Artificial-Lift System Control: A Reduced-Order Transient Model for Real-Time Predictive Control of Electrical Submersible Pump Wells

Abstract

Unstable well operations, driven by reservoir depletion, high gas–oil ratios, unstable inflow, and surface network interactions, have become a major challenge in modern production — especially in Western Siberian fields — causing flow instabilities and production losses. While numerous studies have advanced transient modeling for field optimization, their practical application remains largely limited to recommendation systems running on hourly or daily cycles, making recommendations irrelevant by the time they are applied. At the opposite end, PLCs (programmable logic controllers) relying on PID (proportional–integral–derivative) control cannot solve the main problem: determining the structure of the intermittent cycle. This work proposes a reduced-order transient model of the coupled “reservoir–tubing–annulus” system that captures the essential behavior of transient multiphase flow while remaining compact enough for on-edge real-time model predictive control. Constrained optimization algorithms built on this model provide autonomous closed-loop well control, ensuring equipment and reservoir limits are respected and enabling safe, smooth mode transitions. The solution was validated against high-fidelity simulations and real operating data from Western Siberian fields, demonstrating reliable intermittent ESP (electrical submersible pump) well operation, robust response to rapid operational changes, and effective disturbance rejection.