The Ensemble Kalman filter (EnKF) is a widely used intelligent algorithm in the field of automatic history fitting. The method has a number of drawbacks, such as inaccurate gradient calculation, filter divergence, and pseudo-correlation of parameters, leading to parameter correction errors and model inversion distortion in the process of historical fitting. A history fitting method based on the fast marching method and covariance-localized Ensemble Kalman filter (FMM-CLEnKF) is established to reduce pseudo-correlation in the calculation process of the traditional distance truncation method. According to the static parameter field information of the reservoir geological model combined with the state equation, the fast marching method (FMM) is used to quickly track the propagation time of the pressure wave in every well, determine the sensitive area of a single well, and construct the localization matrix. Combined with the covariance localization Ensemble Kalman filter method, the gradient correction of the data assimilation method is realized, and the pseudo-correlation of parameters is reduced. Finally, the optimal model is improved by gradually fitting and updating the reservoir parameter model. The calculation results of a field example show that the FMM-CLEnKF method has a higher reservoir parameter inversion accuracy, data fitting speed, and production data fitting accuracy than the ensemble Kalman filter method.

集成卡尔曼滤波器（EnKF）是自动历史拟合领域广泛使用的智能算法。该方法存在梯度计算不准确、滤波器发散、参数伪相关等缺点，导致历史拟合过程中存在参数校正误差和模型反演失真。为了减少传统距离截断方法计算过程中的伪相关，建立了一种基于快速行进法和协方差局部化集成卡尔曼滤波器（FMM-CLEnKF）的历史拟合方法。根据油藏地质模型的静态参数场信息结合状态方程，采用快速行进法（FMM）快速跟踪压力波在各井中的传播时间，确定单井敏感区域，构建定位矩阵。结合协方差定位Ensemble Kalman滤波方法，实现了数据同化方法的梯度校正，降低了参数的伪相关性。最后，通过逐步拟合和更新储层参数模型来改进最优模型。现场实例计算结果表明，FMM-CLEnKF方法比集合卡尔曼滤波方法具有更高的储层参数反演精度、数据拟合速度和生产数据拟合精度。并且降低了参数的伪相关性。最后，通过逐步拟合和更新储层参数模型来改进最优模型。现场实例计算结果表明，FMM-CLEnKF方法比集合卡尔曼滤波方法具有更高的储层参数反演精度、数据拟合速度和生产数据拟合精度。并且降低了参数的伪相关性。最后，通过逐步拟合和更新储层参数模型来改进最优模型。现场实例计算结果表明，FMM-CLEnKF方法比集合卡尔曼滤波方法具有更高的储层参数反演精度、数据拟合速度和生产数据拟合精度。