An Artificial Intelligent Cutting-Edge Technology for Automated Field Production Optimization & ESP Failures Prediction

Abstract

Abstract Unpredicted an Electrical Submersible Pump (ESP) failure can have a serious impact on well production and overall field economics; therefore, it is important to reliably predict the starting time of damaging operating conditions and take proactive action to prevent early failure of an ESP. Most operators rely mainly on production engineers to monitor and optimize equipment performance with remote monitoring and surveillance technologies that provide threshold-based diagnostics to detect electrical or mechanical problems. Due to fundamental constraints in exception-based monitoring, it is extremely difficult to reliably predict the root cause of equipment failure and remaining useful life (RUL) of an ESP. Methods, Procedures, Process Predictive Failure Analysis (PFA) leverages artificial intelligence (AI), statistical and physics-based models to constantly predict Remaining Useful Life (RUL) and possible failure cause. The models are qualified and validated using historical sensor time-series from both running and failed ESPs. The validated models are deployed to predict short-term events that may lead to sudden 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 The paper presents a case history for a major operator that was experiencing similar operational challenges in three of its producing assets. unpredicted failures happen frequently since they are hard to predict ahead of time with the data available to production engineers. This case study demonstrates how Advanced ESP Predictive Failure Analytics (PFA) technology has helped this operator to detect such events and extend ESP run life. For one ESP, PFA detected scale deposition and predicted a sharp decline in RUL. After confirming the decrease in production fluid, and other surface and downhole sensor trends indicating scale deposition in the ESP, the production engineer applied chemical injection and avoided the failure. For a second ESP in this case study, PFA detected a grounding condition and predicted sudden decline in RUL. The production engineer noticed motor amps increased beyond the recommended threshold and performed electrical optimization to reduce motor amps. The ESP ran for another year and eventually failed due to grounded downhole sensor failure, which PFA had detected two weeks prior to the failure. Novel/Additive Information PFA is a unique approach that combines AI, physics-based, and expert knowledge-based approaches to provide interpretable predictions of ESP failure. Unlike traditional CBM approaches, PFA generates fewer alarms with longer lead times, enabling the operator to take preventive measures to avoid sudden failure. In addition, PFA ranks ESPs based on criticality, which enables effective surveillance of large number of wells.