Importance of Facies Connectivity in History Matching for 3D Channelized Reservoir Characterization

Abstract

Channelized reservoirs consist of sinusoid patterns for sandstone. It is the most important for decision making to characterize connectivities because they significantly affect reservoir performances. Ensemble Kalman filter (EnKF) and ensemble smoother (ES) modify reservoir models using dynamic data. However, it is difficult for them to apply to channelized reservoirs because they assume that model parameters follow Gaussian distribution. The purpose of this research is to characterize 3D channel connectivities using ensemble-based methods. We use the concept of multiple Kalman gains for improved inverse modeling and it is applied to ES for fast history matching. Multiple Kalman gains are calculated by distance-based method such as hausdorff distance and kernel kmeans clustering. The proposed method, ES with multiple Kalman gains, is compared to EnKF and ES for 3D synthetic case. It solves overshooting problem in ES and describes better channel connectivities and bimodal distribution than EnKF. Furthermore, it requires only 6.4% simulation time of EnKF. When oil production rates and water cuts are predicted by updated ensembles, only the proposed method gives reliable uncertainty quantifications while EnKF and ES deviate from the true productions. Therefore, the proposed method can be utilized for fast decision making tool for 3D channelized reservoirs.