Real-Time Reservoir Model Updating Using Ensemble Kalman Filter

Abstract

Abstract Ensemble Kalman Filter (EnKF) has been reported to be very efficient for real-time updating of reservoir model to match the most current production data. Using EnKF, an ensemble of reservoir models assimilating the most current observations of production data are always available. Thus the estimations of reservoir model parameters, and their associated uncertainty, as well as the forecasts are always up-to-date. In this paper, we apply the EnKF for continuously updating an ensemble of permeability models to match real-time multiphase production data. We improve the previously EnKF by resolving the flow equations after Kalman filter updating so that the updated static and dynamic parameters are always consistent. By doing so, we show that the production data are also better matched for some cases. We investigate the sensitivity of using different number of realizations in the EnKF. Our results show that a relatively large number of realizations are needed to obtain stable results, particularly for the reliable assessment of uncertainty. The sensitivity of using different covariance functions is also investigated. The efficiency and robustness of EnKF is clearly demonstrated using an example. By assimilating more production data, new features of heterogeneity in reservoir model can be revealed with reduced uncertainty, resulting in more accurate predictions.