Identification of Geologic Fault Network Geometry by Using a Grid-Based Ensemble Kalman Filter

Abstract

Discrete geologic features such as faults and highly permeable embedded channels can significantly affect subsurface flow and transport characteristics. Therefore, they must be properly identified, parameterized, and represented in subsurface simulation models. In this work, we use an improved ensemble Kalman filter (EnKF) for history-matching fault network geometry from production data. EnKF is a sequential Monte Carlo data assimilation method that simultaneously propagates and updates an ensemble of model states, resulting in a set of calibrated model realizations that can be readily used for model prediction and uncertainty analysis. A pattern-based stochastic simulation algorithm was used to generate fault network realizations based on a priori fault trace data. The classic EnKF algorithm was enhanced with a grid-based covariance localization scheme to better handle non-Gaussian permeability distributions resulting from the presence of faults. Numerical experiments indicate that the modified EnKF can be a promising method for uncovering unmapped faults by using production data.