f History Matching of Well Test Pressure and Geostatistical Data by Ensemble Kalman Filter Method

In recent years, the ensemble Kalman Filter (EnKF) method has become popular due to its favorable computation speed over the gradient (such as Gauss-Newton and Levenberg Marquardt) based methods in history matching and quantification of uncertainty in performance predictions for oil and gas reservoirs. In this work, we examine the use and applicability of EnKF method for history matching of well test pressure and geostatistical data and for performance prediction using the estimates of reservoir parameters obtained by EnKF history matching. For our investigation, we use a forward or direct model based on pressure diffusion for a slightlycompressible, single-phase fluid flow in a three-dimensional porous medium, where it is assumed that the viscosity is constant, and combine this forward model with the EnKF method for performing history matching. We demonstrate the advantages and disadvantages associated with the EnKF method by considering synthetic example applications. The results indicate that the EnKF method looks promising, particularly due to its superior computational performance compared to gradient based history matching methods for history matching of well test and geostatistical data in heterogeneous reservoir systems, but the number of ensembles to be used, and the prior variogram to be chosen to generate ensembles seem to be critical. Some guidelines are also given for effective use of EnKf method.