Short-Term Production Optimization for Electric Submersible Pump Lifted Oil Field With Parametric Uncertainty

This paper uses a scenario-based optimization method to address the Daily Production Optimization from an Electric Submersible Pump lifted oil field under the presence of uncertainty. The primary contribution of this work lies in addressing the presence of uncertainty in short-term production optimization of the oil industry, a significant aspect that is frequently overlooked. It has been shown that using the dynamic model of the plant in the optimization problem is too computationally expensive, even in a deterministic case. Therefore, the steady-state model of the system has been used in a robust optimization framework. The necessity of considering uncertainty in the optimization problem and the promising results of the proposed robust method is compared with the deterministic optimization counterpart. An additional novelty of this study involves the utilization of a scenario-based optimization framework to explore various forms of uncertainty, including uncertainty in well flow parameters and oil price. It has been shown that the uncertainty in oil price does not affect the optimal solution during normal operation, at least in short-term optimization such as Daily Production Optimization. On the contrary, the uncertainty in the well parameters is important to be considered since well flow parameters influence the optimizer in preferring one well over the other. Consequently, the economic objective for the lucrative business of the oil industry will be translated into production maximization, and the optimizer's task involves allocating the total production capacity among the different wells to maximize the proportion of the oil to water in the produced fluid.