Case Study: Predicting Electrical Submersible Pump Failures Using Artificial Intelligence and Physics-Based Hybrid Models

Abstract

Abstract Despite being the most widely used artificial lift method for high-producing oil wells, ESPs still experience unplanned failures that impact well productivity and overall field economics. Our advanced ESP Predictive Failure Analytics (PFA) can help detect ESP events ahead of time and extend the overall ESP run life. PFA enabled a major Latin American operator, experiencing frequent unplanned ESP failures, to identify critical events while pumps were running and take remedial actions to extend ESPs run life. Methods, Procedures, Process PFA leverages artificial intelligence (AI), statistical and physics-based models to reliably predict Remaining Useful Life (RUL) and possible failure cause. The models are trained using historical sensor time-series from both running and failed ESPs. The trained models are deployed to predict short-term events that may lead to immediate failure, such as a broken shaft, short-circuit, grounded downhole failure; as well as long-term events which build up over time, such as pump low efficiency, sand, scale deposition and gassy conditions. Results, Observations, Conclusions For this study, we used two ESPs. For ESP-1, PFA predicted broken shaft/missed pump stages after a sudden decline in motor current and production rate. As the production rate declined beyond the minimum recommended operating range, PFA identified downthrust condition and estimated a significant RUL reduction. PFA enables the operator to quickly schedule a workover, reducing downtime. For ESP-2, intake pressure and motor current started decreasing and motor temperature started increasing. PFA predicted sand influx and estimated a significant RUL reduction. A chemical injection was applied to reduce sand, and avoid an imminent failure leading to PFA estimating an increased RUL. Novel/Additive Information PFA is an innovative approach which combines AI, statistical and physics-based methods to provide explainable predictions of ESP failure. Unlike commonly used threshold-based approaches, PFA tends to generate fewer alarms which enables proactive optimization of ESP performance, avoiding unplanned failures and extend ESP run life.