A New Method for Economic Prediction of Carbonate Reservoirs Based on Expert Library and Small Database
- Wenbin Chen (China University of Petroleum) | Hanqiao Jiang (China University of Petroleum) | Junjian Li (China University of Petroleum) | Shan Jiang (PetroChina Research Institute of Petroleum Exploration & Development) | Hanxu Yang (China University of Petroleum) | Yan Qiao (China University of Petroleum)
- Document ID
- Society of Petroleum Engineers
- SPE Europec featured at 80th EAGE Conference and Exhibition, 11-14 June, Copenhagen, Denmark
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 5.8 Unconventional and Complex Reservoirs, 5 Reservoir Desciption & Dynamics, 7.2 Risk Management and Decision-Making, 1.6 Drilling Operations, 7 Management and Information, 7.2.1 Risk, Uncertainty and Risk Assessment, 1.6.9 Coring, Fishing, 5.8.7 Carbonate Reservoir, 5.5 Reservoir Simulation, 7.2.3 Decision-making Processes
- Small database, Expert library, Economic prediction method, Delphi-AHP-TOPSIS-MLS-FNPV, Carbonate reservoir
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- 81 since 2007
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The karst caves and fractures are widely developed in carbonate reservoirs, which results in strong spatial heterogeneity. So the parameters obtained from its cores and numerical simulation are limited to reflect its production situation of the entire reservoir, which causes that the traditional economic prediction method for carbonate reservoirs has a high risk. To solve these problems, this paper proposes a new method to accomplish the economic prediction based on expert library and oilfield database, named Delphi-AHP-TOPSIS-MLS-FNPV complex algorithm (DATMF). DATMF method can take account of geological factors, such as sedimentary facies, reservoir types, the characteristics and heterogeneity of caves and fractures. It also considered the impact of production factors on the economic prediction, such as oil production, annual decline rate of production, well spacing density. The process of the DATMF method is as follows: First, establishing a set of hierarchical structure to describe the carbonate reservoirs in the database; Secondly, optimizing the database and analyzing the data based on the expert library; Thirdly, predicting the key development parameters of the new reservoir according to its geological data; Finally, substituting these parameters into the future net present value (FNPV) method to complete the economic prediction of the new carbonate reservoir. Through the calculation example of T7-444CH reservoir, it is found that DATMF method can predict oil production, investment recovery period, and the future net present value, etc. quickly and accurately. On the one hand, it greatly reduces the time and money cost of using traditional economic prediction methods. On the other hand, comparing with the popular big data analysis method, it improves the data's quality and increases the result's professionalism and practicality by using experts’ experience to constraint data, which makes the DATMF method can work on the smaller database. It is very suitable for the DATMF method to be applied in the early or middle stage of oilfield information construction.
|File Size||1 MB||Number of Pages||18|
Campanella, G. and Ribeiro, R. A. 2011. A framework for dynamic multiple-criteria decision making. Decis Support Syst. 52(1): 52-60. http://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. Journal of Engineering Management. 2: 33-36.http://doi.org/10.1016/j.eswa.2010.09.081.
Liang, B., Jiang, H., Li, J., Yang, H., Chen, W., Gong, C., & Qu, S. (2017, January 1). Novel Enhanced-Oil-Recovery Decision-Making Work Flow Derived From the Delphi-AHP-TOPSIS Method: A Case Study. Society of Petroleum Engineers. doi:10.2118/176444-PA
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., and Vargas, L. G. 1984. Inconsistency and rank preservation. J Math Psychol. 28 (2):205-214. http://dx.doi.org/10.1016/0022-2496(84)90027-0.
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.
Spath, J. (2014, January 1). Emerging Frontiers: Big Data! Society of Petroleum Engineers. doi:10.2118/0114-0014-JPT
Tarrahi, M., & Shadravan, A. (2016, April 20). Advanced Big Data Analytics Improves HSE Management. Society of Petroleum Engineers. doi:10.2118/180032-MS
Xiao, J., & Sun, X. (2017, September 5). Big Data Analytics Drive EOR Projects. Society of Petroleum Engineers. doi:10.2118/186159-MS