Nikolaou, Michael2021-07-152021-07-15August 2012015-08August 201https://hdl.handle.net/10657/7866The electrical submersible pump (ESP) is currently the fastest growing artificial-lift pumping technology. Deployed across 15 to 20 percent of oil-wells worldwide, ESPs are an efficient and reliable option at high production volumes and greater depths. However, ESP performance is often observed to decline gradually and reach the point of service interruption due to factors like high gas volumes, high temperature, and corrosion. The financial impact of ESP failure 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 breakdowns. However, such workflows are reactive in nature, i.e., action is taken after tripping or failure. Furthermore, given 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 for predicting and preventing ESP shutdowns using data analytics. Therefore, a data-driven analytical framework is proposed to advance towards a proactive approach to ESP health monitoring based on predictive analytics to detect impending problems, diagnose their cause, and prescribe preventive action.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Electric Submersible Pumps, Predictive Analytics, Data-driven methods, Artificial Lift Technology, Real-time analytics2021-07-15Thesisborn digital