Reconciling geological models to the available dynamic information, commonly known as history matching, is an essential step for optimizing reservoir management and field development strategies, including improved recovery methods. There are several challenges in the current history matching workflow, particularly for high resolution geologic models with multimillion cells and complex geologic architecture. Streamline-based inverse modeling has shown great promise in this respect because of computational efficiency and analytic calculation of sensitivity of production response to reservoir properties. However, the current streamline-based approach is mostly restricted to history matching water-cut and tracer response in two-phase flow.

In this paper we present a novel approach to extend the streamline-based history matching to three-phase flow by incorporating water-cut, gas-oil ratio and bottomhole pressure data while updating high resolution geologic models. The crux of our approach lies in the analytic computation of bottomhole pressure and gas-oil ratio sensitivities which allows for efficient inversion of production and pressure data. Thus, our approach overcomes one of the major limitations of the current state-of-the-art while preserving the computational efficiency and the intuitive appeal of the streamline method. The streamline-based approach can also be used in conjunction with finite difference simulators, further generalizing its applicability to enhanced oil recovery methods. We validate the accuracy and efficiency of the streamline-based sensitivities by comparison with adjoint or numerical methods using finite-difference simulators. In history matching, we incorporate the novel streamline-based method with multiscale approach to account for the disparity in resolution of different types of history data. This method leads to capturing of the large- and fine-scale heterogeneity and reproducing the pressure and production responses efficiently.

We demonstrate the power and utility of our approach using synthetic and field applications. The synthetic example involves the SPE9 benchmark field case with waterflooding and aquifer drive. The field example involves full-field history matching of the Norne Field in the North Sea using water-cut, gas-oil ratio and bottomhole pressure data and subsequent design of a polymer flood. A novel multiscale workflow demonstrates the efficiency and advantage of our proposed approach in achieving geologically consistent history matching at the full-field level.

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