[CCoE Notice] Thesis Defense: Applying Predictive Analytics to Detect and Diagnose Impending Problems in Electric Submersible Pumps

[Grayson, Audrey A]

MS Thesis Defense

Applying Predictive Analytics to Detect and Diagnose Impending Problems in Electric Submersible Pumps

Supriya Gupta

Date: August 3, 2015

Location: ERP Bldg 9 Room 124
Time: 10:30 am

Committee Chair: Dr. Michael Nikolaou

Committee Members:
          Dr. Christine Ehlig-Economides
                   Dr. Karolos Grigoriadis



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. The financial impact of ESP failure is substantial, from both lost production and replacement costs. Furthermore, the emerging trend in the E&P industry of using downhole sensors for real-time surveillance provides an opportunity for predicting and preventing ESP shutdowns using data driven techniques employing multivariate statistics. Therefore, in a quest to reduce costs and optimize maintenance of ESP, a methodology for data-driven detection and diagnosis of impending ESP problems in real time is proposed to advance from a reactive approach towards failure situation to a proactive approach to ESP health monitoring. This would enable prediction of impending events, diagnosis of the cause and prescription of preventive action.  This real-time analytical model enables proactive safeguarding of ESP operation, reduction of intervention costs and production optimization leading to significant improvement in overall oilfield economics.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://Bug.EGR.UH.EDU/pipermail/engi-dist/attachments/20150723/a9db833b/attachment-0001.html