Ensemble-based reservoir characterization method using multiple kalman gains and dynamic data selection

Abstract

The present disclosure relates to an ensemble-based reservoir characterization method using multiple Kalman gains and dynamic data selection. The method includes preparing available data; generating initial ensembles by using the prepared static data; clustering and separating the generated initial models on the basis of a distance-based method; selecting the dynamic data; dynamically simulating the selected dynamic data by using the generated ensembles; calculating multiple Kalman gains by using initial models clustered in the same group as the selected dynamic data; updating ensemble members by means of the selected dynamic data and the multiple Kalman gains; and predicting a movement of a reservoir by using the updated models, and evaluating uncertainty thereof. Therefore, multiple Kalman gains are calculated and a final model is obtained using the selected dynamic data, and a reliable uncertainty evaluation and a future movement prediction can be performed within a short time by using the final model.