A Reactive Approach for Real-Time Optimization of Oil Production Under Uncertainty

This paper proposes a reactive approach based on the moving horizon estimation method for optimization in the presence of parametric uncertainty. The moving horizon estimation scheme uses measurable outputs in order to estimate the states and uncertain parameters jointly where the full states are not measurable. It has been shown that the deterministic certainty-equivalent model predictive control based on estimated states and parameters is less conservative and significantly faster. However, the method's performance deteriorates in transition periods, particularly when the system's parameters change rapidly. Nevertheless, when the estimations converge to the actual values, the adaptive MPC will be adjusted quickly to respect the constraints. These promising features make the adaptive method suitable for circumstances where high performance is more desirable than robustness fulfillment of constraint. The other aspects of the method and its applicability are also discussed thoroughly.