RTO and advanced process control for electric submersible pumps using surrogate models

This paper proposes a real-time optimisation (RTO) framework that uses AI-based surrogate models integrated with the advanced regulatory control (ARC). The main contribution is a method that employs a vertically decomposed control architecture, in which the RTO is formulated using steady-state surrogate models for both objectives and constraints. The RTO layer is responsible for guiding advanced regulatory control (ARC) to the optimal point subject to constraints, and the ARC layer coordinates the PID controllers during dynamic behaviour. The case study is an Electric Submersible Pump (ESP) system controlled with an ARC scheme. Results show effective constraint handling, operation within feasible regions, and adaptability to shifts between production and efficiency maximisation—antagonistic goals in ESP operation. The method is computationally efficient, resulting in a 99% reduction in processing time compared to differential equation methods. The RTO also re-optimised operating conditions following unmeasured disturbances. From a practical standpoint, the results indicate that real-time implementation can be more efficient and requires less computational effort.