Preserving Geological Realism of Facies Estimation on the Brugge Field

Abstract

Facies estimation using ensemble-based methods has been a popular and challenging problem in reservoir history matching. The challenges come from the difficulty of handling the discrete facies variables and preserving the geological realism in the updated facies models from ensemble-based methods. This work proposes the use of a normal score transformation as the facies parameterization approach coupled with Iterative Adaptive Gaussian Mixture (IAGM) filter to estimate the facies and non facies variables simultaneously on the complex reservoirs. We present the novel idea of using dummy wells to condition the facies modeling process for continuous channel regeneration. The overall workflow is an interaction between the data assimilation and the facies property modeling process. The proposed workflow is demonstrated on the Brugge field case and the data assimilation results provide geologically realistic facies models with better match of historical production data.