Integration of Markov Mesh models and ensemble data assimilation in reservoir with complex geology

We present a methodology conducive to updating both facies and petrophysical properties of a reservoir models set characterized by a complex architecture within the context of a history matching procedure based on the Ensemble Kalman Filter (EnKF). Spatial distribution of facies is handled by means of a Markov Mesh (MM) model. The latter is adopted because of its ability to reproduce detailed facies geometries and spatial patterns and can be integrated in a consistent probabilistic Bayesian framework. The MM model is then integrated into a history matching procedure which is based on the EnKF scheme. We test the proposed methodology by means of a synthetic reservoir in the presence of two distinct facies. We analyze the accuracy and computational efficiency of our algorithm with respect to the standard EnKF both in terms of history matching quality and forecast prediction capabilities. The results show that the integration of MM in the data assimilation scheme allows obtaining realistic geological shapes for spatial facies distribution and an improved estimation of petrophysical properties. In addition, the updated ensemble correctly captures the production range in the long term.