A multiscale approach to production-data integration using streamline models

We propose a multiscale approach to data integration that accounts for the varying resolving power of different data types from the very outset. Start ing with a coarse description, we match the production response at the well s by recursively refining the reservoir grid. A multiphase streamline simul ator is used for modeling fluid flow in the reservoir. The well data are th en integrated using conventional, geostatistics, for example sequential sim ulation methods. There are several advantages to our proposed approach. Fir st, we explicitly account for the resolution of the production response by refining the grid only up to a level sufficient to match the data, avoiding over-parameterization and incorporation of artificial regularization const raints. Second, production data are integrated at a coarse scale with fewer parameters, which makes the method significantly faster compared to direct fine-scale inversion of the production data. Third, decomposition of the i nverse problem by scale greatly facilitates the convergence of iterative de scent techniques to the global solution, particularly in the presence of mu ltiple local minima. Finally, the streamline approach allows for parameter sensitivities to be computed analytically using a single simulation run, th us further enhancing the computational speed. The proposed approach has been applied to synthetic as well as field exampl es. The synthetic examples illustrate the validity of the approach and also address several key issues, such as convergence of the algorithm, computat ional efficiency, and advantages of the multiscale approach compared to con ventional methods. The field example is from the Goldsmith San Andres Unit (GSAU) in west Texas and includes multiple patterns consisting of 11 inject ors and 31 producers. Using well-log data and water-cut history from produc ing wells, we characterize the permeability distribution, thus demonstratin g the feasibility of the proposed approach for large-scale field applicatio ns.