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  • Open Access


    An Improved Parameter Dimensionality Reduction Approach Based on a Fast Marching Method for Automatic History Matching

    Hairong Zhang1, Yongde Gao2, Wei Li2, Deng Liu3,*, 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 >

  • Open Access


    Geophysical and Production Data History Matching Based on Ensemble Smoother with Multiple Data Assimilation

    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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