Reservoir Assisted History Matching Method with a Covariance Localization EnKF using Fast Marching Method

Abstract

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