@Article{fdmp.2022.019446,
AUTHOR = {Hairong Zhang, Yongde Gao, Wei Li, Deng Liu, Jing Cao, Luoyi Huang, Xun Zhong},
TITLE = {An Improved Parameter Dimensionality Reduction Approach Based on a Fast Marching Method for Automatic History Matching},
JOURNAL = {Fluid Dynamics \& Materials Processing},
VOLUME = {18},
YEAR = {2022},
NUMBER = {3},
PAGES = {609--628},
URL = {http://www.techscience.com/fdmp/v18n3/46822},
ISSN = {1555-2578},
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 the objective function. This method has been verified through a water drive conceptual example and a real field case. The results have shown that the proposed method can generate more accurate gradient information and predictions compared to the traditional analytical gradient methods and other gradient-free algorithms.},
DOI = {10.32604/fdmp.2022.019446}
}