j-direction 5
Number of grids in k-direction 47
Number of grid blocks 9400
Grid thickness (cm) 1
Porosity (%) 43
Permeability (i, j and k-directions) (D) 10
Temperature (°C) 21
Original oil in place (cm3) 4042
Solvent Propane
Oil saturation (%) 100
No. of injection wells 1
No. of production wells 1
219
(a)
(b)
Figure 5-7: (a) 2D view of the simulated model with the injection and production wells for the small
physical model, (b) 3D view of the simulated model with the injection and production wells for the small
physical model
220
(a)
(b)
Figure 5-8: (a) 2D view of the simulated model with the injection and production wells for the large
physical model, (b) 3D view of the simulated model with the injection and production wells for the large
physical model
221
5.5.2 Injection and production wells’ constraints
As discussed earlier, two horizontal wells were designed in the model as the injection and
production wells. In order to simulate the experimental conditions and to carry out the
VAPEX process, certain well constraints had to be defined for the injection and
production wells.
For the injection well, two operating constraints were defined and implemented. One of
the constraints was the maximum surface injection rate, and the other constraint was the
maximum bottom-hole pressure. The maximum pressure was set close to the dew point of
propane as the experimental conditions.
For the production well, the pressure constraints were selected in a way to represent the
experimental conditions and to establish the connection between the injection and
production well. For this purpose, the first constraint was the minimum bottom-hole
pressure, which was close to atmospheric pressure. Based on the experimental results, the
time required to establish the connection between the injection and production wells was
considered for the first pressure constraint. The second operating constraint was
introduced after the breakthrough time, and it was close to the injection pressure with
about 5kPa pressure difference as the experimental conditions.
5.5.3 History matching
Once the simulation models were built, the models were tuned to match the experimental
results. The only matching parameters in this study were dispersion coefficient and
relative permeability curves. These two parameters were changed to achieve the best
match between the simulation results and experimental observations.
222
Figure 5-9 shows the results obtained for the case of propane injection in the small
model. The results show a good match between the simulated values of recovery factor
and the experimental results. The average absolute error was observed to be about 5.9%.
Figure 5-10 shows the results obtained after injecting propane into the large model. The
number of grids in the large model was considerably greater, and the elapsed time for
running the simulation in the large model was significantly greater. Therefore, it was
more time consuming to get a better match in the large model. The average absolute error
was observed to be about 14.8% in the large model. In both the small and large models,
there was a very promising match between the experimental and simulation results during
the injection/production wells’ connection establishment. However, after the first
breakthrough of the solvent, there is a difference between the experimental and
simulation results, which is more noticeable in the large model. This can be due to the
sudden increase in the solvent production rate and change from single-phase production
to two-phase production. The same issue has been addressed by several researchers
during simulating the VAPEX process (Xu, et al. 2012, Rahnema et al. 2008, and
Yazdani 2007). They have reported that this difference after the solvent breakthrough can
be more noticeable when the distance between the injector and producer increases. Xu et
al. (2012) found that the entire-grid oil dilution by the numerical simulator can be another
reason for this discrepancy; while in the experiments the solvent may find a path between
the injector and producer and therefore the dilution takes place along this path. They
suggested that using finer grades might overcome this issue; however this might result in
significantly longer simulation time, numerical instability, and convergence problems. In
the case of butane injection, the simulation and experimental results were perfectly
223
matched even after the solvent breakthrough (Figure 5-11). This can be due to the low
rate of diffusion and relatively slower production rate compared to propane injection. The
experimental and simulation results were shown for CO2 and methane injection in
Figures 5-12(a) and 5-12(b), respectively. In the case of propane/CO2 and
propane/methane injection, the results showed the same trend as the pure propane
injection. That is, there was an over estimation of recovery factor by the simulated model
after the solvent breakthrough and under estimation of recovery factor towards the end of
the experiments. These results are graphed in Figures 5-13(a) and 5-13(b). The chamber
growth was also monitored during the simulation, is shown in Figure 5-14. It was found
that the simulation results were fairly consistent with the chamber growth observed
during the experiments.
The error analysis showed that the average absolute errors were 8.25%, 9.21%, 13.29%,
12.12%, and 22.57% for butane, CO2, methane, propane/CO2, and propane/methane,
respectively.
224
Time (h)
0 20 40 60 80 100
Rec
over
y F
acto
r(%
OO
IP)
0
20
40
60
80
100
Experimental
Simulation
Figure 5-9: Experimental and simulation results for the recovery factor after injecting propane in the small
model
225
Time (h)
0 20 40 60 80 100 120 140
Rec
over
y F
acto
r(%
OO
IP)
0
20
40
60
80
100
Experimental
Simulation
Figure 5-10: Experimental and simulation results for the recovery factor after injecting propane in the large
model
226
Time (h)
0 10 20 30 40 50 60 70 80 90 100 110
Rec
over
y F
acto
r(%
OO
IP)
0
20
40
60
80
100
Experimental
Simulation
Figure 5-11: Experimental and simulation results for the recovery factor after injecting butane in the small
model
227
Time (h)
0 50 100 150 200 250 300 350 400 450
Rec
over
y F
acto
r(%
OO
IP)
0
10
20
30
40
Experimental
Simulation
(a)
Time (h)
0 50 100 150 200 250 300
Rec
over
y F
acto
r(%
OO
IP)
0
10
20
30
40
Experimental
Simulation
(b)
Figure 5-12: Experimental and simulation results for the recovery factor after injecting (a)CO2 and (b)
methane in the small model
228
Time (h)
0 20 40 60 80 100
Rec
over
y F
acto
r(%
OO
IP)
0
20
40
60
80
100
Experimental
Simulation
(a)
Time (h)
0 20 40 60 80 100 120
Rec
over
y F
acto
r(%
OO
IP)
0
20
40
60
80
100
Experimental
Simulation
(b)
Figure 5-13: Experimental and simulation results for the recovery factor after injecting (a) propane/CO2 and
(b) propane/methane mixtures in the small model
229
(a)
(b)
Figure 5-14: Chamber evolution after 26 h in (a) simulated small model, (b) laboratory model
230
5.5.4 Effect of well configurations
The well configurations for the experimental studies were explained in detail in previous
chapters. In order, to investigate the effect of well spacing on the VAPEX process, a
series of simulation runs was carried out with various well spacing. For the second well
configuration, the injection well was considered to be 16 cm above the production well;
therefore, grid numbers i =12 × j = 1 to 5 × k = 8 were perforated for the injection well
while the production well was at the bottom of the model as it was in the first well
configuration. For the third well configuration, the injection well was considered to be 4
cm above the production well; therefore, grid numbers i =12 × j = 1 to 5 × k = 20 were
perforated for the injection well while the production well was at the bottom of the model
as it was in the first and second well configurations. For the forth configuration, the
injection well and production wells were located at the right corners of the models right
above each other (i.e., injection well: i =20× j = 1 to 5 × k = 1, and production well: i
=20× j = 1 to 5 × k = 24). Finally, the fifth well configuration was the injection well at
the right top corner and the production well at the left bottom corner of the model (i.e.,
injection well: i =20× j = 1 to 5 × k = 1 and production well: i =1× j = 1 to 5 × k = 24).
This sensitivity analysis was carried out in the small model as the simulation in the large
model elapsed over a significantly longer time.
Figure 5-15 depicts the results obtained for all the well configurations used in this study
for injecting propane in the small model. The fifth well configuration resulted in the
highest drainage rate and highest recovery factor. This can be due to the increased
distance between the injection and production wells, which resulted in greater contact
area between the solvent and heavy oil.
231
Time (h)
0 20 40 60 80 100
Rec
over
y F
acto
r(%
OO
IP)
0
20
40
60
80
100
Frist well configuration
Second well configuration
Third well configuration
Forth well configuration
Fifth well configuration
Figure 5-15: Effect of well configuration on the recovery factor. For the first well configuration, the
injection well is located at the top of the model and 24 cm above the production well; for the second well
configuration, the injection well is located 16 cm above the production well; for the third well
configuration, the injection well is 4 cm above the production well; for the forth well configuration, the
injection well is 24 cm above the production well; and for the fifth well configuration, the injection well is
at the right top corner of the model; the production well is at the left bottom corner.
232
The greater contact area would result in better mixing between the solvent and heavy oil,
which would reduce the viscosity of the heavy oil. At the same time, the fifth
configuration has the advantage of great drainage height. When the injection well was
located 16 cm above the production well and in the pay zone, high production rate and
recovery factor were observed. However, when the well spacing was decreased to 4 cm,
the production rate and the recovery factor were drastically decreased. It can be
concluded, then, that the optimum mixing of solvent and heavy oil was achieved in the
fifth well configuration, and the first and second well configurations also showed
promising production rates and recovery factors.
5.5.5 Effect of permeability
Figure 5-16 shows the effect of permeability on the recovery factor after injecting
propane in the small model. Increasing the permeability increased the recovery factor.
The impact was more noticeable after the first breakthrough of the solvent and when the
two-phase production started from the production well.
5.5.6 Effect of grid thickness
The increase in the grid thickness in the j-direction significantly influenced the recovery
performance of the VAPEX after injecting propane. The results are shown in Figure 5-17,
which shows how changing the grid thickness from 1 cm to 8 cm reduced the production
rate and recovery factor.
5.5.7 Effect of time step
The change in the time step did not affect the results, as CMG STARSTM
chooses the
optimum time step based on the range set by the user. Therefore, as the results in Figure
233
5-18 show, no significant change occurred from selecting various time steps. However, it
should be mentioned that setting larger minimum time steps resulted in convergence error
by the software.
234
Time (h)
0 20 40 60 80 100
Rec
over
y F
acto
r(%
OO
IP)
0
20
40
60
80
100
k = 10 D
k = 100 D
k = 150 D
Figure 5-16: Effect of permeability on the recovery factor after injecting propane
235
Time (h)
0 20 40 60 80 100
Rec
over
y F
acto
r(%
OO
IP)
0
20
40
60
80
100
y = 1 cm
y = 4 cm
y = 8 cm
Figure 5-17: Effect of grid thickness on the recovery factor after injecting propane
236
Time (h)
0 20 40 60 80 100
Rec
over
y F
acto
r(%
OO
IP)
0
20
40
60
80
100
t = 1e-8 d
t = 1e-6 d
t = 1e-10 d
t = 1e-12 d
Figure 5-18: Effect of time step change on recovery factor after injecting propane
237
6. CHAPTER 6: SOFT COMPUTING APPROACH
“As complexity increases precise statements lose meaning and meaningful statements lose precision.”___
L. A. Zadeh
Soft computing techniques are tolerant of imprecision and partial truth, and inductive
reasoning is a key factor in this technique. Artificial neural network (ANN) is one of the
components of soft computing science, and its basis is like what a human brain does to
process tasks. The recent progress and success of utilizing artificial neural networks
(ANN) to solve various complicated engineering problems has drawn attention to its
potential applications in the petroleum industry.
ANN has been successfully utilized in several areas, such as permeability prediction, well
testing, PVT properties prediction, identification of sandstone lithofacies, improvement
of gas well production, prediction and optimization of well performance, and integrated
reservoir characterization (Mohammadpoor et al. 2010, 2011, 2012).
ANN is a system of interconnected parallel neurons that takes the input data and
multiplies it by connection weights. A bias value is added, and, then, the result is entered
into the transfer functions. In the case of supervised learning algorithms, the products of
transfer functions are compared with desired targets. If the product is not in the
acceptable range of error, then the initial weights and biases will be changed to match the
desired target.
Figure 6-1 shows a schematic of a neural network model. The parameter I is the input, the
parameter w is the weight, parameter b is the bias, and n is net input for the transfer
function f. Then, the output O is defined by equation (6.2).
238
I1
I2
I3
IN
w1
w2
w3
wN
∑
b
nf O
Figure 6-1: Schematic of an artificial neural network
239
bwIwIwIwIn NN ...332211......................................................................... (6.1)
nfO ....................................................................................................................... (6.2)
Each ANN has an input layer and one or more hidden layers. The input layer includes the
input neurons, and the hidden layers include hidden neurons and transfer functions.
Different types of transfer functions and the learning algorithm will be discussed in more
detail later in this chapter.
6.1 Data handling procedures
ANN is highly dependent on the input and output data. The accuracy and the total
training time are directly related to the number and type of input variables. Therefore, a
comprehensive study is essential prior to developing an ANN model. Data handling
procedures include two main steps: (1) data acquisition, and (2) data pre-processing.
6.1.1 Data acquisition
In order to construct a successful ANN model, choosing the most potent inputs is of
critical importance. Ineffectual inputs may complicate the training procedure and result in
imprecise predictions. After conducting a comprehensive literature review on the
available experimental studies and considering the observations during the experimental
results obtained in this study, five different parameters were considered as the inputs for
training the ANN model. In this study, drainage height, heavy oil viscosity, solvent type,
permeability, and porosity were considered as the inputs to predict the stabilized drainage
rate as the output of the ANN model.
240
To successfully model the complex relationships between the input and output
parameters, a large number of data sets is required to train and test the ANN model. In
order to gather an appropriate number of data sets, the experimental results from this
study were combined with the available experimental results in the literature, and a total
of 223 data sets was collected to develop the ANN model. These data sets were divided
into two categories: one category included 155 data sets for training and validating the
network and the second category consisted of 68 data sets for testing the trained network.
It should be mentioned that the data sets considered for the training procedure must cover
the whole input and output data range. Figure 6-2 shows the data distribution for training
and testing data sets for the input parameters used in this study. The collected data sets
and the sources of data are presented in Table B-1 to B-9 in Appendix B. Table 6-1
shows the input and output parameters and some general properties of data sets used for
this study.
6.1.2 Data normalization
In order to decrease the ANN training time, some data pre-processing such as data
normalization should be carried out. Different types of inputs with different data ranges
and distributions are typically present in the data sets, so data normalization will
significantly reduce this variation. For this purpose, equation (6.3) was used for the input
and output data sets. The approach used for scaling the network inputs and targets was to
normalize the mean and standard deviation of the training and testing data sets.
minmax
min.
XX
XXX norm
…………………………………………………………...……. (6.3)
241
H (cm)
0 20 40 60 80 100
Q (
mL
/h)
0
50
100
150
200
250
Training data sets
Testing data sets
(a)
Pinj
(kPa)
0 500 1000 1500 2000 2500 3000 3500 4000 4500
Q (
mL
/h)
0
50
100
150
200
250
Training data sets
Testing data sets
(b)
Porosity (%)
20 25 30 35 40 45
Q (
mL
/h)
0
50
100
150
200
250
Training data sets
Testing data sets
(c)
k (D)
0 100 200 300 400 500 600 700 800 900 1000 1100 1200
Q (
mL
/h)
0
50
100
150
200
250
Training data sets
Testing data sets
(d)
(cp)
0 50x103 100x103 150x103 200x103 250x103
Q (m
L/h)
0
50
100
150
200
250
Training data sets
Testing data sets
(e)
Figure 6-2: Data distribution for training and testing sets; stabilized drainage rate vs. (a) height (cm), (b)
injection pressure (kPa), (c) porosity (%), (d) permeability (D), and (e) viscosity (mPa.s)
242
Table 6-1: Data range for various input and output parameters used in this study
Variable Minimum Maximum Mean
Standard
deviation
Input
Drainage
height (cm)
7.5 100.5 30.5 19.1
Injection
pressure
(kPa)
69.0 4227.0 599.3 771.4
Oil viscosity
(mPa.s)
1390 225000 31239 47849
Permeability
(D)
3.0 1123.0 265.0 283.5
Porosity (%) 43.1 20.5 36.3 2.7
Output
Drainage
rate (mL/h)
0.02 218.10 35.02 45.74
243
where X denotes the input and output parameters. The subscript max refers to the
maximum and min refers to the minimum value of the variable. The new normalized
variable, Xnorm., takes the range from zero to 1 for all the parameters.
6.2 Neural network development
The number of layers, the interconnections between the layers, and the number of
processing neurons per layer define the ultimate architecture of a neural network model.
Hence, developing an optimal network based on the mentioned variables is not an easy
task and requires following some rules to reduce the number of iterations. There are
different types of supervised and un-supervised ANN architectures utilized for different
science and engineering problems. Among all types of available networks, the multiple-
layer feed-forward back-propagation (BP) architecture is the most widely used neural
network for petroleum engineering applications. This type of network is capable of
representing non-linear functional mappings between inputs and outputs
(Mohammadpoor et al. 2010, 2012). It has been observed by several researchers that a
two-layer BP model with a sigmoid function in the hidden layer and linear function in the
output layer can fit any finite mapping problem (Xu, 2012, Beale et al., 2010, Salahshoor
et al., 2012).
The BP network employed in this study was composed of four hidden layers, and each
layer included one transfer function. There are various types of transfer functions. Three
of the most common functions used are sigmoid, linear and hard limit functions. The
transfer function that was utilized in the hidden layer was a sigmoid function, which is
defined by equation (6.4).
244
jnje
O
1
1……………………………….…………………………………...……. (6.4)
where O is the output of each neuron, and n is the sum weighted inputs and bias.
Equation (6.5) can be used to calculate n:
j
N
i
iijj bOwn
1
……………………………….……………….…………...……. (6.5)
In equation (6.5), N is the number of neurons in each layer, bj is a bias parameter, and wij
is the weight between node j of layer l to node i of layer l-1. The term bias is utilized to
minimize the number of iterations and develop a constant offset.
As mentioned earlier, the linear transfer function was used for the output layer:
jj nO ……………………………….……………………………….………...……. (6.6)
After initializing the network weights and biases, the network is prepared for conducting
the training procedure. Once the training starts, the weights and biases are adjusted
iteratively to minimize average squared error between the network outputs and the
desired targets. The root mean square error (MSE) defined in equation (6.7) is called the
network performance function in BP networks.
n
i
ipi OOn
MSE1
2
,
1...………………..……………..……………………………. (6.7)
where Op is the predicted output of the network and O is the initial target of the network.
Various training algorithms such as Scaled Conjugate Gradient, Gradient Descent, and
Levenberg-Marquardt (LM), which utilize the gradient of network performance function
to adjust the weights, are available. The LM training function is widely accepted because
245
of its robustness and fast training convergence. The goal in the LM algorithm is to
minimize the following equation, which is, in fact, the sum of the squares of deviation
(Salahshoor et al., 2012):
m
j
j xrxf1
2
2
1……………………………………………………………………. (6.8)
where x is a vector and rj denotes the jth residual function. Then, the LM algorithm uses
equation (6.9) as an iteration formula to conduct the search procedure.
iii xfHdiagHxx
1
1 ……..……………………………………………. (6.9)
where H is the Hessian matrix defined by equation (6.10), ixf is the first difference,
and xf is defined by equation (6.11):
m
j
jj
TxrxrxJxJxfH
1
22…………………………………….…. (6.10)
m
j
jj xrxrxf1
…………………………………………………...………... (6.11)
The last step is to define the number of neurons in the hidden layer, which will
significantly affect the ultimate performance of the neural network model. There is no
proven rule about the optimum number of neurons in the hidden layers. Lawrence et al.
(1996) found that the network size is dependent on: 1) the complexity of the
approximation function, 2) the range of data sets distribution, and 3) the size of the
network compared to required size for an optimal solution. A low number of neurons will
result in under-fitting, which will consequently give high training and generalization
246
error. A large number of neurons will result in over-fitting, which will decrease the
training error, but the generalization error will be high. The best way to find the optimum
number of neurons is to start with a reasonable value and monitor the results using cross-
validation technique to find the optimum number of hidden neurons. For the first guess,
there are some rules of thumb that can be used as a guideline, but these rules do not
consider the nature of the problem or the quality and number of data sets. Among these
rules, the followings are more often cited (Heaton, 2008); however, it should be
emphasized that these rules can be used only as a starting point guideline:
The number of hidden neurons should be between the input and output layers’
size.
The number of hidden neurons should be less than the size of the input layer.
sizelayeroutputsizelayerinputneuronshiddenofNumber 3
2
The above-mentioned rules were used as initial guidelines for training BP networks in
this study; therefore, the basic approach to constructing the optimum network was trial
and error, and, then, the results for various network topologies were utilized to find the
optimum network. In this study, MATLAB (R2012a) software from Mathworks was
utilized to train and test the BP networks. Figure 6-3 presents an example of the training
procedure, in which training, validation, and testing stages are shown. In this figure, the
results for each trial are provided for the predicted flow rate versus the actual flow rate
fed to the network.
247
Figure 6-3: An example of network training procedure; plot of: (a) predicted outputs by network for
training data sets, (b) predicted outputs by network for validation data sets, (c) predicted outputs by
network for testing data sets, and (d) predicted outputs by network for the whole group of data sets
chosen for training procedure
0 0.2 0.4 0.6 0.8 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Actual flow rate (normalized Q)
Pred
icte
d f
low
ra
te (
no
rm
ali
zed
Q)
(a) Training: R=0.98268
Flow rate, Q
Fit
y=x
0 0.2 0.4 0.6 0.8
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Actual flow rate (normalized Q)
Pred
icte
d f
low
ra
te (
no
rm
ali
zed
Q)
(b) Validation: R=0.92013
Flow rate, Q
Fit
y=x
0 0.2 0.4 0.6 0.8
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Actual flow rate (normalized Q)
Pred
icte
d f
low
ra
te (
no
rm
ali
zed
Q)
(c) Test: R=0.84419
Flow rate, Q
Fit
y=x
0 0.2 0.4 0.6 0.8 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Actual flow rate (normalized Q)
Pred
icte
d f
low
ra
te (
no
rm
ali
zed
Q)
(d) All: R=0.948
Flow rate, Q
Fit
y=x
248
Training, validation, and testing data sets were chosen randomly among the 155 data sets
for training inputs. Regression analysis was carried out, and R and RMSE values were
monitored for each trial to plan the next network topology. A summary of some of the
various trials for network topologies and the results obtained are presented in Table 6-2.
After constructing each network, the untouched portions of actual data (testing category)
were used to simulate the developed network. For each network, the results were
graphed, and error analysis was carried out to monitor the accuracy of the prediction. To
carry out the error analysis, RMSE (Eq. 6.12) and correlation coefficient (R-coefficient)
(Eq. 6.13) were calculated for the training and testing data sets.
n
i
ipi OOn
RMSE1
2
,
1...……………….………..……………..………………. (6.12)
n
i
n
i
pipi
n
i
pipi
OOOO
OOOO
R
1 1
2
,
2
1
,
………..………...……………....………………. (6.13)
Table 6-2 shows that the optimum network was found to be topology #1 with four hidden
layers and 20 neurons on the first hidden layer,15 neurons on the second hidden layer, 10
neurons on third hidden layer, and 5 neurons on the fourth hidden layer. The schematic of
the developed BP network is presented in Figure 6-4. The number of inputs and output,
number of neurons on each hidden layer, and transfer functions are schematically
presented in this figure.
The network predictions for the training data sets are presented in Figure 6-5.
249
Table 6-2: Summary of the results for some selected training and testing trials
No. Network topology R (Training) RMSE
(Training) R (Testing)
RMSE
(Testing)
1 I-20-15-10-5-O 0.9616 0.0543 0.8177 0.1094
2 I-10-10-O 0.9556 0.0650 0.6233 0.2067
3 I-15-10-O 0.9552 0.0629 0.8187 0.1409
4 I-10-10-5-O 0.9528 0.0645 0.8264 0.0819
5 I-10-O 0.9395 0.0689 0.6901 0.1751
6 I-15-15-15-O 0.9367 0.0782 0.7025 0.1532
7 I-20-20-O 0.9319 0.0699 0.6997 0.1878
8 I-10-10-10-5-O 0.9316 0.0756 0.7175 0.1483
9 I-20-O 0.9297 0.0953 0.5159 0.3341
10 I-10-20-15-O 0.9167 0.0924 0.7278 0.2173
11 I-15-10-10-O 0.9125 0.0821 0.7636 0.1483
12 I-15-O 0.8994 0.1053 0.6762 0.2798
13 I-10-15-10-O 0.8839 0.0715 0.5788 0.1294
14 I-20-25-O 0.8683 0.1025 0.7410 0.1313
15 I-15-15-10-5-O 0.8122 0.1327 0.3324 0.5179
250
Figure 6-4: Schematic of the developed BP network; there are 20 neurons on the first hidden layer and 15
neurons on the second hidden layer. The transfer functions used for hidden layers were log sigmoid
functions, and linear transfer function was used for output layer
251
Actual flow rate (normalized Q)
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Pre
dic
ted f
low
rat
e (n
orm
aliz
ed Q
)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Flow rate, Q
y=x
Fit
Figure 6-5: Output of the developed network vs. the actual data after simulating the model with training
data sets
252
The cross plot of network outputs versus training data sets shows a perfect correlation
with the y = x line. In order to test the validity and accuracy of the model, the outputs
were simulated using the untouched portion of data, and the results are shown in Figure
6-6.
As mentioned earlier, the calculations are based on the number of nodes, transfer
functions on hidden layers, weights for the nodes, and the biases. The matrices of weights
and biases for the hidden layers of the developed network are provided in Appendix C.
These matrices can be used to regenerate the developed BP model and utilize the model
to predict the production rate.
6.3 Sensitivity analysis
As mentioned previously, the selected input variables had been found by several
researchers to be the key parameters governing the ultimate performance of the VAPEX
process. However, the degree of dependency of drainage height on each of these
parameters has yet to be determined. In this research, the developed ANN model was
utilized to conduct a sensitivity analysis on the input variables. For this purpose, the term
relevance factor, r, was employed to study the significance of the five input variables on
the heavy oil production rate. The greater the absolute value of the r-factor for a specific
input, the more significant an effect it would have on the output. On the other hand, the
positive value of r-factor shows the positive impact of the input parameter on the output,
while the negative value of r-factor implied the negative impact of the input parameter on
the output. A very small value of r-factor indicates the negligible impact of the input
parameter on the output. Therefore, the r-factor for each input was calculated based on
the available experimental data using equation (6.14) (Chen et al., 2014):
253
Actual flow rate (normalized Q)
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Pre
dic
ted f
low
rat
e (n
orm
aliz
ed Q
)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Flow rate, Q
y=x
Fit
Figure 6-6: Output of the developed network vs. the actual data after simulating the model with testing data
sets
254
n
i
n
i
ii
n
i
ii
QQII
QQII
QIr
1 1
22
1, …………………………………………………. (6.14)
where I is the input parameter, I is the average value of the input parameter, iQ is the
production rate, and Q is the average value of the production rate.
The results obtained for the relevance factor for each parameter are shown in Figure 6-7.
The highest relevance factor was found to be 0.5751 for the permeability; this was even
higher than the value for drainage height, which was found to be 0.4653. As mentioned in
Chapter 4, Butler proposed that both permeability and drainage height have a square root
relationship with production rate. However, it was found later by Yazdani (2007), that
production rate has a higher dependency on drainage height. This was further proved by
the experimental results obtained in this study, and it was explained in detail in Chapter
4. However, the results obtained based on the sensitivity analysis, which has taken into
account a significantly wider range of data, showed that there is a higher dependency
between the production rate and permeability than previously thought. This can be
another reason for the underestimation of the production rate using Butler’s equation. The
r-factor for the porosity was very low at 0.00001. This shows that porosity did not have
any significant effect on the production rate. The r-factor for viscosity was found to be -
0.1969. As expected, the viscosity had a significant negative impact on the production
rate.
255
Input parameters
Injec
tion pres
sure
Porosit
y
Permeb
ility
Viscosit
y
Drainag
e heig
ht
Rel
evan
cy f
acto
r (r
)
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
0.0824
0.00001
-0.1969
0.4653
0.5751
Figure 6-7: Relevancy (r) factor for various parameters to the production rate
256
6.4 Comparison of results
As discussed in the previous chapter, based on the experimental results, the optimum
value for exponent n for VAPEX scale-up was found to be 1.2. Also, it was mentioned
that Yazdani (2007) found out that exponent n should be between 1.2 and 1.3. During his
experiments, the following correlations were developed based on Butler equation and
new observations for exponent n:
kHQ 26.1017.0 ……………………………………………….…………….... (6.15)
kHQ 13.10288.0 ……………………..…………………………………….... (6.16)
The drainage rate was then calculated using these equations, and the results were graphed
versus the actual testing data sets to check the accuracy of these correlations. Moreover,
the obtained equations based on the experiments in this study were also used, and error
analysis was carried out on the results. R-coefficient and RMSE were calculated for the
testing data sets after utilizing the above-mentioned correlations.
The results after implementing equations (6.15) and (6.16) are presented in Figures6-8
and 6-9, respectively. The same procedure was followed for testing equations (4.15) to
(4.18), and the results are presented in Figures 6-10 to 6-13. In addition, a summary of
the results for the error analysis is provided in Table 6-3.
257
Actual flow rate, Q (cm2/h)
0 20 40 60 80 100 120
Pre
dic
ted f
low
rat
e, Q
(cm
2/h
)
0
20
40
60
80
100
120
Figure 6-8: Plot of predicted stabilized drainage rate by eq. 6.15 versus actual data sets for testing
258
Actual flow rate, Q (cm2/h)
0 20 40 60 80 100 120
Pre
dic
ted f
low
rat
e, Q
(cm
2/h
)
0
20
40
60
80
100
120
Figure 6-9: Plot of predicted stabilized drainage rate by eq. 6.16 versus actual data sets for testing
259
Actual flow rate, Q (cm2/h)
0 20 40 60 108 109 110
Pre
dic
ted f
low
rat
e, Q
(cm
2/h
)
0
10
20
30
40
50140
150
Flow rate, Q
y=x
Fit
Figure 6-10: Plot of predicted stabilized drainage rate by eq. 4.15 versus actual data sets for testing
260
Actual flow rate, Q (cm2/h)
0 20 40 60 108 109 110
Pre
dic
ted f
low
rat
e, Q
(cm
2/h
)
0
10
20
30
40
5095
100
105
Flow rate, Q
y=x
Fit
Figure 6-11: Plot of predicted stabilized drainage rate by eq. 4.16 versus actual data sets for testing
261
Actual flow rate, Q (cm2/h)
0 20 40 60 108 109 110
Pre
dic
ted f
low
rat
e, Q
(cm
2/h
)
0
10
20
30
40
50
96
98
100
Flow rate, Q
y=x
Fit
Figure 6-12: Plot of predicted stabilized drainage rate by eq. 4.17 versus actual data sets for testing
262
Actual flow rate, Q (cm2/h)
0 20 40 60 108 109 110
Pre
dic
ted f
low
rat
e, Q
(cm
2/h
)
0
10
20
30
76
78
80
Flow rate, Q
y=x
Fit
Figure 6-13: Plot of predicted stabilized drainage rate by eq. 4.18 versus actual data sets for testing
263
Table 6-3: Error analysis for various techniques to predict drainage rate
Prediction method R RMSE
BP network 0.8177 0.1094
Eq. 6.15 0.7097 14.8966
Eq. 6.16 0.6968 11.6893
Eq. 4.15 0.7135 18.2123
Eq. 4.16 0.7134 12.3721
Eq. 4.17 0.7135 11.8365
Eq. 4.18 0.7132 9.4831
264
7. CHAPTER 7: CONCLUSIONS AND
RECOMMENDATIONS
7.1 Conclusions
An extensive experimental study involving injecting various solvents in two large-scale
visual physical models was carried out. Various parameters were recorded during the
experiments to investigate the effect of drainage height and solvent type on the VAPEX
process. A comprehensive database was gathered, and the following major conclusions
were drawn:
1. Propane showed promising recovery factor results in both physical models, while
butane injection also showed acceptable results in terms of ultimate recovery
performance. The ultimate recovery factor after injecting propane was found to be
about 75% of original oil in place in the small and large models.
2. Although pure CO2 and methane injection did not show acceptable recovery
performance, CO2 and methane were found to be good carrier gases, while
propane/CO2 and propane/methane mixtures significantly improved recovery
performance. In the case of propane/CO2 injection, an ultimate recovery factor of
54% of original oil in place was observed in the VAPEX models. On the other hand,
after injecting propane/methane mixture, an ultimate recovery factor of 48% of
original oil in place was observed in both the small and large VAPEX models.
3. The main effect of drainage height was observed while comparing the results for
stabilized drainage rates in the small and large physical models. The stabilized
drainage rates were significantly higher in the large model with greater drainage
265
height, which proves the prominent effect of drainage height on the VAPEX process.
For instance, the stabilized drainage rates after injecting propane were found to be
0.22 mL/min and 0.50 mL/min in the small and large models, respectively.
4. The efficiency of propane as an injection solvent was further confirmed by comparing
the solvent utilization curves for various solvents used in this study.
5. It was observed that residual oil saturations close to the injection wells were very low
for all the solvents. Moreover, the lowest residual oil saturation was obtained after
injecting propane for both small and large models. The residual oil saturation for
sample location 1 was 4.3% and 5.1% for the small and large models, respectively.
On the other hand the highest residual oil saturation was observed at the bottom of the
physical models and close to production wells. The highest residual oil saturation was
found to be 80.4% in the small model and 88.9% in the large model for the case of
CO2 injection.
6. Using various solvents, it was observed that more asphaltene precipitation occurred
close to the injection points and at the oil/solvent interface. Comparing the textures of
the asphaltene precipitants from different locations of the models, it was found that
the precipitants close to the injection points where more brittle, while the precipitants
close to the production points were more ductile.
7. The amount of asphaltene precipitation in the large model was slightly greater due the
longer path between the injection and production wells and the longer contact time
between the oil and solvent.
266
8. After comparing the asphaltene precipitation in the small and large models, it was
observed that in the case of propane injection, more asphaltene precipitation was
observed in different physical model locations.
9. The image analysis on the chamber evolution showed that the highest sweep
efficiency was observed after injecting propane, followed by butane, propane/CO2,
propane/methane, CO2, and methane.
10. It was found that establishing a sound connection between the injection and
production wells would significantly affect the ultimate performance of the VAPEX
process. Poor connections between the injection and production wells resulted in
drastically lower production rates and slow rate processes. For instance, the stabilized
drainage rate after propane injection was found to increase from 0.04 mL/min to 0.50
mL/min after injecting propane in the large model by improving the connections
between injection and production well at the beginning of the VAPEX experiment.
11. Further analysis of the experimental results obtained in this study showed that
Butler’s equation, which states square root proportionality between the drainage
height and drainage rate, significantly under predicts the drainage rate. However, it
was found that results proposed by Yazdani (2007) showed better proportionality
between the drainage height and drainage rate in the VAPEX process. The
experimental results obtained in this study indicated that drainage rate is proportional
to the drainage height raised to the power of 1.2 in the VAPEX process.
12. VAPEX number, Ns, increased with increasing drainage height, and it was concluded
that VAPEX number was dependent on the drainage height and oil-solvent properties.
Comparing various solvents used for these experiments, the highest values for
267
VAPEX number were achieved after injecting propane, and VAPEX numbers for
butane and propane/CO2 were also relatively high and close to each other. However,
the lowest values were obtained after injecting pure methane and pure CO2.
13. The experiments were simulated numerically, and satisfactory history matching was
achieved. The major difference between the experimental and simulation results was
observed after the first breakthrough of the solvent.
14. Injection and production wells’ configurations significantly affected the recovery
performance of the VAPEX process. It was observed that longer distance between the
injection and production wells alongside the drainage height will increase the
production rate in VAPEX.
15. It was found that current empirical correlations fail to predict the drainage rate in the
VAPEX process, and they are very limited to the oil-solvent conditions under which
they were initially developed. Hence, a new soft computing-based approach was
utilized to develop a universal model to predict the drainage rate in the VAPEX
process. The estimated drainage rates with the new model showed high accuracy and
a wider range of applicability.
268
7.2 Recommendations
1. The physical models used in this study had maximum injection pressure limitations.
Therefore, in order to evaluate the suitability of various mixtures of propane and other
carried gases, new models with higher pressure tolerance can be designed.
2. In order to obtain more data for various drainage heights and get more accurate height
dependency correlations, new physical models with various drainage heights can be
designed and employed for VAPEX experiments.
3. To further investigate the effect of asphaltene formation and precipitation in the
VAPEX process, a molecular and compositional analysis on the asphaltene
precipitation in the physical models can be carried out.
4. The experimental results on the VAPEX process are very limited so far; therefore, the
ANN model can be further improved by introducing a wider range of data sets in the
future.
5. Other soft computing techniques such as Genetic Algorithm (GA) and Fuzzy Logic
can be incorporated alongside ANN to optimize the developed model in the future.
These soft computing techniques can be utilized to improve the results obtained by
commercial simulators to enhance the performance of the numerical simulation of the
VAPEX process.
269
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Appendix A
Table A-1: Production method versus heavy oil resource (1) (after Clark, 2007)
Production
method or
resource
Open-pit mining
Cold-production
horizontal wells
& multilaterals
Waterflood
Cold production
with sand
(CHOPS)
Status Commercial Commercial Commercial Commercial
Shallowest (<50
m) Only solution No No No
Shallow (50 to 100
m)
Possible but
economically
limited
No No No
Medium depth
(100 to 300 m) No
Unlikely unless
very low viscosity
or high solution
gas along with
high permeability
Unlikely unless
very low viscosity
and high
permeability
Unlikely, may
require solution
gas, but may be
possible
Intermediate depth
(300 to 1,000 m) No
Requires low
viscosity with
solution gas or
high formation
temperature and
high permeability
Requires low
viscosity and/or
high formation
temperature
Requires
unconsolidated
formation and
generally requires
solution gas
Deep (>1,000 m) No
Requires low
viscosity with
solution gas or
high formation
temperature and
high permeability
Requires low
viscosity and/or
high formation
temperature
Unlikely because
requires
unconsolidated
formations
Arctic No Maybe Maybe Disposal of sand
and water an issue
Offshore No Maybe Yes, North Sea Disposal of sand
and water an issue
Carbonate No No No No
Thin beds (<10 m
thick)
Can be mined if
near surface and
thin overburden
Maybe Maybe Yes
Highly laminated
Can be mined if
near surface and
thin overburden
Yes, if
multilaterals can
penetrate multiple
layers
Maybe for vertical
wells Yes
292
Table A-2: Production method versus heavy oil resource (2)(after Clark, 2007)
Production
method or
resource
Cyclic Steam
Stimulation Steamflood SAGD
Solvent without
heat or steam
Status Commercial Commercial Commercial Pilot test
Shallowest (<50
m) No No No No
Shallow (50 to 100
m) No No No
Possible, but
unproven
Medium depth
(100 to 300 m)
No, unless good
sealing Caprock
No, unless good
sealing Caprock
Yes, if good
vertical and
horizontal
permeability and
pay zone> 10m
Unproven, needs
good vertical and
horizontal
permeability
Intermediate depth
(300 to 1,000 m)
Yes, but deep
zones need higher
temperature steam
&are less economic
Yes, but deep
zones need higher
temperature steam
& are less
economic
Yes, but deep
zones need higher
temperature steam
& are less
economic
Unproven, needs
good vertical and
horizontal
permeability
Deep (>1,000 m)
No, needs high
temperature and
high-pressure
steam and too
much heat losses to
overburden through
injection wellbore
No, needs high
temperature and
high-pressure
steam and too
much heat losses
to overburden
through injection
wellbore
No, needs high
temperature and
high-pressure
steam and too
much heat losses
to overburden
through injection
wellbore
Possible, but
unproven
Arctic
Maybe if
permafrost can be
managed
Maybe if
permafrost can be
managed
Maybe if
permafrost can be
managed
Possible, but
unproven
Offshore
No, too much heat
loss in riser to
ocean water
No, too much heat
loss in riser to
ocean water
No, too much heat
loss in riser to
ocean water
Possible, but
unproven
Carbonate No No No Unknown
Thin beds (<10 m
thick)
Possible with
horizontal wells
No, needs at least
10 m bed, heat
losses to
overburden are too
great
No, needs at least
10 m bed great
Possible, but
unproven
Highly laminated Possible with
horizontal wells
May be possible
with horizontal
wells, but
unproven
No, need at least
10mbed Unlikely
293
Table A-3: Production method versus heavy oil resource (3) (after Clark, 2007)
Production
method or
resource
Solvent with heat
or steam
Fire flood with
vertical wells (~20
API oil only)
Fire flood with
vertical and
horizontal wells
Downhole steam
generation (CSS,
flood, SAGD)
Status Pilot test Commercial Pilot test Experimental
Shallowest (<50
m)
No No No No
Shallow (50 to 100
m)
Unknown No Unknown No
Medium depth
(100 to 300 m)
Unproven, needs
good vertical and
horizontal
permeability
Possible Unknown Tested but
commercially
unproven
Intermediate depth
(300 to 1,000 m)
Unproven, needs
good vertical and
horizontal
permeability
Yes Possible Possible, but
unproven
Deep (>1,000 m) Unknown Possible Possible, but
unproven
Unknown, greater
depth means need
high steam
pressure &
temperature
Arctic Unproven, must
manage permafrost
issue
Possible, but
unproven
Possible, but
unproven
Possible, but
unproven
Offshore Unlikely Possible, but
unproven
Possible, but
unproven
Possible, but
unproven
Carbonate Unknown Unknown Unknown Possible, but
unproven
Thin beds (<10 m
thick)
Possible, but
unproven
Unknown Unlikely Possible, but
unproven
Highly laminated Unknown Unknown Unlikely Possible, but
unproven
294
Table A-4: Production method versus heavy oil resource (4)(after Clark, 2007)
Production method or
resource
Electric, induction or
RF heating
Supercritical fluids
(e.g. CO2)
Biotechnology
Status Pilot test Experimental Research
Shallowest (<50 m) No No, needs higher
reservoir pressure
Unknown
Shallow (50 to 100 m) Possible, limited field
successes in isolated
cases
No, needs higher
reservoir pressure
Unknown
Medium depth (100 to
300 m)
Possible, limited field
successes in isolated
cases
No, needs higher
reservoir pressure
Unknown
Intermediate depth (300
to 1,000 m)
Possible, but unproven Unknown Unknown
Deep (>1,000 m) Possible, but unproven Unknown Unknown
Arctic Possible, but unproven Unknown Unknown
Offshore Possible, but unproven Unknown Unknown
Carbonate Possible, but unproven Unknown Unknown
Thin beds (<10 m thick) Possible, but unproven Unknown Unknown
Highly laminated Possible, but unproven Unknown Unknown
295
Table A-5: Technology versus production method (1)(after Clark, 2007)
Technology
or production
method
Simulations
and modeling
Geomechanics Downhole
sampling
In situ
viscosity
Fluid
Characterization
Cold-
production
horizontal &
multilaterals
High High High High High
Waterflood High Medium High High High
Cold
production
with sand
(CHOPS)
Medium High High High High
Cyclic steam
stimulation
(CSS)
High High High High High
Steamflood
with surface
burners
High High High High High
SAGD High High High High High
Solvent
without heat
or steam
High High High High High
Solvent with
heat or steam
High Medium High High High
Fire flood
with vertical
wells (~20
API oil only)
High High High High High
Fire flood
with vertical
and
horizontal
wells
High High High High High
Downhole
steam
generation
(CSS,
steamflood,
SAGD)
High High High High High
Electric,
induction, or
RF heating
downhole
High High High High High
Supercritical
fluids
High High High High High
Biological Unknown Unknown High High High
296
Table A-6: Technology versus production method (2)(after Clark, 2007)
Technology or
production
method
Flow
assurance Drilling
Well
placement Multilaterals Cementing
Cold-
production
horizontal &
multilaterals
High High High High Low
Waterflood High Medium Medium Low Low
Cold
production
with sand
(CHOPS)
High Low Low Low Low
Cyclic steam
stimulation
(CSS)
High Medium Low Medium High
Steamflood
with surface
burners
High Medium Medium Low High
SAGD High High High Low High
Solvent
without heat
or steam
High High High Low Low
Solvent with
heat or steam High High High Low Medium
Fire flood with
vertical wells
(~20 API oil
only)
High Medium Low Low High
Fire flood with
vertical and
horizontal
wells
High High High Low High
Downhole
steam
generation
(CSS,
steamflood,
SAGD)
High Medium Low to High Medium High
Electric,
induction, or
RF heating
downhole
High High High Low to
medium High
Supercritical
fluids High Medium Unknown Unknown High
Biological High Medium Unknown Unknown Low
297
Table A-7: Technology versus production method (3)(after Clark, 2007)
Technology or
production
method
High
temperature
completions
High
temperature,
long life
pumps
Pumps with
high sand and
solids
capability
Sand control Monitoring
and Control
Cold-
production
horizontal &
multilaterals
Low Low High High High
Waterflood Low Low Medium High High
Cold
production
with sand
(CHOPS)
Low Low High Low Medium
Cyclic steam
stimulation
(CSS)
High High Medium High High
Steamflood
with surface
burners
High High Medium High High
SAGD High High Medium High High
Solvent
without heat
or steam
Medium Low Medium High High
Solvent with
heat or steam High Medium Medium High High
Fire flood with
vertical wells
(~20 API oil
only)
High High Medium High High
Fire flood with
vertical and
horizontal
wells
High Medium Medium High High
Downhole
steam
generation
(CSS,
steamflood,
SAGD)
High High Medium High High
Electric,
induction, or
RF heating
downhole
High High Medium High High
Supercritical
fluids High High Medium High High
Biological Low Low Medium High High
298
Table A-8: Technology versus production method (4)(after Clark, 2007)
Technology or
production
method
Devices for
downhole flow
control
Distributed
temperature
Downhole
pressure
High
temperature
electronics &
sensors
(>200°C)
Downhole
multiphase
flow sensors
Cold-
production
horizontal &
multilaterals
High Low Medium Low High
Waterflood High Low High Low High
Cold
production
with sand
(CHOPS)
Low Low Low Low Low
Cyclic steam
stimulation
(CSS)
High High High High Medium
Steamflood
with surface
burners
High High High High High
SAGD High High High High High
Solvent
without heat
or steam
High High Medium Low Low
Solvent with
heat or steam High High High
Medium to
High Low
Fire flood
with vertical
wells (~20 API
oil only)
Medium High High High Low
Fire flood
with vertical
and horizontal
wells
Low High High High Low
Downhole
steam
generation
(CSS,
steamflood,
SAGD)
Low High High High Low
Electric,
induction, or
RF heating
downhole
Low High High High Low
Supercritical
fluids Unknown High High Medium High
Biological Unknown Low Low Low Unknown
299
Table A-9: Technology versus production method (5)(after Clark, 2007)
Technology or
production
method
Microseismic
while
fracturing
Cross-well
EM for fluid
saturation
Cross-well
seismic for
gas saturation
Through-
casing fluid
monitoring
Composition
monitoring for
in situ
upgrading
Cold-
production
horizontal &
multilaterals
Low Low Low Low Low
Waterflood Low High Low Medium Low
Cold
production
with sand
(CHOPS)
Low High Medium High Low
Cyclic steam
stimulation
(CSS)
Medium Medium High Low Low
Steamflood
with surface
burners
High High High High Low
SAGD Medium High Medium High Low
Solvent
without heat
or steam
Low Low Medium Low Medium
Solvent with
heat or steam Medium Low to High High Medium Medium
Fire flood
with vertical
wells (~20 API
oil only)
Low Low to High High Medium High
Fire flood
with vertical
and horizontal
wells
Low Unknown High Medium High
Downhole
steam
generation
(CSS,
steamflood,
SAGD)
Medium Low to High High Low to
Medium Low
Electric,
induction, or
RF heating
downhole
Low Unknown Low High High
Supercritical
fluids Medium Unknown Unknown High High
Biological Unknown Low Low High High
300
Table A-10: Technology versus production method (6)(after Clark, 2007)
Technology or
production
method
Surface
multiphase flow
sensors
4D surface
seismic
Fluids separation
and disposal
Produced- solids
separation
Cold-production
horizontal &
multilaterals
High Medium High Medium
Waterflood High High High Medium
Cold production
with sand
(CHOPS)
High Medium High High
Cyclic steam
stimulation (CSS) High High High Medium
Steamflood with
surface burners High High High Medium
SAGD High Medium High Medium
Solvent without
heat or steam High Medium High Medium
Solvent with heat
or steam High Medium High Medium
Fire flood with
vertical wells (~20
API oil only)
High High High High
Fire flood with
vertical and
horizontal wells
High High High High
Downhole steam
generation (CSS,
steamflood,
SAGD)
High Medium High Medium
Electric,
induction, or RF
heating downhole
High Medium High Medium
Supercritical
fluids High High High Medium
Biological High Low Unknown Medium
301
Appendix B
Table B-1: The experimental data on VAPEX experiments conducted by different researchers
No. Researcher
Solvent/Inj. Pressure
(kPa)
Φ (%) k (D) μ(cp) H (cm) Q (mL/h)
1 Das, 1994 C4/343 35.00 830.00 130000 21.9 19.20
2 Das, 1994 C4/ 343 36.00 217.00 130000 21.9 9.50
3 Das, 1994 C4/ 343 37.00 43.50 130000 21.9 3.60
4 Das, 1994 C4/ 343 35.00 43.50 130000 21.9 4.60
5 Das, 1994 C4/ 343 36.00 27.00 130000 21.9 2.50
6 Das, 1994 C4/ 343 37.00 830.00 10000 21.9 42.00
7 Das, 1994 C4/ 343 35.00 830.00 10000 21.9 39.70
8 Das, 1994 C4/ 343 36.00 830.00 10000 21.9 37.60
9 Das, 1994 C4/ 343 37.00 217.00 10000 21.9 24.00
10 Das, 1994 C4/ 343 35.00 217.00 10000 21.9 25.10
11 Das, 1994 C4/ 343 36.00 43.50 10000 21.9 14.40
12 Das, 1994 C4/ 343 36.00 43.50 10000 21.9 15.60
13 Das, 1994 C4/ 343 37.00 43.50 10000 21.9 16.70
14 Das, 1994 C4/ 343 37.00 27.00 10000 21.9 5.80
15 Butler, 1996 C4/ 240 35.00 220.00 7400 22.9 21.50
16 Jiang, 1997 C4/ 212 35.00 217.00 7000 22.9 21.80
17 Jiang, 1997 C4/ 212 36.00 217.00 7000 22.9 36.20
18 Jiang, 1997 C4/ 212 35.00 217.00 7000 22.9 40.70
19 Jiang, 1997 C4/ 212 36.00 217.00 7000 22.9 39.30
20 Jiang, 1997 C4/ 212 35.00 217.00 7000 22.9 21.90
21 Jiang, 1997 C4/ 212 36.00 217.00 7000 22.9 50.20
22 Jiang, 1997 C4/ 212 35.00 217.00 7000 22.9 31.20
23 Jiang, 1997 (C4/N2)/ 239 35.00 220.00 7400 22.9 11.00
24 Jiang, 1997 (C4/N2)/ 239 35.00 220.00 7400 22.9 14.57
25 Jiang, 1997 (C4/N2)/ 239 35.00 43.00 7400 22.9 8.50
26 Jiang, 1997 C4/ 308 35.00 43.00 7400 22.9 19.40
302
Table B-2: The experimental data on VAPEX experiments conducted by different researchers (Cont'd)
No. Researcher
Solvent/Inj.Pressure
(kPa)
Φ (%) k (D) μ (cp) H (cm) Q (mL/h)
27 Jiang, 1997 C4/ 308 35.00 217.00 7400 22.9 21.50
28 Jiang, 1997 C4/ 308 35.00 43.00 7400 22.9 21.70
29 Jiang, 1997 (C4/C1)/ 308 35.00 217.00 7400 22.9 18.70
30 Jiang, 1997 C4/ 308 35.00 43.00 7400 22.9 15.50
31 James, 2004 C4/114 30.00 74.00 85000 32.5 6.60
32 James, 2004 C4/ 114 30.00 68.00 85000 40.1 12.00
33 James, 2004 C4/114 30.00 66.00 85000 54.5 14.40
34 James, 2004 C4/114 30.00 76.00 85000 60.2 18.60
35 James, 2004 C4/114 38.00 285.00 85000 92.0 12.00
36 James, 2004 C4/114 38.00 350.00 85000 23.7 6.60
37 Talbi, 2003 (CO2/C3)/ 250 35.00 640.00 3300 30.5 73.15
38 Talbi, 2003 (C3/C1)/ 250 35.00 640.00 3300 30.5 76.45
39 Talbi, 2003 (C3/C1)/ 600 35.00 640.00 3300 30.5 71.43
40 Talbi, 2003 (CO2/C3)/ 600 35.00 640.00 3300 30.5 115.02
41 Yazdani, 2007 C4/ 240 34.10 220.00 18000 7.5 4.00
42 Yazdani, 2007 C4/ 240 36.80 330.00 18000 7.5 5.00
43 Yazdani, 2007 C4/ 240 36.50 640.00 18000 7.5 7.00
44 Yazdani, 2007 C4/ 240 34.10 220.00 18000 15.0 11.00
45 Yazdani, 2007 C4/ 240 36.80 330.00 18000 15.0 14.00
46 Yazdani, 2007 C4/ 240 36.50 640.00 18000 15.0 20.00
47 Yazdani, 2007 C4/ 240 34.10 220.00 18000 30.0 20.00
48 Yazdani, 2007 C4/ 240 36.80 330.00 18000 30.0 25.00
49 Yazdani, 2007 C4/ 240 36.50 640.00 18000 30.0 38.00
50 Yazdani, 2007 C4/ 240 34.10 220.00 18000 30.0 34.00
51 Yazdani, 2007 C4/ 240 36.80 330.00 18000 30.0 40.00
52 Yazdani, 2007 C4/ 240 36.50 640.00 18000 30.0 60.00
53 Yazdani, 2007 C4/ 240 34.10 220.00 18000 60.1 71.00
303
Table B-3: The experimental data on VAPEX experiments conducted by different researchers (Cont'd)
No. Researcher
Solvent/Inj.
Pressure (kPa)
Φ (%) k (D) μ (cp) H (cm) Q (mL/h)
54 Yazdani, 2007 C4/ 240 36.80 330.00 18000 60.1 97.00
55 Yazdani, 2007 C4/ 240 36.50 640.00 18000 60.1 135.00
56 Yazdani, 2007 C4/ 240 34.10 220.00 18000 100.5 90.00
57 Yazdani, 2007 C4/ 240 36.80 330.00 18000 100.5 120.00
58 Yazdani, 2007 C4/ 240 36.50 640.00 18000 100.5 160.00
59 Yazdani, 2007 C4/ 240 34.10 220.00 18656 60.1 67.80
60 Yazdani, 2007 C4/ 240 36.80 330.00 18656 60.1 86.10
61 Yazdani, 2007 C4/ 240 36.50 640.00 18656 60.1 123.30
62 Tuhinuzzaman, 2006 C4/ 240 40.00 13.00 5800 35.6 6.00
63 Tuhinuzzaman, 2006 C4/ 240 40.00 13.00 14400 35.6 4.00
64 Xu, 2006 C4/ 204 40.00 13.00 150000 30.48 20.00
65 Xu, 2006 C4/ 188 40.00 13.00 150000 30.48 4.00
66 Zhang, 2006 C4/ 240 36.80 441.30 38347 10.0 6.15
67 Zhang, 2006 C4/ 240 37.50 132.00 38347 10.0 3.76
68 Etminan, 2007 C4/ 240 35.18 10.00 18600 15.2 7.00
69 Etminan, 2007 C4/ 240 33.29 10.00 18600 15.2 6.00
70 Tam, 2007 C4/ 103 38.00 1123.00 23200 100.0 69.00
71 Tam, 2007 C4/ 103 38.00 1123.00 23200 100.0 75.00
72 Tam, 2007 C4/97 39.00 300.00 23200 100.0 39.48
73 Tam, 2007 C4/ 93 39.00 300.00 23200 100.0 33.60
74 Tam, 2007 C4/ 110 38.00 1123.00 23200 100.0 129.00
75 Zhang, 2007 C3/ 800 36.20 438.00 38347 10.0 1.29
76 Zhang, 2007 C3/ 800 36.80 158.00 38347 10.0 0.88
77 Zhang, 2007 C3/ 800 37.40 418.00 38347 10.0 2.91
78 Zhang, 2007 C3/ 800 36.50 417.00 38347 10.0 4.93
79 Zhang, 2007 C3/ 800 36.50 122.00 38347 10.0 2.95
80 Zhang, 2007 C3/ 800 35.80 424.00 38347 10.0 3.63
304
Table B-4: The experimental data on VAPEX experiments conducted by different researchers (Cont'd)
No. Researcher
Solvent/Inj.
Pressure (kPa)
Φ (%) k (D) μ (cp) H (cm) Q (mL/h)
81 Zhang, 2007 C3/ 800 35.30 410.00 38347 10.0 2.21
82 Zhang, 2007 C3/ 800 36.70 143.00 38347 10.0 9.14
83 Zhang, 2007 C3/ 800 36.10 118.00 38347 10.0 1.99
84 Azin, 2008 (C3/C1)/ 1069 39.50 830.00 58770 30.0 19.90
85 Azin, 2008 (C3/C1)/ 1655 39.50 830.00 58770 30.0 29.08
86 Azin, 2008 (C3/C1)/ 1069 39.50 830.00 58770 30.0 27.35
87 Azin, 2008 (C3/C1)/ 689 39.50 830.00 58770 30.0 43.37
88 Haghighat, 2008 C3/ 814 36.42 3.00 2050 30.0 1.23
89 Haghighat, 2008 C3/ 850 34.24 3.00 2050 30.0 1.09
90 Haghighat, 2008 (C3/Toluene)/ 850 34.71 3.00 2050 30.0 2.12
91 Haghighat, 2008 (C3/Toluene)/ 850 36.44 3.00 2050 30.0 1.06
92 Haghighat, 2008 C3/ 750 37.70 3.00 2050 30.0 1.07
93 Haghighat, 2008 C4/ 240 39.93 3.00 2050 30.0 1.40
94 Moghadam, 2008 C3/ 800 32.50 310.00 11900 10.0 14.55
95 Moghadam, 2008 C3/ 800 32.90 103.00 11900 10.0 3.70
96 Moghadam, 2008 C3/ 800 33.10 96.00 11900 10.0 1.36
97 Moghadam, 2008 C3/ 800 35.40 49.00 11900 10.0 3.28
98 Moghadam, 2008 C3/ 800 35.70 25.00 11900 10.0 1.88
99 Moghadam, 2008 C3/ 800 36.30 16.00 11900 10.0 1.30
100 Talbi, 2008 (CO2/C3)/ 1814 35.40 640.00 4500 30.5 70.35
101 Talbi, 2008 (C3/C1)/ 1814 35.20 640.00 4500 30.5 77.05
102 Talbi, 2008 (CO2/C3)/ 1814 35.00 640.00 4500 30.5 73.10
103 Talbi, 2008 (C3/C1)/ 4227 35.30 640.00 4500 30.5 70.92
104 Talbi, 2008 (CO2/C3)/ 4227 35.10 640.00 4500 30.5 97.00
105 Talbi, 2008 (CO2/C3)/ 4227 35.10 640.00 18600 30.5 52.92
106 Talbi, 2008 (CO2/C3)/ 2848 35.15 640.00 18600 30.5 43.10
107 Talbi, 2008 (CO2/C3)/ 1469 35.20 640.00 18600 30.5 34.28
108 Talbi, 2008 (C3/C1)/ 4227 35.20 640.00 18600 30.5 36.33
305
Table B-5: The experimental data on VAPEX experiments conducted by different researchers (Cont'd)
No. Researcher
Solvent/Inj.
Pressure (kPa)
Φ (%) k (D) μ (cp) H (cm) Q (mL/h)
109 Talbi, 2008 (C3/C1)/ 2848 35.00 640.00 18600 30.5 45.28
110 Talbi, 2008 (C3/C1)/ 1469 35.10 640.00 18600 30.5 44.00
111 Talbi, 2008 CO2/ 4227 35.30 640.00 18600 30.5 23.31
112 Derakhshanfar, 2009 C3/ 860 35.70 40.00 21000 15.3 7.00
113 Derakhshanfar, 2009 (C3/C1) 1480 36.60 40.00 21000 15.3 5.50
114 Derakhshanfar, 2009 (C3/C1)/ 2859 37.00 40.00 21000 15.3 3.50
115 Derakhshanfar, 2009 (C3/CO2)/ 2859 36.90 40.00 21000 15.3 4.50
116 Luo, 2009 C4/ 240 36.80 441.00 24137 10.0 4.06
117 Luo, 2009 C4/ 240 37.50 132.00 24137 10.0 1.85
118 Luo, 2009 C3/ 800 36.50 122.00 24137 10.0 2.95
119 Luo2009 C3/ 918 36.70 143.00 24137 10.0 9.14
120 Luo, 2009 C3/ 800 32.50 310.00 12900 10.0 17.82
121 Luo, 2009 C3/ 800 32.90 103.00 12900 10.0 4.29
122 Luo, 2009 C3/ 800 35.40 49.00 12900 10.0 3.48
123 Luo, 2009 C3/ 800 36.30 16.00 12900 10.0 1.18
124 Alkindi, 2010 Ethanol/ 198 39.00 43.00 1390 30.0 0.48
125 Alkindi, 2010 Ethanol/ 198 39.00 43.00 1390 30.0 0.75
126 Alkindi, 2010 Ethanol/ 198 39.00 43.00 1390 30.0 1.03
127 Alkindi, 2010 Ethanol/ 198 39.00 43.00 1390 15.0 0.41
128 Alkindi, 2010 Ethanol/ 198 39.00 43.00 1390 15.0 0.52
129 Abukhalifeh, 2010 C3/ 791 38.00 204.00 225000 25.0 29.46
130 Abukhalifeh, 2010 C3/ 791 38.00 204.00 225000 35.0 34.38
131 Abukhalifeh, 2010 C3/ 791 38.00 204.00 225000 45.0 38.46
132 Abukhalifeh, 2010 C3/ 791 37.80 102.00 225000 25.0 23.52
133 Abukhalifeh, 2010 C3/ 791 37.80 102.00 225000 35.0 26.52
134 Abukhalifeh, 2010 C3/ 791 37.80 102.00 225000 45.0 31.32
135 Abukhalifeh, 2010 C3/ 791 37.60 51.00 225000 25.0 15.60
306
Table B-6: The experimental data on VAPEX experiments conducted by different researchers (Cont'd)
No. Researcher
Solvent/Inj.
Pressure (kPa)
Φ (%) k (D) μ (cp) H (cm) Q (mL/h)
136 Abukhalifeh, 2010 C3/ 791 37.60 51.00 225000 35.0 17.34
137 Abukhalifeh, 2010 C3/ 791 37.60 51.00 225000 45.0 20.34
138 Rezaei, 2010 C5/69 37.10 220.00 40500 41.9 61.72
139 Rezaei, 2010 C5/69 37.10 220.00 40500 41.9 68.58
140 Rezaei, 2010 C5/69 37.10 220.00 40500 41.9 72.24
141 Rezaei, 2010 C5/69 37.10 220.00 40500 41.9 76.80
142 Rezaei, 2010 C5/69 37.10 220.00 40500 41.9 79.56
143 Rezaei, 2010 C5/69 37.10 220.00 40500 41.9 80.04
144 Rezaei, 2010 C5/69 37.10 220.00 40500 41.9 80.04
145 Rezaei, 2010 C5/69 37.10 220.00 40500 41.9 81.84
146 Rezaei, 2010 C5/69 37.10 780.80 40500 36.0 132.96
147 Rezaei, 2010 C5/69 30.50 148.80 40500 36.0 14.28
148 Rezaei, 2010 C5/69 20.50 19.10 40500 36.0 67.26
149 Rezaei, 2010 C5/69 28.00 119.10 40500 35.8 64.92
150 Rezaei, 2010 C5/69 29.40 147.10 40500 36.0 69.72
151 Rezaei, 2010 C5/69 30.10 132.40 40500 36.0 74.40
152 Rezaei, 2010 C5/69 32.10 143.70 40500 36.0 83.22
153 Rezaei, 2010 C5/69 37.10 830.00 40500 41.9 111.56
154 Rezaei, 2010 C5/69 37.10 220.00 40500 41.9 61.72
155 Rezaei, 2010 C5/69 37.10 830.00 5400 41.9 169.62
156 Rezaei, 2010 C5/69 37.10 220.00 5400 41.9 96.90
157 Rezaei, 2010 C5/69 37.10 830.00 40500 41.9 119.76
158 Rezaei, 2010 C5/69 37.10 220.00 40500 41.9 70.38
159 Rezaei, 2010 C5/69 37.10 830.00 5400 41.9 184.68
160 Rezaei, 2010 C5/69 37.10 220.00 5400 41.9 104.70
161 Rezaei, 2010 C5/69 37.10 830.00 40500 41.9 176.94
162 Rezaei, 2010 C5/69 37.10 830.00 40500 41.9 170.52
307
Table B-7: The experimental data on VAPEX experiments conducted by different researchers (Cont'd)
No. Researcher
Solvent/Inj.
Pressure (kPa)
Φ (%) k (D) μ (cp) H (cm) Q (mL/h)
163 Rezaei, 2010 C5/69 37.10 830.00 5400 41.9 208.92
164 Rezaei, 2010 C5/69 37.10 830.00 5400 41.9 218.1
165 Rezaei, 2010 C5/69 37.10 220.00 40500 41.9 100.56
166 Rezaei, 2010 C5/69 37.10 220.00 40500 41.9 104.22
167 Rezaei, 2010 C5/69 37.10 220.00 5400 41.9 128.94
168 Rezaei, 2010 C5/69 37.10 220.00 5400 41.9 119.34
169 Rezaei, 2010 C5/69 37.10 830.00 40500 41.9 121.158
170 Rezaei, 2010 C5/69 37.10 220.00 40500 41.9 45.72
171 Rezaei, 2010 C5/69 37.10 830.00 5400 41.9 208.5
172 Rezaei, 2010 C5/69 37.10 220.00 5400 41.9 109.26
173 Ahmadloo, 2012 C4/ 240 37.10 6.46 10541 24.5 0.32
174 Ahmadloo, 2012 C4/ 240 37.10 6.46 10541 47.5 0.62
175 Ahmadloo, 2012 C4/ 240 30.30 5.19 10541 24.5 0.02
176 Ahmadloo, 2012 C4/ 240 30.30 5.19 10541 47.5 0.05
177 Ahmadloo, 2012 C4/ 240 35.60 5.62 10541 24.5 0.04
178 Ahmadloo, 2012 C4/ 240 35.60 5.62 10541 47.5 0.17
179 Ahmadloo, 2012 C4/ 240 37.10 6.46 10541 24.5 0.80
180 Ahmadloo, 2012 C4/ 240 37.10 6.46 10541 47.5 2.80
181 Ahmadloo, 2012 C4/ 240 30.30 5.19 10541 24.5 0.90
182 Ahmadloo, 2012 C4/ 240 30.30 5.19 10541 47.5 4.00
183 Ahmadloo, 2012 C4/ 240 35.60 5.62 10541 24.5 0.60
184 Derakhshanfar, 2012 C3/800 33.80 9.20 11900 10.0 2.07
185 Derakhshanfar, 2012 C3/800 35.20 8.30 11900 10.0 1.38
186 Derakhshanfar, 2012 C3/800 34.60 10.80 11900 10.0 1.61
187 Derakhshanfar, 2012 C3/800 34.30 10.10 11900 10.0 1.17
188 Derakhshanfar, 2012 (C4/C3)/ 300 35.50 4.70 11900 10.0 0.37
189 Derakhshanfar, 2012 (C4/C3)/ 300 35.20 5.80 11900 10.0 0.32
308
Table B-8: The experimental data on VAPEX experiments conducted by different researchers (Cont'd)
No. Researcher
Solvent/Inj.
Pressure (kPa)
Φ (%) k (D) μ (cp) H (cm) Q (mL/h)
190 Muhamad, 2012 C3/ 689 38.50 439.20 14500 25.0 1.46
191 Muhamad, 2012 C3/ 689 38.00 220.00 14500 25.0 1.17
192 Muhamad, 2012 C3/ 689 37.80 97.40 14500 25.0 0.62
193 Muhamad, 2012 C3/ 689 37.60 44.40 14500 25.0 0.49
194 Muhamad, 2012 C4/ 192 38.00 204.00 14500 25.0 0.30
195 Muhamad, 2012 C4/ 200 38.00 204.00 14500 25.0 0.38
196 Muhamad, 2012 C4/ 208 38.00 204.00 14500 25.0 0.48
197 Muhamad, 2012 C4/ 214 38.00 204.00 14500 25.0 0.54
198 Badamchizadeh, 2013 (CO2/C3)/ 1974 35.70 640.00 15000 30.5 4.00
199 Badamchizadeh, 2013 (CO2/C3)/ 2016 35.70 640.00 15000 30.5 4.00
200 Badamchizadeh, 2013 (CO2/C3)/ 3407 35.70 640.00 15000 30.5 6.00
201 Badamchizadeh, 2013 C3/ 784 35.90 640.00 15000 30.5 15.00
202 Jia, 2013 C3/ 800 35.46 4.50 8411 10.0 4.15
203 Jia, 2013 C3/ 800 35.47 4.23 8411 10.0 5.51
204 Jia, 2013 C3/ 800 35.79 4.22 8411 10.0 3.28
205 Jia, 2013 C3/ 800 35.17 4.20 8411 10.0 3.36
206 Jia, 2013 C3/ 800 35.83 4.79 8411 10.0 3.46
207 Jia, 2013 C3/ 800 35.66 4.20 8411 10.0 1.88
208 Jia, 2013 C3/ 800 35.75 5.05 8411 10.0 1.94
209 Jia, 2013 C3/ 800 35.88 4.75 8411 10.0 2.81
210 Jia, 2013 C3/ 800 36.00 5.20 5875 10.0 2.75
211 Jia, 2013 C3/ 800 35.60 5.60 5875 10.0 11.99
212 This study C3/ 800 42.20 8.78 5650 24.5 13.20
213 This study C3/ 800 43.10 9.12 5650 45.5 30.00
214 This study (C3/CO2)/ 850 41.80 8.64 5650 24.5 9.00
215 This study (C3/CO2)/ 850 42.40 8.87 5650 45.5 19.80
216 This study (C3/C1)/850 42.00 8.50 5650 24.5 7.80
217 This study (C3/C1)/850 38.50 439.20 5650 45.5 15.00
309
Table B-9: The experimental data on VAPEX experiments conducted by different researchers (Cont'd)
No. Researcher
Solvent/Inj.
Pressure (kPa)
Φ (%) k (D) μ (cp) H (cm) Q (mL/h)
218 This study C4/ 140 42.10 8.69 5650 24.5 8.40
219 This study C4/ 140 42.30 9.08 5650 45.5 19.20
220 This study CO2/ 850 42.10 6.11 5650 24.5 0.72
221 This study CO2/ 850 42.60 6.70 5650 45.5 1.68
222 This study C1/ 850 40.70 5.12 5650 24.5 1.62
223 This study C1/ 850 41.80 5.88 5650 45.5 3.42
310
Appendix C
The matrix of weights from input parameters to the first hidden layer nodes (iw 1, 1):
[3.8342 2.6339 -1.7228 -1.2711 0.28176;
2.6878 1.68 -3.5471 0.51937 1.8157;
-2.9572 2.6228 2.6736 -1.0331 1.5031;
-2.8597 -0.33989 2.7232 -0.19416 -3.0003;
0.5961 3.7079 -3.1228 -0.91768 -1.2655;
2.7566 0.52986 -2.0731 -2.0107 3.7189;
-4.1331 1.348 0.084948 2.8174 0.45116;
-0.032271 0.094057 0.091646 -4.9785 -1.1014;
-0.7139 -2.6992 0.094485 0.84579 4.3104;
2.238 -1.0346 -3.2367 2.4626 2.0406;
0.81488 -0.78138 2.6493 -1.6253 4.658;
-1.6764 0.57618 -0.88634 4.6013 1.4561;
-3.6315 -2.1886 -1.3978 -1.8416 -2.178;
-3.8408 0.060839 3.4376 -2.0979 0.29411;
1.6419 2.3604 2.6563 -1.2682 1.3958;
4.0527 -2.9136 0.9637 -0.9541 1.49;
-0.60968 -1.1512 0.35623 -4.5634 -1.2164;
-4.7813 -0.1938 -2.4813 0.45669 1.6036;
3.4817 2.0315 -2.4783 3.2969 1.5177;
-0.91766 3.1251 -3.0631 -0.20696 -1.2271]
311
The matrix of weights from the first hidden layer nodes to the second hidden layer nodes
(iw 2, 1):
[2.6318 -2.0514 -0.80709 2.3836 0.093621 0.5588 1.2372 0.64943 -2.3911 -0.58394 -
0.4018 0.19941 0.10041 -0.079929 -0.7413 0.051628 2.1146 2.6368 1.2221 -0.67989;
-2.1714 -0.77674 1.3303 0.96678 2.0044 1.295 0.95583 -1.8506 0.37636 0.30933 2.0122
-2.0375 0.17967 1.8269 -0.64497 -0.29409 1.6621 1.6449 -0.58706 2.0361;
-0.8789 -0.82931 -0.33006 2.4585 0.25847 0.59257 -1.3263 -1.4221 0.56028 -1.246
0.48434 2.6359 2.567 0.33864 2.1691 2.0583 2.2656 0.75952 1.062 -0.69716;
-0.92178 1.8881 2.0184 -1.9775 0.88015 1.6529 1.9165 0.28952 1.7327 1.8705 2.0931 -
0.18077 1.1225 1.6229 -0.11249 1.4712 -1.5148 0.12491 1.7676 0.34114;
1.1154 -1.3387 -0.11071 -0.30272 -0.69325 2.3929 -1.6084 -0.21867 -1.5819 -0.43299
2.2543 2.148 1.1971 -1.1345 1.5195 0.43341 -2.5531 -0.78669 1.445 1.334;
-0.6284 1.5918 1.1481 0.45122 2.8126 -1.2392 -2.1094 -0.66093 -1.1668 0.91009
0.60498 1.0852 1.1834 2.3248 1.9419 0.26604 -2.0877 0.20785 1.944 -0.90947;
1.9587 -1.7722 -0.50812 -2.319 -1.6095 2.4513 -2.3669 0.54037 1.172 -2.1491 -1.3738 -
0.32042 1.3534 -0.053012 1.4311 0.18885 1.3234 0.96526 -0.77075 -1.2046;
0.62395 0.67625 -0.077309 -1.9163 -0.99189 -0.15776 0.45703 1.1481 2.2203 -0.80612
2.4831 -1.2634 0.97829 2.1727 0.37317 1.1661 0.28517 2.7068 2.6351 -2.3245;
-0.19732 -0.71551 0.083086 2.4232 0.52199 1.2527 -0.89368 0.56382 -3.0429 -1.4215
1.8027 2.7168 1.9995 -0.91605 0.83622 0.58416 -0.93651 0.70624 -1.9017 0.62462;
-0.21302 2.4763 0.62008 -0.52747 0.030618 0.797 2.4542 1.807 1.8153 -1.1298 -1.2273
-1.2086 -0.5148 2.7781 -1.5728 -1.1492 0.15256 1.4514 -1.657 -1.3266;
1.8093 -0.62835 -0.010542 0.43175 1.8556 0.85602 0.27024 -0.69986 2.3253 -1.7988
2.4142 -0.8184 1.6639 1.9188 -2.342 2.1695 0.63335 -1.358 0.21704 0.25233;
-0.62302 0.44494 -2.5205 -1.9005 2.4151 -0.72544 -0.87981 -0.62693 1.8809 1.7572
1.2932 0.39357 2.3716 -1.08 0.43669 -2.2839 0.65154 -0.50548 -0.29694 -1.2114;
1.1464 -0.41652 2.1002 1.4334 2.0287 1.1927 -0.48917 0.49421 1.0455 -2.5653 -
0.41121 2.4319 1.081 -1.8942 1.4026 0.12931 0.4948 -1.018 -2.2073 -0.99635;
-1.6046 -2.2245 1.677 -1.6962 -1.182 -0.065035 0.18135 -1.6376 -0.28411 -1.846
1.9622 -0.38106 -0.12648 1.2878 1.5887 0.27783 -1.1348 2.5239 -1.8249 1.6319;
2.1892 -1.2199 -2.3435 -0.72842 -1.6442 -0.37102 -2.2672 1.6081 -1.4481 1.3056 -
0.63831 -1.0642 -1.881 1.7183 0.70666 -0.12495 -1.1613 0.21133 -2.2819 -1.6896]
312
The matrix of weights from the second hidden layer nodes to the third hidden layer nodes
(iw 3, 2):
[1.8746 -0.89305 1.744 2.1698 2.0149 -1.8158 1.7097 0.30429 -0.73645 -2.1121 1.9202 -
1.5021 -2.3995 -1.3412 1.0464;
-0.24969 0.20851 2.7371 -0.55734 -0.68594 1.1659 0.32128 2.8112 -1.838 2.5849
2.6554 -1.9633 0.22612 -1.0195 -1.7012;
0.43593 0.83728 -0.44766 -2.7619 0.57858 -2.6619 -0.81414 2.6012 0.7144 -0.19577
1.5826 -1.5387 2.2225 2.0047 1.0532;
-1.8505 -0.069614 -0.3922 -3.2289 -0.026621 -0.28225 1.1239 1.8144 -2.575 -0.63929
1.371 -2.6888 0.49397 2.7586 -0.91565;
-2.2247 -2.1071 1.843 -1.7703 -0.424 1.0666 1.3701 1.5006 2.304 0.41289 -2.1081 -
2.446 2.0402 1.1134 1.5589;
-1.317 2.6514 -2.464 0.66646 -2.5365 0.51663 -0.14216 -0.37108 2.7571 2.5638
0.81146 -0.14786 -1.3037 1.8148 0.50924;
0.77755 -0.15352 -2.0285 2.3663 1.8349 3.2685 0.64157 -2.4044 1.1428 1.3576 2.2002
-0.58845 -1.5505 -1.2971 -0.72603;
0.060605 -1.2487 1.0722 1.1642 -1.6616 1.8614 -1.9825 3.2714 -0.28992 -2.7026 1.157
-0.2449 1.3993 3.3683 1.0698;
2.281 -1.8388 1.848 -1.2109 -1.8779 1.9401 0.89893 1.8977 1.8982 2.0604 2.5457
0.62028 -0.30926 2.14 1.6572;
-2.5277 0.3542 2.4269 0.83718 -0.91481 0.094006 -2.7763 -2.1455 -2.7416 -1.3561
1.7942 -1.3213 -0.50729 -1.2123 -2.0684]
313
The matrix of weights from the third hidden layer nodes to the fourth hidden layer nodes
(iw 4, 3):
[3.3118 -0.029973 -1.7086 2.6421 2.5819 -3.2451 -0.90743 -0.85697 -2.0989 -0.48801;
-2.6397 1.4878 0.224 -3.3185 -1.46 -0.72539 -2.6905 -1.2204 1.3719 -3.0077;
1.2133 -2.7204 -0.63364 0.38662 -3.9798 -2.9172 0.30322 -0.55424 3.3309 0.32613;
2.9842 0.6636 -0.95366 -0.45138 1.6837 -3.0945 -0.55417 -3.4008 1.3252 3.8397;
3.1591 2.6323 -0.21436 -1.425 -3.2038 0.66739 2.93 0.084489 2.0293 -0.72111]
The matrix of weights from the fourth hidden layer nodes to the fifth hidden layer nodes
(iw 5, 4):
[1.6703 1.0973 -0.65058 -2.1238 -0.56007]
314
The matrix of biases for the first hidden layer nodes (b 1):
[-5.1636;
-4.4262;
4.0208;
3.755;
-3.0667;
-2.0715;
1.8703;
1.2979;
1.3744;
-0.33229;
0.5332;
-1.3046;
-1.1322;
-1.688;
3.1306;
2.49;
-3.9999;
-4.1369;
3.4163;
-5.4958]
315
The matrix of biases for the second hidden layer nodes (b 2):
[-5.662;
-2.255;
-1.6396;
-5.5168;
-4.4382;
-3.6276;
0.68071;
-4.7479;
-3.9795;
-0.29637;
-2.7047;
-2.4574;
0.91688;
-0.10902;
7.9531]
316
The matrix of biases for the third hidden layer nodes (b 3):
[-4.817;
0.2902;
-5.5327;
2.8447;
-0.32041;
-3.2198;
0.14043;
-4.2837;
-2.6078;
4.1015]
The matrix of biases for the forth hidden layer nodes (b 4):
[-1.5436;
7.8936;
1.9165;
1.289;
0.84813]
The matrix of biases for the output layer node (b 5):
[0.75002]