Estimation of Facies Boundaries Using Categorical Indicators with P-Field Simulation and Ensemble Kalman Filter (EnKF)

Abstract

The Ensemble Kalman Filter (EnKF) often fails to reproduce realistic facies distribution and proportion corresponding to the prior spatial distribution. In this paper, a new step is proposed for inclusion in the history matching of multiple facies reservoir models using EnKF. The new step consists of constructing a facies probability map and application of probability field (P-Field) simulation to re-sample a new ensemble. After certain number of assimilation steps in EnKF, the updated ensemble members are used to propose a probability map for facies distribution. P-Field simulation is performed subsequently using the facies probability map to generate a new ensemble, which honors the static geologic data and is more consistent with the early production data. After the re-sampling step, the forecast model is applied to the new ensemble from the beginning. EnKF is again applied on the ensemble members to assimilate the remaining production history. Two case studies with different facies distribution and well configurations were conducted. Initial ensemble was created using known facies classification at the well locations and populating binary facies data throughout the reservoir using prior information of spatial facies distribution and facies proportions. The qualities of the history-matched models were assessed by comparing the spatial facies distribution and proportions of the updated ensemble, in addition to the root mean square error of the predicted data mismatch. Implementation of EnKF together with re-sampling of the new realizations using probability maps demonstrates reasonable improvement in the history matching and uncertainty assessment of multiple facies reservoir models.