Integrated Data Assimilation and Distance-Based Model Selection with Ensemble Kalman Filter for Characterization of Uncertain Geological Scenarios

This paper develops a reliable workflow for multi-objective history matching coupled with a distance-based model selection and ensemble-based data assimilation at a clastic channel reservoir with uncertain geological scenarios. The distance map allocated to each producer determines the probabilities of training images which assess the uncertainty of geological scenarios. K-medoids clustering selects the reservoir models within the ensemble set applied with some training images with less error. These geo-models play as initial ensembles suitable to explain the geological scenarios and ensemble Kalman filter recursively assimilates the oil rates of each producer. The developed workflow, updating reliable reservoir models suitable for well-performance-based history matching, more accurately forecasts water breakthrough and improves the predictability of unknown oil rates with a lower error than those of the conventional ensemble Kalman filter. This framework is able to preserve the spatial characteristics of facies models and reservoir properties without interpreting one fixed scenario. The proposed method can contribute to a reasonable design for data analytics with uncertain geological scenarios and for matching different-scaled well production histories.