Quantitative methods for reservoir characterization and improved recovery: Application to heavy oil sands

Improved prediction of interwell reservoir heterogeneity is needed to increase productivity and to reduce recovery cost for California's heavy oil sands, which contain approximately 2.3 billion barrels of remaining reserves in the Temblor Formation and in other formations of the San Joaquin Valley. This investigation involves application of advanced analytical property-distribution methods conditioned to continuous outcrop control for improved reservoir characterization and simulation. The proposed investigation is being performed in collaboration with Chevron Production Company U.S.A. as an industrial partner, and incorporates data from the Temblor Formation in Chevron's West Coalinga Field. A portion of Section 36 of West Coalinga Field was selected as the site to conduct a comparison of geologic and fractal models with results from an ongoing steam flood. The layer structure and permeability distributions of the different models were incorporated into a numerical flow simulator. The modeling objectives with regard to facies tract and facies group models have been achieved, with satisfactory matches for the oil and water production. The injection and production histories, including shutdowns, of the wells in the study area were accurately modeled, including the occasional conversion of production wells to steam injection wells. An exhaustive sensitivity analysis was performed to determine which reservoir and fluid properties have the greatest control on oil and water production. These results indicate that predictions of oil production are strongly influenced by the geologic framework and by the boundary conditions. The framework provided by facies groups provides a more realistic representation of the reservoir conditions than facies tracts, which is revealed by a comparison of the history matching for the oil production. Numerical difficulties encountered during multi-phase flow between cells with large permeability differences have created challenges in simulation using the fractal permeability distribution. This impediment is currently being addressed by the use of a new matrix solver for the numerical flow simulator, as well as modifications to the fractal field.