Razi Gaskari - Tecnica Integral de Datos de Produccion
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Transcript of Razi Gaskari - Tecnica Integral de Datos de Produccion
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An Integrated Technique forProduction Data Analysis with
Application to Mature Fields.
Razi Gaskari
Shahab D. Mohaghegh, Jalal Jalali
West Virginian University
SPE 100562
SPE Gas Technology Symposium, Calgary, Alberta, Canada - May 2006.
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Introduction
The most common data that engineers cancount on, specially in the case of mature
fields is PRODUCTION RATE DATA.
SPE 100562
Introduction Objective Methodology Result and Discussion Conclusion
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Introduction
History of production data analysis:
SPE 100562
Introduction Objective Methodology Result and Discussion Conclusion
1945 Arps et al. Liquid systems, Rate-time type curves, PSSconditions, parameter b, no physical bases.
1980 Fetkovich: Liquid systems, Constant pressure, Early time,
Transient analytical solution + Arps.
1985 Carter: Liquid and gas systems, constant BHP, parameter ()
1987 Wattenbarger: Gas systems, modified Fetkovich type curves,
normalize time, long term boundary dominated (PSS).
1993 Palacio, Blasingame: Liquid and gas systems , Radial flow,
dimensionless variables, equivalent constant rate liquid data,
derivative methods. 1995 Cox: Gas flow, tight and hydraulically fractured reservoir.
1999 Agarwal et al.: Liquid and Gas Radial systems, Fractured wells,
Finite and Infinite conductivity
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Introduction
Shortcoming of Sate-of-the-art ProductionData Analysis:
Inherent subjectivity.
It requires pressure data (bottom-hole or well-head).
Addresses individual wells rather than the entire field.
SPE 100562
Introduction Objective Methodology Result and Discussion Conclusion
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Objective
Development of an integrated & comprehensiveproduction rate analysis technique:
Minimize subjectivity.
With reasonable repeatability.
With reasonable geological resolution.
Field wide production analysis
Addresses Entire Field (depletion, remaining reserve, etc.) Sweet spots.
Detect underperforming wells.
SPE 100562
Introduction Objective Methodology Result and Discussion Conclusion
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Methodology
Methodology is demonstrated through applicationto a mature field in mid-continent U.S.
Wattenberg field in D.J. basin of Rockies. 137 wells used in the analysis.
Only publicly available production rate data was used
for analysis.
SPE 100562Introduction Objective Methodology Result and Discussion Conclusion
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Methodology
An iterative approach that integrates:
Decline Curve Analysis
Type Curve Matching
Single-well Numerical Reservoir Simulation
& then applies all the findings to the entire field:
Fuzzy Pattern Recognition & Analysis
SPE 100562Introduction Objective Methodology Result and Discussion Conclusion
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Methodology
SPE 100562
Step One:
Qi, Di, bEUR
EUR, b, h , K
S, A, , Xf
EUR, b, h ,K
S, A, , Xf
Introduction Objective Methodology Result and Discussion Conclusion
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Methodology
Decline Curve AnalysisType Curve MatchingHistory Matching
TCM
DCA
HM
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Methodology
Step Two:
SPE 100562
SweetSpots
RemainingReserves
Underperformingwells
Trends inthe field
Fuzzy pattern recognition technique.
Introduction Objective Methodology Result and Discussion Conclusion
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Decline Curve Analysis
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Introduction Objective Methodology Result and Discussion Conclusion
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Type Curve Matching
SPE 100562
b= 0.6 EUR = 204 MMCF
Decrease subjectivity in type curve matching
b= 1.69 EUR = 286.5 MMCF
Introduction Objective Methodology Result and Discussion Conclusion
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Type Curve Matching
EUR30 = 532 MMCF
EUR30 = 401 MMCF
Adding uniqueness to the type curve match.
Actual Production Data
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Type Curve Matching
SPE 100562
To be used in History Matching
Introduction Objective Methodology Result and Discussion Conclusion
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Type Curve Matching
SPE 100562
EUR30 =590.57 MMCF
EUR30 =116.09 MMCF
Introduction Objective Methodology Result and Discussion Conclusion
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Reservoir Simulations
History Matching of production data using asingle-well radial numerical model.
Use the results of type curve matching
procedure as a guideline to achieve reasonablehistory match.
In order to achieve a reasonable match we have
to go back to TCM and DCA and iterativelymodify some parameters.
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Reservoir Simulations
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Reservoir Simulations
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Introduction Objective Methodology Result and Discussion Conclusion
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Reservoir Simulations
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Monte Carlo Simulation
Introduction Objective Methodology Result and Discussion Conclusion
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Combined DCA, TCM and HM
SPE 100562
Well ID Qi (MCF) Di b EUR (MMCF)
51231540400 9,500 0.064 0.773 376.87
51231556200 47,191 0.846 1.333 590.57
51231558900 8,177 0.084 1.14 378.27
51231559100 18,140 0.218 1.523 669.5
Decline Curve Analysis
K (md) Xf (ft) A (acre) EUR (MMCF)
0.48 57.62 14.94 374.57
0.884 137.46 6.94 587.481
0.557 21.77 9.79 376.6
0.977 42.92 8.29 662.9
Type Curve Matching
Reservoir Simulator (history matching)
Monte Carlo Simulator
(EUR)
Introduction Objective Methodology Result and Discussion Conclusion
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Reservoir Quality Index
Upon completion of the first step, we have a set ofreservoir characteristics that are reasonably close toreality, in quality and range.
The second part of analysis uses Fuzzy PatternRecognition technique to integrate the above informationin the context of the entire field.
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Reservoir Quality Index
Fuzzy Pattern Recognition based on Longitude
Fuzzy Pattern
Recognition
based onLatitude
Low Relative
Reservoir Quality
Index RRQI
represents higher
quality reservoir
characteristics.
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Reservoir Quality Index
SPE 100562
Note the
movement of
a well fromone RRQI to
another with
time.
Introduction Objective Methodology Result and Discussion Conclusion
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Reservoir Quality Index
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Reservoir Characteristics
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Introduction Objective Methodology Result and Discussion Conclusion
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Remaining Reserves
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Remaining Reserves
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Conclusions
An Integrated technique has been introduced for
production rate data analysis.
The integrated technique uses DCA, TCM and HM in aniterative fashion to converge to a reasonable set of
reservoir characteristics. The integrated technique uses the fuzzy pattern recognition
with the results of above integration and produces 2D and3D maps of the filed for:
Reservoir Quality
Reservoir Characteristics
Remaining Reserves
SPE 100562
Introduction Objective Methodology Result and Discussion Conclusion