This research focuses on combining physics-based expert rules with machine learning (ML) to improve the detection of failure-related events in electrical submersible pumps (ESPs). The goal is to create a generalized framework for abnormal-event detection that addresses the challenge of limited labeled data and sensor data, providing a practical solution for ESP management and maintenance planning. By using ML as a denoising layer over the physics rules, the method bridges rule-based and data-driven approaches.
Introduction
ESP reliability remains a decisive lever for production uptime, yet unplanned shutdowns still account for a significant share of lifting costs. Years of field digitalization have turned each pump into a high-frequency data source, but limited ground-truth failure labels and heterogeneous sensor layouts still hinder the move from reactive maintenance to predictive decision-making.
This study tackles that constraint by treating physics knowledge as a source of weak supervision. A library of expert rules—encapsulating known degradation signatures—runs continuously on sliding windows of normalized sensor trends, producing numeric scores that act as soft labels.
