College of Information and Mathematics, Yangtze University
集合卡尔曼滤波（Ensemble Kalman Filter，EnKF）是自动历史拟合中应用最为广泛的方法之一，但该算法在应用过程中会产生伪相关、滤波发散等问题。本文建立了一种新的基于连通性分析的单井敏感性局域化集合卡尔曼滤波自动历史拟合方法，解决了传统距离截断方法处理伪相关时与实际油藏不匹配问题。该方法将油藏网格视为连通的有向图，利用连通性分析和Floyd算法计算任意两个网格点间的最短路径，从而确定单井敏感性区域和井点到各网格的相关系数矩阵，再结合集合卡尔曼滤波方法有效削弱了伪相关问题。将改进的算法编程实现并运用实例进行验证，结果表明，基于连通性分析局域化的EnKF方法在生产动态拟合和模型参数场反演等方面均优于标准EnKF方法。
Ensemble Kalman Filter (EnKF) is one of the most widely used methods in automatic history matching, but the algorithm will produce problems such as pseudo-correlation and filter divergence during application. In this paper, a new localized ensemble Kalman filter method of single-well sensitivity region based on connectivity analysis for automatic history matching is established, which solves the problem of mismatch between the traditional distance truncation method and the actual reservoir when dealing with pseudo-correlation. In this method, the reservoir grid is regarded as a connected directed map, and the shortest path between any two grid points is calculated by connectivity analysis and the Floyd algorithm to determine the sensitive area of a single well and the correlation coefficient matrix from the well point to each grid point, and then it combines with the ensemble Kalman filter method, in the end the pseudo-correlation problem is effectively weakened. The improved algorithm is programmed and verified by examples, and the results showes that the EnKF method based on localization of connectivity analysis is superior to the standard EnKF method in terms of production dynamic matching and model parameter field inversion.
曹静,陈玉,辛显康. 基于连通性分析局域化的集合卡尔曼滤波的油藏自动历史拟合方法[J]. 科学技术与工程, , ():复制