Facies Parameterization and Estimation for Complex Reservoirs - The Brugge Field

Abstract Data assimilation using ensemble-based inversion methods has been successfully applied for parameter estimation in reservoir models. However, in certain complex reservoir models, it remains challenging to estimate the model parameters and preserve the geological realism simultaneously. In particular, when handling special reservoir model parameters such as facies types concerning fluvial channels, geological realism becomes one of the key concerns. The main objective of this work is to address this issue on a real field with a newly extended version of a recently proposed facies parameterization approach coupled with ensemble-based data assimilation techniques. The proposed workflow combines the new facies parameterization and the IAGM filter into the data assimilation framework for channelized reservoirs. In order to handle discrete facies parameters, we combine probability maps and truncated Gaussian fields to obtain a continuous parameterization of the facies fields. For the data assimilation we use the Iterative Adaptive Gaussian Mixture (IAGM) Filter, which is an efficient history matching approach that incorporates a resampling routine which allows us to regenerate facies fields using information from the updated probability maps. This workflow is evaluated, for the first time, on a complex field case - the Brugge field. This reservoir model consist of layers with complex channelized structures and layers characterized by reservoir properties generated with variograms. With limited prior knowledge on uncertain facies models, this workflow is shown to be able to preserve the channel continuity while reducing the reservoir model uncertainty with IAGM. When applied to complex real field studies, it provides a geologically consistent and realistic reservoir model which leads to improved capability of predicting subsurface flow behaviors.