Facies estimation through data assimilation and structure parameterization

Abstract

Data assimilation in a reservoir with facies description using the ensemble Kalman filter (EnKF) is challenging. An important reason is that probability density functions for pixel-based representations of facies fields seldom follow the unimodal Gaussian assumption underlying traditional EnKF implementations. Different approaches for identification of facies fields, aiming to overcome this challenge, have been proposed within the EnKF framework. Level set (LS) representations of the facies field have been reported to alleviate the problems of multimodality. Several authors have, however, pointed out that the most commonly applied LS representation suffers from topological constraints that can create difficulties in an estimation setting. An alternative LS representation, that overcomes these topological constraints, leads to instabilities in the assimilated ensemble members. To overcome topological constraints, the recently proposed hierarchical LS representation is applied in an estimation setting for the first time in this paper. To improve stability and to alleviate challenges associated with model nonlinearities, we apply regularization by reduced representation of the LS functions and adjustable smoothing of the LS representation. The resolution of the reduced LS representation is selected based on the variability of the initial ensemble, aiming at preserving enough flexibility to disclose unexpected features. 2D and 3D estimation results demonstrate that the hierarchical LS representation does avoid topological constraints and that instabilities are avoided. The results suggest that the method is capable of handling estimation of facies fields while preserving geological plausibility.