Comparison of economic model predictive controllers for gas-lift optimization in offshore oil and gas rigs

This paper presents a comparative study of different control strategies to solve the gas-lift optimization (GLO) problem of offshore rigs. GLO consists of distributing the compressed gas between the wells to maximize oil production, considering several operational and process aspects such as the cost of flaring, price fluctuations, measurable noise, external disturbances, and plant-model mismatches. We compare and evaluate the performance of economic nonlinear model predictive control (ENMPC), Modifier-based EMPC (EMPC-Mod), EMPC with Local Linearization on Trajectory (EMPC-LLT), the static Real-Time Optimizer with Parameter Adaptation (ROPA), and the Active Constraint Control (ACC) based on feedback controllers. The study points out the advantages and drawbacks of each approach being useful for engineers to choose the most appropriate strategy. Moreover, the results show that the linear EMPCs and ROPA have similar performance to the theoretical optimal while maintaining minimal computational burden, and also that ACC is satisfactory for this case study.