Hairong Zhang¹, Yongde Gao², Wei Li², Deng Liu^3,*, Jing Cao³, Luoyi Huang³, Xun Zhong³

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.3, pp. 609-628, 2022, DOI:10.32604/fdmp.2022.019446

Abstract History matching is a critical step in reservoir numerical simulation algorithms. It is typically hindered by difficulties associated with the high-dimensionality of the problem and the gradient calculation approach. Here, a multi-step solving method is proposed by which, first, a Fast marching method (FMM) is used to calculate the pressure propagation time and determine the single-well sensitive area. Second, a mathematical model for history matching is implemented using a Bayesian framework. Third, an effective decomposition strategy is adopted for parameter dimensionality reduction. Finally, a localization matrix is constructed based on the single-well sensitive area data to modify the gradient of… More >

Zelong Wang^{1, 2, 3, *}, Xiangui Liu^{1, 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 >