An intelligent integrated production optimization technique for waterflooding reservoirs

Abstract

The production optimization in the closed-loop reservoir management is generally empirical, and challenged by the issues such as low precision, low efficiency, and difficulty in solving constrained optimization problems. This paper outlines the main principles, advantages and disadvantages of commonly used production optimization methods/models, and then proposes an intelligent integrated production optimization method for waterflooding reservoirs that considers efficiency and precision, real-time and long-term effects, and the interaction and synergy between a variety of optimization models. This method integrates multiple optimization methods/models, such as reservoir performance analysis, reduced-physics models, and reservoir numerical models, with these model results and insights organically coupled to facilitate model construction and matching. This proposed method is elucidated and verified by field examples. The findings indicate that the optimal production optimization model varies depending on the specific application scenario. Reduced-physics models are conducive to short-term real-time optimization, whereas the simulator-based surrogate optimization and streamline-based simulation optimization methods are more suitable for long-term optimization strategy formulation, both of which need to be implemented under reasonable constraints from the perspective of reservoir engineering in order to be of practical value.