A Study on a Marine Reservoir and a Fluvial Reservoir History Matching Based on Ensemble Kalman Filter

In reservoir management, utilizing all the observed data to update the reservoir models is the key to make accurate forecast on the parameters changing and future production. Ensemble Kalman Filter (EnKF) provides a practical way to continuously update the petroleum reservoir models, but its application reliability in different reservoirs types and the proper design of the ensemble size are still remain unknown. In this paper, we mathematically demonstrated Ensemble Kalman Filter method; discussed its advantages over standard Kalman Filter and Extended Kalman Filter (EKF) in reservoir history matching, and the limitations of EnKF. We also carried out two numerical experiments on a marine reservoir and a fluvial reservoir by EnKF history matching method to update the static geological models by fitting bottom-hole pressure and well water cut, and found the optimal way of designing the ensemble size. A comparison of those the two numerical experiments is also presented. Lastly, we suggested some adjustments of the EnKF for its application in fluvial reservoirs.