Investigation of EOR Performance in Shale Oil Reservoirs ...
Transcript of Investigation of EOR Performance in Shale Oil Reservoirs ...
Investigation of EOR Performance in Shale Oil Reservoirs by Cyclic Gas
Injection
BY
Tao Wan, M.S.
A Dissertation
Submitted to the Graduate Faculty of
Texas Tech University
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
IN
PETROLEUM ENGINEERING
Approved
by
Dr. James J. Sheng
Chair of Committee
Dr. Habib K. Menouar
Dr. Lloyd Heinze
Dr. Marshall Watson
Dr. Mohamed Y. Soliman
Dr. Mark Sheridan
Dean of the Graduate School
May, 2015
© Copyright 2015, Tao Wan
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Acknowledgements
First and foremost I would like to express sincere gratitude to the committee members,
Dr. James J. Sheng, Dr. Habib K. Menouar, Dr. Lloyd Heinze, Dr. Marshall Watson and
Dr. Mohamed Soliman. I should give my strong appreciation to my advisor Dr. Sheng for
working with me patiently and directing me throughout this expedited process. I was
grateful to him for assisting me in improving my writing skills for journal publication. I
was impressed by his intensity and enthusiasm in doing every piece of work. His advice
on both research as well as on career has been priceless to me. My time at Texas Tech U
was made enjoyable in large part due to talk with Dr. Sheng, faculty and our research
groups.
I gratefully acknowledge the Petroleum Engineering Department for the financial support
and funding sources from Dr. Sheng that made my Ph.D. work possible.
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Table of Contents
ACKNOWLEDGEMENTS ......................................................................................... II
ABSTRACT ................................................................................................................ VI
LIST OF TABLES ................................................................................................... VIII
LIST OF FIGURES ...................................................................................................... X
NOMENCLATURE ................................................................................................... XV
CHAPTER 1 ..................................................................................................................1
INTRODUCTION .........................................................................................................1
Objectives ....................................................................................................................4
Organization of this dissertation ...................................................................................5
CHAPTER 2 MODELING OF THE EOR PROCESS IN STIMULATED SHALE
OIL RESERVOIRS BY CYCLIC GAS INJECTION ..................................................6
2.1. Abstract.................................................................................................................6
2.2. Introduction ..........................................................................................................6
2.3. Description of Models ...........................................................................................8
2.3.1. MMP determination ......................................................................................... 10
2.4. Simulation Results and Discussion ...................................................................... 17
2.5. Summary ............................................................................................................ 32
CHAPTER 3 EVALUATION OF THE EOR POTENTIAL IN FRACTURED
SHALE OIL RESERVOIRS BY CYCLIC GAS INJECTION .................................. 40
3.1. Abstract............................................................................................................... 40
3.2. Introduction ........................................................................................................ 41
3.3. Model Setup and Validation ................................................................................ 43
3.4. Simulation Results and Discussion ...................................................................... 45
3.5. Conclusions ........................................................................................................ 55
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3.6. References .......................................................................................................... 55
CHAPTER 4 COMPOSITIONAL MODELING OF THE DIFFUSION EFFECT
ON EOR PROCESS IN FRACTURED SHALE OIL RESERVOIRS BY GAS
FLOODING ................................................................................................................. 59
4.1. Abstract............................................................................................................... 59
4.2. Introduction ........................................................................................................ 60
4.3. Description of Mathematical Model .................................................................... 62
4.4. Model Validation ................................................................................................. 64
4.5. Simulation Results and Discussion ...................................................................... 70
4.6. Conclusions ........................................................................................................ 81
4.7. References .......................................................................................................... 82
CHAPTER 5 EVALUATE THE EOR POTENTIAL OF CO2 DISPLACEMENT IN
SHALE RESERVOIRS USING STAGGERED ZIPPER FRACTURED
HORIZONTAL WELLS ............................................................................................. 87
5.1. Abstract............................................................................................................... 87
5.2. Introduction ........................................................................................................ 88
5.3. Model Description .............................................................................................. 90
5.4. Results and Discussion ........................................................................................ 94
5.5. Conclusions ...................................................................................................... 102
5.6. References: ....................................................................................................... 103
CHAPTER 6 NUMERICAL SIMULATION OF THE EXPERIMENTAL DATA IN
LIQUID-RICH SHALES BY CYCLIC GAS INJECTION ..................................... 106
6.1. Introduction ...................................................................................................... 106
6.2. Material and Methods ....................................................................................... 108
6.3. Simulation model description ............................................................................ 110
6.4. Results and Discussion ...................................................................................... 113
6.5. Conclusions ...................................................................................................... 124
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6.6. Acknowledgments ............................................................................................. 125
6.7. References ........................................................................................................ 125
CHAPTER 7 CONCLUSIONS ................................................................................. 129
The contribution of this study .................................................................................. 131
Recommendations for future work ........................................................................... 131
References ............................................................................................................... 132
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Abstract
The primary oil recovery factor from shale oil reservoirs is only a small percentage of the
in-place reserves. The low primary recovery efficiency and the abundance of shale
reservoirs provide huge potential for enhanced oil recovery. Well production performance
in shale oil or gas reservoirs strongly depends on the size of fracture-network. The
induced fractures that connect the natural fracture complexity provide high flow capacity
for injected fluid to access the hydrocarbons trapped in shale matrix. However, in such a
system, flooding may not be effective to enhanced oil recovery because the injected
fluids may break-through to production wells via the fracture network. A cyclic injection
scheme is one way to solve this problem.
A simulation approach is used to evaluate the EOR potential from cyclic gas injection.
The reason why we initiate such a project is because there is limited research on studying
the approaches on recovering shale oil. Conventional gas flooding may not be a good
candidate for improving oil recovery in shale oil reservoirs because of the reasons stated
above. The deficiency of gas fingering is avoided by using the gas huff-and-puff method
(also called cyclic gas injection in this dissertation). Cyclic gas injection is not subject to
early breakthrough. It can take the advantage of natural fractures to increase the contact
area of the injected solvent with reservoir rocks.
This dissertation also addresses the role of diffusion effect on EOR process in fractured
shale oil reservoirs by gas flooding in field-scale displacements and in the presence of
viscous flow. A dual permeability model coupled with diffusion that characterized the
dispersive-convective flux through nanopores in shale oil reservoirs during gas injection
process is presented. The results produced by this model are in good agreement with
experimentally measured data in shale rocks. The significance of inclusion of matrix-
fracture diffusion rate in the oil phase and diffusion within the matrices in tight shale oil
reservoirs is highlighted. The diffusion effect on gas flooding efficiency is summarized,
which is implemented in two hydraulically zipper fractured horizontal wells in liquid-rich
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shales.
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List of Tables
Table 2.1. Peng-Robinson EOS Fluid Description .......................................................... 12
Table 2.2. Binary interaction coefficients ....................................................................... 12
Table 2.3. Reservoir Fluid Composition ......................................................................... 12
Table 2.4. Pressure dependent mixing parameter ............................................................ 13
Table 2.5. Black-oil and solvent PVT table .................................................................... 13
Table 2.6. Reservoir properties for the model input ........................................................ 13
Table 3.1. Eagle Ford Fluid properties ........................................................................... 45
Table 4.1. Core sample properties .................................................................................. 66
Table 4.2. Compositional description of reservoir fluid in experimental simulation ........ 66
Table 4.3. Binary coefficient used for experimental simulation ...................................... 66
Table 4.4. Peng-Robinson EOS Fluid Description .......................................................... 71
Table 4.5. Binary coefficient for Eagle Ford fluid and reservoir flooding case ................ 71
Table 4.6. The effective diffusion coefficients of different components at 2000 psi ........ 71
Table 4.7. Reservoir properties for the model input ........................................................ 71
Table 4.8. Required inputs for the NPV calculations ...................................................... 79
Table 5.1. Peng-Robinson EOS Fluid Description of Eagle Ford Condensate lumping ... 91
Table 5.2. Reservoir properties for the model input ........................................................ 92
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Table 6.1. Properties of Soltrol-130 (Chevron-Phillips Chemical Company LP) ........... 108
Table 6.2. Properties of C15 at 95 ˚F ............................................................................. 110
Table 6.3. Reservoir and fluid properties used in this study .......................................... 111
Table 6.4. Measured shale oil recovery factor and oil saturation with a deletion time of
0.05-hr (Yu, 2015) ....................................................................................................... 113
Table 6.5. Measured recovery factor and oil saturation with a deletion time of 40-hr (Yu,
2015) ........................................................................................................................... 114
Table 6.6. Operational schedules in a cycle .................................................................. 119
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List of Figures
Figure 2.1-P-T phase envelopes with varying composition for reservoir fluid ..................9
Figure 2.2-Pseudo-ternary diagram cell-to-cell MMP simulation ................................... 11
Figure 2.3-Slim-tube simulation of recovery for lean gas injection................................. 11
Figure 2.4-Yuan’s MMP correlation graph ..................................................................... 12
Figure 2.5-Effect of solution lean gas on swelling of reservoir fluid ............................... 14
Figure 2.6-Effect of lean gas mole fraction on relative volume ...................................... 14
Figure 2.7-Reservoir model ........................................................................................... 17
Figure 2.8-Simulation model of natural fracture-network (implicit) ............................... 19
Figure 2.9-Effect of numerical dispersion on oil R.F. vs. time ........................................ 19
Figure 2.10-Effect of numerical dispersion on C1 prod. vs. time .................................... 20
Figure 2.11-Comparison of oil recovery for four component model vs. fully
compositional model...................................................................................................... 21
Figure 2.12-Comparison of oil R.F. for different fracture spacing .................................. 22
Figure 2.13-Impact of hydraulic fracture on oil R.F........................................................ 22
Figure 2.14-Extrapolation of recovery to zero grid-block size (slim-tube model) ........... 24
Figure 2.15-Gas production rate (RC) and hydrocarbon pore volume injection .............. 25
Figure 2.16-Oil production rate and average reservoir pressure (unit fracture) ............... 26
Figure 2.17-C20 recovery vs. time at 0.2 PVI.................................................................. 26
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Figure 2.18-Impact of natural fracture spacing and hydraulic fracture on CO2 huff-puff oil
recovery, BHP of producer = 2500 psi ........................................................................... 28
Figure 2.19-BHP effect on ultimate oil recovery (Fracture spacing = 50-ft) ................... 29
Figure 2.20-Reservoir pressure, CO2 mole fraction in oil phase and oil viscosity
variations during CO2 huff-and-puff .............................................................................. 31
Figure 2.21-Period oil production (unit fracture production, entire horizontal well
production should be 20 times) ...................................................................................... 32
Figure 3.1-Stimulated fracture-network in SRV (Dx=200) ............................................. 46
Figure 3.2-Stimulated fracture-network in SRV (Dx=100) ............................................. 46
Figure 3.3-Impact of stimulated fracture-network spacing on EOR ................................ 47
Figure 3.4-Impact of the conductivity of fracture-network in the SRV on production
profiles .......................................................................................................................... 48
Figure 3.5-Irreversible process of fracture conductivity reduction .................................. 49
Figure 3.6-Stress-dependent FCD of fracture-network effect on EOR .............................. 50
Figure 3.7-Permeability distribution for the non-uniform fracture network .................... 51
Figure 3.8-Effect of stress-dependent FCD on EOR performance .................................. 51
Figure 3.9-Non-SRV embedded in SRV (Hydraulic fracture spacing = 400-ft) ............... 53
Figure 3.10-Hydraulic fracture spacing effect on EOR ................................................... 54
Figure 3.11-Fracture scenarios effect on EOR (DX=200-ft) ........................................... 54
Figure 4.1-Comparative cumulative oil recovery ........................................................... 67
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Figure 4.2-2D single porosity model with 10 x 10-m matrix blocks ............................... 68
Figure 4.3-Comparison of C3 recovery for the dual permeability model with single
porosity model ............................................................................................................... 69
Figure 4.4-Comparison of C3 recovery by CO2 injection in shale gas reservoirs ............. 70
Figure 4.5-The horizontal well pair perforated and stimulated in a staggered pattern ..... 72
Figure 4.6-Simulation Unit ............................................................................................ 73
Figure 4.7-Comparison of Coats’s model results with our model for matrix permeability
km = 1 mD ..................................................................................................................... 74
Figure 4.8-Effect of diffusion in the oil phase and within matrices on shale oil recovery 74
Figure 4.9-Peclet number in the oil phase in the matrix (km=1 md) at 7000 days ............ 75
Figure 4.10-Peclet number in the oil phase in the shale matrix (km=1E-04 md) at 7000
days ............................................................................................................................... 75
Figure 4.11-Oil recovery vs. PVI ................................................................................... 77
Figure 4.12-Oil recovery vs. time .................................................................................. 77
Figure 4.13-Comparative NPV by two different injection rates ...................................... 79
Figure 4.14-Comparison of gas production rates and cumulative gas injection by two
different injection rates .................................................................................................. 80
Figure 4.15-Effect of natural fracture spacing on gas injection performance .................. 80
Figure 5.1A-The horizontal well pair perforated and stimulated in a staggered pattern ... 89
Figure 5.2-The horizontal well pair stimulated in a staggered pattern ............................. 90
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Figure 5.3-Phase diagram of gas condensate behavoir .................................................... 91
Figure 5.4-Reservoir pressure changes during gas injection ........................................... 92
Figure 5.5-Effect of solution gas on swelling of ............................................................. 93
Figure 5.6-Relative volume curve reservoir fluid by CO2 ............................................... 94
Figure 5.7-The liquid dropout curve for constant-composition expansion experiment at
335F on the gas condensate mixture. ............................................................................. 94
Figure 5.8-Effect of numerical dispersion on C1 recovery vs. time ................................. 96
Figure 5.9-Effect of numerical dispersion on gas R.F. vs. time ....................................... 96
Figure 5.10-Unit fracture controlled stimulated reservoir volume vs. entire horizontal
well SRV results ............................................................................................................ 97
Figure 5.11-BHP impact on gas recovery performance ................................................... 97
Figure 5.12-Gas recovery factor-Darcy flow vs. gas recovery-non-Darcy flow .............. 98
Figure 5.13-Impact of hydraulic fracture spacing between injection well and production
well on gas recovery and C9 recovery ............................................................................ 99
Figure 5.14-Global mole fraction of CO2 and C1 changes during CO2 flooding .............. 99
Figure 5.15-Condensate saturation distribution in or at vicinity of the fracture ............. 100
Figure 5.16-Gas relative permeability on or vicinity of the fracture .............................. 101
Figure 6.1-Experimental setup and apparatus ............................................................... 110
Figure 6.2-Base simulation model ................................................................................ 111
Figure 6.3-Shale oil production response in cyclic gas injection processes from 1th-5th
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cycle ............................................................................................................................ 114
Figure 6.4-Effect of depletion time on CGI recovery performance ............................... 115
Figure 6.5-Effect of grid block size on calculated oil R.F. ............................................ 116
Figure 6.6-Effect of fracture permeability on cyclic gas injection performance ............ 117
Figure 6.7-Comparison of simulation results with experimental data (0.05 hours) ........ 117
Figure 6.8-Comparison of simulation results and experimental data (40 hours) ............ 118
Figure 6.9-Comparison of simulated oil saturation and measured data ......................... 119
Figure 6.10-Pressure variations in one cycle of huff-n-puff process (40-hour depletion)
.................................................................................................................................... 119
Figure 6.11-Comparison of simulation results and experimental data (Pi=1000 psi,
depletion time = 40-hr) ................................................................................................ 121
Figure 6.12-Effect of diffusion on ultimate oil recovery ............................................... 121
Figure 6.13 -Effect of soak duration on production response ........................................ 122
Figure 6.14-N2 mole fraction in the shale matrix from 1th -8th cycle (1-hour soak time) 123
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Nomenclature
B = the phase formation volume factor
��=1/��, ��=1/��
BHP= bottom-hole flowing pressure
���=Binary diffusion coefficient between component i and j in the mixture
Fo= Forchheimer Number
FCD= fracture conductivity
���= the well bore pressure head between the connection and the well’s bottom-hole
datum depth.
HF = Hydraulic fractures
�=Mass flux
k=Permeability
��=Relative permeability
Kfeff =effective fracture permeability
x y zL , L , L =Fracture spacing in x, y and z direction
MMP= minimum miscibility pressure
NF = natural fracture-network
PV= pore volume
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PVI= injected pore volume
��=the bottom hole pressure of the well
Po,Pg = Oil and gas phase pressure, respectively
Pcog=Capillary pressure between oil and gas
��,�= the component i molar rate in the j phase
��,�= the volumetric flow rate of phase p in connection j at reservoir condition
�= the pressure equivalent radius
�=the well bore radius
Rs=solution gas, scf/STB
Rv=oil vapor in gas, STB/scf
So, Sg= Saturation of oil and gas, respectively
=Velocity
Vblock=Volume of the grid-block
�=Porosity
�= the angle of segment of connecting with the well
= mixing parameter
���=stock tank oil density
���=stock tank gas density
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�=non-Darcy beta factor
σ =Shape factor, 1/ft2
ijτ =mass transfer of component i in phase j caused by both convection and diffusion
��, ��=Viscosity of oil and gas, cp
ijω =The mass fraction of component i in phase j divided by the total mass of all
components in that same phase
�= Molar density
Subscripts
c=components
f=fracture
i=Component index
j=Phase index, o=oil phase, g=gas phase
m=matrix
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Chapter 1
Introduction
Cyclic gas injection is an effective and quick responding enhanced-oil-recovery method
in intensely naturally fractured or hydraulically fractured reservoirs. The short payout is a
good characteristic to attract industry’s interest in investing in these projects. One of the
limitations of gas or water injection in tight shale oil reservoirs is that the fluid injectivity
is low due to the nature of very low permeability of shale. EIA (2013) published a report
about the world shale gas and shale oil resource assessment, which provided the oil
recovery efficiency factor of the 28 U.S. tight oil plays. The primary oil recovery factors
in shale oil plays range from 1.2% to 8.4%. Another challenge of gas flooding is that the
injected gas is subject to early breakthrough in densely fractured shale gas or oil
reservoirs, resulting in poor flooding performance. Cyclic gas injection (CGI) in a single
horizontal well is not affected by early gas breakthrough. Compared to gas flooding,
cyclic gas injection is a more effective recovery process in tight shale oil reservoirs. This
dissertation presents our simulation work on using cyclic injection method to improve
shale oil recovery. Core flooding and simulation outputs showed that it is favorable to
implement cyclic gas injection enhanced oil recovery process in shale oil reservoirs.
Enriched-gas displacements are widely used as secondary recovery process because it is
possible to obtain high local displacement efficiencies for enriched gas with reservoir oil
(Johns et al. 2000). Laboratory studies (Zhang et al. 2004) of the effect of CO2 impurities
on oil recovery efficiency showed that the addition of nitrogen or methane in the CO2
injection stream tends to increase the minimum miscibility pressure. Shyeh-Yung (1995)
found that lower solvent mobility may still recover oil efficiently in field displacements
owing to better sweep efficiency compensating for the loss in local displacement
efficiency. Chapter 3 examines the effect of injection gas composition effect in
recovering oil from liquid-rich shales.
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Integrated technologies including argon ion-milled scanning electron microscopy (SEM),
lateral resistivity logging and core analyses were used by Kurtoglu (2013) to characterize
the mineralogy, pore structure and fracture properties in the Bakken formation. She
discovered that micro-fracture network in shale reservoirs provides the main pathway for
injected fluid to transport to the matrix and contact with oil in the matrix. It is concluded
that the well productivity in the Bakken formation is largely attributed to the presence of
micro-fracture network. Landry et al. (2014) used the SEM to study the Eagle Ford shale
matrix-fracture connectivity and they observed two types of fractures. The first type of
calcite filled fracture is thick and short parallel to bedding, which is less likely to
significantly influence fluid flow. The second type of fracture is thin and long oriented
preferentially perpendicular to bedding that contributes to fluid flow and well
productivity in shale reservoirs. Chapter 3 mainly addresses the effect of natural fracture
properties on well production performance during enhanced oil recovery process in shale
oil reservoirs by cyclic gas injection.
Most of the available literature on performance of cyclic gas injection focused on
reservoir conditions that have high permeability. Recent studies (Chen et al. 2014;
Gamadi, et al. 2013; Wan et al. 2013) showed that cyclic gas injection could be a viable
method to improve the oil recovery in shale oil reservoirs. Simulation results showed
cyclic gas injection combined with modern technologies such as horizontal well drilling
and hydraulic fracturing has achieved promising recovery results in low permeability
formation. Kovscek et al. (2008) presented a series of experimental results of using CO2
injection to improve oil recovery in low permeability shale rocks (0.02-1.3 mD).
Countercurrent flow and concurrent injection schemes were employed to evaluate the oil
recovery potential. Their experimental results showed that the incremental oil recovery
from near miscible CO2 injection is around 35%, in which 25% oil recovery obtained for
the countercurrent flow mode and 10% for the concurrent flow. Wang et al. (2013)
reported experimental results of CO2 huff-and-puff process operated in a 973 mm long
composite core with an average permeability of 2.3 mD. Their experimental results
showed that the first operation cycle contributes above half of the total oil production and
additional oil produced from subsequent cycles is significantly decreased compared to
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previous cycles. Kurtoglu (2013) evaluated the feasibility of enhanced oil recovery by
conventional gas injection and gas huff-n-puff in Bakken fields using simulation
techniques. He used a dual-porosity reservoir model to simulate the CO2 huff-n-puff
flooding performance in the Bakken field. Unfortunately, the diffusion effect was not
included in their model because of numerical convergence issues. However, studies
(Javadpour et al. 2007; Sakhaee-Pour and Bryant 2012; Ozkan et al. 2010) suggest that
molecular diffusion is an important recovery mechanism in the mobilization and recovery
of oil in very low permeability shale oil or gas reservoirs. Chapter 4 is dedicated to
investigating the effect of diffusion on shale oil recovery performance by secondary gas
injection process.
Cyclic gas flooding in conventional field applications (Miller et al. 1998; Lino 1994;
Bardon et al. 1986; Gondiken 1987) has been performed successfully. In shale gas or oil
reservoirs, the presence of fissures or induced hydraulic fractures provides highly
conductive paths for injected gas to diffuse or penetrate into the nano-permeable matrix,
which makes it favorable to perform cyclic gas injection. Chen et al. (2014) investigated
the effect of reservoir heterogeneity on CO2 huff-n-puff recovery process using (UT-
COMP) simulation approaches. Gamadi et al. (2013) presented a series of experimental
data of cyclic gas injection in Barnett, Mancos and Eagle Ford shale cores. They
investigated the effect of injection pressure, soaking time and the number of injection
cycles on oil recovery performance by N2 huff-n-puff process. Improved oil recovery
efficiency factor was observed both in their simulation work and experimental work.
However, the available literature provides limited information on the laboratory
examination of the applicability of gas huff-n-puff in very low permeability shale rocks.
Very limited field or laboratory data are available on the performance of cyclic gas
injection in shale oil reservoirs. In this dissertation, chapter 6 focuses on examining the
relevant parameters that affect the performance of cyclic gas injection process in detail.
The principle recovery mechanisms in shale oil reservoirs were discussed. Chapter 6
interrelated numerical simulation approach with the laboratory data to analyze the
significance of possible parameters that have on the performance of cyclic injection
recovery process.
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Sanaei et al. found that desorption has negligible effect on gas and condensate recoveries
from Eagle Ford shales. It is believed that desorption effect has to be considered in shales
with high total organic content (TOC). Otherwise, an underestimation of ultimate gas
recovery is expected. The significance of desorption effect on oil or gas recovery is not
considered in this dissertation.
Objectives
The objectives of this work include:
1. Evaluate the performance of cyclic gas injection to improve oil recovery in liquid-
rich shales using numerical simulation approaches.
2. Evaluate the effects of fracture spacing, the size of fracture-network, fracture
connectivity (uniform and non-uniform) and stress-dependent fracture-network
conductivity on production performance of shale oil reservoirs by secondary
cyclic gas injection.
3. Simulate the importance of molecular diffusion in fractured shale oil reservoirs.
Proper representation of diffusion-convention mass transfer between matrix and
fracture is critical to examining the recovery mechanism in shale reservoirs.
4. Investigate the significance of possible factors on gas huff-n-puff recovery
process in shale oil reservoirs via simulation approaches.
5. Combined simulation approaches with experimental data to show the potential of
cyclic gas injection method in shale oil reservoirs and discuss the diffusion effect
in laboratory-scale flooding in liquid-rich shales.
Our simulation results have demonstrated the potential of gas huff-n-puff injection to
improve oil recovery in shale oil reservoirs. We also examined the effect of diffusion on
improved oil recovery performance by cyclic injection process. Our simulation results
benchmarked with experimental observations showed that molecular diffusion played a
significant role in the mobilization of oil in lab scale. The Computer Modeling Group
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(CMG) software is used in this dissertation to perform the numerical simulation studies.
Organization of this dissertation
Chapter 1 gives an introduction, background and objectives of this study.
Chapter 2 describes the simulation results of the effect of injected fluid compositions on
enhanced oil recovery process.
Chapter 3 primarily discusses the effect of fracture properties on enhanced oil recovery
process by cyclic gas injection. It first only considers the presence of natural fractures.
Then, both natural fracture-network and hydraulic fractures are included to show the
impact of stress-dependent fracture conductivity on gas injection performance.
Chapter 4 presents the effect of diffusion on well productivity performance in liquid-rich
shales by gas flooding. This chapter focuses on discussing the recovery mechanism in
recovering oil in tight shale reservoirs.
Chapter 5 proposes an approach to increase stimulated reservoir volume in shale oil or
gas reservoirs. The design of staggered pattern of zipper frac is to expose more reservoir
rocks to injected solvent. The existence of micro-fracture network or highly conductive
induced fractures is a key factor that impacts oil recovery by secondary cyclic gas
injection.
Chapter 6 presents a numerical simulation study of experimental data in shale oil
reservoirs by cyclic nitrogen injection at an immiscible condition. A model is developed
to achieve a good history matching of the experimental data. Then, this model is used to
evaluate the significance of possible factors that affect the secondary recovery
performance in shale oil reservoirs.
Chapter 7 provides the conclusions of this dissertation, recommendations for future work
and contributions of this study.
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Chapter 2
Modeling of the EOR Process in Stimulated Shale Oil
Reservoirs by Cyclic Gas Injection
2.1. Abstract
Cyclic gas injection is considered as an effective and quick responding recovery process
that has been widely used in the worldwide oil industry. Cyclic gas injection technique
was introduced in our earlier publication (Wan et al., 2013a) for improving oil recovery
in hydraulically fractured shale oil reservoirs. In this chapter, we focus on the effect of
injected gas composition on oil recovery. Different injection gas scenarios such as lean
gas, rich gas and CO2 were included in the simulation models to represent the EOR
mechanisms of vaporizing, condensing or a combined condensing/vaporizing process.
Our simulation results indicate that the stimulated natural fractures are critical to
improving oil recovery and well productivity performance in shale oil reservoirs. Since
the interaction of the induced hydraulic fractures with pre-existing natural fractures and
fissures makes the hydraulically fractured reservoir modeling very challenging in shale
oil/gas reservoirs, a dual-continuum model was used by varying the fracture permeability
and intensity to attain a better characterization of the natural fractures. We conclude that
cyclic gas injection in shale oil reservoirs employing hydraulically stimulated fractures is
feasible to improve substantial amounts of oil production than primary production.
2.2. Introduction
Cyclic gas injection is an efficient enhanced-oil-recovery method in intensely naturally
fractured or hydraulically fractured reservoirs. Artun et al. (2011) used a neural-network
based proxy models to perform the parametric study of the cyclic gas injection process in
order to develop optimized design schemes to maximize the production efficiency of the
process in the naturally fractured reservoirs. Artificial neural networks model has the
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advantage of extracting complex and non-linear relationships, but this black-box does not
represent the actual physics phenomena of reservoir fluid flow and production
performance. Ivory et al. (2010) investigated the cyclic solvent injection process in Cold
Lake and Lloydminster heavy oil reservoirs. Their experiment result indicated that the
potential secondary recovery was 50% by cyclic solvent (28% C3H8 -72% CO2) injection
process after primary production. Haines et al. (1990) studied the cyclic natural gas
injection for enhanced recovery of light oil from waterflooded fields. Their core flooding
and numerical simulation results indicated that response to natural gas huff-n-puff
enhanced oil recovery was a function of cyclic injection process variables, such as cycles
used and gas slug size. They concluded that repressurization and gas relative permeability
hysteresis are the major recovery mechanisms. Alves et al. (1990) used a Peng-Robinson
equation-of-state (EOS) to predict the potential of miscible gas displacement as a
secondary production in tight carbonate formation in which rich gas or lean gas were
used as injection gas. The PR-EOS fluid model has to be tuned and compared with the
PVT experimental data (Moortgat et al. 2009). Vega et al. (2010) performed an
experimental study to investigate the oil recovery from CO2 injection in 1.3 mD
permeability siliceous reservoirs, as they showed the improved oil recovery was
significant. Unfortunately, their compositional simulation results could not reproduce
their experimental results.
Cyclic gas recovery in field applications (Miller et al. 1998; Lino 1994; Bardon et al.
1986; Gondiken 1987) has been performed successfully. The above cases demonstrate
that cyclic gas injection played an important role in enhanced-oil-recovery in
conventional reservoirs. Recent studies (Chen et al. 2013; Gamadi, et al. 2013; Wan et al.
2013) showed that cyclic gas injection could be a viable method to improve the oil
recovery in shale oil reservoirs, but those studies were performed without detailed
analysis of the phase behavior of reservoir oil/injected gas system and the effect of
injected gas compositions on enhanced oil recovery process.
Texas Tech University, Tao Wan, May 2015
8
2.3. Description of Models
The reservoir fluid composition data (There was no field shale oil compositional data
available when this work was done in 2013) is from fifth SPE comparative solution
project published data (Killough et al. 1987) which aimed to illustrate the comparison
results between a four-component black-oil miscible flood simulator and a fully
compositional reservoir simulation model. Later on, Pu (2013) presented the Bakken
reservoir fluid composition data which was collected from a well T23N R58E SEC19 of
the Elm Coulee Field. Kurtoglu (2013) presented the Bakken PVT data in Reunion Bay,
Bailey and Murphy Creek field. She also used the software to tune the fluid composition
data to match the lab measured data. It is very difficult to obtain in-situ fluid composition
data. Fig.2.1 shows the P-T phase envelope and quality fraction lines of reservoir oil
calculated by Heidemann method (1980). Table 2.1 presents the pseudo-component
description and input for Peng-Robinson equation of state calculations. Binary interaction
coefficients were given in Table 2.2. The initial reservoir oil compositions are shown in
Table 2.3 which represent extremely light oil. The injected solvent contained 77% C1, 20%
C3 and 3% C6. Todd and Longstaff (1972) proposed a method of modeling miscible gas
displacement performance without reproducing the fine structure of the flow. One
prominent feature of their method is that it requires modifying the physical properties and
flowing characteristics of the miscible fluids in a three-phase black-oil model. A mixing
parameter is required to determine the degree of miscibility between the miscible fluids
within a grid-block. Fayers et al. (1992) recommended a formula (Eq. 2.1) to quantify the
mixing parameter by comparing the effective mobilities in the Todd-Longstaff model
with the Koval model (1963). Table 2.4 shows the pressure dependent mixing parameter.
� = 1 − 4 log �0.78 + 0.22�������
�� / log������ (2.1)
Texas Tech University, Tao Wan, May 2015
9
Figure 2.1-P-T phase envelopes with varying composition for reservoir fluid
In order to compare the four-component Todd-Longstaff miscible flood model with fully
compositional simulation results, a differential liberation expansion was simulated by
Winprop to generate the black-oil PVT properties that correspond with the EOS
characterization (Killough et al. 1987). The Coats (1982) method uses the conservation
equations to obtain the oil phase properties such as formation volume factor and gas-oil
ratio.
����� + ������ = ����� + ������ (2.2)
������� + ���� = ������� + ���� (2.3)
Where �� = 1/��, �� = 1/��. The stock tank densities are obtained from the output of
the separators at the saturation pressure. �� and �� can be obtained by using Whitson and
Torp (1983) method by flashing the reservoir fluids at surface separation conditions.
Most existing commercial software uses this method to simulate the differential libration
expansion. Table 2.5 presents the black-oil and solvent PVT data generated from Table
Bubblepoint=2302
Critical point
Texas Tech University, Tao Wan, May 2015
10
2.1 EOS characterization.
2.3.1. MMP determination
Local displacement efficiency by gas or solvent injection process is closely dependent
upon the minimum miscibility pressure (MMP). Most gas or solvent injection cases
operate in a regime in which true miscibility is not achieved, but high recoveries could be
possibly attained even so. The analytical method (Wang et al. 1998; Yuan et al.2005) for
MMP determination focused on finding the key crossover tie lines for a dispersion-free
displacement when one of the key tie lines becomes a critical tie-line (a tie line of zero
length). Johns and Orr (1993) developed a method to find the key crossover tie lines from
the geometric construction in the analytical solution to control the development of
miscibility in condensing/vaporizing systems. Jessen (1998) developed an algorithm
based on the key tie line approach to reduce the calculation of MMP time consumption
and improve the method robustness. In this study, we use cell-to-cell simulation method
provided by Winprop to determine the MMP of the given solvent composition and
reservoir fluids. The pseudo-ternary diagram (Fig.2.2) is generated from the calculations
to study the vaporization or extraction process which interprets MMP of solvent as 3440
psi. We also developed a slim-tube simulation model that has good agreement with the
calculated MMP by tie-line method. The grid sensitivity was run by using 10, 20, 50, 100
1D gridblocks to illustrate the effect of numerical dispersion. The slim-tube simulation
results from these different grid-block sensitivity studies show that there is only slight
difference in cumulative oil recovery vs. time if the number of grid-blocks is larger than
100. Then, slim tube displacement simulation was performed at eight different pressures
(Fig.2.3) by employing 100 1D grid-blocks. CO2 is more favorable to achieve miscibility
with reservoir oil than lean gas. The miscibility development is achieved by a combined
vaporizing/condensing mechanism. The miscibility achieved at 2300 psia by pure CO2
displacement calculated by the key tie line method. Yuan et al. (2005) proposed a
correlation to calculate the MMP for CO2 floods that gives us 2617 psi. As compared to
what we get from key tie-line method, the result from correlation overestimates a little bit
Texas Tech University, Tao Wan, May 2015
11
the minimum miscibility pressure.
Figure 2.2-Pseudo-ternary diagram cell-to-cell MMP simulation
Figure 2.3-Slim-tube simulation of recovery for lean gas injection
50
60
70
80
90
100
2000 2500 3000 3500 4000 4500
R.F
. %
at
1.2
PV
Pressure, psi
Recovery at 1.2 PV
Texas Tech University, Tao Wan, May 2015
12
Figure 2.4-Yuan’s MMP correlation graph
Table 2.1. Peng-Robinson EOS Fluid Description
Components Initial Comp.
Pc (atm) Tc (k) Acentric Fac. MW Vc Parachor
C1 0.5 45.44 190.6 0.013 16.04 0.0998 39.84318 C3 0.03 41.94 369.8 0.1524 44.1 0.2005 126.7644 C6 0.07 29.73 507.4 0.3007 86.18 0.3698 250.622 C10-15 0.2 20.69 617.7 0.4885 142.29 0.6297 403.6545 C15-20 0.15 13.61 705.6 0.65 206 1.0423 560.6208 C20+ 0.05 11.02 766.7 0.85 282 1.3412 724.5072
Table 2.2. Binary interaction coefficients
C1 C3 C6 C10-15 C15-20 C20+
C1 zero 0.0 0.0 0.0 0.05 0.05 C3 0.0 zero 0.0 0.0 0.005 0.005 C6 0.0 0.0 zero 0.0 0.0 0.0 C10-15 0.0 0.0 0.0 zero 0.0 0.0 C15-20 0.05 0.005 0.0 0.0 zero 0.0 C20+ 0.05 0.005 0.0 0.0 0.0 zero
Table 2.3. Reservoir Fluid Composition
Comp. Initial Comp. Injection solvent Comp. C1 0.5 0.77 C3 0.03 0.20 C6 0.07 0.03 C10-15 0.2 0.00 C15-20 0.15 0.00 C20+ 0.05 0.00
100
150
200
250
300
350
0
0.1
0.2
0.3
0.4
1500
2000
2500
3000
3500
4000
MWc7+mol% of C2-6
MM
P fo
r C
O2 inje
ctio
n
Texas Tech University, Tao Wan, May 2015
13
Table 2.4. Pressure dependent mixing parameter
Pressure (psi) Oil viscosity Gas viscosity Solvent viscosity � 3500 0.175 0.0214 0.031 0.730605 4000 0.167 0.0232 0.034 0.733841 4500 0.159 0.025 0.037 0.736953 4800 0.155 0.0261 0.038 0.738665
Table 2.5. Black-oil and solvent PVT table
Pressure (PSIA)
GOR (SCF/STB)
Bo Bg (bbl/ft3) Oil Visc (cp)
Gas Visc Solvent Visc
14.7 0.0 1.03480 0.211416 0.310 0.0107 0.011
500.0 117.600 1.10170 0.00592417 0.295 0.0127 0.012
1000.0 222.600 1.14780 0.0028506 0.274 0.0134 0.013
1200.0 267.700 1.16770 0.0023441 0.264 0.0138 0.014
1500.0 341.400 1.19970 0.0018457 0.249 0.0145 0.016
1800.0 421.500 1.23500 0.0015202 0.234 0.0153 0.018
2000.0 479.000 1.26000 0.00136023 0.224 0.0159 0.019
2302.3 572.800 1.30100 0.0011751 0.208 0.0170 0.022
2500.0 634.100 1.32780 0.00110168 0.200 0.0177 0.023
3000.0 789.300 1.39560 0.0009852 0.187 0.0195 0.027
3500.0 944.400 1.46340 0.0009116 0.175 0.0214 0.031
4000.0 1099.500 1.53210 0.0008621 0.167 0.0232 0.034
4500.0 1254.700 1.59910 0.0008224 0.159 0.0250 0.037
4800.0 1347.800 1.63980 0.0008032 0.155 0.0261 0.038
Table 2.6. Reservoir properties for the model input
Initial Reservoir Pressure 6425 psia Reservoir Temperature 320 Fo
Saturation Pressure 2302 psia Rock Compressibility Porosity Permeability of shale
5.0E-06 6 % 100 nano-Darcy
Water Density 62.4 lb/cuft
Texas Tech University, Tao Wan, May 2015
14
Figure 2.5-Effect of solution lean gas on swelling of reservoir fluid
Figure 2.6-Effect of lean gas mole fraction on relative volume
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
2200
2400
2600
2800
3000
3200
3400
3600
3800
4000
0 0.2 0.4 0.6 0.8 1
Sa
tura
tio
n P
ress
ure
, p
sia
Injected lean gas mole composition
Psat
Swelling Factor
0.7
1.2
1.7
2.2
2.7
0 1000 2000 3000 4000 5000 6000 7000
Re
lati
ve
Vo
lum
e
Pressure, psia
Lean gas injection
0, mole fraction
0.2, mole fraction
0.4, mole fraction
0.6, mole fraction
Texas Tech University, Tao Wan, May 2015
15
The swelling test was simulated by varying proportions of injection gas mixed with
original reservoir oil. The initial reservoir pressure is 6425 psi which is far above the
bubble point pressure at 2302 psi. When CO2 or gas dissolves in oil, the liquid volume
will expand and increase. Fig.2.5 shows the effect of lean gas injection on the saturation
pressure. Constant composition expansion experiment (CCE) is started at a pressure
higher than the saturation point. As the pressure is lowered, oil volume expands and is
recorded. When the pressure dropped below the bubble point pressure, the measured
volume will increase rapidly because gas evolves from the oil. CCE simulation can give
us information about the relative volumetric amounts of oil and gas in the reservoir at
various stages of the lifetime of the reservoir (Pedersen et al. 2006). Fig.2.6 illustrates the
effects of injected gas mole fraction on the relative volumes. As injected gas mole
fraction increases, the relative volume goes up because more gas can evolve from the oil
when pressure drops below the bubble-point pressure.
The reservoir rock properties we used in this model are based on the published data in
Eagle Ford shale as was previously used as shown in Table 2.6 (Hsu and Nelson, 2002;
Chaudhary et al., 2008; Bazan et al., 2010; Wan et al., 2013). The initial reservoir
pressure for this field is 6,425 psi. The producer is subject to minimum bottom-hole
pressure constraint (BHP) of 2500 psi and is produced for 1800 days (5 years) as the
natural depletion. Local displacement efficiency by gas or solvent injection process is
related to the controlled BHP that dictates whether local miscible flooding can occur or
not. In this study, we propose cyclic CO2/solvent injection in a single hydraulically
stimulated horizontal well to improve oil recovery in 0.0001-mD shale oil reservoirs. The
horizontal well drilling combined with hydraulic fracturing technique was undertaken in
combination with cyclic gas injection, which indicates as an effective technique for
improving oil recovery in shale oil reservoirs. The dimension of the shale reservoir is
2000-ft long×1000-ft wide×200-ft thick, as shown in Fig.2.7. The horizontal well is
stimulated with 10 transverse fractures each placed 200-ft apart. We will simulate only
one single hydraulic fractured stimulated reservoir volume on the basis of flow symmetry.
Texas Tech University, Tao Wan, May 2015
16
The field cumulative oil production and production rate can be obtained simply
multiplied by the number of effective fractures.
Due to the complex propagation of induced hydraulic fractures in gas shale reservoirs
associated with the interaction of pre-existing fissures or natural fractures, it has been
suggested that modeling the complex fracture system should be classified into three
categories (Cipolla et al., 2008; Cipolla et al., 2009; Dershowitz et al., 2011):
1: Planar hydraulic fractures provide the dominated conductive flow path to the wellbore
if the injected proppant is mainly concentrated in the single hydraulic fracture.
2: Planar hydraulic fractures act as the primary flow path to the wellbore supplemented
by the natural fracture if there is slight amount of frac fluids leakage.
3: Natural fracture-network dominates the flow path in the gas reservoirs if the proppant
is evenly distributed in the entire fracture-network.
According to the classifications of stimulated reservoirs, the reservoir simulation in this
study is separated into two stages:
1. Investigate the cyclic gas injection EOR in naturally fractured shale oil reservoirs,
without induced hydraulic fractures.
2. Investigate the gas huff-n-puff EOR in hydraulic fractures combined with natural
fractures.
Texas Tech University, Tao Wan, May 2015
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Figure 2.7-Reservoir model
2.4. Simulation Results and Discussion
In this study, the dual permeability model was used to simulate the natural fracture-
network. The dual permeability model allows the communication between matrices of the
adjacent grid blocks in addition to the expected inter-block fracture to fracture flows and
the matrix to fracture fluid flow. In the base model, the fracture spacing was assumed to
be 200-ft in both X and Y direction. The natural fracture network is assumed to be
contained within an orthogonal system of continuous, uniformly spaced and constant
width. In each of these blocks, two perpendicularly crossed 0.001-ft wide fracture
(approximately actual fracture width) is assumed to exist which runs through the 200-ft x
200-ft block. Therefore, the average fracture porosity is 0.00001=2xVfrac/Vblock (2x (0.001
x 200 x 200) / (200 x200x200)). The fracture spacing is used to calculate the shape factor
developed by Gilman and Kazemi (1983). Specifying small values of fracture spacing
will result in a large value of shape factor, hence the matrix-fracture transfer rate will also
increase (CMG Manual, 2009).
The simulation model assumes that the SRV consists of a uniform network-fracture with
conductivity of 4 mD-ft (4 mD-ft is a reasonable value used for the conductivity in the
Texas Tech University, Tao Wan, May 2015
18
stimulated volume as discussed by Rubin (2010)). The 200-ft x 1000-ft single fracture
controlled SRV is simulated by using 80 blocks (50 ft x 50 ft). In the case with 4 mD-ft
fracture, the effective fracture permeability equals to 0.08 mD (4/50) which should
conserve the fracture conductivity of 4 mD-ft. The non-Darcy flow effect should be
considered for gas injection in fractured reservoirs. The Forchheimer equations are
written as (Dake, 1978):
� � =�� � + ����, � =
2.73 × 10����.��� (2.4)
� � =�� �1 + ����, �� =
����� (2.5)
Fo is referred to as the Forchheimer Number (CMG Manual, 2009). In actual 0.001-ft
width size fracture model, the fracture permeability is 4000 mD (FCD=4 md-ft), whereas
the effective permeability is 0.08 mD in 50-ft grid-block model to preserve the fracture
conductivity. Similarly, the hydraulic fracture permeability is 83300 mD (Hydraulic
fracture conductivity = 83.3 md-ft) and the effective fracture permeability should be
41.65 mD in a 2-ft width pseudo LS-LGR-DK (Rubin 2010; Wan et al. 2013) model. This
model uses fine grid LGR near the fracture regions so better representation of pressure
and saturation changes could be captured near the fractures. The non-Darcy flow
Forchheimer correction factor was used in this model to ensure the coarse fracture model
will produce similar results to the actual 0.001-ft width fracture model.
Texas Tech University, Tao Wan, May 2015
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Figure 2.8-Simulation model of natural fracture-network (implicit)
Figure 2.9-Effect of numerical dispersion on oil R.F. vs. time
Fig.2.8 shows the base reservoir model using 80 grid blocks of 50 ft x 50 ft to simulate a
unit fracture-network. In this study, we examined the impact of grid refinement on oil
recovery by performing a series of numerical sensitivity calculations. Figs.2.9 and 2.10
illustrate that using course grid-blocks is producing similar results with more refined
0
1
2
3
4
5
6
7
8
9
10
0 1000 2000 3000 4000 5000 6000
Oil
R.F
. %
Time, days
10x50x1
4x20x1
1x5x1
Texas Tech University, Tao Wan, May 2015
20
grid-blocks. The induced numerical dispersion effect is not that significant. The reservoir
initially operates in naturally depletion for 1800 days. Then, we start 20 cycles of
secondary recovery. Each cycle consists of 100 days gas/solvent injection and 100 days
production. The injection rate decreases rapidly in the huff phase due to low permeability
in shale rocks. The minimum bottom-hole pressure of the production well is 2500 psi.
Figure 2.10-Effect of numerical dispersion on C1 prod. vs. time
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
0 1000 2000 3000 4000 5000 6000
C1
pro
du
ced
, l
b
Time, days
10x50x1
4x20x1
1x5x1
Texas Tech University, Tao Wan, May 2015
21
Figure 2.11-Comparison of oil recovery for four component model vs. fully compositional model
Fig.2.11 compares the oil recovery efficiency factor of the four-component model with a
fully compositional model for the scenario that the average reservoir pressure was
maintained well above the original saturation pressure. Although the black-oil model
gives close results with the composition model, the black-oil model lack the ability to
consider the mass transfer between injected solvent and the reservoir oil.
Vaporizing/condensing gas drive process controls the development of miscibility in gas
displacement in which gas extracts intermediate molecular weight hydrocarbon
components from oil and oil may take up components from the gas phase. For immiscible
condition, the black-oil model gives lower oil recovery compared to the compositional
model because the black-oil model can’t carry condensable liquids in the gaseous phase
(Killough et al. 1987). The oil recovery given by the base model is low because of the
sparse fracture spacing (200-ft) and no presence of hydraulic fracture.
0
1
2
3
4
5
6
7
8
9
10
0 1000 2000 3000 4000 5000 6000
Oil
Re
cov
ery
, %
Time, days
Black oil model
Composition model
Texas Tech University, Tao Wan, May 2015
22
Figure 2.12-Comparison of oil R.F. for different fracture spacing
Figure 2.13-Impact of hydraulic fracture on oil R.F.
0
10
20
30
40
50
60
0 1000 2000 3000 4000 5000 6000
Oil
Re
cov
ery
, %
Time, days
100-ft fracture spacing
200-ft fracture spacing
50-ft fracture spacing
0
5
10
15
20
25
0 1000 2000 3000 4000 5000 6000
Oil
Re
cov
ery
, %
Time, days
Hydraulic fractures + natural fracture-network
Fracture-network
Texas Tech University, Tao Wan, May 2015
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Fig.2.12 shows the impact of natural fracture spacing on the ultimate oil recovery factor
by lean gas (77% C1, 20% C3 and 3% C6) huff-and-puff process. As shown in the graph,
reducing fracture spacing from 200-ft to 50-ft would result in an almost six fold increase
in cumulative oil recovery factor. The results show that natural fracture spacing has a
significant impact on enhanced oil recovery. The fracture-network complexity is critical
to well productivity in shale reservoirs because they maximize fracture-surface contact
area with the shale through both size and fracture density (Mayerhofer et al.2010). The
well production performance in shale oil reservoirs is strongly dependent upon the
fracture complexity. Fig.2.13 compares the shale oil recovery from a case that hydraulic
fractures connect to the natural fracture-network and a case that has natural fracture-
network exclusively. The role of hydraulic fractures in well productivity in shale
reservoirs is observed. The introduction of hydraulic fractures on the base model of
natural fracture-network gives much higher oil recovery than the base model (natural
fracture-network). The hydraulic fracture conductivity is set as 83.3 mD-ft. Cipolla (2009)
showed that the presence of highly connected fracture-network can reduce the
requirement for fracture conductivity in shale gas reservoirs. If the fracture density
exceeds beyond a point, the addition of hydraulic fractures would not significantly
improve oil recovery.
Lean gas, rich gas and CO2 injection performance
Texas Tech University, Tao Wan, May 2015
24
Figure 2.14-Extrapolation of recovery to zero grid-block size (slim-tube model)
Fig.2.14 shows effects of numerical dispersion on the component of C6 recovery after 1.2
pore volumes injection (PVI) slim-tube test. It also shows the recovery as a function of
the dilution or enrichment of incipient solvent with C1 or C3. The slim-tube displacements
were performed at 320 ˚F and 2500 psig with high matrix permeabilities. The solvent or
diluted solvent with C1 will become immiscible flooding at this designated pressure,
which is below the required minimum miscibility pressure (3400 psi) for incipient solvent
to be miscible with reservoir fluid. An increase of C1 content in the solvent makes it more
difficult to develop miscibility with oil. Solvent was injected at a rate of 10% pore
volumes per day to ensure a low pressure drawdown and low dispersion. The use of
square root of dimensionless grid block size method will give approximately linear
extrapolation to the correct answer. The dimensionless grid block size is the actual grid
block size divided by the slim-tube length (Stalkup, 1987). For example, use of 100 grid-
blocks with an equal block size of 0.1-ft will result in a square root of dimensionless
block size at 0.1. Fig.2.14 shows that as the incipient solvent is diluted by C1, the
extrapolated recovery of injection gases falls off. However, the addition of 20% C3 in the
incipient solvent gives an effective miscible displacement. It suggests that enrichment of
injected solvent by C3 or heavier components contributes to achieve miscibility with
64
69
74
79
84
89
94
99
0 0.05 0.1 0.15 0.2
C6
reco
ve
ry a
t 1
.2 P
V
Square root of grid-block size (Dimensionless grid block size)
Solvent
Solvent + 50% C1
C1
Solvent + 20% C3
Texas Tech University, Tao Wan, May 2015
25
reservoir oil. The MMP required for the enriched solvent with 20% C3 to form miscibility
with reservoir oil is reduced to 2210 psi. Zick (1986) proposed a vaporizing/condensing
drive mechanism to explain this. The light intermediates of the injection gas condense
into oil when gas first contacts with oil, while the light gas will pick up small amount of
middle intermediates from oil during the gas transportation. The intermediates that were
originally present in the gas combined with those stripped from oil will make the gas
become richer that is able to condense with fresh oil downstream. When injection gas
enrichment exceeds a critical value, displacement behavior of the reservoir fluid becomes
a miscible flooding. It important to notice immiscible displacements are not as efficient
as miscible displacements, but may still recover oil and the gas utilization factor is higher
for the immiscible flooding.
Figure 2.15-Gas production rate (RC) and hydrocarbon pore volume injection
Texas Tech University, Tao Wan, May 2015
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Figure 2.16-Oil production rate and average reservoir pressure (unit fracture)
Figure 2.17-C20 recovery vs. time at 0.2 PVI
0
5
10
15
20
0 1000 2000 3000 4000 5000 6000 7000
C2
0 R
.F ,
%
Time, days
50-ft fracture-network, BHP=2500
C1
CO2
Solvent + 20% C3
Solvent
Texas Tech University, Tao Wan, May 2015
27
Fig.2.15 displays the gas injection rate and the total injected hydrocarbon pore volume.
The injector maintained a injection rate at 240 rft3/day at reservoir condition. In order to
compare the performance of miscible and immiscible flooding, it is critical to keep
injected pore volume the same for each case. For the same injection pressure, different
gas components have different injection rates. The inflow performance relationship in
terms of the volumetric production or injection rate of each phase is expressed as Eq. 2.6
and 2.7.
�,� = ���
� ,��,� ��� − �� −���� (2.6)
��,� = ��,��,�� ��� + ��,��,�� ��,� (2.7)
Fig.2.16 shows daily oil production rate and average reservoir pressure changes during
cyclic gas injection process. In the first few cycles, the average reservoir pressure slowly
builds up and maintains about 4,000 psi which is sufficient for the solvent to achive
miscible displacement with reservoir oil. The oil production rate responses agree with the
average reservoir pressure. After producing several number of cycles, the oil recovery
efficiencies start to decrease. It should be noticed that all the production rate or injection
rate in this study are referred as unit fracture value. Fig.2.17 shows the C6 component
recovery after 20 cycles of gas injection in a 50-ft spacing of fracture-network at 0.01 PV
injection for each cycle. CO2 and solvent enriched with C3 give miscible displacement.
They perform better than incipient solvent or C1. The MMP required for methane to form
miscibility with reservoir oil is too high that can’t happen at current injection case. Even
so, the improved oil recovery due to immiscible methane injection is remarkable.
CO2 Huff-n-Puff
Carbon dioxide is believed to be a favorable injection fluid for implementing EOR
projects because CO2 has the advantage of extracting or vaporizing some hydrocarbon
Texas Tech University, Tao Wan, May 2015
28
components when it comes in contact with reservoir oils. CO2 recovery mechanisms
include CO2 dissolution in the oil leads to oil swelling, oil viscosity reduction,
vaporization of intermediate to heavy hydrocarbons and development of multi-contact
miscibility (Whitson et al).
Figure 2.18-Impact of natural fracture spacing and hydraulic fracture on CO2 huff-puff oil recovery, BHP of producer = 2500 psi
Texas Tech University, Tao Wan, May 2015
29
Figure 2.19-BHP effect on ultimate oil recovery (Fracture spacing = 50-ft)
The results shown in Fig.2.18 indicate that the natural fracture density (spacing) is a
dominant factor that dictates well production performance in shale oil reservoirs by
secondary cyclic CO2 injection. It shows the effect of an increase of fracture
permeabilities by induced hydraulic fractures is not as significant as the increase of
fracture-network densities on shale oil recovery efficiency. In other words, the
conductivity requirement is not as critical as fracture spacing to cyclic gas injection
enhanced oil recovery process. Fig.2.18 shows that when the natural fracture-network
spacing reduced from 200-ft to 50-ft, it results in an increase of 50% oil production.
However, it is important to notice that the model assumes that the conductivity of natural
fracture is constant (4 md-ft). In field cases, without hydraulic fracturing stimulation, the
conductivity of natural fracture is not likely to reach 4 md-ft. More importantly, natural
fracture-network properties (conductivity, density, size) are highly dependent on
hydraulic fracture treatment. For instance, high injection pressure in hydraulic fracture
treatment may cause slip on natural fractures resulting in an increase of the conductivity
of fracture-network. The presence of highly conductive hydraulic fracture also provides
0
1000
2000
3000
4000
5000
6000
7000
0
10
20
30
40
50
60
70
80
90
0 1000 2000 3000 4000 5000 6000 7000
Av
era
ge
re
serv
oir
pre
ssu
re,
psi
a
Oil
re
cov
ery
, %
Time, days
BHP=500
BHP=2500
Average pressure for
BHP=500
Texas Tech University, Tao Wan, May 2015
30
more contact area with injected solvent which is considered as a major influence on
recovery efficiencies. The relationship between natural fracture network properties and
hydraulic fracturing has not been investigated in this work by including a geomechanical
model. The comparisons of results for the various flowing bottom-hole pressure of
producer are presented in Fig.2.19. The reservoir pressure in each gridblock is controlled
above the minimum miscibility pressure with 2500 psi of bottom-hole-flowing pressure.
When the BHP of the producer is 500 psi, the reservoir pressure in most blocks will drop
below the MMP during the production phases. Lowering the bottom-hole pressure has the
advantage of improving inflow performance by gaining higher pressure drawdown, thus
recovering more oil.
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31
Figure 2.20-Reservoir pressure, CO2 mole fraction in oil phase and oil viscosity
variations during CO2 huff-and-puff
Fig.2.20 shows the reservoir pressure, CO2 mole fraction in oil phase and oil viscosity
variations during CO2 huff-n-puff process at time 0, after 1800 days of primary recovery,
after the first 100 days of injection, after the first 100 days of production and after 20
cycles of secondary recovery. As cyclic CO2 injection continues in 20 cycles, there is
increasing amount of CO2 retained in the oil phase as shown in Fig.2.19. This loss of CO2
in the oil production cycle is actually a form of geological storage as CO2 will be
contained within the reservoir (Whittaker et al. 2013). With an increase the amount of
CO2 dissolved in oil, it results in an oil viscosity reduction from initial 0.295 cp to 0.08
cp that allows the oil to flow more easily toward the production well. CO2 cycling
process can be repeated several times, but the oil recovery efficiency decreases (Fig.2.21).
It is important to determine an optimized injection length in each cycle to make the
displacements most efficient. Long injection cycle will result in a loss of production time
during the injection period. Oil will be pushed far away from the fractures by long
injection time that makes it more difficult to produce back, which is not a good strategy.
Texas Tech University, Tao Wan, May 2015
32
Figure 2.21-Period oil production (unit fracture production, entire horizontal well production should be 20 times)
2.5. Summary
The objective of this chapter is to evaluate the response of cyclic gas injection as an
enhanced-oil-recovery method in intensely naturally fractured and hydraulically fractured
reservoirs using a compositional CMG model. The compositional model was compared
with generated black-oil model. They give similar results in the case of miscible
displacements. Detailed reservoir black-oil and gas condensate PVT characterization
analysis was performed to illustrate the lean gas injection and CO2 injection mechanisms.
A series of slim-tube simulations were performed to study the interaction of injected
solvent with reservoir oil. Miscible and immiscible gas flooding performances were
compared at the same injection pore volumes for each case. When injection solvent
enrichment exceeds a certain value, miscible displacement can happen at a lower pressure.
Enriched solvent or CO2 performed better than pure solvent or C1 because it is difficult
for C1 to develop miscible displacement with reservoir oil at a low pressure.
Texas Tech University, Tao Wan, May 2015
33
The impact of fracture densities of fracture network on oil recovery were investigated in
this chapter, the simulation results indicate that smaller fracture spacing is crucial to
improving oil recovery in shale oil reservoirs. The primary decision in designing fracture
treatment in shale oil reservoirs is to exploit fracture complexity. The role of hydraulic
fractures should be honored because hydraulic fracture treatment induces the fracture
complexity to shale reservoirs. CO2 cycling process can be repeated several times, but the
oil recovery efficiency decreases with the increase of cycles. Next chapter will focus on
investigating the effect of stress-dependent natural-fracture network permeability on
cyclic gas injection performance in shale oil reservoirs.
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34
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Texas Tech University, Tao Wan, May 2015
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Chapter 3
Evaluation of the EOR Potential in Fractured Shale Oil
Reservoirs by Cyclic Gas Injection
3.1. Abstract
The current technique to produce shale oil is to use horizontal wells with multi-stage
stimulation. However, the primary oil recovery factor is only a few percent. The low
recovery and the abundance of shale reservoirs provide huge potential for enhanced oil
recovery.
Well productivity in shale oil and gas reservoirs primarily depends upon the size of
fracture network and the stimulated reservoir volume (SRV) which provides highly
conductive conduits to communicate the matrix with the wellbore. The natural fracture
complexity is critical to the well production performance and it also provides an avenue
for injected fluids to displace the oil. However, the disadvantage of gas flooding in
fractured reservoirs is that injected fluids may break through to production wells via the
fracture network. Therefore, a preferred method is to use cyclic gas injection to overcome
this problem.
In this chapter, we used a numerical simulation approach to evaluate the EOR potential in
fractured shale oil reservoirs by cyclic gas injection. Simulation results indicate that the
stimulated fracture network contributes significantly to the well productivity via its large
contact volume with the matrix, which prominently enhances the macroscopic sweep
efficiency in secondary cyclic gas injection process. In our previous simulation work, the
EOR potential was evaluated in hydraulic planar traverse fractures without considering
the propagation of natural fracture-network. In this chapter, we examined the effect of
fracture networks on shale oil secondary production performance. The impacts of fracture
spacing and stress-dependent fracture conductivity on the ultimate oil recovery are
investigated. The results presented in this chapter demonstrate an EOR potential by cyclic
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gas injection in shale oil reservoirs. The objective of this chapter focus on evaluating the
effects of fracture spacing, the size of fracture-network, fracture connectivity (uniform
and non-uniform) and stress-dependent fracture-network conductivity on production
performance of shale oil reservoirs by secondary cyclic gas injection.
3.2. Introduction
This chapter builds upon and extends our earlier work that investigated the impact of the
planar fractures on the enhanced oil recovery by cyclic gas injection (Wan et al. 2013).
We investigated the impact of different well operating schedules (injection time and
production time in each cycle), degree of in-situ miscibility between solvent with oil and
propped planar hydraulic fractures spacing on the ultimate oil recovery. The previous
work mainly focused on examining the effects of planar hydraulic fractures on the well
primary production and secondary gas injection performance without taking into account
of the contribution from fracture-network.
In naturally fractured shale formations, the fracture network permeability is sensitive to
changes in stress, especially by secondary gas injection process (Palmer and Mansoori,
1998). There are some difficulties in simulation of fluid flow in unconventional reservoirs
because a large permeability contrast exists between the hydraulic fractures and their
neighboring tight shale matrix (Moinfar et al., 2013). Rubin (2010) used a logarithmically
spaced, finely-gridded local grid refinement (LGR) method based on dual permeability
model to model gas flow from unconventional shale gas reservoirs inside the SRV region.
Unrefined dual permeability grids were used to simulate the fluid flow outside the SRV
because of low pressure drop. The LS-LR-DK (logarithmically spaced, locally refined
and dual permeability) model is able to accurately simulate the gas flow in fractured shale
gas reservoirs. Rubin also compared the results with the actual 0.001-ft width fracture
reference model and they matched very well. The fracture-network characterization in
shale reservoirs is a challenging task because the location of proppant transported by
fracturing fluids and fracture conductivity are difficult to be determined. The initial
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microseismic-mapping work in the Barnett shale (Fisher et al. 2002; Fisher et al. 2004)
has shown that the fracture propagation can be highly unpredictable and complex,
ranging from simple planar fractures to very complex fracture systems. In horizontal well
multiple-cluster completions, the propagation of subsequent fractures is affected by the
reorientation of stress field from the previous propagated fractures. The occurring of
stress shadow effect in multiple clusters of fracturing completion makes the simulation of
hydraulic fracturing in naturally fractured shale plays more complex. The drawback of
using a single-porosity approach to simulate intensely fractured shale oil reservoirs is that
it would require tremendous number of fine grid-block when fracture-network spacing is
narrow. The advantage of LS-LR-DK model over a single-porosity model appears in the
modeling of low permeability shale reservoirs because it can produce similar results as
the finely gridded single-porosity model with much less LGR grids as showed by Rubin
(2010). The LS-LR-DK model can be implemented with significantly less number of
grids in comparison with an explicit representation of both matrix and fracture regions.
Chen et al. (2013) investigated the effect of reservoir heterogeneity on improved shale oil
recovery by CO2 huff and puff. In their simulation model, they used the log-normally
distributed permeability field and Dykstra-Parsons function to represent the permeability
heterogeneity in shale reservoirs. However, this permeability heterogeneity representation
cannot physically simulate the fracture distributions, fracture network complexity and
fluids flow in naturally fractured reservoirs. Cipolla et al. (2008) examined the effect of
fracture characteristics on gas well performance. In their fracture model, two types of
fractures were assumed based on proppant distribution: planar fracture and fracture-
network in which proppant was evenly distributed in.
Their reservoir simulation results indicated that with an increase of fracture complexity it
requires lower conductivity to achieve the same production compared with production
from simple fracture system. Cipolla et al. (2011) and Moinfar et al. (2013) showed that
the well primary productivity was significantly affected by stress-dependent network-
fracture conductivity in shale gas reservoirs. Tremendous work presented approaches for
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reservoir modeling the primary production in naturally fractured shale gas reservoirs
(Mayerhofer et al. 2006; Cipolla et al. 2010). But limited work was dedicated to
addressing the impact of fracture network characteristics on improved oil recovery
process in shale oil reservoirs. The objective of this chapter is to investigate the
applicability of cyclic gas injection in very low permeability shale oil reservoirs with the
LS-LR-DK approach and examine the characteristics of fracture network on enhanced oil
recovery process.
In this chapter, the simulation results showed that in the uniform conductivity of
unpropped fracture-network, production from shale oil reservoirs is affected by the
reduction of natural fracture conductivity due to stress effect, resulting in a lower
recovery. However, the presence of highly conductive propped hydraulic fractures that
connect to the natural fractures can compensate the recovery loss due to reduction of the
network-fracture conductivity by stress effect.
3.3. Model Setup and Validation
The LS-LR-DK method is used to simulate the fluid flow in fractured shale oil reservoirs.
In this chapter, we use finely gridded dual permeability LGR cells to model the fracture-
network and hydraulic fractures inside the SRV region. We simply use coarse dual
permeability grids to model the non-SRV region because the pressure drop in non-SRV is
low. The use of coarse dual permeability grids is enough to capture the low pressure drop
resolution in the shale non-SRV. The structure of this chapter is first to examine the effect
of uniform conductivity of fracture network inside the SRV on improving shale oil
recovery by cyclic gas injection scheme. We then consider the effect of non-uniform
conductivity of fracture-network by including hydraulic fractures that connect the natural
fracture complexity.
The reservoir rock and fluid properties used in this model are based on the published data
in Eagle Ford shale as previously used (Bazan, 2010). The initial reservoir pressure for
this field is 6,425 psi. The producer is subject to minimum bottom-hole pressure
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constraint (BHP) of 2500 psi and opened production for 1800 days (5 years) as primary
production. The bubble point pressure of the reservoir is 2398 psi. The reservoir rock
properties are referred to Table 3.1 (Wan et al. 2013). The reservoir fluid properties are
presented in Table 3.1. The injection gas is comprised of the produced gas from the
reservoir and the gas produced from previous gas lift injection. The dimension of
fracture-network is 2,000 ft×1,000 ft. We assume the fracture network is orthogonal to
each other and centered in the grid-blocks. The simulation model assumes that the SRV
consists of a uniform network-fracture with a conductivity of 4 mD-ft (this value is
reasonable according to Cipolla et al. 2008). The fracture network is assumed to be
contained within an orthogonal system of continuous, uniformly spaced and constant
width. The distance between network-fracture in both the X and Y directions is 200 ft in
the base model. Later on we will examine the fracture spacing effect on the enhanced oil
recovery performance. The local grid refinement is applied to discretize each large 200
ft×200 ft block in the SRV region into 9×9 logarithmically spaced smaller cells, as shown
in Fig.3.1. In each of these cells, two perpendicularly crossed fractures are assumed to
exist. We performed grid sensitivity study using finer grids with better resolution. It is
found that further grid refinement does not affect the numerical simulation results. We
use a computationally efficient 2-foot width pseudoization fracture model for
representing the fluids flow in the fracture (Better discussion referred to Rubin, 2010).
The shale matrix permeability is 0.0001 mD. The 2-foot width fracture pseudoization
model was already tested to be able to accurately simulate the fluid flow in fractured
shale gas reservoirs by including the non-Darcy flow correction (CMG Manual, 2009).
The 2-foot width fracture model is validated against the reference model that represents
fractures by narrow 0.001-ft width cells and they both produce similar results. The 2-foot
width fracture pseudoization model has to preserve the same fracture conductivity as the
standard fracture model. The fracture permeability of the 0.001-ft width fracture model is
4000 mD (FCD=0.001×4000=4 mD-ft). Thus, the permeability of the 2-ft pseudoization
fracture model is 2 mD (FCD=2×2=4 mD-ft).
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Table 3.1. Eagle Ford Fluid properties
Pressure (psi) Rs (ft3/bbl) Bo (bbl/STB) Eg (ft3/bbl) Oil viscosity (cp) Gas viscosity (cp)
14.69 4.68 1.09917 4.10159 0.9026 0.013601
332.47 65.28 1.12711 95.3676 0.7194 0.013905
650.24 140.36 1.16295 191.364 0.5973 0.014385
968.02 223.32 1.20393 291.506 0.5154 0.015001
1285.79 311.98 1.24913 394.75 0.4567 0.015745
1603.57 405.21 1.29803 499.604 0.4126 0.016612
1921.34 502.26 1.3503 604.264 0.3779 0.01759
2239.11 602.62 1.40566 706.874 0.3498 0.018664
3218.4 929.14 1.59372 995.379 0.2889 0.022371
4859.2 1521.47 1.95964 1360.49 0.2299 0.028854
6500.0 2193.14 2.37939 1609.67 0.1946 0.034795
3.4. Simulation Results and Discussion
We only simulated one branch of fractures instead of modeling the entire stimulated
reservoir volume considering the flow symmetry, as illustrated in Fig.3.1. The implicit
assumption is that the fracture-network has the identical properties such as the same the
same geometric shape, spacing, aperture and the same conductivity. Although these
parameters may vary substantially in real reservoirs, the more effective simulation of
complex fracture networks or fracture connectivity by using discrete fracture-network
models (DFN) is out of the scope of chapter. We specified the maximum surface solvent
rate of 80 Mscf/day for injector in a unit (200-ft×1000-ft×200-ft) SRV region and the
maximum allowable surface injection pressure is 6000 psi. For primary production stage,
the producer opens for producing 1800 days. Then, the well operation mode changes into
huff-n-puff for secondary recovery. In the huff-n-puff process, each cycle consists of 100
days of solvent injection and 100 days of producing. We implement 60 cycles of cyclic
gas injection after 1800 days of primary production. Fig.3.1 and Fig.3.2 showed the
pressure distribution after the 60 cycles of gas huff and puff for the entire stimulated
fracture-network with 200-ft spacing and 100-ft spacing, respectively.
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Figure 3.1-Stimulated fracture-network in SRV (Dx=200)
Figure 3.2-Stimulated fracture-network in SRV (Dx=100)
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Figure 3.3-Impact of stimulated fracture-network spacing on EOR
The uniform fracture conductivity (4 mD-ft) of stimulated fracture network with 200-ft
spacing is displayed in Fig.3.1. Fig.3.3 compares the production profiles for a network-
fracture with spacing of 200-ft to 100-ft. After 60 cycles of cyclic gas injection, the oil
recovery is increased from 5.5% of primary recovery to 45% of ultimate oil recovery for
the 200-ft spacing of fracture-network, while it obtains 70% of ultimate oil recovery for
100-ft spacing of fracture-network. The oil response after 60 cycles of gas injection is
encouraging. The injected gas increases the reservoir energy and dissolves in the crude
oil to reduce its viscosity. In shale oil reservoirs, increasing the fracture-network density
is one avenue to increase the contact volume between injected solvent and shale matrix.
The above simulation results indicate that the stimulated network-fracture spacing has a
significant impact on the production performance of shale oil reservoirs. The stimulated
fracture-network density and complexity are the key components that dictate the well
productivity in shale oil reservoirs.
Impact of Fracture-network Conductivity on Cyclic Gas Injection EOR
Performance
0 2000 4000 6000 8000 10000 12000 140000
10
20
30
40
50
60
70
80
Time (days)
OO
IP r
eco
very
fa
cto
r, %
Entire SRV, DX=100-ft
Entire SRV, DX=200-ft
Simulation unit, DX=100-ft
Simulation unit, DX=200-ft
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Figure 3.4-Impact of the conductivity of fracture-network in the SRV on production profiles
Fig.3.4 presents the results of the conductivity of fracture-network in the SRV on ultimate
oil production profiles by cyclic gas injection. It is noted that the impact of fracture-
network conductivity is not as significant as stimulated fracture spacing. For 100-ft
spacing fracture networks, there is roughly 6% more oil production due to an increase of
fracture conductivity from 2 md-ft to 4 md-ft, while more than 27% oil production could
be achieved with fracture network spacing decrease from 200-ft to 100-ft.
Impact of Stress-Dependent Conductivity of the Uniform Conductivity
Fracture-Network on Production Performance
Production performance of shale reservoirs is primarily dictated by the size or
conductivity of network of fractures. It is critical to consider the productivity losses in
reservoirs due to stress-dependent fracture-network permeability reduction. If the stress-
dependent fracture-network conductivity reduction process is irreversible, the
conductivity will never be able to recover its initial value with the decreasing of closure
pressure (Fig.3.5). On the other hand, if we assume the stress-dependent fracture-network
0 2000 4000 6000 8000 10000 12000 140000
10
20
30
40
50
60
70
80
Time (days)
OO
IP r
eco
ve
ry fa
cto
r, %
4 mD-ft, DX=100-ft
2 mD-ft, DX=100-ft
4 mD-ft, DX=200-ft
2 mD-ft, DX=200-ft
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conductivity reduction process is reversible, the fracture conductivity will return to its
initial value with the increase of reservoir pressure by gas injection.
Figure 3.5-Irreversible process of fracture conductivity reduction
Cipolla et al. (2010) studied the impact of stress dependent network-fracture conductivity
on well performance in shale gas reservoirs. Their simulation results showed that the well
productivity in many shale gas reservoirs is impaired by the insufficient fracture
conductivity. Raghavan and Chin (2004) presented correlations to evaluate the stress-
induced productivity losses in stress-sensitivity reservoirs. However, their focus was on
investigating the effect of pressure-dependent rock-matrix permeability, not the fracture-
network permeability, on production performance. Cho et al. (2012) used the
experimental data from Bakken cores to screen and calibrate the correlations proposed by
Raghavan and Chin (2004). They modified those correlations and applied them to analyze
the effect of fracture permeability on the productivity of shale gas reservoirs. The
modified model prediction obtained a good history match to the performance of field
examples. The correlation developed by Cho et al. (2012) is used in this chapter to
provide us a clear understanding about stress-dependent fracture-network permeability
effect on EOR performance in shale oil reservoirs.
Texas Tech University, Tao Wan, May 2015
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Figure 3.6-Stress-dependent FCD of fracture-network effect on EOR
Fig.3.6 illustrates that the stimulated fracture-network spacing has more significant effect
on improved oil recovery than stress-dependent conductivity. For 100-ft spacing uniform
fracture-network, the oil recovery loss due to the irreversible reduction of fracture
conductivity is roughly 10% compared to the case without stress-dependent effect.
However, the secondary shale oil recovery from 100-ft spacing fracture-network is
substantially higher than 200-ft scenarios.
Impact of Stress-Dependent Conductivity of the Non-Uniform Conductivity
Fracture-Network on Production Performance
0 2000 4000 6000 8000 10000 12000 140000
10
20
30
40
50
60
70
80
Time (days)
OO
IP r
eco
very
fa
cto
r, %
Without stress effect, DX=100-ft
Reversible stress effect, DX=100-ft
Irreversible stress effect, DX=100-ft
Without stress effect, DX=200-ft
Reversible stress effect, DX=200-ft
Irreversible stress effect, DX=200-ft
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Figure 3.7-Permeability distribution for the non-uniform fracture network
Fig.3.7 presents the fracture conductivity distribution for the non-uniform fracture
network. The unpropped network fracture (blue lines) conductivity in the SRV remains to
be 4 mD-ft. The hydraulic fracture (red lines) conductivity is 83.3 mD-ft. The non-
uniform conductivity of fracture-network is represented by the infinite conductivity
primary hydraulic fractures connecting with finite conductivity natural fractures.
Figure 3.8-Effect of stress-dependent FCD on EOR performance
Fig.3.8 considers the effect of stress-dependent conductivity of the non-uniform
0 2000 4000 6000 8000 10000 12000 140000
10
20
30
40
50
Time, days
Ulti
mate
Oil
R.F
., %
Without stress effect
Stress-dependent FCD
Irreversible Stress-dependent FCD
Dx=200
PrimaryRecovery
Secondary Recovery
Hydraulic fractures communicate with natural fracture network
FCD=83.
FCD=83.3
FCD=4
Texas Tech University, Tao Wan, May 2015
52
conductivity fracture-network on well performance in tight shale reservoirs. The
simulation results from Fig.3.8 indicate that the impact of stress-dependent fracture
conductivity on ultimate oil recovery is inconsequential in the presence of hydraulic
fractures communication with the fracture-network. As compared to Fig.3.6, without the
presence of highly conductive propped fractures, the results illustrate that the existence of
highly conductive hydraulic fractures connecting to the fracture- network complexity can
reduce the requirement for fracture conductivity. When the long, narrow and relatively
high conductivity closely spaced directional fracture swarms are communicated,
hydraulic fracture length requirements are very minimum and highly conductive fractures
are not demanded (Gale et al. 2007). In many shale gas reservoirs, the primary gas
production is strongly dependent on fracture conductivity. However, it is interesting to
note that the well completion design for improving oil recovery in shale oil reservoirs by
secondary cyclic gas injection is different from the primary production from shale gas
reservoirs. The fracture complexity is more critical than fracture conductivity in the
cyclic gas injection enhanced-oil-recovery process. When a certain level of fracture
conductivity has been reached, increasing the fracture conductivity has a diminishing
effect on improved oil recovery. The simulation results from Fig. 3.8 indicate that the
conductivity of fracture network is not the dominating factor anymore if the fracture
networks were communicated with the propped hydraulic fractures.
The Effect of Stimulated Fracture-Network Size on Cyclic Gas Injection
EOR Performance
Texas Tech University, Tao Wan, May 2015
53
Figure 3.9-Non-SRV embedded in SRV (Hydraulic fracture spacing = 400-ft)
Fig.3.9 shows a realistic case in the field in which the stimulated rock volume is not
always continuous and is separated by the non-SRV region. The hydraulic fracture
spacing is dependent upon the number of fracture treatment stages along the lateral.
Implementing more fracturing stages results in smaller fracture spacing. Exploiting the
fracture complexity in unconventional reservoirs is a main consideration for improving
shale oil recovery by secondary cyclic gas injection as discussed. Slick water has been
the primary fracturing fluid in treating shale gas reservoirs because low-viscosity water
leaks off easily to fracture networks to widen the zone of stimulation away from a single
fracture plane. Fig.3.9 presented a case of 400-ft spacing hydraulically fractured shale
model in which 200-ft interval is not stimulated between two adjacent fracturing
stimulated volumes. The fracture permeability in non-SRV is 0.001-mD that is 10 times
higher than the shale matrix. The relationship between hydraulic fracturing spacing and
the well performance is illustrated in Fig.3.10. The results indicate that the hydraulic
fracturing spacing is a dominant factor that dictates secondary well performance because
the size of induced natural fracture-network closely depends on the spacing of hydraulic
fracturing treatment.
Non-SRV
HF=400
Texas Tech University, Tao Wan, May 2015
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Figure 3.10-Hydraulic fracture spacing effect on EOR
Figure 3.11-Fracture scenarios effect on EOR (DX=200-ft)
The results in Fig.3.11 show that the ultimate shale oil recovery from planar hydraulic
fracture is approximately 30% by gas huff-n-puff process that is consistent with the
results of previous work (Wan et al.2013). Fig.3.11 shows that the ultimate oil recovery
from a system of hydraulic fractures connected to natural fractures is the highest, which
obtains about 20% more oil recovery than production exclusively from propped hydraulic
fractures. There is slight difference between production from fracture-network connecting
to hydraulic fractures system and exclusive fracture-network system. The priority in
optimizing fracture treatments for improving oil recovery in shale oil reservoirs should be
maximizing the fracture-network complexity.
0 2000 4000 6000 8000 10000 12000 140000
10
20
30
40
50
60
Time (days)U
ltim
ate
sh
ale
oil
reco
ve
ry fa
cto
r, %
HF spacing=200-ft
HF spacing =400-ft
HF spacing=600-ft
0 2000 4000 6000 8000 10000 12000 140000
5
10
15
20
25
30
35
40
45
50
Time (days)
Ulti
ma
te s
hale
oil
reco
ve
ry fa
cto
r, %
HF + NF
Fracture-network only
Hydraulical fractures only
Texas Tech University, Tao Wan, May 2015
55
3.5. Conclusions
Natural fractures are critical to the well productivity in shale oil reservoirs. A distinct
feature of exploiting shale oil reservoirs by secondary cyclic gas injection is that the
fracture-network spacing is more critical than fracture conductivity. The fracture-network
spacing is predominating and plays a more important role than fracture network
conductivity in enhancing oil recovery.
If the an infinite conductivity primary hydraulic fractures were connected to a finite
conductivity network, the hydraulic fracture or fracture-network conductivity reduction
due to stress effect will not result in significant ultimate oil recovery loss by using cyclic
gas injection technique.
The priority in designing fracture treatments for enhancing oil recovery in shale oil
reservoirs should be maximizing the fracture network complexity rather than spending
money by pumping large volumes of proppant to maximize the fracture conductivity. It is
recommended that creating dense fracture spacing in shale oil reservoirs will lead to more
effective completion designs and bring more productivity to enhanced oil recovery by
cyclic gas injection process.
The main well productivity is from the stimulated reservoir volume (SRV). The number
of hydraulic fracturing treatment stages is crucial to improving the stimulated rock
volume, which provides the main drainage area and the flow conduits for hydrocarbons.
The role of diffusion in a field-scale displacement on enhanced-oil-recovery process by
cyclic gas injection will be discussed in next chapter.
3.6. References
Bazan, L.W., Larkin, S.D., Lattibeaudiere, M.G., and Palisch, T.T. 2010. Improving
Production in the Eagle Ford Shale with Fracture Modeling, Increased Fracture
Conductivity, and Optimized Stage and Cluster Spacing Along the Horizontal
Texas Tech University, Tao Wan, May 2015
56
Wellbore. SPE 138425 presented at Tight Gas Completions Conference, San Antonio,
Texas, USA, 2-3 November 2010. Doi: 10.2118/138425-MS.
Chen, C., Balhoff, M., and Mohanty, K. K. 2013. Effect of Reservoir Heterogeneity on
Improved Shale Oil Recovery by CO Huff-n-Puff. Paper SPE 164553 presented at the
SPE Unconventional Resources Conference - USA, Apr 10 - 12, The Woodlands, TX,
USA. Doi: 10.2118/164553-MS.
Cho, Y., Apaydin, O.G., and Ozkan, E. 2012. Pressure-Dependent Natural-Fracture
Permeability in Shale and its Effect on Shale-Gas Well Production. Paper SPE 159801
presented at SPE Annual Technical Conference and Exhibition, 8-10 October 2012,
San Antonio, Texas, USA. Doi:10.2118/159801-MS.
Cipolla, C.L., Warpinski, N.R., Mayerhofer, M.J., Lolon, E.P., and Vincent, M.C. 2008.
The Relationship Between Fracture Complexity, Reservoir Properties, and Fracture
Treatment Design. Paper SPE 115769 presented at the SPE Annual Technical
Conference and Exhibition, Denver, 21–24 September. Doi: 10.2118/115769-MS.
Cipolla, C.L., Lolon, E.P., Erdle, J.C., and Rubin,B. 2010. Reservoir Modeling in Shale-
Gas Reservoirs. SPEREE 13(04): 638-653. Doi: 10.2118/125530-PA.
Cipolla, C., Weng, X., Mack, M., Ganguly, U., Gu, H., Kreese, O., and Cohen, C. 2011.
Integrating micro-seismic mapping and complex fracture modeling to characterize
fracture complexity, SPE 140185 presented at the SPE Hydraulic Fracturing
Technology Conference, The Woodlands, Texas, 24-26 January. Doi: 10.2118/140185-
MS.
CMG Manual. 2009. Modelling Non Darcy Flow in Hydraulic Fractures Accurately
Using a Grid Based Approach. IMEX, Advanced Oil/Gas Reservoir Simulator Version,
PP. 167-190 .
Fisher, M.K., Wright, C.A., Davidson, B.M., Goodwin, A.K., Fielder, E.O., Buckler, W.S.,
Texas Tech University, Tao Wan, May 2015
57
and Steinberger, N.P. 2002. Integrating Fracture Mapping Technologies to Optimize
Stimulations in the Barnett Shale. Paper SPE 77441 presented at the SPE Annual
Technical Conference and Exhibition, San Antonio, Texas, USA, 29 September–2
October. Doi: 10.2118/77441-MS.
Fisher, M.K., Heinze, J.R., Harris, C.D., Davidson, B.M., Wright, C.A., and Dunn, K.P.
2004. Optimizing Horizontal Completion Techniques in the Barnett Shale Using
Microseismic Fracture Mapping. Paper SPE 90051 presented at the SPE Annual
Technical Conference and Exhibition, Houston, 26–29 September. Doi:
10.2118/90051-MS.
Gale, J.F., Reed, RM, and Holder, J. 2007. Natural fractures in the Barnett Shale and their
importance for hydraulic fracture treatments. AAPG Bulletin 91(4) : 603-622.
Mayerhofer, M.J., Lolon, E.P., Youngblood, J.E., and Heinze, J.R. 2006. Integration of
Microseismic-Fracture-Mapping Results With Numerical Fracture Network
Production Modeling in the Barnett Shale. Paper SPE 102103 presented at the SPE
Annual Technical Conference and Exhibition, 24-27 Septembe, San Antonio, Texas,
USA. Doi: 10.2118/102103-MS.
Moinfar, A., Varavei, A., Sepehrnoori, K., and Johns, R.T. 2013. Development of a
Coupled Dual Continuum and Discrete Fracture Model for the Simulation of
Unconventional Reservoirs. Paper SPE 163647 presented at the SPE Reservoir
Simulation Symposium, The Woodlands, TX, USA, Feb 18 – 20. Doi:
10.2118/163647-MS.
Palmer, I., Mansoori, J. 1998. How Permeability Depends on Stress and Pore Pressure in
Coalbeds: A New Model. SPE Reservoir Evaluation & Engineering 1(06):539-544.
Doi: http://dx.doi.org/10.2118/52607-PA.
Raghavan, R., and Chin, L.Y. 2004. Productivity Changes in Reservoirs With Stress-
Dependent Permeability. Paper SPE 88870 presented at the SPE Annual Technical
Texas Tech University, Tao Wan, May 2015
58
Conference and Exhibition, 29 September-2 October, San Antonio, TX. Doi:
10.2118/88870-PA.
Rubin, B. 2010. Accurate Simulation of Non Darcy Flow in Stimulated Fractured Shale
Reservoirs. Paper SPE 132093 presented at Western Regional Meeting, Anaheim,
California, USA, 27-29, May. Doi: 10.2118/132093-MS.
Wan, T., Sheng, J., Soliman, M.Y. 2013. Evaluation of the EOR Potential in Shale Oil
Reservoirs by Cyclic Gas Injection. Paper SPWLA-D-12-00119 presented at the
SPWLA 54th Annual Logging Symposium held in New Orleans, Louisiana, June 22-
26, 2013.
Texas Tech University, Tao Wan, May 2015
59
Chapter 4
Compositional Modeling of the Diffusion Effect on EOR
Process in Fractured Shale Oil Reservoirs by Gas Flooding
4.1. Abstract
Gas injection is considered as an effective recovery process that has been widely used in
the worldwide. There are limited pilot field projects conducted on EOR process by gas
injection in shale oil reservoirs. Although many studies have been conducted on gas
injection in tight gas or oil reservoirs, the main recovery mechanism in shale oil
reservoirs is not well understood. Diffusion plays an important role in the oil recovery
process in fractured shale reservoirs. Most of the current studies on diffusion are
performed in such a way that the producing pressure is equal to the initial reservoir
pressure or core pressure, thus, the convective displacement is eliminated or minimized.
One of the challenges is to evaluate the role of diffusion in field scale displacements in
the presence of viscous flow. This chapter discusses the role of diffusion in improving oil
recovery in fractured shale oil reservoirs.
Hoteit and Firoozabadi (2009) investigated the diffusion effect on recovery performance
in a fractured gas/condensate reservoir. Their simulation results showed that molecular
diffusion has a significant effect on gas recovery if the reservoir pressure is below the
minimum miscible pressure. Modeling of the diffusion effect on ultimate oil recovery in
extensively fractured shale reservoir is crucial to the development of these marginal shale
oil or gas projects. Evaluation of the recovery contribution from diffusion will provide
important insights into the recovery mechanisms in intensely fractured shale gas/oil
reservoirs. Currently, a majority of the diffusion models were developed on the basis of
the single-porosity model that demands tremendous grid refinement in intensely fractured
shale oil reservoirs. The grid refinement is necessary surrounding the fracture
intersections that makes the system become computationally expensive. In this chapter,
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60
the matrix-matrix and matrix-fracture diffusion is coupled in a dual permeability model to
overcome the drawback of single-porosity model. The simulation results demonstrate that
the enhanced oil recovery by gas injection process in the Eagle Ford shale oil reservoir
will benefit from matrix-matrix and matrix-fracture molecular diffusion.
4.2. Introduction
It is recognized that diffusion is an important recovery mechanism in recovering oil by
gas injection in fractured reservoirs (Coats, 1989; Da Silva et al. 1989; Karimaie et al.
2007; Morel et al.1990). Ertekin et al. (1986) derived a slippage factor under the
assumption that the driving mechanisms exerted by the concentration and pressure field
are acting in parallel. The combination of Darcian flow velocity and molecular diffusion
velocity yields a slippage factor that depends on composition, pressure and saturation.
Followed Ertekin’s work, a lot of successive studies on gas diffusion (Allan et al. 2012;
Javadpour 2009; Ozkan et al. 2010; Sakhaee-Pour and Bryant 2012; Shi et al.2013) used
the dusty gas model (DGM) to model gas flow through nanoscale pores and throats based
on the assumption that overall flow rate is a linear combination of gas transport
mechanisms. Ozkan et al. (2010) incorporated Knudsen flow into the dual porosity
formulation to simulate gas migration from matrix to fracture system. Javadpour (2009)
developed the concept and formulation of apparent gas permeability in shale by adding
the Knudsen diffusion and viscous forces as a total mass flux, similar to Ertekin’s model.
Roy and Raju (2003) stated that different flow regimes are dependent on the Knudsen
number. They modeled gas flow characteristics through microchannels and nanopores
beyond the slip flow regime. They found that in the case of nanopore systems the
continuum assumption is not valid. Although some studies (Grogan & Pinczewski, 1987;
Darvish et al., 2006) suggests that molecular diffusion is an important recovery
mechanism in the mobilization of oil in laboratory-scale floods, little work has addressed
the role of diffusion and determined the time scales necessary for diffusion to be an
effective recovery mechanism in the reservoir-scale flooding. This work focuses on
examining the contribution of diffusion at reservoir flooding conditions.
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The study of diffusion effect on fluid flow dynamics in shale resource plays that have
complex pore networks starts to draw operators’ attention (Javadpour et al. 2007;
Sakhaee-Pour & Bryant 2012; Schettler et al. 1989; Yuan et al. 2013). Most of the studies
(Ghorayeb and Firoozabadi 2000; Hoteit et al. 2006, 2009, 2011; Jamili 2010) on
numerical modeling of the diffusion role in fractured media used a single porosity, dual-
continuum model to simulate naturally fractured reservoirs. The fractures are set as high
permeable blocks and fine grid blocks are demanded surrounding the fractures. The
construction of fracture-network requires using very fine grid-block near the fractures to
accurately capture the rapid pressure, saturation changes and multi-phase flow effects
surrounding the fractures. The disadvantage of this approach is self-explanatory because
it demands a large number of refined grids and a huge amount of computing time. Hoteit
and Firoozabadi (2009) presented a numerical simulation model of molecular diffusion
for gas injection in 10-m spacing of fracture network. It demands more than one day of
running time on a 2.5-GHz, Pentium 4 PC. It is almost impractical to use such a grid-
refinement and multi-component EOS model in a full-field simulation. The deficiency of
the single-porosity diffusion model is avoided by using a dual permeability model to
represent fracture network that includes the diffusion transfer flux in it. The aim of
incorporating diffusion flow into a dual permeability model is to significantly reduce the
runtime but produce the same accurate results as a single-porosity diffusion model. Coats
(1989) proposed a fully implicit numerical model for compositional simulation of fluid
flow that includes the effect of diffusion in the dual-porosity model. He solved the
diffusion equation in 1D and extended this approach to compositional simulation.
However, this model only considered the gas phase diffusion flux between matrix and
fracture. Matrix-matrix diffusion and diffusion rates in the oil phase were neglected.
Jamili (2010) used a dual-continuum approach to examine the mass transfer between the
fractures and the matrix blocks in naturally fractured reservoirs. The matrix is discretized
into fine grids and the fractures act as the boundaries of the matrix. It simply comprises
of the matrix with four fractures surrounding it. A lot of current studies on modeling the
diffusion effect on gas flooding efficiency used a single porosity model to explicitly
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represent the fracture network (Ghorayeb and Firoozabadi (2000); Hoteit et al. 2006,
2009, 2011). In this chapter, we coupled the diffusion equation in a dual permeability
model that can properly simulate the fluids flow in shale oil reservoirs.
4.3. Description of Mathematical Model
The species balance for component i in an nc-component system is given by (Hoteit and
Firoozabadi 2009; Jamili 2010) in the following convection-diffusion equation:
,
convective flux diffusion flux
0, i=1,...,nc j=o, gj j ij j ij j i j
j j j
accumulation
S u Jt
φρ ω ρ ω
∂
+∇⋅ + = ∂
∑ ∑ ∑�
����� ����������
(4.1)
The velocity for each phase is described by Darcy’s law and the diffusion is followed by
Fick’s law
( ) j=o, grj
j j j
j
kku p gρ
µ= − ∇ +
�
� �
(4.2)
,
, i=1,...,n ; j=o, gi j j j ij ij c
J S Dφρ ω= − ∇ (4.3)
The composition �� is constrained by
1
1, j=o, gcn
ij
i
ω
=
=∑ (4.4)
Therefore, the governing matrix flow equation of the diffusion model can be expressed as:
( ), ,
0, i=1,...,nc j=o, gj j ij j ij j i j mf ij
j j jmm
S u Jt
φρ ω ρ ω τ ∂
+ ∇ ⋅ + + = ∂ ∑ ∑ ∑
�
(4.5)
The fluids flow conservation equation in the fracture is:
( ), ,
0, i=1,...,nc j=o, gj j ij j ij j i j mf ij
j j jff
S u Jt
φρ ω ρ ω τ ∂
+∇⋅ + − = ∂ ∑ ∑ ∑
�
(4.6)
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63
The matrix-fracture transfer term can be obtained by modifying the equation proposed by
Kazemi et al. (1976). In our work, the transfer of a component between matrix-fracture
by diffusion is simply described by Fick’s law that preserves the same form as viscous
displacement. Modeling of matrix-to-cleat diffusion in the coal-bed methane adopts the
same approach to simulate the diffusive mass flow rate of a species (CMG, 2011).
The mass transfer formulation between the matrix and fracture is expressed as:
( ) ( ), , , , ,
- i=1,2...nc , j=o,grj j
mf ij j m j f j j ij ij m ij f
j
kp p S D
ρτ σ ρ ω ω
µ
= + −
(4.7)
g o cog
p p p= + (4.8)
1o g
S S+ = (4.9)
2 2 2
4yx z
x y z
kk k
L L Lσ
= + +
(4.10)
Where ��denotes the molar density of phase j (j=o, g). ��,��,���, �� and �� represent the
saturation, pressure, relative permeability, velocity and viscosity of phase j, respectively.
The subscript i with i=1,…,nc corresponds to the components. ��� is the effective
diffusion coefficient of component i in the phase j. �,�is the mass flux of component i in
the phase j by diffusion. ��� represents the molar fraction of component i in the phase j.
��,��is the matrix-fracture transfer of component i in the phase j.
Equation 4.1 can be derived from the fundamental phase conservation equation (Lake,
1989). Jamili (2010) developed the same mathematical model as Hoteit’s model to
simulate diffusion and convection mechanisms for gas injection in naturally fractured
reservoirs. The governing matrix transport equation for each species in the oil and gas
phases due to convective and molar diffusive flux is shown in Eq.4.5. The governing flow
equation in the fracture is represented by Eq.4.6. Eqs.4.5 and 4.6 are developed on the
basis of Eq.4.1. The governing equations of matrix and fracture are similar to the dual
permeability formulation, except that the diffusion of components in the oil and gas
Texas Tech University, Tao Wan, May 2015
64
phases is considered. We only included the molecular diffusion in this chapter without
containing mechanical dispersion. We used the Gilman and Kazemi shape factor to
characterize the matrix-fracture transfer coefficient.
The system of diffusion equations in the dual permeability model consists of 2×(2nc+4)
equations. The 2×(2nc+4) unknowns are ���,�� , ��, ��, �, … , ��� and
���,��, ��, ��, �, … , ��� . There are 2nc equations from Eqs. 4.5 and 4.6 and 2nc
equations that can be derived from thermodynamic equilibrium (fugacity) equations. The
rest of the required equations are from Eqs.4.4, 4.8 and 4.9, that is, mass fraction
conservation equation, capillary pressure equation and saturation equations in the matrix
and fracture. We used an adaptive-implicit approach that is developed by (Collins et al.
1992) to solve the system of equations. The essence of adaptive-implicit approach is that
the task of solving the flow equations is independent from solving the equilibrium
equations. It avoids the drawback of solving the flow and phase equilibrium equations
simultaneously that has a high level of complexity to find the solution. The solution
method of this model is well-established in the literature (Collins et al. 1992; Jamili
2010).
The difference between this model and the previously developed model by Hoteit and
Firoozabadi (2009) is that we coupled the diffusion mechanism into a dual permeability
model and included the matrix-fracture mass transfer due to diffusive flux (as shown in
Eq.4.7). The difference between this model and the Coats (1989) model is that the latter
does not include diffusion rate of a component in the oil phase and the diffusive flux
within matrices and fractures.
4.4. Model Validation
Our numerical simulation results from the model are benchmarked using experimental
data and published numerical simulation results. Then, simulation results of gas diffusion
in nanopores are presented and compared with conventional numerical model by Coats
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65
(1989). Kovscek et al. (2008) reported a series of experimental results of using CO2
injection to enhance oil recovery in low permeability shale rocks (0.02-1.3 mD). Initially,
the shale core sample is saturated with live oil at 1300 psi. Then, it is depleted to a
pressure at 350-psi. Two CO2 injection modes followed the primary depletion.
Countercurrent flow and concurrent injection schemes were employed to evaluate the oil
recovery potential after primary production. The core sample is placed in the horizontal
direction. Gravity segregation effect is not considered in their study. In the countercurrent
mode, CO2 is flushed through the inlet at constant pressures while the outlet is sealed. A
sketch of experimental apparatus for countercurrent CO2 flow and concurrent flow is
shown by Kovscek et al. (2008) (their Fig.4.5 and Fig.4.6). The injected carbon dioxide
diffuses from fractures into the porous matrix to displace oil. The experimental setup of
countercurrent mode is designed to evaluate the oil recovery in the absence of viscous
displacement. The experimental results for 0.023 mD shale rock sample showed no
incremental oil production during the countercurrent flooding stage. Concurrent flow is
performed at an injection pressure at which viscous displacement becomes the dominant
oil recovery mechanism. Later, Vega et al. (2010) tried to simulate the miscible gas
injection process (1.32 mD shale sample) including countercurrent and concurrent flow
modes. But the individual experimental recovery process was unable to be matched.
There is a huge gap between their simulation results and experimental data. They used a
Carman-Kozeny type of porosity-permeability correlation to generate the permeability
distribution of the shale rock sample. There is no evidence to support that the approach
they used can represent the fracture and matrix permeability distribution in shale. The
fact that there is significant deviation between their simulation results and experimental
results is an indication that the complexity of fracture network in shale rocks is not fully
characterized by using this correlation. In this chapter, we will use a dual permeability
model to simulate the experimental results of gas injection in ultra-tight siliceous shale of
0.023 mD presented in Kovscek’s work (Table 4.1). The matrix permeability of the
sample is 0.023 mD. The fracture permeability used in dual permeability model is 1 mD.
The lumped reservoir fluids pseudo-component description is given by Vega et al. (2010).
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66
Table 4.2 and 4.3 summarizes the compositional description of reservoir fluid and binary
interaction coefficients used in experimental simulation. Sigmund (1976) method is used
to model the molecular diffusion and calculate binary diffusion coefficient between
component i and j in the mixture.
Table 4.1. Core sample properties
Property Sample
Length, cm 7.3
Diameter, cm 3.2
Matrix Permeability, mD 0.023
Porosity, fraction 0.3
Initial pressure, psi 1400
CO2 injection Yes
Table 4.2. Compositional description of reservoir fluid in experimental simulation
Component Mole Fraction Pc (atm) Tc (K) Acentric factor Mol. Weight (g/mol) CO2 0.0036 72.8 304.2 0.225 44.01
C1 0.1565 45.4 190.6 0.008 16.04
C2-C3 0.0815 45.05 338.93 0.1246 36.99
C4-C6 0.1002 34.28 462.55 0.2258 70.14
C7-C15 0.4494 24.13 627.20 0.3984 135.20
C16-C34 0.1576 12.44 828.07 0.8581 305.01
C35+ 0.0512 6.50 786.09 0.8421 644.85
Table 4.3. Binary coefficient used for experimental simulation
Component CO2 C1 C2-C3 C4-C6 C7-C15 C16-C34 C35+
CO2 zero 0.103 0.0 0.0 0.0 0.0 0.0
C1 0.103 zero 0.0 0.0 0.0 0.0 0.0
C2-C3 0.0 0.0 zero 0.0 0.0 0.0 0.0
C4-C6 0.0 0.0 0.0 zero 0.0 0.0 0.0
C7-C15 0.0 0.0 0.0 0.0 zero 0.0 0.0
C16-C34 0.0 0.0 0.0 0.0 0.0 zero 0.0
C35+ 0.0 0.0 0.0 0.0 0.0 0.0 zero
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Figure 4.1-Comparative cumulative oil recovery
Fig.4.1 shows the cumulative oil recovery of the simulation model contrasting with the
experimental data at depletion, countercurrent flow and concurrent flow stages. Results
produced by the model are in good agreement with the experimental results. There is little
noticeable oil recovery from the countercurrent flow stage because the production is
performed in a short span of time and there is no viscous displacement and gravity
drainage assisted recovery. Another reason may attribute to that the fluid is injected at
high flow velocities so that diffusion is unable to effectively equalize concentration in
such a short residence time. Most of the oil production is recovered during pressure-
driven stage.
The model is also compared with Hoteit and Firboozabadi (2009) numerical model. In
Hoteit’s example, the reservoir fluid is simplified that only contains C1/C3 mixture.
Methane is injected as a solvent at a rate of 1.3x10-4 pore volume per day to displace
C1/C3 mixture. The reservoir domain is assumed to be a 2D cross section with 500-m
length and 100-m height, as shown in Fig.4.2. In Hoteit’s model, different sizes of matrix
blocks (100x10, 10x10 and 10x5) were used to construct the fracture-networks that have
different fracture spacing. The natural fracture spacing is adjusted by varying the sizes of
matrix blocks. The fracture aperture is equal to 0.5 mm. Grid refinement is needed for the
0
5
10
15
20
25
30
35
40
1 10 100
Oil
Re
cov
ery
, %
Days
Experimental Results
Simulation Results
Primary depletion Concurrent stage
Countercurrent
Texas Tech University, Tao Wan, May 2015
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area surrounding the fractures. The drawback of this approach is that it would require
tremendous grid blocks when the fracture-network spacing is smaller. The most important
advantage of a dual permeability model over a single-porosity model is that it does not
require detailed grid-refinement because the natural fractures are incorporated into the
model. For example, if the spacing of fracture-network is 10-m, we can use 10mx10m
(32.8 ft x32.8 ft) blocks in which two perpendicularly crossed 0.001-ft wide fractures
(approximately actual fracture width) are assumed to exist which runs through each
32.8ft x32.8 ft block. Therefore, the input fracture porosity in the dual permeability
model is 6.1E-05 = (2xVfrac/Vblock). The simulation results in Hoteit’s simulation example
1 was reproduced. Methane is injected at the top of the right corner matrix blocks to
displace C3 in the domain. The fracture network spacing is 10-m apart away in both X
and Z directions. We used 10 x 10 m grid-blocks in dual permeability model that implies
the fracture spacing is 10-m. The fracture porosity is 6.1E-05. The same diffusion
coefficients as Hoteit was used. They are in the range of 8.7 x 10-8 and 2.2 x 10-9 m2/sec
in gas and oil phase, respectively. The bottom-hole flowing pressure at the production
well is maintained equal to the initial reservoir pressure to eliminate the viscous
displacement caused by pressure gradient. The reservoir model setup probes recovery by
gas injection in fractured reservoirs under a zero pressure gradient. In this case, the
gravity drainage and diffusion flow becomes the dominant recovery mechanisms in
fractured reservoirs.
Figure 4.2-2D single porosity model with 10 x 10-m matrix blocks
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Figure 4.3-Comparison of C3 recovery for the dual permeability model with single
porosity model
Our simulation results are well-matched with the single porosity model that uses grid-
block refinement, as shown in Fig.4.3. It takes only few minutes to simulate the test by
using the dual permeability model coupled with diffusion, while their model takes hours
to run (Hoteit and Firboozabadi, 2009). Fig.4.3 clearly shows that diffusion contributes to
a large percentage of oil recovery in absence of pressure gradient driven convective
transport.
Then, the importance of diffusion effect on fractured shale gas reservoirs was
investigated. Rock and fluid properties (relative permeabilities, phase behavior and grid-
block distribution) were the same as used in the Hoteit’s model. The matrix permeability
was simply changed into 0.0001-mD. The C3 recovery, with and without considering
diffusion effect, by CO2 injection is presented in Fig.4.4. It obtained 86% of C3 recovery
in CO2 injection process due to contribution of diffusion in absence of viscous
displacement. Fig.4.4 indicates that diffusion plays a crucial role for improving gas
recovery in fractured shale reservoirs. Similar observations of the importance of diffusion
effects on shale gas production have been reported in the literature (Javadpour et al. 2007;
Sakhaee-Pour & Bryant 2012; Schettler et al. 1989; Yuan et al. 2013).
0
10
20
30
40
50
60
70
80
90
100
0 20 40 60 80 100
C3
Re
cov
ery
, fr
act
ion
PVI, %
Dual Perm-With Diffusion
Dual Perm-No Diffusion
Hoteit (2009) -Without Diffusion
Hoteit (2009) -With Diffusion
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Figure 4.4-Comparison of C3 recovery by CO2 injection in shale gas reservoirs
4.5. Simulation Results and Discussion
The main objective of this chapter is to model the dispersive-convective flux through
nanopores in shale oil reservoirs during gas injection process using the dual permeability
model coupled with diffusion. The reservoir fluid and rock properties used in this model
are based on the published data in Eagle Ford shale as previously used (Wan et al. 2014).
The initial reservoir oil compositions are shown in Table 4.4 which represents light oil.
Table 4.5 presents the binary coefficients used for Eagle Ford fluid. The effective
diffusion coefficients for different components in the oil and gas phases are presented in
Table 4.6 which were calculated from Wilke-Chang equation (Wilke and Chang, 1955).
Hydrodynamic dispersion includes both molecular diffusion and mechanical dispersion.
The molecular diffusion coefficients between component i and j in the gas phase are
calculated by Sigmund (1976) method. The Wilke-Chang equation is used to estimate
effective oil phase molecular diffusion coefficients. The basic reservoir properties are
presented in Table 4.7.
0
10
20
30
40
50
60
70
80
90
100
0 20 40 60 80 100
C3
Re
cov
ery
, fr
act
ion
PVI, %
Without Diffusion in Shale
With Diffusion in Shale
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Table 4.4. Peng-Robinson EOS Fluid Description
Components Initial Comp.
Pc (atm) Tc (k) Acentric Fac. MW Vc
C1 0.5 45.44 190.6 0.013 16.0 0.0998 CO2 0.0 72.8 304.2 0.225 44.01 0.094 C2-3 0.03 41.94 369.8 0.152 44.1 0.2005 C4-6 0.07 29.73 507.4 0.300 86.2 0.3698 C7-10 0.2 20.69 617.7 0.488 142.2 0.6297 C11-15 0.15 13.61 705.6 0.65 206 1.0423 C16+ 0.05 11.02 766.7 0.85 282 1.3412
Table 4.5. Binary coefficient for Eagle Ford fluid and reservoir flooding case
Component C1 CO2 C2-C3 C4-C6 C7-C10 C11-C15 C16+
C1 zero 0.103 0.0 0.0 0.0 0.05 0.05
CO2 0.103 zero 0.135 0.0 0.0 0.0 0.0
C2-C3 0.0 0.135 zero 0.0 0.0 0.005 0.005
C4-C6 0.0 0.0 0.0 zero 0.0 0.0 0.0
C7-C10 0.0 0.0 0.0 0.0 zero 0.0 0.0
C11-C15 0.05 0.0 0.005 0.0 0.0 zero 0.0
C16+ 0.05 0.0 0.005 0.0 0.0 0.0 zero
Table 4.6. The effective diffusion coefficients of different components at 2000 psi
Components C1 CO2 C2-C3 C4-C6 C7-C10 C11-C15 C16+
Gas Phase, cm2/s 1.30E-04 2.13E-04 1.60E-04 1.07E-04 7.80E-05 5.77E-05 4.88E-05
Oil Phase, cm2/s 4.38E-05 3.99E-05 3.11E-05 1.93E-05 1.53E-05 1.21E-05 8.82E-06
Table 4.7. Reservoir properties for the model input
Initial Reservoir Pressure 6425 psia
Reservoir Temperature 160 Fo
Saturation Pressure 2302 psia
Rock Compressibility
Porosity
Permeability of shale matrix
5.0E-06
6 %
100 nano-Darcy
The diffusion effect on gas flooding efficiency in liquid-rich shale is examined that is
implemented in two hydraulically zipper fractured horizontal wells. Considering that CO2
Texas Tech University, Tao Wan, May 2015
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EOR process is limited by the cost and availability of CO2 in the field, it is favorable to
use produced natural gas as the injection gas. The composition of injector fluid is
specified as 70% C1, 20% C3 and 10% C6. Reinjection of produced gas into existing
reservoirs is an economically available avenue to perform EOR projects due to low
natural gas prices. We applied staggered zipper frac technique to stimulate two adjacent
horizontal wells to maximize the exposure of reservoir rock. Two horizontal wells drilled
through the reservoir, which are closely spaced, one adjacent to the other, forming a gas
injector and producer pair, as shown in Fig.4.5. The dimension of the shale reservoir is
2000-ft long×1000-ft wide×200-ft thick. An effective fracture permeability of 0.04 mD
is used to simulate the natural fractures in the stimulated reservoir volume (SRV). The
hydraulic fracture conductivity is 83.3 mD-ft. The hydraulic fracture half-length is 250-ft.
The horizontal well is stimulated with 10 transverse hydraulic fractures at the spacing of
200-ft apart from each other. The LS-LR-DK (logarithmically spaced, locally refined and
dual permeability) model is used to accurately simulate the fluid flow in fractured shale
oil reservoirs. The LGR coupled to standard DK grids is used to represent the hydraulic
fractures owing to the high permeability contrast between fracture and matrix. Better
discussions about the advantages of this LS-LR-DK model can be found in the literature
(Rubin 2010).
Figure 4.5-The horizontal well pair perforated and stimulated in a staggered pattern
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Figure 4.6-Simulation Unit
The producer is subject to minimum bottom-hole pressure constraint (BHP) of 2000 psi.
Our operational bottom-hole flowing pressure is close to the minimum miscibility
pressure requirement for injected solvent to achieve miscible flooding with reservoir oil.
We specified the solvent injection rate at 1 PVI/10000 days for injector and the maximum
allowed bottom-hole injection pressure is 5000 psi. Another similar case with an injection
rate at 1 PVI/5000 days is also simulated to investigate the effects of injection rate on oil
recovery in multistage hydraulically fractured horizontal wells. In our simulation model,
we only simulate a half unit of the hydraulic fracture controlled volume based on the
symmetry of fluid flow, as shown in Fig.4.6. The red lines represent hydraulic fractures.
In Fig.4.6, we used a fine logarithmic gridding surrounding the hydraulic fractures and
near the wellbore. The simulation domain was a two-dimensional Cartesian section (x-y).
Twenty two gridblocks were used in the x direction with a variable gridblock size and
forty two gridblocks in the y direction. We performed grid sensitivity study and found
that further grid refinement did not affect the numerical results.
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Figure 4.7-Comparison of Coats’s model results with our model for matrix permeability km = 1 mD
Figure 4.8-Effect of diffusion in the oil phase and within matrices on shale oil recovery
0 1000 2000 3000 4000 5000 6000 70000
10
20
30
40
50
60
70
Time (days)
C15
com
pon
ent
Reco
very
, %
The matrix permeability is 1-md
Coats model (1989)
Our simulation model
0 1000 2000 3000 4000 5000 6000 70000
2
4
6
8
10
12
14
16
18
Time (days)
C15
com
pon
ent
Reco
very
, %
The matrix permeability is 0.0001-md
Coats model (1989)
Our simulation model
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Figure 4.9-Peclet number in the oil phase in the matrix (km=1 md) at 7000 days
Figure 4.10-Peclet number in the oil phase in the shale matrix (km=1E-04 md) at 7000 days
Figs.4.7 and 4.8 compare our simulation results with Coats (1989) model output results
for the high matrix permeability case (1 mD) and the shale matrix permeability case
(0.0001-mD). In Coats’s model, it only includes the gas phase diffusion term in the
molecular flux between a fracture block and a matrix block. He did not consider the
5 10 15 200
2
4
6
8
10
Grid away from the producer
Pe
cle
t n
um
be
r
Matrix permeability = 1 md
5 10 15 200
0.5
1
1.5
2
2.5
3
3.5
4x 10
-3
Grid away from the producer
Pe
cle
t n
um
be
r
Matrix permeability = 1E-04 md
Texas Tech University, Tao Wan, May 2015
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diffusion rate of components in the oil phase and the diffusive flux from matrix to matrix,
which may be an important recovery mechanism in tight shale oil or gas reservoirs.
Fig.4.7 shows the results produced by Coats’s model are in good agreement with our
simulation results for conventional reservoirs (km=1 md). The importance of matrix-
matrix diffusion and diffusion rate in the oil phase is observed in Fig.4.8 in the low
permeability shale case. Fig.4.8 shows that the impact of matrix-fracture diffusion rate in
the oil phase and matrix-matrix diffusion on ultimate oil recovery is significant in tight
shale reservoirs (km= 1e-04 mD). In very low permeability shale, it is essential to model
the diffusion rate in the oil phase and within matrices. Figs.4.9 and 4.10 show the Peclet
number in the oil phase in the matrix blocks along the horizontal producer (1~22, 32, 1).
The Peclet number is defined as the ratio of convective transport rate to dispersive
transport rate (Lake, 1989). The Peclet number is a measure of the relative importance of
advection to diffusion. Perkins and Johnson (1963) have studied the influence of
diffusion and dispersion on miscible displacement processes. They found that when
Peclet number is less than 0.02, the transport is controlled by diffusion. The Peclet
number in the shale matrix is considerably lower than that of in the conventional
reservoirs (as shown in Figs.4.9 and 4.10). This observation tends to confirm the idea that
diffusion plays an important role in recovering oil from nano-scale shale oil reservoirs.
Without including the matrix-fracture diffusion in the oil phase and matrix-matrix
diffusion, the oil recovery from the shale matrix is significantly lower. It reflects that
matrix-fracture diffusion in the oil phase and diffusion within matrices play a crucial role
in recovering shale matrix oil.
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Figure 4.11-Oil recovery vs. PVI
Figure 4.12-Oil recovery vs. time
0 0.2 0.4 0.6 0.8 10
5
10
15
20
25
30
35
PVI
Sha
le o
il re
co
very
fa
cto
r, %
Diffusion, 1 PVI/10000D
Diffusion, 1 PVI/5000D
Without Diffusion, 1 PVI/10000D
Without Diffusion, 1 PVI/5000D
0 2000 4000 6000 8000 10000 120000
5
10
15
20
25
30
35
Time (days)
Sh
ale
oil
reco
very
facto
r, %
Diffusion, 1 PVI/5000D
Without Diffusion, 1 PVI/5000D
Diffusion, 1 PVI/10000D
Without Diffusion, 1 PVI/10000D
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Fig. 4.11 and Fig.4.12 show the effect of diffusion and injection rate on shale oil recovery.
One can observe that the diffusion plays a role in improving oil recovery at two
hydraulically fractured horizontal wells flooding case. With the diffusion, the velocity of
a component is owing to the summation of both convection and diffusion velocities.
Vapor-vapor diffusion is about tenfold faster than vapor-liquid diffusion (da Silva and
Belery 1989). Thus, the injected gas will diffuse from the fracture to the matrix with a
velocity considerably higher than the velocity of crude oil being flushed out by
convective flow. In terms of one pore volume injection, the scenario with an injection rate
at 1 PVI per 10000 days achieved higher oil recovery than the rate of 1 PVI per 5000
days (Fig.4.11). If the injected fluid flows at a high interstitial velocity through the porous
medium, the oil recovery due to diffusion will be impaired because there is less residence
time for fluids in high concentration side to diffuse in low concentration pore spaces
(Perkins and Johnston, 1963). However, Fig.4.12 shows that higher gas injection rate
achieves higher oil recovery at a given injection time because it requires more pore
volumes of solvent injection.
The incremental oil recovery due to diffusion by using lower injection rates at a given
pore volume injection has raised the consideration on the trade-offs between the potential
economic gains and maximization of ultimate hydrocarbons recovery. We performed an
economic analysis to evaluate the potential economic benefits from these two injection
rates. The study assesses shale oil production in two different injection schemes
(1PVI/10000D and 1PVI/5000D) in order to determine how to extract shale oil
economically in the field. Well economics vary greatly across the basin as a function of
productivity, geology, drilling and stimulation cost. Presumably, in unconventional
reservoirs, it will cost approximately $4 million to drill a horizontal well and $3 million
to stimulate and complete it (Alexander et al., 2009). Table 4.8 presents the input
economic parameters for calculation of the NPV. All capital expenditures occur in the
year of first production. The capital investment in shale oil production includes drilling,
completion and stimulation costs. The operating expenditure is assumed to be $4/boe.
The oil prices are assumed to be flat over the life cycle of production ($100 per barrel).
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Royalty is levied on gross production at a rate of 20%.
Table 4.8. Required inputs for the NPV calculations
Economic Parameters Unit Value
Discount rate fraction/yr 10%
Working interest fraction/yr 100%
Royalty rate fraction/yr 20%
Net revenue interest fraction/yr 80%
Severance tax fraction 4.6%
Ad valorem tax fraction 3%
CAPEX, $ $MM 14 (2 wells)
OPEX, $ $/boe 4
Oil price, $ $/barrel 100
Figure 4.13-Comparative NPV by two different injection rates
-20
-15
-10
-5
0
5
10
15
0 5 10 15 20 25 30 35
NP
V,
$M
illi
on
s
Years
1PVI/10000 days
1PVI/5000 days
0 2000 4000 6000 8000 10000 120000
10
20
30
40
50
60
Pro
du
ctio
n g
as r
ate
, M
scf/
da
y
Time,Days
0 2000 4000 6000 8000 10000 120000
0.5
1
1.5
2
2.5x 10
5
Cu
mu
lative g
as in
jectio
n,
Mscf
1 PVI/10000D, Gas prod rate
1 PVI/5000D, Gas prod rate
1 PVI/10000D, Cumu gas injection
1 PVI/5000D, Cumu gas injection
Primary
Secondary recovery
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Figure 4.14-Comparison of gas production rates and cumulative gas injection by two different injection rates
Fig.4.13 shows the results of the net present value (NPV) calculations for two different
injection rates at one pore volume injection. Fig.4.14 presents the comparison of gas
production rates and cumulative gas injection by two different injection schemes. The
insignificant difference of NPV in Fig.4.13 reflects the project cannot achieve more
return by enduring the injection time for a given injection pore volume. Although it
yielded higher oil recovery by using slower injection rate that extends injection time, the
NPV was even lower. Depreciation of cash flow has a negative influence on return that
comes from diffusion contributed oil recovery. Along with the continuous operating
expenditures including compression cost and gas reinjection cost, it is not a good idea to
take advantage of diffusion increased oil recovery by extending injection time. The NPV
calculations aim to provide an insight for designing the solvent injection rates in shale oil
reservoirs and to estimate the project’s return.
Figure 4.15-Effect of natural fracture spacing on gas injection performance
Fig.4.15 compares the effect of natural fracture spacing on gas injection recovery
performance. It is observed that the diffusion effect is not quite significant at the primary
production stage. The importance of molecular diffusion is magnified in gas injection
0 1000 2000 3000 4000 5000 6000 70000
5
10
15
20
25
30
35
40
Time (days)
Sh
ale
oil
fie
ld r
eco
very
, %
Injection rate = 1 PVI/5000 D
200-ft, Diffusion
200-ft, Without Diffusion
100-ft, Diffusion
100-ft, Without Diffusion
50-ft, Diffusion
50-ft, Without Diffusion
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enhanced recovery process in highly fractured shale oil reservoirs with smaller fracture
spacing. The increase of oil recovery by inclusion of diffusion effect for the case of 50-ft
fracture spacing is much higher than 200-ft spacing case. In densely fractured reservoirs,
diffusion recovery mechanism predominates with a decrease of fracture spacing.
It is noteworthy that selection of different shape factors may lead to different reservoir
behaviors. Gilman and Kazemi shape factor assumes that all fractures are instantly
immersed in water and under quasi-steady state conditions (Rangel-German and Kovscek,
2003). One deficiency in use of Kazemi’s shape factor is that assumptions of pseudo-
steady state and instantly immersed fractures may break down in shale reservoirs. The
shape factors are treated as adjustable parameters which are used to reproduce observed
field or laboratory results. However, this approach does not necessarily capture the
physics of matrix-fracture transfer flow behavior. Bahrami et al. (2008) presented an
approach of integrating image log data associated with well-test analysis to determine the
shape factor. Combining of core analysis, well-test data and well-logging data might be
an effective approach to estimate the shape factor. More effort is needed to develop a
shape factor that can characterize the transient behavior and match the production
performance in shale reservoirs.
4.6. Conclusions
In this chapter, we coupled the diffusion equation with a dual permeability model so that
the enhanced oil recovery processes by gas flooding can be properly simulated. There are
difficulties to use a single porosity dual-continuum model to match the experimental
results of gas flooding in the low permeability shale rocks, because it can not properly
capture the matrix-fracture mass transfer rates of densely distributed microfractures. The
results produced by dual permeability model coupled with diffusion are in good
agreement with the experimentally measured data in shale rocks. The simulation work
addressed the role of diffusion in the field scale flooding. The impact of matrix-fracture
diffusion rate in the oil phase and matrix-matrix diffusion on oil recovery is significant in
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tight shale oil reservoirs. It is observed that the interstitial velocity of oil phase in the
shale matrix is considerably lower than that of the conventional reservoirs. In very low
permeability shale oil or gas reservoirs, the dominant recovery mechanism is by diffusion
according to the analysis of Peclet number. One noticeable finding of gas injection in
shale reservoirs is that without including the matrix-fracture diffusion in the oil phase, it
results in lower oil recoveries. It is essential to model the matrix-fracture diffusion rate in
the oil phase and diffusion within the matrices for tight shale oil reservoirs. In
conventional reservoirs, Coats’s (1989) model is able to produce good results because the
transport is controlled by viscous flow.
The importance of diffusion effect on fractured shale gas reservoirs and shale oil
reservoirs is observed. It is important to notice that the oil recovery by including
diffusion effect is much higher than the case without considering diffusion in shale oil
reservoirs. In the cases where the convective flux for a component is negligible because
of low pressure drawdown, diffusion due to compositional differences between matrix
and fracture tends to become the main recovery mechanism. From a reservoir
management perspective, relying on diffusion enhanced oil recovery by decreasing
injection rate is not the best way to exploit shale resource plays.
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Lake, L.W.1989. Enhanced Oil Recovery, Prentice-Hall, Englewood Cliffs, New Jersey.
Morel, D., D., Bourbiaux, B., Latil, M., and Thiebot, B.1990. Diffusion Effects in Gas-
Flooded Light Oil Fractured Reservoirs. SPE Paper SPE 20516 presented at the
Annual Technical Conference and Exhibition, Sept. 23-26.
Ozkan, E., Raghavan, R.S., and Apaydin, O.G. 2010. Modeling of Fluid Transfer From
Shale Matrix to Fracture Network. Paper 134830 presented at SPE Annual Technical
Conference and Exhibition, 19-22 September, Florence, Italy.
Perkins,T.K. and Johnston,O.C. 1963. A Review of Diffusion and Dispersion in Porous
Media. SPE J 3 (1):70-84. SPE-480-PA.
Rangel-German, E. R. and Kovscek, A. R. 2003. Time-Dependent Matrix-Fracture Shape
Factors for Partially and Completely Immersed Fractures. SPE 84411 presented at SPE
Annual Technical Conference and Exhibition, 5-8 October, Denver, Colorado.
Roy, S., Raju, R., Chuang, H.F., Cruden, B.A., and Meyyappan, M. 2003. Modeling Gas
Flow through Microchannels and Nanopores. Journal of Applied Physics 93(8): 4870-
4879.
Rubin, B. 2010. Accurate Simulation of Non Darcy Flow in Stimulated Fractured Shale
Reservoirs. Paper SPE 132093 presented at Western Regional Meeting, Anaheim,
California, USA, 27-29 May.
Sakhaee-Pour, A. and Bryant, S. 2012. Gas Permeability of Shale. SPE J 15 (4): 401-409.
SPE-146944-PA.
Schettler, PD., Parmely, CR., and Lee, WJ. 1989. Gas storage and transport in Devonian
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shales. SPEFE 4 (3):371–376. SPE-17070-PA.
Shi, J., Zhang, L., Li, Y., Yu, W., He, X., Liu, N., Li, X., and Wang, T. 2013. Diffusion
and Flow Mechanisms of Shale Gas through Matrix Pores and Gas Production
Forecasting. Paper 167226 presented at SPE Unconventional Resources Conference
Canada, 5-7 November, Calgary, Alberta, Canada.
Sigmund, P.M. 1976. Prediction of Molecular Diffusion At Reservoir Conditions. Part 1-
Measurement And Prediction of Binary Dense Gas Diffusion Coefficients. JCPT 15
(2):48-57.
Vega, B., O’Brien, W.J., and Kovscek, A.R. 2010. Experimental Investigation of Oil
Recovery from Siliceous Shale by Miscible CO2 Injection. Paper SPE 135627
presented at the SPE Annual Technical Conference and Exhibition held in Florence,
Italy, 19–22 September.
Wan, T., Meng, X., Sheng, J., and Watson, M. 2014. Compositional Modeling of EOR
Process in Stimulated Shale Oil Reservoirs by Cyclic Gas Injection. Paper SPE
169069 presented at SPE Improved Oil Recovery Symposium, 12-16 April, Tulsa,
Oklahoma, USA.
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Yuan, W., Pan, Z., Li, X., Yang, Y., Zhao, C., Connell, L.D., Li, S., and He, J. 2013.
Experimental study and modelling of methane adsorption and diffusion in shale. Fuel
117 (Part A):509-519.
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Chapter 5
Evaluate the EOR Potential Of CO2 Displacement In Shale
Reservoirs Using Staggered Zipper Fractured Horizontal
Wells
5.1. Abstract
Modified zipper frac technique is developed in a manner different from zipper frac in
which the fractures are stimulated in a staggered pattern. The benefit of the modified
zipper frac is that it will improve the contact area with the reservoir and increase the
effective stimulated volume. Studies showed that enhancing fracture complexities in
shale gas resources is critical to improving stimulation treatment and well production
performance. CO2 injection EOR process under the miscible flooding condition can
significantly reduce oil viscosity. Oil viscosity reduction combined with the increased
contact area by hydraulic fractures could be the dominant recovery mechanisms.
Problems associated with gas injection in conventional well patterns such as early
breakthrough and channeling through high permeability zones will not likely happen in
nano-permeable shale oil or gas reservoirs, except that two wells are communicated by
fractures.
In this chapter, we propose gas injection to enhance gas condensate recovery in a
horizontal well pair, a gas injection well and a production well, which is stimulated in a
staggered zipper frac pattern. The approach integrates the advantages of hydraulic zipper
fracturing, horizontal wells and miscible gas flooding. Miscible gas flooding has shown
the IOR potential in shale gas and oil reservoirs in this simulation work. We develop a
compositional model to simulate complex interactions between the injected gas and
reservoir fluids. Our simulation results of the Eagle Ford Shale indicate that the
secondary recovery is increased after 5 years of primary recovery in a 100-ft fracture
spacing staggered zipper fracture pattern. The investigation of CO2 injection in a
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staggered zipper fractured horizontal well pair provides an insight into the EOR
performance in nano-shale reservoirs.
5.2. Introduction
In this chapter, staggered zipper frac technique was used to stimulate two adjacent
horizontal wells to maximize the exposure of new reservoir rock. Although the laterals
expose more of the nano-Darcy permeability shale rocks, the desired end result in
hydraulic fracture stimulation is to maximize coverage around each lateral. The benefit of
modified zipper frac technique from a geomechanical consideration is to increase
stimulated reservoir volume and complexity development in successive fracturing stages
because the net pressure created by the stimulation stage on the adjacent first well help
divert the fracture direction (Belhadi, J. et al 2011; Rafiee et al. 2012).
Numerous numerical simulations (Mayerhofer et al. 2006; Warpinski et al. 2009) show
that well productivity in shale oil and gas reservoirs primarily depends upon the size of
fracture network and the stimulated reservoir volume (SRV), which provides highly
conductive conduits to communicate the matrix with the wellbore. The natural fracture
complexity is critical to the well production performance and it also provides an avenue
for injected fluids to displace the oils (Cipolla et al. 2008; Wan et al 2013; Cipolla et al.
2011 and Moinfar et al. 2013).
Two horizontal wells are drilled through the reservoir and casing is set and cemented in
place. The two horizontal wells are closely spaced, one above the other, forming a gas
injector and producer pair like steam-assisted gravity drainage (SAGD) system.
Hydraulic fracturing is introduced to improve the flow capacity of the reservoir and well
productivity performance in shale reservoirs. The horizontal well fracture stimulation
requires pumping fluids into a wellbore at high rate and pressure that is too high for the
formation to accept without breaking. The perforating design plays a critical role in the
stimulation treatment. The effectiveness of the perforating process in this two horizontal
well pair cased-hole completion system depends on the perforating location. Before
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selecting components for a perforating job, the first task is to understand how to avoid the
hydraulic fracturing in the injection well directly communicating with producer. This
requires perforating in a staggered pattern of effective entrance hole through the pipe and
cement. Once the perforation and staging design were completed for one lateral, the
perforation scheme for successive laterals was based on a staggered design with respect
to adjacent wells (Belhadi, J. et al 2011). There is no perforating in the injector at the
location where there are perforations in the producer. The primary objective is to prevent
the created two wings of the injector from communicating with the producer. In this case,
there is no connection between the propagated hydraulic fractures in the injector and the
production horizontal well. The injected solvent would not break through to the producer
by the created hydraulic fractures of the injector. The injected CO2 from the hydraulic
fractures of the injector effectively pushes the oil towards the hydraulic fractures of the
production well and components in the injection gas dissolve in the oil phase as chemical
equilibrium is established.
Figure 5.1A-The horizontal well pair perforated and stimulated in a staggered pattern
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Figure 5.1B-Unit fracture SRV
Figure 5.2-The horizontal well pair stimulated in a staggered pattern
5.3. Model Description
The reservoir fluid composition description and phase behavior is from fifth SPE
published data (Pope et al; Wu et al; Wang et al) which is aimed to model the behavior of
a gas condensate fluid using six hydrocarbon components. Table 5.1 presents the pseudo-
component description and input for Peng-Robinson equation of state calculations. The
initial reservoir fluid compositions are also given in Table 5.1 which represent a gas
condensate, as shown in Fig.5.3. The reservoir rock properties we used in this model are
based on the published data in Eagle Ford shale as was previously used (Hsu and Nelson,
2002; Chaudhary et al., 2008; Bazanet al., 2010; Wan et al., 2013). The dimension of the
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shale reservoir is 2000-ft long×1000-ft wide×200-ft thick, as shown in Fig.5.4. The
producer is located at the beneath of reservoir domain and the injection well is placed at
the upper layer. The producing horizontal well is stimulated with 10 transverse fractures
each placed 200-ft apart as well as the injection well. The fracture spacing between the
injector and producer is 100-ft. The initial reservoir pressure for this field is 6,425 psi.
The producer is subject to minimum bottom-hole pressure constraint (BHP) of 500 psi
and is produced for 1800 days (5 years) as the natural depletion. Our operational flowing
bottom-hole pressure 500 psi is far below the dew-point pressure which results in the
condensate dropout.
Table 5.1. Peng-Robinson EOS Fluid Description of Eagle Ford Condensate lumping
Components Initial
Comp.
Pc
(atm)
Tc (k) Acentric
Fac.
MW Shift
CO2 0.1618 72.8 304.2 0.225 44.01 0.094 C1 0.7098 45.4 190.6 0.008 16.043 0.099 C2-3 0.079 45.7 330.5 0.119 36.0 0.16964 C4-6 0.026 34.9 453.8 0.226 72.0 0.2986 C9 0.02 26.0 606.0 0.359 128.0 0.4904 C22+ 0.0034 14.9 869.7 0.788 310.0 1.0589
Figure 5.3-Phase diagram of gas condensate behavoir
Dew point line
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Figure 5.4-Reservoir pressure changes during gas injection
Table 5.2. Reservoir properties for the model input
Initial Reservoir Pressure 6425 psia Reservoir Temperature 335Fo
Saturation Pressure 4456 psia Rock Compressibility Porosity Permeability of shale
5.0E-06 6 % 100 nano-Darcy
Water Density 62.4 lb/cuft Hydraulic fracture conductivity 83.3 md-ft
CO2 recovery mechanisms include CO2 dissolution in the oil leads to an increase in its
volume, oil viscosity reduction, vaporization of intermediate to heavy hydrocarbons and
development of multi-contact miscibility (Whitson et al). The swelling test was simulated
by varying proportions of injection gas mixed with original reservoir oil. The swelling
test provides useful phase and volumetric data to investigate how a reservoir fluid reacts
with gas injection. Fig.5.5 shows that the reservoir fluids remain in a single gas phase
preceding to gas injection because the initial reservoir pressure at 6425 psi is higher than
saturation pressure. With increasing injection percentage of CO2, the saturation pressure
decreases. The swelling factor increases with increasing CO2 solubility. Fig.5.6 shows the
simulated relative volumes in a CCE experiment at 335 F for the gas condensate mixture.
Fig.5.7 shows the simulated liquid dropout curve in a CVD experiment at 335 F for the
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gas condensate mixture. Liquid dropout curve expressed as Vro=Vo/Vs. Relative oil
volume is defined as the volume of oil, Vo at a given pressure, divided by the original
saturation volume. The relative volume provides a measurement of the average reservoir
oil saturation that will develop during depletion of a gas condensate reservoir (Whitson et
al). The reservoir oil saturation can be calculated from Vro with So=(1-Sw)Vro.
Liquid dropout starts at the dew point pressure 4456 psi and continue to increase until the
pressure reducing to 1500 psi when a maximum of condensate liquid reached.
Figure 5.5-Effect of solution gas on swelling of
1
1.5
2
2.5
3
3.5
4
4.5
5
2400
2900
3400
3900
4400
0 20 40 60
Sa
tura
tio
n P
ress
ure
, p
sia
Injected CO2, mole composition %
Psat
Swelling Factor
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Figure 5.6-Relative volume curve reservoir fluid by CO2
Figure 5.7-The liquid dropout curve for constant-composition expansion experiment at 335F on the gas condensate mixture.
5.4. Results and Discussion
In this study, we examined the impact of grid refinement through numerical sensitivity
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1000 2000 3000 4000 5000
Re
lati
ve
Vo
lum
e
Pressure
0
10
20
30
40
50
60
70
80
90
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1000 1500 2000 2500 3000 3500 4000 4500 5000
Ga
s p
rod
,%
Liq
uid
dro
po
ut
Pressure
Liquid volume
Gas prod
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calculations. Figs.5.8 and 5.9 show the reservoir model using 10 x 21 coarse grids
produces similar results to refined 22 x 21 grids. Fig.5.8 illustrates that using course grids
for hydraulic fracture representation (as shown in Fig.5.1B) is getting close to the result
as 21 grid-blocks. There is no large error caused by numerical dispersion. From these
results we found that a 10 x 21 grid discretization around the fractures is able to properly
model the rapidly varying pressure in and near the fractures. In Fig.5.8 and 5.9, the gas
recovery ‘tail’ flatten out is due to the software results output issue which does not
precisely reflect the simulation cases. On the basis of symmetry, Fig.5.1B simulates only
a unit fracture SRV consisted of two ‘mirror’ half fractures stimulated reservoir volume.
Fig.5.10 shows the production performance from the unit fracture controlled SRV
produces the similar results with the simulated entire fractured horizontal well controlled
SRV. Initially the reservoir operates in naturally depletion for 1800 days. Then, we start
CO2 injection for 4000 days. The injection well is constraint to inject at a maximum
injection pressure 7000 psi. Fig.5.11 shows the results for the different flowing bottom-
hole pressure of the producer on the gas recovery factor. The BHP used in the base case
simulation is 500 psi. The dew point pressure is 4456. The largest pressure drops occur
near producing wells and hydraulic fractures. Condensate liquid saturation will build up
near a well because of drawdown below the dewpoint pressure. The results in Fig.5.11
take into account the effect of condensate blockage, namely gas productivity index
reduction due to build up of condensate in the near wellbore region. The benefit of large
pressure drawdown in the producing well to the gas inflow performance overwhelms the
gas recovery loss due to gas relative permeability reduction and condensate dropout.
There is no visible distinction for the BHP of 500 psi and 1000 psi cases. It illustrates that
the gas recovery loss due to condensate banking effect is equivalent to the benefit from
pressure drawdown.
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Figure 5.8-Effect of numerical dispersion on C1 recovery vs. time
Figure 5.9-Effect of numerical dispersion on gas R.F. vs. time
0
10
20
30
40
50
60
70
80
90
0 1000 2000 3000 4000 5000 6000 7000
C1
Re
cov
ery
Time, days
6X21X2
22X21X2
10X21X2
0
10
20
30
40
50
60
70
80
90
0 1000 2000 3000 4000 5000 6000 7000
Ga
s R
eco
ve
ry,
%
Time, days
6X21X2
22X21X2
10X21X2
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Figure 5.10-Unit fracture controlled stimulated reservoir volume vs. entire horizontal well SRV results
Figure 5.11-BHP impact on gas recovery performance
The non-Darcy flow effect should be considered for gas injection in fractured reservoirs.
0
1000
2000
3000
4000
5000
6000
7000
0
10
20
30
40
50
60
70
80
90
0 2000 4000 6000
Av
era
ge
re
serv
oir
pre
ssu
re
Ga
s R
eco
ve
ry,
%
Time, days
Entire HW
Unit Fracture
Entire HW PAVG
Unit Fracture PAVG
0
10
20
30
40
50
60
70
80
90
0 1000 2000 3000 4000 5000 6000 7000
Ga
s R
eco
ve
ry,
%
Time, days
BHP=500
BHP=1000
BHP=2500
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The description of non-Darcy effect in hydraulic fractures is similar to the content
presented in chapter 2.
Figure 5.12-Gas recovery factor-Darcy flow vs. gas recovery-non-Darcy flow
Fig.5.12 compares the condensate and gas recovery results of Darcy flow model with the
non-Darcy flow model. The non-Darcy flow corrected model in 2-ft wide fracture
conduits exhibit a little bit lower recovery than Darcy flow model. Rubin (2010) showed
that the non-Darcy correction could be used to accurately model a pseudo fracture 1000
times wider than the actual fracture size in fractured shale gas reservoirs for horizontal
wells. Fig.5.13 shows the impact of fracture spacing on the cumulative gas recovery
factor. As shown in the graph, reducing fracture spacing from 200-ft to 100-ft would
result in a more than two fold increase in cumulative gas recovery efficiency factor. The
horizontal well production is the total sum of all fracture network segments which is
strongly dependent on the fracture spacing. Increasing stimulation stages can be used to
increase fracture-network size and SRV. Mayerhofer et al. (2010) illustrated that well
production in shale reservoirs is related directly to reservoir volume stimulated during the
fracture treatments which is in context with the fracture spacing.
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Figure 5.13-Impact of hydraulic fracture spacing between injection well and production well on gas recovery and C9 recovery
Figure 5.14-Global mole fraction of CO2 and C1 changes during CO2 flooding
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Fig.5.14 shows global mole fraction of CO2 and C1 changes at time 0, after 1800 days of
primary depletion and after 4000 days of CO2 flooding. The initial reservoir gas viscosity
at 335 Fcalculated by Jossi-Stiel-Thodos (JST) correlation (Fong and Nghiem, 1980) is
0.0373 cp. The CO2 viscosity at 335 F and at 6425 psi is 0.052 cp. When injected CO2
contacts with reservoir fluids, the injected CO2 effectively pushes C1 and other
components towards the hydraulic fractures of production well. Injected CO2 channel,
bypassing most of in-situ fluid, is not observed. After 4000 days of continuous CO2
injection from the top of the reservoir, most of the C1 and other components have been
uniformly swept to the hydraulic fracture of the producing well.
Figure 5.15-Condensate saturation distribution in or at vicinity of the fracture
0
0.05
0.1
0.15
0.2
0.25
0.3
0 2000 4000 6000
Co
nd
en
sate
sa
tura
tio
n
Time, days
BHP=500, 1 10 2
BHP=500, 2 11 2
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Figure 5.16-Gas relative permeability on or vicinity of the fracture
It is well known that well deliverability would be compromised by accumulation of
condensate near the wellbore. However, the presence of hydraulic fracture can mitigate
the condensate banking effect due to large pressure drawdown. Pope et al (1998) and Fan
et al (2005) developed a radial symmetric and single well model to demonstrate that
condensate blockage could result in a rapid falloff in productivity. Narayanaswamy et al.
(1999) concluded that when both non-Darcy and capillary number effects were
considered, the capillary number effect can in some case overshadow the two-phase non-
Darcy effects which results in smaller descrease in productivity indx (PI) than the case
with capillary number effect only. In Pope’s model, they purposely set the initial reservoir
pressure below the dewpoint that allows the condensate saturation goes up immediately.
Fig.5.15 shows that as pressure declines rapidly in the fracture block (1, 10, 2),
condensate saturation build-up very fast due to the large pressure drawdown in the
fracture blocks. However, the condensate dropout near the fracture blocks (2, 11, 2) is not
as high as the fracture blocks. Condensate blockage at the face of hydraulic fracture
results in gas relative permeability reduction.
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1000 2000 3000 4000 5000 6000
Ga
s re
lati
ve
pe
rm
Time, days
Gas relative perm at 1 10 2
Gas relative perm at 2 11 2
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5.5. Conclusions
The objective of this chapter is to propose a new approach to implant the staggered zipper
frac technique to stimulate a horizontal well pair. This requires perforation in a staggered
pattern of effective entrance hole through the pipe and cement. Gas injection EOR
process was performed in this staggered zipper fractured horizontal wells. Unit fracture
controlled volume simulation was able to represent the entire horizontal well recovery on
the basis of flow symmetry. The phase behavior of CO2 mixed with reservoir fluids were
investigated and proved to be efficient for enhanced oil recovery projects. From the
compositional modeling of the gas injection in a fractured horizontal well pair, it is
demonstrated that injected CO2 from hydraulic fractures of the injection well uniformly
pushes the reservoir fluids to the producing well and its fractures, which increases the
cumulative gas recovery significantly. The well production performance is strongly
dependent on the fracture spacing. The condensate saturation builds up by the large
pressure drawdown at the face of hydraulic fractures and gas relative permeability
reduction results were presented. The main goal of this chapter is to examine the EOR
potential by gas injection in a staggered zipper fractured horizontal well pair, which tends
to produce promising results. Next chapter will focus on examining the numerical
simulation of experimental data about cyclic gas injection in shale rocks. The role of
diffusion in laboratory floods will be discussed. The impact of withdrawal rates and soak
periods on liquid recovery in shale reservoirs will be presented.
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5.6. References:
Bazan, L.W., Larkin, S.D., Lattibeaudiere, M.G., Palisch, T.T. 2010. Improving
Production in the Eagle Ford Shale with Fracture Modeling, Increased Fracture
Conductivity, and Optimized Stage and Cluster Spacing Along the Horizontal Wellbore.
SPE 138425 presented at Tight Gas Completions Conference, San Antonio, Texas, USA.
Doi: 10.2118/138425-MS.
Belhadi, J., Ramakrishnan, H., Yuyan, R. 2011. Approach Optimizes Frac Treatments.
American Oil and Gas Report.
Chaudhary, A.S., Ehlig-Economides, C., Wattenbarger, R. 2011. Shale Oil Production
Performance from a Stimulated Reservoir Volume. Paper SPE 147596 presented at
Annual Technical Conference and Exhibition, Denver, Colorado, USA. Doi:
10.2118/147596-MS.
Cipolla, C.L., Warpinski, N.R., Mayerhofer, M.J., Lolon, E.P., and Vincent, M.C. 2008.
The Relationship Between Fracture Complexity, Reservoir Properties, and Fracture
Treatment Design. Paper SPE 115769 presented at the SPE Annual Technical Conference
and Exhibition, Denver, doi: 10.2118/115769-MS.
Cipolla, C., Weng, X., Mack, M., Ganguly, U., Gu, H., Kreese, O., and Cohen, C. 2011.
Integrating micro-seismic mapping and complex fracture modeling to characterize
fracture complexity. SPE 140185 presented at the SPE Hydraulic Fracturing Technology
Conference, The Woodlands, Texas. Doi: 10.2118/140185-MS.
Fan, L., Harris, B.W., Jamal, A., Kamath, J., Mott, R., Pope, G.A., Shandrygin, A.,
Whitson, C.H. 2005. Understanding Gas-Condensate Reservoirs. Oilfield Review, Vol.17.
Fong, D.K.S., and Nghiem, L.X. 1980. A Viscosity Model for Reservoir Fluids.
Computer Modelling Group Research Report R7.02.
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Hsu, S.-C., and Nelson, P.P. 2002. Characterization of Eagle Ford Shale. Engineering
Geology, Volume 67, PP. 169-183.
Mayerhofer, M.J., Lolon, E.P., Youngblood, J.E., and Heinze, J.R. 2006. Integration of
Microseismic-Fracture-Mapping Results With Numerical Fracture Network Production
Modeling in the Barnett Shale. Paper SPE 102103 presented at the SPE Annual Technical
Conference and Exhibition, San Antonio, Texas, USA. Doi: 10.2118/102103-MS.
Mayerhofer, M.J., Lolon, E.P., Warpinski, N.R., Cipolla, C.L., Walser,D., Rightmire, C.M.
2010. What Is Stimulated Reservoir Volume? SPEJ, Vol.25, No1. Pp 89-98. Doi:
10.2118/119890-PA.
Moinfar, A., Varavei, A., Sepehrnoori, K., Johns, R.T. 2013. Development of a Coupled
Dual Continuum and Discrete Fracture Model for the Simulation of Unconventional
Reservoirs. Paper SPE 163647 presented at the SPE Reservoir Simulation Symposium,
The Woodlands, TX, USA. Doi: 10.2118/163647-MS.
Narayanaswamy, G., Pope, G.A., Sharma, M.M. 1999. Predicting Gas Condensate Well
Productivity Using Capillary Number and Non-Darcy Effects. SPE paper 51910
presented at SPE Reservoir Simulation Symposium, Houston, Texas.
Pope, G.A., Wu, W., Narayanaswamy, G., Delshad, M., Sharma, M., Wang, P. 1998.
Modeling Relative Permeability Effects in Gas-Condensate Reservoirs. Paper SPE 49266
presented at SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana.
Doi: 10.2118/49266-MS.
Rafiee, M., Soliman, M.Y., Pirayesh, E. 2012. Hydraulic Fracturing Design and
Optimization: A Modification to Zipper Frac. SPE 159786 presented at SPE Annual
Technical Conference and Exhibition, 8-10 October, San Antonio, Texas, USA.
Rubin, B. 2010. Accurate Simulation of Non Darcy Flow in Stimulated Fractured Shale
Reservoirs. Paper SPE 132093 presented at Western Regional Meeting, Anaheim,
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California, USA. Doi: 10.2118/132093-MS.
Straight, D. 1989. Antelope Field: Preliminary Horizontal Drilling Evaluation, Bakken
Formation. Simtech Consulting Services, Inc., Golden, CO.
Wan, T., Sheng, J.J., Soliman, M.Y. 2013. Evaluate EOR Potential in Fractured Shale Oil
Reservoirs by Cyclic Gas Injection. Paper 168880 presented at the Unconventional
Resources Technology Conference held in Denver, Colorado, USA.
Wang, P., G.A, Pope and K. Sepehrnoori. 1997. Development of Equations of State for
Gas Condensates for Compositional Petroleum Reservoir Simulation. Industrial &
Engineering Chemistry Research.
Warpinski, N.R., Mayerhofer, M.J., Vincent, M.C., Cipolla, C.L., and Lolon, E.P. 2009.
Stimulating Unconventional Reservoirs: Maximizing Network Growth While Optimizing
Fracture Conductivity. JCPT, Vol.48, No 10. Pp 39-51. Doi: 10.2118/114173-PA.
Whitson, Curtis H. & Brule, Michael R. 2000. Phase Behavior. SPE Monograph Series
Vol. 20. Richardson, Texas.
Wu, Wei-Jr, P. Wang, M. Delshad, C. Wang, G.A. Pope and M. Sharma. 1998. Modeling
Non-Equilibrium Mass Transfer Effects for a Gas Condensate Field. Paper SPE 39746
presented at the Asia Pacific Conference held in Kuala Lumpur, Malaysia.
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Chapter 6
Numerical Simulation of the Experimental data in Liquid-Rich
Shales by Cyclic Gas Injection
6.1. Introduction
The objective of this chapter is to integrate the numerical simulation approaches with the
experimental data to examine the role of molecular diffusion, soak period and withdrawal
rates in laboratory floods. All the experimental data presented in this chapter is conducted
by Yu Yang (The experimental procedures are presented based on the discussion with Yu).
The simulation model history matched the experimental data clearly showed the
importance of diffusion in recovering oil in laboratory-scale flooding.
Monger and Coma (1988) conducted a laboratory-scale evaluation of the CO2 huff-n-puff
(also called cyclic gas injection) process with 32 experiments. The significance of soak
periods on the ultimate oil recovery is identified. The inclusion of a soak period led to an
incremental oil recovery than in absence of soak periods. The soak periods are
characterized by injected gas in the fracture transporting to the shale matrix by diffusion
process. Experimental results by Shayegi et al. (1996) revealed that the first cycle yielded
highest oil recovery. A second cycle recovered additional incremental oil with some
decline in process efficiency. Laboratory results by Monger et al. (1991) showed that
more favorable recovery performance is achieved by CO2 huff-n-puff injection at near-
miscible conditions than miscible. Their experimental data and reported field test results
suggest that CO2 retention and CO2 utilization factor increases as pressure decreases. The
conclusion was made under the presumption of same mass of CO2 injection. The
recovery performance by a large CO2 slug injection that develops miscibility with
reservoir oil is more favorable than a small CO2 slug.
Most of the available literature on performance of cyclic gas injection focused on
reservoir conditions that have high permeability. Recent studies (Chen et al. 2014;
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Gamadi, et al. 2013; Wan et al. 2013) showed that cyclic gas injection could be a viable
method to improve the oil recovery in shale oil reservoirs. Wang et al. (2013) reported
experimental results of CO2 huff-and-puff process operated in a 973 mm long composite
core with an average permeability of 2.3 mD. Their experimental results showed that the
first operation cycle contributes above half of the total oil production and additional oil
produced from subsequent cycles is significantly decreased compared to previous cycles.
Kurtoglu (2013) evaluated the feasibility of enhanced oil recovery by conventional gas
injection and gas huff-n-puff in Bakken fields using simulation techniques. She used a
dual-porosity reservoir model to simulate the CO2 huff-n-puff flooding performance in
the Bakken field. Unfortunately, the diffusion effect was not included in their model
because of numerical convergence issues. However, studies (Javadpour et al. 2007;
Sakhaee-Pour and Bryant 2012; Ozkan et al. 2010) suggest that molecular diffusion is an
important recovery mechanism in the mobilization and recovery of oil in very low
permeability shale oil or gas reservoirs. Chen et al. (2014) investigated the effect of
reservoir heterogeneity on CO2 huff-n-puff recovery process using (UT-COMP)
simulation approaches. The drawback of current simulation work on enhanced oil
recovery methods in unconventional reservoirs is that there is no sound laboratory or
field data to support the model prediction. Gamadi et al. (2013) presented a series of
experimental data of cyclic gas injection in Barnett, Mancos and Eagle Ford shale cores.
They investigated the effect of injection pressure, soaking time and the number of
injection cycles on oil recovery performance by N2 huff-n-puff process.
The available literature provides limited information for the laboratory examination of
the applicability of gas huff-n-puff in very low permeability shale rocks. Very limited
field or laboratory data are available on the performance of cyclic gas injection in shale
oil reservoirs. The purpose of this chapter is to extend earlier work performed by our
research group (Gamadi et al., 2013). In this chapter, the relevant parameters that affect
the performance of cyclic gas injection process are examined in detail. The principle
recovery mechanisms in shale oil reservoirs were discussed. A series of experiments
using immiscible cyclic nitrogen injection in Eagle Ford shale cores were conducted by
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Yu. The gas huff-n-puff core floods were conducted with the same 2-in long, 1.5-in
diameter Eagle Ford shale cores. This chapter interrelated numerical simulation approach
with the laboratory data to analyze the significance of possible parameters that have on
the performance of cyclic injection recovery process.
6.2. Material and Methods
Experimental Design
All the experiments were performed at the temperature of 95 ˚F in the oven. The cores
used in current experiments are from the Eagle Ford and Barnett. Each core has the same
dimension with 1.5-inch in diameter and 2-inch in length. A mineral oil Soltrol-130 was
used to represent the reservoir fluids. Table 6.1 shows the basic properties of Soltrol-130
provided by the manufacturer.
Table 6.1. Properties of Soltrol-130 (Chevron-Phillips Chemical Company LP)
Properties Value
Boiling Point 181˚C-209˚C
Specific Gravity 0.762 @ 15.6˚C (47 lb/ft3)
Viscosity 1.55 cSt @ 38˚C
Vapor Pressure 1.5 mmHg @ 38˚C
The experimental procedures (Core saturation and cyclic gas injection processes)
1. The cores were dried prior to the saturation process. In order to know the weight
of oil saturated into the shale cores, the dry weight of each core is required.
Analyses of the microstructures in tight shales suggest that the organic porosity
may play a dominant role for hydrocarbon accumulation and production
(Handwerger, D.A. et al., 2012). The effect of nitrogen adsorption on the organic
matter was not considered in simulation work. In this study, only oil and gas
phases were considered in the displacement performance (no interstitial water).
Since the pore size in tight shale formations is in the magnitude of microns in size,
it is very difficult to saturate the oil in shale cores up to a desired oil saturation. To
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inject oil into tight shales, it is necessary to vacuum the cores using a vacuum
pump to remove the air and create a high pressure difference between the
injection line and shale cores. The cores were placed in the desiccators and
vacuumed for 2 days.
2. The shale cores were saturated with Soltrol-130 oil at an injection pressure of
1000 psi for 24 hours.
3. Weigh all the cores again and record their saturated weight to calculate the oil
saturation of the shale rocks.
4. After the saturation processes, the saturated shale cores were placed in a
cylindrical core container. The N2 injection pump is opened to pressurize the
system up to 1000 psi (shown in Fig.6.1). The shale cores were exposed to the
injected gas that allows the injected gas to penetrate into the shale matrix. The
core container is placed in the oven which was heated to a constant temperature,
95 ˚F.
5. When the pressure showed on the pressure gauge reached to 1000 psi, shut down
the injection pump. In order to allow injected gas to diffuse into oil phase in the
shale matrix and to make the system reach equilibrium, the cores were soaked for
48 hours under a constant pressure (1000 psi). A uniform injection pressure in the
system is maintained in the soak duration.
6. Once the soak period is finished, the pressure in the system is exposed to
withdrawing down to the atmospheric pressure (14.7 psi). The pressure in the
tight shale cores may drop at a slower rate than that in the cylinder. In the
simulation model, the permeability of the fracture space between the core
container and the shale cores is assumed to be much higher than the shale matrix.
Pressure in the shale cores is required to be depleted to the atmospheric pressure
before processing the next stage.
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7. After the depletion process, the shale cores are weighed again to calculate the oil
production during this gas huff-and-puff process.
8. Repeated above 4-7 steps at the same injection and soaking conditions for 8
cycles to observe oil recovery profile with increasing number of cycles.
Figure 6.1-Experimental setup and apparatus
6.3. Simulation model description
Table 6.2. Properties of C15 at 95 ˚F
C15 at 95 F liquid
Ideal Cp, BTU/lbmol-R 75.82
MW, g/mol 206.00
Density, lb/ft3 47.28
Phase volume, % 100.000
A numerical simulation model (CMG-GEM model) was developed whose validity was
established by accurately simulating the cyclic gas injection results performed in the
laboratory. In the simulation model, C15 is used to represent the mineral Soltrol-130 oil.
Chevron-Phillips chemical company stated that the Soltrol-130 solvent is a complex
mixture of many different isoparaffinic molecules. Soltrol-130 isoparaffin solvent has the
carbon number distribution in the C11-C15 range. Based on the given product properties,
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C15 that has similar properties (density and viscosity) as mineral oil is used to simulate
the experiments. Although other critical properties have not been analyzed in detail yet, at
this point C15 is simply used to represent the oil properties. Table 6.2 shows the properties
of C15 at 95 ˚F.
Table 6.3. Reservoir and fluid properties used in this study
Parameters Value Unit
Initial core pressure 15 Psi Soaking pressure 1000 Psi
Reservoir temperature 95 ˚F
Porosity of shale matrix 5% value
Compressibility of shale 5*10-6 psi-1
Shale matrix permeability 0.0005 mD
Fracture permeability 1000 mD
Oil density 47.3 lb/ft3
Figure 6.2-Base simulation model
A radial coordinate model and Computer Modeling Group (CMG-GEM) reservoir
simulator are used to simulate the cyclic gas injection process in shale cores. The height
of the core holder is approximately two times higher than the cylindrical shale sample.
The dimension of the core holder (a diameter of 2.4-inch and 5.6-inch in height) is
Sector 2
Sector 1
Annular fracture
Shale matrix
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designed with a larger diameter and height than the shale cores, because the fracture was
represented by the surrounding annular volume. All the faces of the shale sample are
open during gas injection, diffusion and production stages. The permeabilities in the
annular fracture space between the shale core and inside core container are set as 1000
mD (fracture permeability sensitivity studies are performed as shown in Fig.6.6). The
Eagle Ford shale matrix permeability is assumed to be 0.0005 mD. In order to history
match the experimental data, the model input parameters including fracture permeability,
shale matrix permeability, relative permeability and diffusivities of oil and gas phases are
tuned within an acceptable range. A two-dimensional radial cross section (x-z) was used
to form the simulation domain. Table 6.3 shows us the reservoir rock and fluid properties
in the simulation model. The simulation model has 46 layers with the highest
permeability layer at the top (fracture permeability = 1000 mD). The shale matrix that
has the lowest permeability is located at the bottom. A variable grid-block size ranges
from 0.048 to 0.072 inch was used in the x direction and with refined gridblocks located
near the fracture. The simulation domain is separated into two sectors in order to control
the output of reservoir data on a regional basis. The shale cores are set as sector 1 and the
fracture region is set as sector 2. The production well and injection well are located at
gridblock (1,1,1) at the top of sector 2. It is important to notice that the actual volumes of
oil and gas produced by wells are different from the oil and gas recoveries from sector 1.
Once the production well was shut down, there were no oil and gas recoveries from the
well. However, oil and gas production from sector 1 is still possible because pressure
drawdown in the shale cores may be slower than the fracture space which results in oil
being driven out of the sector 1 by pressure gradients. Thus, the oil and gas recoveries by
wells are different from the recoveries in sector 1. The injection well is constraint to
inject at a maximum injection pressure at 1000 psi and a maximum surface gas rate at 1.2
MSCF/D. The injector will automatically change its mode of control whenever the
existing control mode would violate one of these limits. The production well is subject to
minimum bottom-hole pressure at 14.7 psi. This allows us to conveniently implement the
displacement process of the cyclic gas injection in laboratory. Once we open the injection
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valve, pressure in the annular fracture will reach to 1000 psi within 1-2 minutes. However,
it requires some time for the gas in the fracture space to diffuse into tight shale matrix and
achieve pressure and concentration equilibriums.
6.4. Results and Discussion
Fig.6.3 shows oil production response from 1th-5th cycle in the course of cyclic gas
injection. Cyclic gas injection was quite effective, especially in the first few cycles.
Subsequent cycles of N2 injection recovered additional incremental oil with some decline
in process efficiency. Table 6.4 and 6.5 investigated the effect of depletion time on
ultimate oil recovery. These two group comparison experiments probe the relationship
between the rate of withdrawal and recovery performance of shale reservoirs. Table 6.4
and 6.5 presented shale oil recovery results with 0.05-hr and 40-hr of depletion time,
respectively. The core pressure was systematically decreased to 15 psi over depletion
time. The depletion time refers to the time required for the initial fracture pressure
declined to atmospheric pressure in a huff-n-puff cycle. Fig.6.4 shows the comparison of
shale oil recovery performance during 8 cycles of gas injection process using two
different withdrawal rates. The experimental results presented in Fig.6.4 showed that
more oil recovery will be obtained with an increase of withdrawal rates.
Table 6.4. Measured shale oil recovery factor and oil saturation with a deletion time of 0.05-hr (Yu, 2015)
Number of cycles 1 2 3 4 5 6 7 8
Cumulative R.F. 14.23% 21.37% 26.47% 31.50% 35.72% 39.51% 42.79% 45.45%
Oil Saturation 0.86 0.79 0.74 0.68 0.64 0.60 0.57 0.55
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Table 6.5. Measured recovery factor and oil saturation with a deletion time of 40-hr (Yu, 2015)
Number of cycles 1 2 3 4 5 6 7 8
Cumulative R.F. 11.41% 16.95% 21.65% 25.82% 29.36% 32.62% 36.15% 39.66%
Oil Saturation 0.88 0.83 0.78 0.74 0.71 0.67 0.64 0.60
Figure 6.3-Shale oil production response in cyclic gas injection processes from 1th-5th cycle
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Figure 6.4-Effect of depletion time on CGI recovery performance
The effect of producing rate on ultimate oil recovery has been studied by many
researchers. Results from conventional reservoirs demonstrated an increase of ultimate
recovery was yielded at high producing rates purely from the perspectives of reservoir
flow mechanics (Beveridge, 1974; Permyakov and Gadok, 1961). Previous studies
(Morel et al.1993) showed that capillary end effect comes into play in lab-scale core
flooding, with an accumulation of liquid near the fracture that delays liquid production.
With a fast withdrawal rate, the capillary end effect is significantly reduced. However,
other investigations showed that high production rates might lead to a reduction in the
ultimate recovery compared to that with a slow withdrawal rate (Miller et al. 1949).
Longer producing time periods will assist in recovering oil more effectively by the
mechanisms of gravity drainage and cross-current. The recovery is affected by so many
parameters that the variations in recovery are not constrained to any one factor such as
rates.
In this simulation study, the impact of grid refinement on the recovery performance is
examined by performing a series of numerical sensitivity calculations. Fig.6.5 illustrates
that using 22x1x46 2D grid-blocks is producing similar results as more refined 50x1x46
grid-blocks. Grid refinement near the transition zone between highly conductive fracture
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
0 1 2 3 4 5 6 7 8 9
Cu
mu
lati
ve
Oil
Re
cov
ery
, %
Number of Cycles
Eagle Ford shales-Effect of Production Rate
Experimental results-0.05 hr
Experimental results-40 hrs
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and shale matrix is needed to produce steady numerical solutions. Fig.6.5 shows that a
22x1x46 grid-block model is able to properly simulate the gas injection processes. There
are some unknown parameters in this simulation model such as annular fracture
permeability, relative permeability curves and shale matrix permeability. The simulation
model that obtains a good agreement with the laboratory data is not unique since
simulation output results are affected by these unknown input parameters. Fig.6.6
investigated the effect of fracture permeability on enhanced oil recovery performance. It
is seen from Fig.6.6 that annular space permeability does not affect the cumulative oil
recovery significantly provided that it is larger than 1000 mD.
Figure 6.5-Effect of grid block size on calculated oil R.F.
1 2 3 4 5 6 7 80
5
10
15
20
25
30
35
40
45
Number of cycles
Cu
mu
lativ
e o
il re
co
ve
ry,%
50x1x46
22x1x46
10x1x46
22x1x90
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Figure 6.6-Effect of fracture permeability on cyclic gas injection performance
Figure 6.7-Comparison of simulation results with experimental data (0.05 hours)
1 2 3 4 5 6 7 85
10
15
20
25
30
35
40
45
Number of cycles
Cum
ula
tive o
il r
ecove
ry,%
Kf = 10-md
Kf = 100-md
Kf = 1000-md
Kf = 100,000-md
1 2 3 4 5 6 7 80
5
10
15
20
25
30
35
40
45
50
Number of cycles
Cum
ula
tive
oil
reco
ve
ry,%
Injection P = 1000 psi, depletion time = 0.05-hr
Experimental data
Simulation (W ilke-Chang)
Simulation (Sigmund)
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Figure 6.8-Comparison of simulation results and experimental data (40 hours)
The diffusion mechanism is considered in computing the matrix and fracture molecular
fluxes. The dispersive-convective flux through nanopores in shale oil reservoirs is
modeled during gas injection process. Fig.6.7 shows the comparison of simulation results
with the experimental data at 1000 psi injection pressure with 0.05 hrs of depletion time.
The molecular diffusion coefficients could be calculated by Sigmund (1976) method. The
effective diffusion coefficients for each component of the mixture could also be estimated
by Wilke-Chang approach (Wilke and Chang, 1955). The oil recovery produced by
Wilke-Chang correlation is closer to the experimental data than using Sigmund’s
correlation. Fig.6.8 presented the history matching results of the measured cumulative oil
recovery in eight cycles with 40-hour of depletion time. There is a slight deviation
between the experimentally measured data and simulation results. This is primarily due to
the fact that the micro-fractures are not considered in the simulation model, while natural
fractures might exist in the shale cores that result in a higher oil recovery. Fig.6.9 shows a
good agreement between the measured oil saturations and calculated oil saturations by
simulation.
1 2 3 4 5 6 7 80
5
10
15
20
25
30
35
40
45
50
Number of cycles
Cum
ula
tive o
il r
ecove
ry,%
Injection P = 1000 psi, depletion time = 40-hr
Simulation (Sigmund)
Simulation (W ilke-Chang)
Experimental data
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Figure 6.9-Comparison of simulated oil saturation and measured data
Figure 6.10-Pressure variations in one cycle of huff-n-puff process (40-hour depletion)
Table 6.6. Operational schedules in a cycle
Time (days) 0-0.001 0.001-2 2-3.67 (40 hrs) 3.67-4
Operations Injector open Soak Producer open Further depletion
Table 6.6 shows the operational schedule in a huff-n-puff cycle. Fig.6.10 displays the
reservoir pressure variations during the nitrogen huff-n-puff processes at time 0, 0.001
1 2 3 4 5 6 7 80
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Number of cycles
Ave
rag
e o
il sa
tura
tion
Injection P = 1000 psi, depletion time = 0.05-hr
Simulation (Wilke-Chang)
Experimental data
1 2 3
4 5 6
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days injection, 2 (soaking), 2.1 (after 0.1-day of production), 3 (after 1 day of production)
and time 4 (depleted to atmospheric pressure), respectively. It is observed that the
pressure buildup in the fracture space is much faster than that in the shale cores. When
pressure in the fracture was increased to 1000 psi, the pressure in the inner shale cores
still remained around 15 psi. The injected nitrogen front slowly propagates through the
shale matrix. The region beyond the gas invaded region remains at initial reservoir
pressure. As time progresses, nitrogen penetrates deep along the radial direction into the
shale matrix with a progressive increase in the size of the treated shale zone. The soak
period is designed to allow the injected gas in the fracture to diffuse into the shale matrix
to achieve compositional and pressure equilibriums. In regarding to how long it takes to
attain an equilibrium state in the system, it depends on a lot of factors, such as fracture
permeability, shale cores permeability and fluids properties (diffusive velocity). During
the gas production phase, the pressure drawdown in the fracture spacing decreases at a
higher rate than the tight shale cores. The pressure in the fracture declines to the
minimum allowable bottom-hole flowing pressure (15 psi) in a short period of time,
while the pressure in the inner gridblocks of shale cores remained 1000 psi. In the
production cycle, the average pressure in the fracture is lower than the shale matrix,
which results in oil and nitrogen being displaced out by pressure difference. Owing to the
high oil concentration and high pressure resided in the shale matrix, the transport
mechanisms of oil component are a combination of diffusive and viscous displacement.
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Figure 6.11-Comparison of simulation results and experimental data (Pi=1000 psi, depletion time = 40-hr)
Figure 6.12-Effect of diffusion on ultimate oil recovery
Fig.6.11 shows comparisons between the simulation model output results and
experimental results on time scale. Due to the fact that we only measured oil recovery at
the end of each cycle, the oil recovery was increased instantaneously and sharply for
0 5 10 15 20 25 300
5
10
15
20
25
30
35
40
45
50
Time, days
Cu
mu
lativ
e o
il re
co
ve
ry,%
Simulation results
Experimental data
1 2 3 4 5 6 7 80
5
10
15
20
25
30
35
40
45
Number of cycles
Cum
ula
tive
oil
reco
ve
ry,%
With diffusion (0.05-hr depletion)
With diffusion (40-hr depletion)
Without diffusion (0.05-hr depletion)
Without diffusion (40-hr depletion)
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experimental data. Although there is some deviation at the first few cycles between
simulation results that include molecular diffusion model with the experimental results,
they are in good agreement at subsequent cycles. It is recognized that oil is continuously
produced until the pressure in the shale matrix is in equilibrium with the fracture, which
is clearly presented in the Fig.6.11. After having been subjected to multiple cycles of
nitrogen injection, the cumulative oil recovery from the treated shale cores increased
substantially. The importance of diffusion effect is observed in Fig.6.12. It is noteworthy
that the impact of diffusion is more significant than production time on ultimate oil
recovery. With diffusion included in the simulation model, the results exhibited roughly
10% higher oil recovery. Without considering the diffusion effect, the principle drive
mechanism is viscous displacement. In absence of diffusion, the incremental oil
recoveries at each cycle are increasing at roughly equal rates because applied pressure
differentials in each cycle are the same. Simulation results showed that withdrawing the
nitrogen in the annual fracture space at rapid rates produced a higher cumulative oil
recovery than low rates, which is consistent with the experimental observations showed
in Fig.6.4.
Figure 6.13 -Effect of soak duration on production response
1 2 3 4 5 6 7 80
10
20
30
40
50
60
Number of cycles
Cum
ula
tive o
il r
ecove
ry,%
Injection P = 1000 psi, depletion time = 0.05-hr
Exp, Soak 1-hour
Exp, Soak 3-hour
Exp, Soak 24-hour
Exp, Soak 72-hour
Simulation, Soak 1-hour
Simulation, Soak 24-hour
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The effect of soak duration on ultimate oil response remains controversial. Monger and
Coma (1988) pointed out that the cyclic process response was not sensitive to the soak
duration in huff-n-puff field tests. However, Thomas and Monger (1991) showed that
incremental oil recovery increased by extending the soak periods up to 4 weeks, provided
that incremental oil not discounted for production lost during soak periods. Recent
experimental observations reported by Yu and Sheng (2015) furthered investigation of the
effect of soak duration on process response in Eagle-Ford shale reservoirs by cyclic
nitrogen injection (Fig.13). Based on their experimental data, we used a numerical
simulation approach to enhance our understanding of cyclic gas recovery mechanisms.
Fig.13 shows that a 24-hour soak time results in a more favorable oil recovery profile
than 1-hour of soak time. However, extending the soak length from 24-hour to 72-hour
has immaterial effect on the production response improvement. Production losses during
the soak periods can be reduced by employing the optimal cycle length at current
reservoir conditions. The comparison conducted between 24-hour and 72-hour soak
showed that production response is less efficient by the long soak duration. It was
speculated that experimental errors existed in the measurements of the 24-hour and 72-
hour soak.
Figure 6.14-N2 mole fraction in the shale matrix from 1th -8th cycle (1-hour soak time)
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The effect of soak periods during cyclic gas injection process in nano-permeable shales is
presented in Fig.13. The importance of diffusion is underlined by the relation between
process performance and soak duration. A soak period is dominated by diffusive transport
of injected nitrogen in the fracture to the shale matrix. A soak period allows free gas to
contact with oil that is not near the core inlet. The N2 mole fraction in the shale matrix
from 1th -8th cycle is shown in Fig.14. Nitrogen dissolved in the oil phase is increasing
progressively as the cycles proliferate. In the cases of cyclic CO2 injection, laboratory
core-flooding results by Monger and Coma (1988) suggested that hydrocarbon extraction
into CO2 rich phase also requires a soak period. Detailed discussion about the role of
diffusion in a field-scale gas flooding in fractured shale oil reservoirs is referred to Wan
and Sheng (2015). It is recognized that diffusion effect is an important recovery
mechanism in mobilizing and recovering oil from nanopore systems. Gamadi et al. (2013)
presented the cyclic N2 injection performance in shale cores from different fields at 1000-
psi, 2000-psi and 3000-psi. Their experimental data (in their Fig.5) showed that gas
injection pressure had a significant effect on incremental oil recovery. High injection
pressures achieve a favorable cyclic response.
6.5. Conclusions
This study focused on evaluating the potential of oil recovery by cyclic nitrogen injection
in shale oil reservoirs. Our understanding about huff-n-puff in shale plays can be
furthered by comparing model predictions with experimental observations. Pressure
depletion rate, soak duration and diffusion effect were discussed and how they influence
process performance. Further studies are needed to better understand the mechanisms of
oil recovery in shale oil reservoirs by cyclic gas injection.
1. Both experimental data and simulation results show the potential and applicability
of cyclic gas injection to improve oil recovery in liquid-rich shales.
2. The first cycle yielded highest oil recovery by immiscible cyclic nitrogen
injection in liquid-rich shales. Additional oil produced from subsequent cycles is
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significantly decreased compared to previous cycles.
3. Results of the simulation and experiments showed that higher ultimate recovery
was yielded at higher producing rates.
4. The simulation model that includes the molecular diffusion recovery mechanism
produced well-matched results with experimental data. The diffusion effect should
be considered in recovering oil from nano-permeable shale rocks by secondary
cyclic injection method.
5. It requires some time for the gas in the fracture space to diffuse into the oil phase
in tight shale cores and achieve pressure and concentration equilibriums with the
shale cores. The significance of soak periods is observed in experimental results.
6.6. Acknowledgments
I would like to express my appreciation to ConocoPhillips. Their support is greatly
appreciated. I also appreciate Yu give me the experimental data to complete this
simulation work. I extend my appreciation to Computer Modeling Group (CMG) for
providing the software for reservoir simulation.
6.7. References
Beveridge, S.B., Coats, K.H., Agrawal, R.K. and Modine, A.D., 1974. A Study of the
Sensitivity of Oil Recovery to Production Rate. SPE 5129, Fall Meeting of the Society of
Petroleum Engineers of AIME, 6-9 October, Houston, Texas.
Black, D.J., Aziz, N.I., Ren, T.X., 2011. Enhanced Gas Drainage from Undersaturated
Coalbed Methane Reservoirs. No.50, The 3rd Asia Pacific Coalbed Methane Symposium,
May 3-6, Brisbane, Australia.
Chen, C., Balhoff, B., Mohanty, K. K., 2014. Effect of Reservoir Heterogeneity on
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Primary Recovery and CO2 Huff-n-Puff Recovery in Shale-Oil Reservoirs. SPEREE 17
(03), 404-413. Doi: 10.2118/164553-MS.
Gamadi, T.D., Sheng, J.J., Soliman, M.Y., 2013. An Experimental Study of Cyclic Gas
Injection to Improve Shale Oil Recovery. SPE 166334, SPE Annual Technical
Conference and Exhibition held in New Orleans, Louisiana, USA, 30 September–2
October.
Handwerger, D.A., Suarez-Rivera, R., Vaughn, K.I., Keller, J.F., 2012. Methods Improve
Shale Core Analysis. The American Oil and Gas Reporter.
Javadpour, F., Fisher, D., and Unsworth, M., 2007. Nanoscale Gas Flow in Shale Gas
Sediments. JCPT 46 (10), 55-61. Doi: 10.2118/ 10.2118/07-10-06.
Kurtoglu, B., 2013. Integrated reservoir characterization and modeling in support of
enhanced oil recovery for Bakken. Ph.D. dissertation at petroleum engineering at the
Colorado School of Mines.
Lofton, L.K. and Morse, R.A., 1978. The Effects Of Injection Pressure On Condensing
Gas Drive Recovery. SPE 7472, SPE Annual Fall Technical Conference and Exhibition,
1-3 October, Houston, Texas.
Miller, C. C., Brownscombe, E. R., & Kieschnick Jr, W. F., 1949. A Calculation of the
Effect of Production Rate upon Ultimate Recovery by Solution Gas Drive. JPT, 1(09),
235-247.
Monger, T.G., and Coma, J.M., 1988. A Laboratory and Field Evaluation of the CO2 Huff
‘n’ Puff Process for Light-Oil Recovery. SPE Reservoir Engineering, 3 (04), 1168-1176,
SPE-15501-PA.
Monger, T.G., Ramos, J.C., Thomas, J., 1991. Light Oil Recovery from Cyclic CO2
Injection: Influence of Low Pressures, Impure CO2, and Reservoir Gas. SPE Reservoir
Engineering, 01(6), 25-32.
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Morel, D., Bourbiaux, B., Latil, M., Thiebot, B., 1993. Diffusion Effects in Gasflooded
Light-Oil Fractured Reservoirs. SPE J. 1(02), 100-109.
Ozkan, E., Raghavan, R.S., Apaydin, O.G., 2010. Modeling of Fluid Transfer From Shale
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SPE-146944-PA.
Shayegi,S., Jin, Z., Schenewerk, P., Wolcott, J., 1996. Improved Cyclic Stimulation Using
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Reservoirs by Cyclic Gas Injection. SPE 168880, Unconventional Resources Technology
Conference held in Denver, Colorado, USA, 12-14 August.
Wang, Z., Ma, J., Gao, R., Zeng, F., Huang, C., Tontiwachwuthikul, P., Liang, Z., 2013.
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Chapter 7 Conclusions
The objective of this dissertation is to evaluate the response of cyclic gas injection as an
enhanced-oil-recovery method in intensely naturally fractured and hydraulically fractured
reservoirs. The impact of spacing of fracture network on oil recovery were investigated in
chapter 3, the simulation results indicate that smaller fracture spacing is constructive to
improving oil recovery in shale oil reservoirs. The primary decision in designing fracture
treatments in shale oil reservoirs is to exploit fracture complexity.
In this dissertation, we also coupled the diffusion equation with a dual permeability
model so that the enhanced-oil-recovery process by gas flooding can be properly
simulated. There are difficulties in use a single porosity dual-continuum model to match
the experimental results of gas flooding in the low permeability shale rocks, because it
cannot properly capture the matrix-fracture mass transfer rates of densely distributed
microfractures. The results produced by dual permeability model coupled with diffusion
are in good agreement with the experimentally measured data in shale rocks. One
deficiency in use of Kazemi’s shape factor in the simulation model is that assumptions of
pseudo-steady state and instantly immersed fractures may break down in shale reservoirs.
The shape factors are treated as adjustable parameters which are used to reproduce
observed field or laboratory results. However, this approach does not necessarily capture
the physics of matrix-fracture transfer flow behavior.
The simulation work in chapter 4 addressed the role of diffusion in the field scale
flooding. The impact of matrix-fracture diffusion rate in the oil phase and matrix-matrix
diffusion on oil recovery is significant in tight shale oil reservoirs. It is observed that the
interstitial velocity of oil phase in the shale matrix is considerably lower than that of the
conventional reservoirs. In very low permeability shale oil or gas reservoirs, the dominant
recovery mechanism is by diffusion according to the analysis of Peclet number. One
noticeable finding of gas injection in shale reservoirs is that without including the matrix-
fracture diffusion in the oil phase, it results in lower oil recoveries. It is essential to model
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the matrix-fracture diffusion rate in the oil phase and diffusion within the matrices for
tight shale oil reservoirs. In conventional reservoirs, Coats’s (1989) model is able to
produce good results because the transport is controlled by viscous flow.
The importance of diffusion effect on fractured shale gas reservoirs and shale oil
reservoirs is observed. In the cases where the convective flux for a component is
negligible because of low pressure drawdown, diffusion due to compositional differences
between matrix and fracture tends to become the main recovery mechanism. From a
reservoir management perspective, relying on diffusion enhanced oil recovery by
decreasing injection rate is not the best way to exploit shale resource plays.
Chapter 5 proposed a new approach to implant the staggered zipper frac technique to
stimulate a horizontal well pair. This requires perforating a staggered pattern of effective
entrance hole through the pipe and cement. Gas injection EOR process was performed in
this modified zipper fractured horizontal wells. The phase behavior of CO2 mixed with
reservoir fluids were investigated and proved to be efficient for EOR. From our
compositional modeling of the gas injection in a fractured horizontal well pair, it is
demonstrated that injected CO2 from the injection well fracture uniformly pushes the
reservoir fluids to the producing well and its fracture, which increases the cumulative gas
recovery significantly. The well production performance is strongly dependent on the
fracture spacing. The condensate saturation builds up by the large pressure drawdown at
the face of hydraulic fractures and gas relative permeability reduction results were
presented. The main goal of chapter 5 is to examine the EOR potential by gas injection in
modified zipper fractured horizontal well pair, which tends to produce promising results.
Both experimental data and simulation results substantiated the applicability of cyclic gas
injection to improve oil recovery in liquid-rich shales in chapter 6. The first cycle yielded
highest oil recovery by immiscible cyclic nitrogen injection in liquid-rich shales.
Additional oil produced from subsequent cycles is significantly decreased compared to
previous cycles. The simulation model that includes the molecular diffusion recovery
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mechanism produced well-matched results with experimental data. The diffusion effect
should be considered in recovering oil from nano-permeable shale rocks by secondary
cyclic injection method. It requires some time for the gas in the fracture space to diffuse
into the oil phase in tight shale cores and achieve pressure and concentration equilibriums
with the shale cores. The significance of soak periods is observed in experimental results.
The contribution of this study
Very limited work has been dedicated to studying possible avenues for enhanced oil
recovery in shale oil reservoirs. Cyclic gas injection is proposed in this dissertation to
improve shale oil recovery. It is discovered that the impact of matrix-fracture diffusion
rate in the oil phase and matrix-matrix diffusion on oil recovery is significant in tight
shale oil reservoirs. The recovery mechanisms in very low permeability shale oil and gas
reservoirs were reexamined. The numerical simulation approaches with experimental data
were interrelated to investigate significance of related parameters on enhanced oil
recovery process. It is found that diffusion plays an important role in recovering oil from
nano-permeable shale rocks by secondary cyclic injection method in laboratory-scale
flooding.
Recommendations for future work
1. Affinity of organic-rich shales to carbon dioxide has been reported by many
researchers in the literature. The adsorption of CO2 could be significant on the
finely-dispersed organics that have a large internal surface area. The higher CO2
concentration in the fracture or adsorbed on organic-matter surface is critical to
diffusive flow of injected CO2 into shale matrix. The adsorption effect of shale
gas and injected gas on shale gas recovery is not included in this dissertation yet.
In terms of adsorption effect, some literature states adsorbed gas comprises only
a small fraction of produced gas. Sanaei et al. (2014) found that desorption has
negligible effect on gas and condensate recoveries from Eagle Ford shales. It is
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believed that desorption effect has to be considered in shales with high total
organic content (TOC). Otherwise, an underestimation of ultimate gas recovery
is expected. The significance of desorption effect on gas and oil recovery is not
considered in this dissertation.
2. Literature points out that fracture growth in naturally fractured shale reservoirs
can be very complex due to interactions between induced fractures with the pre-
existing fracture network (Maxwell et al., 2002). Microseismic imaging in the
Barnett shale has shown a high degree of complexity of the simulated fracture
network. The stimulated fractures are interesting with pre-existing fracture
network. The most difficult part is to describe the natural fractures in shale
reservoirs, which are critical to well productivity by enhanced oil recovery
process.
3. One common problem during CO2 injection is that injected CO2 could induce
asphaltene precipitation that may cause formation plugging. The likelihood of
asphaltene precipitation and deposition effect on oil recovery in secondary
miscible CO2 flooding is not considered in this dissertation. In low permeability
shale reservoirs, the further permeability reduction induced by wax could be an
important consideration to oil recovery. Many factors can affect the asphaltene
precipitation process such as the asphaltene to resin ratio, the nature of injection
gas, reservoir pressure and temperature and CO2 concentration (Srivastava et al.,
1999).
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