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
An Ensemble Kalman Filter Method Based on Localization of Connectivity Analysis for Reservoir Automatic History Matching
曹静 1陈玉 1辛显康2
作者信息
- 1. 长江大学信息与数学学院,荆州 434023
- 2. 长江大学石油工程学院,武汉 430100;油气钻采工程湖北省重点实验室(长江大学),武汉 430100;长江大学石油工程学院,油气钻完井技术国家工程研究中心,武汉 430100
- 折叠
摘要
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.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.
Abstract
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.A new localized ensemble Kalman filter method of single-well sensitivity region based on connectivity analysis for automatic history matching was established,which solved 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 was regarded as a connected directed map,and the shortest path between any two grid points was 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 combined with the ensemble Kalman filter method,in the end the pseudo-correlation problem was 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.
关键词
集合卡尔曼滤波/连通性/局域化/Floyd算法Key words
EnKF/connectivity/localization/Floyd algorithm引用本文复制引用
基金项目
国家自然科学基金青年科学基金(52104020)
出版年
2024
NETL
NSTL
万方数据