Enhancing ESP Uptime: Hybrid Approach Integrating Deep Learning, Expert Knowledge, and Physics-Based Modeling for ESP Failure Prediction

Abstract

Electric Submersible Pumps (ESPs) are widely used in oilfield artificial lift operations but are prone to frequent failures due to complex downhole conditions, including multiphase flow, harsh temperature–pressure environments, gas, solid, and strong coupling between pump and motor dynamics. Conventional approaches based solely on empirical knowledge or purely data-driven models often face limitations and uncertainty in balancing accuracy and interpretability. The study leveraged high-frequency time-series monitoring data collected from more than 100 production and water source wells, including key electrical, pressure, temperature, and vibration parameters, along with failure labels derived from DIFA reports and pull/failure records. The proposed workflow combined experience-based threshold monitoring, expert troubleshooting rules, and several AI modules such as BP neural networks for current-card diagnostics, CNN–LSTM rolling forecasting for short-term parameter prediction, LSTM–VGGNet dual-branch classification for fault-type identification, and wavelet-transform LSTM for remaining useful life (RUL) prediction. In addition, a mechanistic ESP performance model was incorporated to constrain predictions within physically plausible operating regimes and improve robustness. Field case studies demonstrated that the framework could provide early warnings for failures such as cable faults and pump mechanical issues, enabling proactive operational decisions and reducing unplanned downtime. The results showed that the hybrid approach achieved up to 90% fault-type identification accuracy, delivered 1–30 days of early warning in representative scenarios, and increased ESP uptime by approximately 8%–12% on average. The findings indicated that integrating AI with physics-based models and expert knowledge can not only improve prediction accuracy and generalization but also enhance interpretability and field applicability. The presented framework integrates essential elements of ESP surveillance and prediction analysis into fully digitized intelligent software for ESP systems, allowing engineers to monitor early signs, diagnose potential causes, and take preventive actions.