Implementation of a new proxy algorithm in earth science - A case study: Automatic history matching in one of oil reservoirs

Document Type : Original Article

Authors

Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

History matching is still one of the main challenging parts of reservoir study especially in giant brown oil fields with lots of wells. It would be a challenge in reservoir engineering that due to various parameters and uncertainties in study of reservoirs, many simulation runs are needed to reach a good match for responses in conventional mechanism of history matching. However, for accelerating history matching part, new methods, which are called as assisted or automated history matching (AHM), have been established. In this paper, the latest approach for automated history matching (AHM) has been applied in a real brown field containing 14 wells with multiple responses that is located in south of Iran. Least square support vector machine (LSSVM) has been applied to create proxy model based on cubic centered face method. The optimization algorithms, used in this research, consist of genetic algorithm (GA) and particle swarm optimization (PSO). 
Introduction
In the latest studies in geosciences and reservoir characterization, employing a proxy model that acts faster, instead of real reservoir model, has led to good results. One of the most important sections in fulfilled study (FFS) and master development plan is history matching, which plays an important role in production scenarios and future production plan of the under study reservoir. In this paper, one of the newest methods is used for making proxy model and then, the model for history matching is optimized.
 Methodology and Approaches
Least square support vector machine (LSSVM) has been employed to create proxy model based on cubic centered face (CCF) method. The optimization algorithms of genetic algorithm (GA) and particle swarm optimization (PSO) have been used in this research.
 Results and Conclusions
A new proxy model has been successfully constructed using 1086 samples leading into determination coefficient (R2)

Keywords

References

Ahmadi, M.A., & Bahadori, A. (2015). ALSSVM approach for determining well placement and conning phenomena in horizontal wells. Fuel, 153, 276–283.
Al-Thuwaini, J., Zangl, G., & Earl Phelps, R. (2006). Innovative Approach to Assist History Matching Using Artificial Intelligence. Paper presented at the Intelligent Energy Conference and Exhibition, Amsterdam, The Netherlands.
Arief, I. H. (2013). Assisted History Matching : A Comprehensive Study of Methodology. Stavanger.
Arwini, S.G., & Stephen, K.D. (2011). Combining Experimental Design with Proxy Derived Sensitivities to Improve Convergence Rates in Seismic History Matching. Paper presented at the SPE EUROPEC/EAGE Annual Conference and Exhibition, Vienna, Austria
Askari Firoozjaee, R., & Khamehchi, E. (2014). A Novel Approach to Assist History Matching Using Artificial Intelligence. Chemical Engineering Communications, 202, 513–519. doi: 10.1080/00986445.2013.852977
Azad, A., & Chalaturnyk, R. (2013). Application of Analytical Proxy Models in Reservoir Estimation for SAGD Process: UTF-Project Case Study SPE-165576-PA.
Bhark, E., & K,B. (2014). Assisted History Matching Benchmarking: Design of Experiments-based Techniques. Paper presented at the SPE Annual Confrence, Amsterdam,Netherlands.
Dehghan Monfared, A., Helalizadeh, A., Parvizi, H., & Zobeidi, K. (2014). A Global Optimization Technique Using Gradient Information for History Matching. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 36(Taylor & Francis), 1414–1428.
Denney, D. (2010). Pros and Cons of Applying a Proxy Model as a Substitute for Full Reservoir Simulations. Journal of Petroleum Technology, 62(07).
Espinoza, M., Suykens, A.K., & Moor, B.D. (2003). Least Square support Vector Machines and Primal Space Estimation. Paper presented at the 42nd IEEE Conference on Decision and Control, Maui,Hawaii, USA.
Fedutenko, E., Yang, C., Card, C., & Nghiem, L. X. (2013). Time-Dependent Proxy Modeling of SAGD Process Paper presented at the SPE Heavy Oil Conference-Canada, Calgary, Alberta, Canada
Fedutenko, E., Yang, C., Card, C., & Nghiem, L. X. (2014). Time-Dependent Neural Network Based Proxy Modeling of SAGD Process. Paper presented at the SPE Heavy Oil Conference-Canada, Calgary, Alberta, Canada
Ghasemi, M., & Whitson, C. H. (2011). Modeling SAGD with a Black-Oil Proxy. Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, USA
He, J., Xie, J., Wen, X.-H., & Chen, W. (2015). Improved Proxy For History Matching Using Proxy-for-data Approach And Reduced Order Modeling. Paper presented at the SPE Western Regional Meeting, California, USA.
Mohaghegh, S.D., Abdulla, F., Gaskari, R., & Maysami, M. (2015). Smart Proxy: An Innovative Reservoir Management Tool; Case Study of a Giant Mature Oilfield in the UAE. Paper presented at the Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, UAE.
Mohaghegh, S.D., & Abdulla, F.A.S. (2014). Production Management Decision Analysis Using AI-Based Proxy Modeling of Reservoir Simulations – A Look-Back Case Study. Paper presented at the SPE Annual Technical Conference and Exhibition, Amsterdam, The Netherlands
Mohaghegh, S.D., Liu, J., Gaskari, R., Maysami, M., & Olukoko, O. (2012). Application of Well-Based Surrogate Reservoir Models (SRMs) to Two Offshore Fields in Saudi Arabia, Case Study. Paper presented at the SPE Western North American Regional Meeting, Bakersfield, California, USA.
Mohaghegh, S.D., Modavi, A., Hafez, M., & Haajizadeh. Y. (2006). Development of Surrogate Reservoir Models (SRM) For Fast Track Analysis of Complex Reservoirs Paper presented at the 2006 SPE Intelligent Energy Conference and Exhibition, Amsterdam, The Netherlands.
Mohamed Al-akhdar, S., Yu Ding, D., Dambrine, M., & Jourdan, A. (2012). An Integrated Parameterization and Optimization Methodology for Assisted History Matching: Application to Libyan Field Case. Paper presented at the North Africa Technical Conference and Exhibition, Cairo, Egypt.
Panja, P., Pathak, M., Velasco, R., & Deo, M. (2016). Least Square Support Vector Machine :An Emerging Tool for Data Analysis. Paper presented at the SPE Low Perm Symposium, Denver, Colorado, USA.
Ramgulam, A. (2006). Utilization Of Artificial Neural Networks In The Optimization Of History Matching (Master of Science), Pennsylvania State University.
Shahkarami, A. (2012). Artificial Intelligence Assisted History Matching – Proof Of Concept. (Master of Science),West Virginia University.
Shahkarami, A., Mohaghegh, S.D., & Hajizadeh, Y. (2015). Assisted History Matching Using Pattern Recognition Technology. Paper presented at the Digital Energy Confrence, Texas,USA.
Shahkarami, A., Mohaghegh, S.D., Gholami, V., & Haghighat, S.A. (2014). Artificial Intelligence (AI) Assisted History Matching. Paper presented at the SPE Western North American and Rocky Mountain Joint Regional Meetingheld, Denver, Colorado, USA.
Silva, P. C., Maschio, C., & Schiozer, D. J. (2008). Application of Neural Network and Global Optimization in History Matching. Journal of Canadian Petroleum Technology.
Suykens, A.K., Gestel, J., Brabanter, T.V., Moor,J.D., & walle, J.V. (2002). Least Square Support Vector Machines. Singapore World Scientific Publishing Co.
Van Doren, J., Van Essen, G., Wilson, O., Zijlstra, E., & (2012). A Comprehensive Workflow for Assisted History Matching Applied to a complex Mature Reservoir. Paper presented at the EAGE Annual Confrence, Copenhagen,Denmark.
Wang, J., & Buckley, J.S. (2006). Automatic History Matching Using Differential Evoluttion Algorithm Paper presented at the International Symposium of the Society of Core Analysts, Trondheim, Norway.
Wang, S., Zhao, G., Xu, L., Guo, D., & Sun, S. (2005). Optimization for Automatic History Matching. International Journal Of Numerical AnalysisAnd Modeling, 2, 131-137.
Yao, S., & Prasad, V. (2015). Proxy Modeling of the Production Profiles of SAGD Reservoirs Based on System Identification. Industrial & Engineering Chemistry Research.
Zangl, G., Graf, T., & Al-Kinani, A. (2006). Proxy Modeling in Production Optimization. Paper presented at the SPE Europec/EAGE Annual Conference and Exhibition, Vienna, Austria.
Journal of Petroleum Geomechanics
Volume 1, Issue 1
August 2017
Pages 1-14

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