– Building and Ranking of Geostatistical Petroleum Reservoir Models –
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ABSTRACT
Techniques in Geostatistics are increasingly being used to generate reservoir models and quantify uncertainty in reservoir properties.
This is achieved through the construction of multiple realizations to capture the physically significant features in the reservoir. However, only a limited number of these realizations are required for complex fluid flow simulation to predict reservoir future performance.
Therefore, there is the need to adequately rank and select a few of the realizations for detailed flow simulation. This thesis presents a methodology for building and ranking equiprobable realizations of the reservoir by both static and dynamic measures.
Sequential Gaussian Simulation was used to build 30 realizations of the reservoir. The volume of oil originally in place, which is a static measure, was applied in ranking the realizations.
Also, this study utilizes Geometric Average Permeability, Cumulative Recovery and Average Breakthrough times from streamline simulation as the dynamic measures to rank the realizations. A couple of realizations selected from both static and dynamic measures were used to conduct a successful history match of field water cut in a case study.
TABLE OF CONTENTS
SIGNATURE PAGE……………..………………….…………………………………………………………..I
TITLE PAGE…………………………………………………………………………….……………………….II
ABSTRACT ……………………………………………………………………………………………………………….. III
DEDICATION …………………………………………………………………………………………………………….. IV
ACKNOWLEGDEMENTS ……………………………………………………………………………………………. V
TABLE OF CONTENTS ………………………………………………………………………………………………. VI
LIST OF FIGURES ……………………………………………………………………………………………………… IX
LIST OF TABLES ………………………………………………………………………………………………………… X
LIST OF APPENDICES ………………………………………………………………………………………………. XI
CHAPTER ONE – INTRODUCTION …………………………………………………………………………… 1
1.1 PROBLEM DEFINITION ……………………………………………………………………………………………… 1
1.2 OBJECTIVES ………………………………………………………………………………………………………………….. 2
1.3 SCOPE OF WORK …………………………………………………………………………………………………………. 2
CHAPTER TWO – LITERATURE REVIEW ……………………………………………………………….. 4
2.1 INTRODUCTION ………………………………………………………………………………………………………….. 4
2.2 BUILDING OF GEOSTATISTICAL RESERVOIR MODELS ……………………………………… 4
2.2.1 Stochastic Simulation ………………………………………………………………………………………………. 6
2.2.1.1 Sequential Gaussian Simulation (SGSIM) …………………………………………………….. 6
2.3 RANKING OF GEOSTATISTICAL RESERVOIR MODELS ………………………………………. 8
2.3.1 Static Criteria …………………………………………………………………………………………………………….. 8
2.3.2 Dynamic Criteria ………………………………………………………………………………………………………. 9
2.4 LITERATURE SUMMARY ………………………………………………………………………………………… 12
CHAPTER THREE – STUDY METHODOLOGY ……………………………………………………….. 13
3.1 INTRODUCTION ……………………………………………………………………………………………………….. 13
3.2 STUDY AREA ……………………………………………………………………………………………………………… 14
3.3 DATA SUMMARY AND ANALYSIS …………………………………………………………………………. 15
3.3.1 Analysis ……………………………………………………………………………………………………………………. 15
3.4 BUILDING OF RESERVOIR REALIZATIONS ………………………………………………………… 22
3.5 RANKING OF RESERVOIR REALIZATIONS ………………………………………………………. 26
3.5.1 Stock Tank Oil Originally in Place ………………………………………………………………………. 26
3.5.2 Geometric Average Permeability ……………………………………………………………………….. 27
3.5.3 Connected Hydrocarbon Pore Volume ……………………………………………………………… 28
3.5.4 Breakthrough times ………………………………………………………………………………………………. 29
3.5.5 Cumulative Recovery ……………………………………………………………………………………………. 31
3.6 FILTERING SELECTED REALIZATIONS BY RECOVERY AND WATER CUT ….. 31
3.7 CHAPTER SUMMARY ……………………………………………………………………………………………….. 32
CHAPTER FOUR –RESULTS AND DISCUSSION ……………………………………………………… 33
4.1 BUILDING OF RESERVOIR MODELS …………………………………………………………………….. 33
4.2 RANKING OF THE RESERVOIR MODELS …………………………………………………………….. 36
4.2.1 Static Ranking …………………………………………………………………………………………………………. 36
4.2.2 Dynamic Ranking …………………………………………………………………………………………………… 37
4.2.2.1 Geometric Average Permeability (kga) ………………………………………………………. 37
4.2.2.2 Connected Hydrocarbon Pore Volume (CHPV) ……………………………………….. 37
4.2.2.3 Average Breakthrough times (ABT) …………………………………………………………… 38
4.2.2.4 Cumulative Recovery (CR) ……………………………………………………………………………. 40
4.2.3 Relationship between Dynamic Ranking Criteria ………………………………………….. 40
4.2.4 Application of Cumulative Recovery …………………………………………………………………. 41
4.2.5 Application of Field Water Cut ……………………………………………………………………………. 44
4.3 HISTORY MATCHING……………………………………………………………………………………………….. 45
4.4 CHAPTER SUMMARY ……………………………………………………………………………………………….. 48
CHAPTER FIVE – CONCLUSIONS AND RECOMMENDATIONS……………………………… 49
5.1 SUMMARY AND CONCLUSIONS …………………………………………………………………………….. 49
5.2 RECOMMENDATIONS ……………………………………………………………………………………………… 50
REFERENCES ……………………………………………………………………………………………………………. 52
INTRODUCTION
In Geostatistical reservoir characterization, it is a common practice to generate a large number of realizations of the reservoir model to assess the uncertainty in reservoir descriptions for performance predictions.
However, only a limited fraction of these models can be considered for comprehensive fluid flow simulations because of the high computational costs.
There is therefore the need to rank these equiprobable reservoir models based on an appropriate performance criterion that adequately reflects the interaction between reservoir heterogeneity and flow mechanisms.
Most techniques used in ranking of realizations are based on static properties such as highest pore volume, highest average permeability, and closest reproduction of input statistics.
The drawback of these simple techniques is that they do not account for dynamic flow behavior which is very essential in predicting future reservoir performance.
This thesis work seeks to build and rank equally probable representations of the reservoir using petrophysical properties such as porosity, water saturation, and permeability. The multiple reservoir descriptions are ranked using both static and dynamic measures.
REFERENCES
Al-Khalifa, M.A., “Advances in Generating and Ranking Integrated Geological Models
for Fluvial Reservoir”, SPE 86999, presented at the SPE Asia Pacific Conference on
Integrated Modeling for Asset Management, held in Kuala Lumpur, Malaysia, 20-30
March 2004.Ates, H., Bahar, A., Salem, E., Mohsen, C., and Akhil, D., “Ranking and Upscaling of
Geostatistical Reservoir Models Using Streamline Simulation: A field Case Study”,
SPE 81497, presented at the SPE 13th Middle East Oil Show and Conference, held in
Bahrain, 9-12 June 2003.Ates, H., Kelka, M., and Datta-Gupta, A., “The description of Reservoir Properties by
integrating Geological, Geophysical and Engineering Data”, The University of Tulsa
and Texas A&M University, Joint Industry Project, 2003, pp. 3-16.Ballin, P., Journel, A., and Aziz, K., “Prediction of Uncertainty in Reservoir
Performance Forecasting”, Journal of Canadian Petroleum Technology (JCPT), no. 4,
April 1992.Datta-Gupta, A., and King, M.J., Streamline Simulation: Theory and Practice, SPE
Textbook Series, 2007.Deutsch, C., Geostatistical Reservoir Modeling, Oxford University Press, 2002b.
Deutsch, C and Srinivasan, S., “Improved Reservoir Management through Ranking
Reservoir Models”, Society of Petroleum Engineers (SPE), Paper 35411, 1996.Fenik, D.R., Nouri, A., and Deutsch, C.V., “Criteria for Ranking Realizations in the
Investigation of SAGD Reservoir Performance”, Paper 2009-191, presented at the
Canadian International Petroleum Conference, held in Calgary, Alberta, Canada, 16-
18 June 2009.
StudentsandScholarship Team.
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