Research on Automatic History Matching Method Based on Multi Models

doi:10.11885/j.issn.1674-5086.2020.11.10.01

Abstract

Abstract: In traditional reservoir numerical simulation, only a single random geological model is established, and the artificial history fitting method is used to obtain the geological model that conforms to reservoir production history and is used for project prediction. However, due to the relative scarcity of geological data and the heterogeneity of reservoir, the historical fitting problem must have multiple solutions, and the single geological model obtained cannot guarantee the accurate reflection of the real underground situation. In the numerical simulation study in this research, static geological data are used to produce a large number of random implementations, PCA dimensionality reduction method is used to reduce the amount of model data, and then the clustering method is used to select a number of implementations with different characteristics as the initial model. The automatic history fitting method based on SPSA algorithm was used to obtain several historical fitting models which conform to reservoir dynamics but contain different characteristics. Multi models can reflect the real underground situation more completely, and the prediction result is no longer a single dynamic curve, but a series of curves with multiple development possibilities, making the prediction more scientific and reliable.

Key words: automatic history matching, PCA dimensionality reduction, K-medoids clustering, SPSA algorithm, uncertainty evaluation

CLC Number:

TE319

LU Yi, HU Hao, CHENG Yabin, XIA Guochao, REN Guangwen. Research on Automatic History Matching Method Based on Multi Models[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2022, 44(6): 97-104.

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References

[1] JACQUARD P. Permeability distribution from field pressure data[C]. SPE 1307-PA, 1965. doi:10.2118/1307-PA
[2] THOMAS L K, HELLUMS L J, REHEIS G M. A nonlinear automatic history matching technique for reservoir simulation models[C]. SPE 3475-PA, 1972. doi:10.2118/3475-PA
[3] CHEN W H, GAVALAS G R, SEINFELD J H, et al. A new algorithm for automatic history matching[C]. SPE 4545-PA, 1974. doi:10.2118/4545-PA
[4] GAVALAS G R, SHAH P C, SEINFELD J H. Reservoir history matching by bayesian estimation[C]. SPE 5740-PA, 1976. doi:10.2118/5740-PA
[5] OUENES A, BREFORT B, MEUNIER G, et al. A new algorithm for automatic history matching:Application of simulated annealing method (SAM) to reservoir inverse modeling[C]. SPE 26297-PA, 1993. doi:10.2118/26297-PA
[6] 王曙光, 郭德志. Nelder-Mead单纯形法的推广及其在自动历史拟合中的应用[J]. 大庆石油地质与开发, 1998, 17(4):25-27. WANG Shuguang, GUO Dezhi. Extension of Nelder-Mead simplex method and its application in automatic history fitting[J]. Petroleum Geology and Development in Daqing, 1998, 17(4):25-27.
[7] ZHANG F J, SKJERVHEIM J A, REYNOLDS A C, et al. Automatic history matching in a Bayesian framework, example applications[C]. SPE 84461-PA, 2003. doi:10.2118/84461-PA
[8] CHOUDHARY M. Generation of multiple history match models using multistart optimization[D]. Stanford:Stanford University, 2012.
[9] 冯国庆, 何玉俊, 刘红林, 等.利用试井数据约束的随机地质建模方法[J].石油地球物理勘探, 2020, 55(2):435-441. doi:10.13810/j.cnki.issn.1000-7210.2020.02.023 FENG Guoqing, HE Yujun, LIU Honglin, et al. Stochastic geological modeling constrained by well test data[J]. Oil Geophysical Prospecting, 2020, 55(2):435-441. doi:10.13810/j.cnki.issn.1000-7210.2020.02.023
[10] PARK H, SCHEIDT C, FENWICK D, et al. History matching and uncertainty quantification of facies models with multiple geological interpretations[J]. Computational Geosciences, 2013, 17(4):609-621. doi:10.1007/s10596-013-9343-5
[11] SUN W, VINK J C, GAO G. A practical method to mitigate spurious uncertainty reduction in history matching workflows with imperfect reservoir models[C]. SPE 182599-MS, 2017. doi:10.2118/182599-MS
[12] WANG Jianzhong. Geometric structure of high-dimensional data and dimensionality reduction[M]. Beijing:Higher Education Press, 2012. doi:10.1007/978-3-642-27497-8_3
[13] TENENBAUM J B, SILVA V, UNGFORD J C. A global geometrie framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500):2319-2323. doi:10.1126/science.290.5500.2319
[14] ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500):2323-2326. doi:10.1126/science.290.5500.2323
[15] WOLD S, ESBENSEN K H, GELADI P. Principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, 1987, 2(1-3):37-52. doi:10.1016/0169-7439(87)80084-9
[16] HOTELLING H. Analysis of a complex of statistical variables into principal components[J]. Journal of Educational Psychology, 1933, 24(6):417-441. doi:10.1037/h0071325
[17] PARK H S, JUN C H. A simple and fast algorithm for K-medoids clustering[J]. Expert Systems with Applications, 2009, 36(2):3336-3341. doi:10.1016/j.eswa.2008.01.039
[18] 谢娟英, 郭文娟, 谢维信. 基于邻域的K中心点聚类算法[J]. 陕西师范大学学报(自然科学版), 2012, 40(4):16-22. doi:10.3969/j.issn.1672-4291.2012.04.005 XIE Juanying, GUO Wenjuan, XIE Weixin. A neighborhood-based K-medoids clustering algorithm[J]. Journal of Shaanxi Normal University (Natural Science Edition), 2012, 40(4):16-22. doi:10.3969/j.issn.1672-4291.2012.04.005
[19] SPALL J C. Implementation of the simultaneous perturbation algorithm for stochastic optimization[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(3):817-823. doi:10.1109/7.705889
[20] 赵辉, 曹琳, 李阳, 等. 基于改进随机扰动近似算法的油藏生产优化[J]. 石油学报, 2011, 32(6):1031-1036. ZHAO Hui, CAO Lin, LI Yang, et al. Production optimization of oil reservoirs based on an improved simulaneous perturbation stochastic approximation algorithm[J]. Acta Petrolei Sinica, 2011, 32(6):1031-1036.
[21] CHEN Y, OLIVER D S. Ensemble-based closed-loop optimization applied to Brugge Field[C]. SPE 118926-PA, 2010. doi:10.2118/118926-PA