Uncertainty Quantification of Channelized Reservoir Using Ensemble Smoother with Selective Measurement Data

Abstract

Ensemble smoother (ES) assimilates all available dynamic data without iterations as global update. Therefore, ES is much faster than ensemble Kalman filter (EnKF), which uses recursive updates. Iterative concepts are introduced for ES to increase accuracy of history matching. However, they lose advantages of simulation time and cost over EnKF. We propose ES with selective use of observation data in assimilation to improve history matching results and to keep simulation time short. Three methods, EnKF with all data, ES with all data, and the proposed, are applied to 2D synthetic channelized reservoirs with a nine spot waterflooding. Ensemble-based methods interpret the reason of reduced oil production rate after water breakthrough as low permeability. In this research, we suggest selective measurement data to manage misinterpretable data for the ensemble-based methods. As a logical choice, oil production rate before water breakthrough and water cut after water breakthrough are used for assimilation. EnKF with all data cannot predict true performances of oil and water productions on each well. ES with all data shows severe overshooting and filter divergence problems, which are two typical problems in the ensemble-based methods. However, the proposed method overcomes the two problems and shows good history matching results. It provides reliable uncertainty quantification of reservoir performances for both each well production and field total productions. Simulation cost of the proposed method is about 2.2% of that of EnKF, which uses 45 times update. It has clear advantage over EnKF or iterative ES methods.