ENSEMBLE-BASED METHOD FOR RESERVOIR CHARACTERIZATION USING MULTIPLE KALMAN GAINS AND SELECTIVE USE OF DYNAMIC DATA

摘要

The present invention relates to an ensemble-based reservoir characterization method through multiple Kalman gains and dynamic data selection. The method includes: a step of preparing available data including static data and dynamic data; a step of generating initial ensembles using the prepared static data; a step of dividing and clustering the generated initial ensembles based on the distance based method; a step of selecting the dynamic data; a step of performing dynamic simulation of the selected dynamic data using the generated ensembles; a step of calculating multiple Kalman gains using the initial models clustered in the same group and the selected dynamic data; a step of updating the ensemble members using the selected dynamic data and multiple Kalman gains; and a step of predicting the movement of the reservoir using the updated models and evaluating the uncertainty. By doing so, the present invention can calculate the multiple Kalman gains appropriate for the initial static model, obtain the final model using the selected dynamic data, and perform reliable uncertainty evaluation and future movement prediction within a short time using the same.