Zelong Wang1, 2, 3, *, Xiangui Liu1, 2, 3, Haifa Tang3, Zhikai Lv3, Qunming Liu3
CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.2, pp. 873-893, 2020, DOI:10.32604/cmes.2020.08993
Abstract The Ensemble Kalman Filter (EnKF), as the most popular sequential data
assimilation algorithm for history matching, has the intrinsic problem of high computational
cost and the potential inconsistency of state variables updated at each loop of data
assimilation and its corresponding reservoir simulated result. This problem forbids the
reservoir engineers to make the best use of the 4D seismic data, which provides valuable
information about the fluid change inside the reservoir. Moreover, only matching the
production data in the past is not enough to accurately forecast the future, and the
development plan based on the false forecast is very likely… More >
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