Integrated modeling and parallelized optimization of enhanced oil recovery processes in hydrocarbon reservoirs under uncertainty

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A considerable portion of current world oil production comes from mature oilfields by applying enhanced oil recovery (EOR) techniques since new discoveries have been declining steadily over the last few decades. Prior to successfully implementing an EOR process, it is essential that the amount and location of the residual oil in a mature oilfield be accurately determined by calibrating a static geologic model to dynamic production data and then the production-injection strategy can be optimized. Due mainly to the complex and nonlinear relationship between inputs of a simulator and production data, simulation methods used, data collected, and model scale, accuracy of a reservoir geological model is normally compromised. The presence of geological and economic uncertainty not only makes it more difficult to accurately and dynamically update the geological model, but also elevates the difficulty of optimization to the level of a risk assessment problem. Therefore, it is of fundamental and practical importance to generate multiple reservoir models conditional to static and dynamic data and subsequently optimize the corresponding reservoir performance under uncertainty.