A Digital Analytics Solution for Analyzing a Population of Electric Submersible Pumps

Abstract

The electrical submersible pump (ESP), an efficient artificial lift method, was developed to increase production rates from wellbores (Bates et al. 2004). As the number of ESP installations increases annually, there is a greater awareness of their environmental impact and a growing responsibility to reduce the associated carbon footprint because frequent workovers to replace failed ESPs are primary sources of carbon dioxide emissions from the oil and gas industry. Because of this, companies are beginning to pursue cost reductions and look for methods to mitigate the consequences of production. Systems for monitoring ESP performance in real time are currently being developed on the basis of data analysis to detect potential problems in advance. This paper presents a digital solution for tracking ESP performance that includes an automated data processing pipeline and the use of statistical metrics to analyze the dynamics of failure and run life at the system node level. This comprehensive analysis helps diagnose problematic equipment nodes using developed web applications. The recommendation system determines the most-reliable ESP configurations under the necessary operating conditions.