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.He...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.展开更多
基金This study was supported by National Natural Science Foundation of China(Nos.52104017,51874044,51922007)Southern Marine Science and Engineering Guangdong Laboratory(Zhanjiang)(No.zjw-2019-04).
文摘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.
