The electrical submersible pump (ESP) is currently the fastest growing artificial-lift pumping technology (Bates, Cosad et al. 2004). Deployed across 15 to 20 percent of oil-wells worldwide, ESPs are an efficient and reliable option at high production volumes and greater depths (Breit and Ferrier 2008). However, ESP performance often declines without much warning and reaches the point of service interruption due to factors like high gas volumes, high temperature, and corrosive environments. The financial impact of ESP service interruption is substantial, from both lost production and replacement costs. Therefore, ESP performance in extensively monitored, and numerous workflows exist to suggest actions in case of service interruption events such as trippings or breakdowns. However, such workflows are reactive in nature, i.e. action is taken after an event occurs. Furthermore, with the emerging trend in the E&P industry of using downhole sensors for real-time surveillance of parameters impacting ESP performance, there is an opportunity to leverage real-time data obtained from sensors for predicting and preventing ESP shutdowns using data analytics.

This paper presents an analytical framework towards proactive ESP health monitoring based on data-driven predictive modeling and analysis. This framework can be used to automatically identify real-time patterns and assess ESP health in real time, thus offering to engineers and field personnel early detection of impending problems well before they occur, for mitigation and prevention of ESP service interruptions.

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