Fast history matching of finite-difference model, compressible and three-phase flow using streamline-derived sensitivities

Abstract

Reconciling high-resolution geologic models to field production history is still a very time-consuming procedure. Recently streamline-based assisted and automatic history matching techniques, especially production data integration by “travel-time matching,” have shown great potential in this regard. But no systematic study was done to examine the merits of travel-time matching compared to more traditional amplitude matching for field-scale application. Besides, most applications were limited to two-phase water-oil flow because current streamline models are limited in their ability to incorporate highly compressible flow in a rigorous and computationally efficient manner. The purpose of this work is fourfold. First, we quantitatively investigated the nonlinearities in the inverse problems related to travel time, generalized travel time, and amplitude matching during production data integration and their impact on the solution and its convergence. Results show that the commonly used amplitude inversion can be orders of magnitude more nonlinear compared to the travel-time inversion. Both the travel-time and generalized travel time inversion (GTTI) are shown to be more robust and exhibit superior convergence characteristics. Second, the streamline-based assisted history matching was enhanced in two important aspects that significantly improve its efficiency and effectiveness. We utilize streamline-derived analytic sensitivities to determine the location and magnitude of the changes to improve the history match, and we use the iterative GTTI for model updating. Our approach leads to significant savings in time and manpower. Third, a novel approach to history matching finite-difference models that combines the efficiency of analytical sensitivity computation of the streamline models with the versatility of finite-difference simulation was developed. Use of finite-difference simulation can account for complex physics. Finally, we developed an approach to history matching three-phase flow using a novel compressible streamline formulation and streamline-derived analytic sensitivities. Streamline models were generalized to account for compressible flow by introducing a relative density of total fluids along streamlines and a density-dependent source term in the saturation equation. The analytical sensitivities are calculated based on the rigorous streamline formulation. The power and utility of our approaches have been demonstrated using both synthetic and field examples.