Reinforcement Learning Control Scheme for Electrical Submersible Pumps

Abstract

Electrical submersible pumps (ESPs) are closely monitored in surveillance operations because they operate in challenging environments and are subject to stressful events that, if left without intervention, may lead to unplanned shutdowns, decreased run life, or even failures. These events can occur unannounced with different magnitudes of severity due to the large range of operating conditions. Thus, a universally prescriptive response is challenging because each well may require a tailored and dynamic course of action over time. This paper proposes leveraging a powerful multidimensional state engine known as automated events detection (AED), working together with an artificial intelligence agent, to respond to these stressful events and subsequently improve actions using a reinforcement learning (RL) scheme. Motivations of this approach are to move toward more autonomous, self-protecting systems with closed-loop actions and to achieve this at scale across many wells.