An optimal water injection policy maximizes oil recovery per barrel of injected water while minimizing formation damage and maintaining reservoir pressure. Optimal water injection into low permeability, fractured oil reservoirs is problematic because of highly nonlinear and complex reservoir dynamics. Likewise, current first principle models of fluid movement in fractured, low permeability rock systems are insufficient to design, operate, and predict the performance of large scale waterfloods Historically, the conflict between prudent reservoir management and meeting field injection-production targets has resulted in reservoir and well damage, injectant recirculation and irreversibly lost oil production.
Here we present the next generation of "intelligent" field surveillance and prediction software based on neural networks and implemented on a PC. We demonstrate a new approach to field-wise performance prediction and optimization of waterfloods that recognizes an oil field as a coupled, highly nonlinear system of injectors and producers. With lease-wide historical data from a waterflood in the Lost Hills Diatomite (Kern County, CA), we construct several neural networks which recognize that individual well behavior may depend on well history and the injection-production conditions of surrounding wells. Some of our neural networks accurately predict wellhead pressure as a function of injection rate, and vice versa, for all injectors. Other networks history-match oil and water production on the well-by-well basis, and predict future production on a quarterly or half-year basis. Finally, our neural networks recognize and suggest water injection policies that lead to the minimum injected water and the best oil recovery.
