Saturation-based localization within EnKF approach for history matching in SAGD processes

Abstract

The history matching method with the ensemble Kalman filter (EnKF) technique has been successfully applied in the steam-assisted gravity drainage (SAGD) processes. In the application of the EnKF method, usually, large ensemble size is required to eliminate spurious correlations, which also increases the computation cost. In this paper, a newly defined saturation-based localized EnKF approach is proposed and proved to be much more efficient than the standard EnKF method. This approach is able to use a small ensemble to obtain the desired history match and prediction results. Therefore, the computation cost can be reduced. Simulation results from a large ensemble (1000 ensembles) size are used to conduct cross-covariance analysis to determine the regions of the localization function. Based on the positive covariance regions, the localization regions are defined. Oil saturation is used as localization scale to define the saturation-based localization function. Good history match results and prediction results are obtained by adopting saturation-based localized EnKF approach. A small ensemble (10 ensembles) size is employed to test the efficiency of the newly defined localization function. Without localization, the ensemble variability collapses quickly when using small ensembles which will have no data assimilation ability. The saturation-based localized EnKF approach with small ensembles (10 ensembles) can avoid ensemble variability collapse and obtain desired history matching results.