Real - Time Reservoir Model Updating Using Ensemble Kalman Filter

This paper was selected for presentation by an SPE Program Committee following review of information contained in a proposal submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to a proposal of not more than 300 words; illustrations may not be copied. The proposal must contain conspicuous acknowledgment of where and by whom the paper was presented. Abstract The ensemble Kalman Filter technique (EnKF) has been reported to be very efficient for real-time updating of reservoir models 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 previous 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 the EnKF is clearly demonstrated using an example. By assimilating more production data, new features of heterogeneity in the reservoir model can be revealed with reduced uncertainty, resulting in more accurate predictions. Introduction The reliability of reservoir models increases as more data are included in their construction. Traditionally, static (hard and soft) data, such as geological, geophysical, and well log/core data are incorporated into reservoir geological models through conditional geostatistical simulation