Ensemble-based method for reservoir characterization using multiple kalman gains and selective use of dynamic data

The present invention relates to an ensemble-based reservoir characterization methods using multiple Kalman gain and dynamic data selection, the method comprising: preparing a soluble material containing static data and dynamic data; Wherein generating the initial ensemble by using the ready-static material; Separating by clustering the initial model is generated based on the distance-based method; The step of selecting the dynamic data; Further comprising: a dynamic simulation of a selected dynamic data using the generated ensemble; Calculating a multiple Kalman gain using the initial model tied to the selected dynamic data and the same group; Steps to update the ensemble members with selected materials and a dynamic multi Kalman gain; And predicting the reservoir behavior using the updated model and evaluating uncertainty; composed, including getting a final model with the dynamic data calculation and selecting an appropriate multiple Kalman gain to the initial static model using this fast there is an effect that it is possible to perform a reliable evaluation of uncertainty in the future behavior can be predicted in time.