Novel Enhanced-Oil-Recovery Decision-Making Work Flow Derived From the Delphi-AHP-TOPSIS Method: A Case Study
- Bin Liang (China University of Petroleum Beijing) | Hanqiao Jiang (China University of Petroleum Beijing) | Junjian Li (China University of Petroleum Beijing) | Hanxu Yang (China University of Petroleum Beijing) | Wenbin Chen (China University of Petroleum Beijing) | Changcheng Gong (China University of Petroleum Beijing) | Shiyuan Qu (China University of Petroleum Beijing)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- May 2018
- Document Type
- Journal Paper
- 325 - 343
- 2018.Society of Petroleum Engineers
- Synthesis influence, Delphi-AHP-TOPSIS, Decision making workflow, EOR strategy
- 6 in the last 30 days
- 269 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
The process for the enhanced-oil-recovery (EOR) strategic decision is a thorny process that depends on proper evaluation, rational understanding of complex relationships, and quantitative assessment of multiple categories, including economic considerations, dynamic field production, reservoir numerical simulation, and other relevant elements. Using the Delphi-AHP-TOPSIS method (Joshi et al. 2011), which combines the Delphi method, analytic hierarchy process (AHP), and the technique for order preference by similarity to ideal solution (TOPSIS), we develop and present in this paper a decision-making work flow to meet this challenge.
This technological process is divided into three phases. The first phase is the Delphi method, in which key performance factors and subfactors are identified and quantitatively evaluated using expert judgment. The second is the AHP, in which a synthesis of the influence of factors and subfactors is acquired with a pairwise-comparison matrix. The third phase is the TOPSIS, in which the final ranking of candidate EOR projects is given using the monotonic utility function. A typical polymer-flooding EOR decision-making case is presented for better understanding.
The proposed work flow helps geoscientists acquire a comprehensive understanding of the factors in an existing project and select the optimal alternative. The approach not only minimizes the decision makers’ bias, combining the influence of technical and nontechnical factors, but also helps practitioners respond quickly to the oil market and reservoir dynamic performance rationally. This is, to our knowledge, the first time that the Delphi-AHP-TOPSIS method has been introduced to EOR decision making in the petroleum field.
|File Size||604 KB||Number of Pages||19|
Acosta, L. M., Jimenez, J. G., Guedez, A. et al. 2005. Integrated Modeling of the Furrial Field Asset Applying Risk and Uncertainty Analysis for the Decision taking. Presented at the SPE Europec/EAGE Annual Conference, Madrid, Spain, 13–16 June. SPE-94093-MS. https://doi.org/10.2118/94093-MS.
Agarwal, A. and Parsons, J. 2011. Commercial Structures for Integrated CCS-EOR Projects. Energy Procedia 4: 5786–5793. https://doi.org/10.1016/j.egypro.2011.02.575.
Alvarado, V. and Manrique, E. 2010. Enhanced Oil Recovery: Field Planning and Development Strategies. Oxford, UK: Elsevier.
Alvarado, V., Ranson, A., Hernandez, K. et al. 2002. Selection of EOR/IOR Opportunities Based on Machine Learning. Presented at the European Petroleum Conference, Aberdeen, 29–31 October. SPE-78332-MS. https://doi.org/10.2118/78332-MS.
Anikin, I. 2014. Knowledge Representation Model and Decision Support System for Enhanced Oil Recovery Methods. Oral presentation given at the International Conference on Intelligent Systems, Data Mining, and Information Technology, Bangkok, 21–22 April.
Campanella, G. and Ribeiro, R. A. 2011. A Framework for Dynamic Multiple-Criteria Decision Making. Decis. Support Syst. 52 (1): 52–60. https://doi.org/10.1016/j.dss.2011.05.003.
Chen, T. T. and Song, Y. F. 2012. Construction Planning Decision of Subway Stations Based on AHP-TOPSIS Method. J. Eng. Ma. 2: 33–36.
Diaz, D., Bassiouni, Z., Kimbrell, W. et al. 1996. Screening Criteria for Application of Carbon Dioxide Miscible Displacement in Waterflooded Reservoirs Containing Light Oil. Presented at SPE/DOE Improved Oil Recovery Symposium, Tulsa, 21–24 April. SPE-35431-MS. https://doi.org/10.2118/35431-MS.
Efe, B. 2016. An Integrated Fuzzy Multi Criteria Group Decision Making Approach for ERP System Selection. Appl. Soft. Comput. 38 (January):106–117. https://doi.org/10.1016/j.asoc.2015.09.037.
Goodlett, G. O., Honarpour, M. M., Chung, F. T. et al. 1986. The Role of Screening and Laboratory Flow Studies in EOR Process Evaluation. Presented at SPE Rocky Mountain Regional Meeting, Billings, Montana, 19–21 May. SPE-15172-MS. https://doi.org/10.2118/15172-MS.
Hwang, C. L. and Yoon, K. 1981. Methods for Multiple Attribute Decision Making. In Multiple Attribute Decision Making, Chap. 3, 58–191. New York: Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-48318-9.
Joshi, R., Banwet, D. K., and Shankar, R. 2011. A Delphi-AHP-TOPSIS Based Benchmarking Framework for Performance Improvement of a Cold Chain. Expert Syst. Appl. 38 (8): 10170–10182. https://doi.org/10.1016/j.eswa.2011.02.072.
Karahalios, H. 2017. The Application of the AHP-TOPSIS for Evaluating Ballast Water Treatment Systems by Ship Operators. Transport Res. D-Tr. E. 52A (May): 172–184. https://doi.org/10.1016/j.trd.2017.03.001.
Krohling, R. A. and Campanharo, V. C. 2011. Fuzzy TOPSIS for Group Decision Making: A Case Study for Accidents with Oil Spill in the Sea. Expert Syst. Appl. 38 (4): 4190–4197. https://doi.org/10.1016/j.eswa.2010.09.081.
Lake, L. W., Johns, R. T., Rossen, W. R. et al. 2014a. Fundamentals of Enhanced Oil Recovery, Richardson, Texas: Society of Petroleum Engineers.
Lake, L. W., Yang, A., and Pan, Z. 2014b. Listening to the Data: An Analysis of the Oil and Gas Journal Database. Presented at the SPE Improved Oil Recovery Symposium, Tulsa, 12–16 April. SPE-169056-MS. https://doi.org/10.2118/169056-MS.
Manrique, E., Ranson, A., and Alvarado, V. 2003. Perspectives of CO2 Injection in Venezuela. Oral presentation given at the Annual Workshop & Symposium, IEA Collaborative Project on EOR, Regina, Saskatchewan, Canada, 7–10 September.
Manrique, E. J., Izadi, M., Kitchen, C. D. et al. 2009. Effective EOR Decision Strategies With Limited Data: Field Cases Demonstration. SPE Res Eval & Eng 12 (4): 551–561. SPE-113269-PA. https://doi.org/10.2118/113269-PA.
Manrique, E. J., Thomas, C. P., Ravikiran, R. et al. 2010. EOR: Current Status and Opportunities. Presented at the SPE Improved Oil Recovery Symposium, Tulsa, 24–28 April. SPE-130113-MS. https://doi.org/10.2118/130113-MS.
Moreno, J., Gurpinar, O., and Liu, Y. 2014. EOR Advisor System: A Comprehensive Approach to EOR Selection. Presented at the International Petroleum Technology Conference, Kuala Lumpur, 10–12 December. IPTC-17798-MS. https://doi.org/10.2523/IPTC-17798-MS.
Ren, B., Zhang, L., Huang, H. et al. 2015. Performance Evaluation and Mechanisms Study of Near-Miscible CO2 Flooding in a Tight Oil Reservoir of Jilin Oilfield China. J. Nat. Gas Sci. Eng. 27 (November): 1796–1805. https://doi.org/10.1016/j.jngse.2015.11.005.
Saaty, R. W. 1987. The Analytic Hierarchy Process—What It Is and How It Is Used. Math. Modelling 9 (3–5): 161–176. https://doi.org/10.1016/0270-0255(87)90473-8.
Saaty, T. L. 1990. How To Make a Decision: The Analytic Hierarchy Process. Eur J Oper Res. 48 (1): 9–26. https://doi.org/10.1016/0377-2217(90)90057-I.
Saaty, T. L. 2003. Decision-Making with the AHP: Why is the Principal Eigenvector Necessary? Eur. J. Oper. Res. 145 (1): 85–91. https://doi.org/10.1016/S0377-2217(02)00227-8.
Saaty, T. L. 2008. Decision Making with the Analytic Hierarchy Process. Int. J. Serv. Sci. 1 (1): 83–98. https://doi.org/10.1504/IJSSci.2008.01759.
Saaty, T. L., and Hu, G. 1998. Ranking by Eigenvector versus Other Methods in the Analytic Hierarchy Process. Appl. Math. Lett. 11 (4): 121–125. https://doi.org/10.1016/S0893-9659(98)00068-8.
Saaty, T. L. and Vargas, L. G. 1984. Inconsistency and Rank Preservation. J. Math. Psychol. 28 (2): 205–214. https://doi.org/10.1016/0022-2496(84)90027-0.
Sadi-Nezhad, S. and Damghani, K. 2010. Application of a Fuzzy TOPSIS Method Base on Modified Preference Ratio and Fuzzy Distance Measurement in Assessment of Traffic Police Centers Performance. Appl. Soft. Comput. 10 (4): 1028–1039. https://doi.org/10.1016/j.asoc.2009.08.036.
Sanz, C. A. and Miller, M. A. 1994. A Decision Analysis Approach to the Design of a Chemical Flooding Process. Presented at the SPE Latin America/Caribbean Petroleum Engineering Conference, Buenos Aires, 27–29 April. SPE-27036-MS. https://doi.org/10.2118/27036-MS.
Sheng, J. 2010. Modern Chemical Enhanced Oil Recovery: Theory and Practice. Burlington, Massachusetts: Gulf Professional Publishing.
Siena, M., Guadagnini, A., Rossa, E. D. et al. 2015. A New Bayesian Approach for Analogs Evaluation in Advanced EOR Screening. Presented at the EUROPEC 2015, Madrid, Spain, 1–4 June. SPE-174315-MS. https://doi.org/10.2118/174315-MS.
Skinner, D. 2001. Introduction to Decision Analysis: A Practitioner’s Guide to Improving Decision Quality. Gainesville, Florida: Probabilistic Publishing.
Surguchev, L. and Li, L. 2000. IOR Evaluation and Applicability Screening Using Artificial Neural Networks. Presented at the SPE/DOE Improved Oil Recovery Symposium, Tulsa, 3–5 April. SPE-59308-MS. https://doi.org/10.2118/59308-MS.
Taber, J. J., Martin, F. D., and Seright, R. S. 1997a. EOR Screening Criteria Revisited—Part 1: Introduction to Screening Criteria and Enhanced Recovery Field Projects. SPE Res Eval & Eng 12 (3): 189–198. SPE-35385-PA. https://doi.org/10.2118/35385-PA.
Taber, J. J., Martin, F. D., and Seright, R. S. 1997b. EOR Screening Criteria Revisited—Part 2: Applications and Impact of Oil Prices. SPE Res Eval & Eng 12 (3): 199–206. SPE-39234-PA. https://doi.org/10.2118/39234-PA.
Tsaur, S.-H., Chang, T.-Y., and Yen, C.-H. 2002. The Evaluation of Airline Service Quality by Fuzzy MCDM. Tourism Manage. 23 (2): 107–115. https://doi.org/10.1016/S0261-5177(01)00050-4.
Wang, X. M., Qin, J. C., Zhang, Q. L. et al. 2013. Mining Method Optimization of Gu Mountain Remaining Ore Based on AHP-TOPSIS Evaluation Model. J. Central South Univ. Sci. Technol. 44 (3): 1131–1137.
Wilkinson, J. H. 1965. The Algebraic Eigenvalue Problem. Oxford, UK: Clarendon Press.
Xu, B. B. 2008. Optimization Method Study of Enhanced Oil Recovery for Conventional Heavy Oil Reservoir. Master’s thesis, China University of Petroleum.
Yang, Z. L., Bonsall, S., and Wang, J. 2011. Approximate TOPSIS for Vessel Selection Under Uncertain Environment. Expert Syst. Appl. 38 (12): 14523–14534. https://doi.org/10.1016/j.eswa.2011.05.032.
Ying, C. 2012. Core Competitiveness Evaluation and Promotion Strategy of Z Logistics Company Based on the AHP-TOPSIS. Master’s thesis, South China University of Technology.
Zerafat, M. M., Ayatollahi, S., Mehranbod, N. et al. 2011. Bayesian Network Analysis as a Tool for Efficient EOR Screening. Presented at the SPE Enhanced Oil Recovery Conference, Kuala Lumpur, 19–21 July. SPE-143282-MS. https://doi.org/10.2118/143282-MS.