Aquifer characterization of gas reservoirs using Ensemble Kalman filter and covariance localization

For decision making, it is necessary to predict reservoir behaviors using reliable models. We can forecast future performances with less uncertainty after reservoir characterization, which is an essential step for integrating available static and dynamic data in history matching. Ensemble Kalman filter (EnKF) is one of the powerful methods for reservoir characterization. It uses recursive updates and provides uncertainty assessment. EnKF has been rarely applied to characterization of gas reservoirs in spite of its active research for oil reservoirs. Gas reservoirs show typically high recovery and are less sensitive to permeability uncertainty. However, the recovery of gas reservoirs is severely affected by an aquifer, which has considerable uncertainty. Therefore, aquifer characterization is crucial for production management and uncertainty assessment of gas reservoirs. This paper presents a method to characterize permeability distribution and aquifer sizes of gas reservoirs using static data and production data available. Covariance localization is applied for taking account of proper relationship between well production data and the properties of grid cells. The proposed method manages not only permeability overshooting but also successful assimilation of permeability distribution. Besides, the aquifer factors come closer to the reference values as compared with a standard EnKF. Therefore, it obviously improves future predictions of gas and water productions.