Multi-stage scenario-based MPC for short term oil production optimization under the presence of uncertainty. (October 2022)

• Record Type: Journal Article
• Authors: Janatian, Nima; Sharma, Roshan
• Is Part Of: Journal of process control. Volume 118(2022)
• Page Start: 95
• Page End: 105
• Publication Date: 2022-10
• Subjects: Multi-stage stochastic model predictive control -- Real-time optimization -- Parametric uncertainty -- Scenario tree -- Gas lift oil wells; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.08.012
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: This paper considers the problem of daily production optimization for a gas lift oil network under the presence of parametric uncertainty. The objective is to find the optimal distribution of injected lift gas to maximize total oil production from an oil network that contains parametric uncertainties subject to some constraints. Typically, the model-based optimization methods in such processes overlook uncertainty and go along with the optimal solution based on the nominal model. Nevertheless, the effect of uncertainty may lead to infeasibility when implemented in real applications. The proposed scenario-based optimization framework in this paper ensures robust feasibility compared to deterministic optimization. Additionally, the superiority of the method has been illustrated in comparison with the other robust optimization counterparts such as Min–Max NMPC in terms of conservativeness and execution time. The simulation results of the nominal, min–max, and scenario-based optimization are compared and discussed. Highlights: Multi-stage model predictive control for real-time optimization under uncertainty. Multi-stage method is faster and less conservative than worst-case optimization. Effect of the uncertainty description on the control performance. Application of the methods for optimal operation in a gas lifted case example.