Electrical Submersible Pump Prognostics and Health Monitoring Using Machine Learning and Natural Language Processing

Abstract

Abstract Electrical Submersible Pump (ESP) systems efficiently pump high volumes of production fluids from the wellbore to the surface. They are extensively used in the oil and gas industry due to their adaptability, low maintenance, safety, relatively low environmental impact. They require specific operating conditions with respect to the power, fluid level and fluid content. Oilfield operation workflows often require extensive surveillance and monitoring by subject-matter experts (SMEs). Detecting issues like formation of unwanted gas and emulsions in ESPs requires constant analysis of downhole data by SMEs. The lack of adequate and accurate monitoring of the downhole pumps can lead to low efficiency, high lifting costs, and frequent repair and replacements. There are 3 workflows described in the paper which demonstrate that the maintenance costs of the ESPs can be significantly reduced, and production optimized with the augmentation of machine learning approaches typically unused in ESP surveillance and failure analysis. Downtime due to ESP failures cause loss of production, disrupt logistics planning and require weeks for replacement. Pull-out due to poor reservoir conditions complicates the inventory issue. Therefore, it is desirable to reduce workover time and optimize production by providing early warning of ESP failures (Fig. 1).