(2022003) Impact from Analyzing The Run Life Statistics Using Survivability Curves Methodology On ESP Key Performance Indicators


Christopher Bryan and Miguel Irausquin
Baker Hughes

Managing extensive Electrical Submersible Pump (ESP) operations and evaluating their performance can be a challenging task, especially in unconventional reservoirs. Varied operational environments, expansive geographical areas, large ESP populations, different declination patterns, diverse fluid properties and well designs and different service providers are some of the complications that operators face every day. Many companies measure the success of any artificial lift project focus on simple run life statistics as the central key performance indicator; however, these types of statistics may not always be enough in providing significant information to decision makers. It is vital to the success of any project to establish a performance evaluation structure that can effectively capture deficiencies and highlight potential improvements. Survivability curves are the result of the statistical model based on Kaplan-Meier analysis, which was originally created to measure the fraction of subjects living for a certain amount of time after treatment in clinical trials, so similar methodology was deployed to analyze an important dataset of ESPs to deeper understand by factoring and comparing elements which influence ESP run life, showing results that are easier to understand and represent real value to operators on several areas as safety, engineering, reliability and operations. As a result, from this comprehensive study jointly initiated between an oil operator and ESP vendor, corrective actions were taken that drive improvements in all ESP aspects, which can be seen not only in today’s KPIs, also influence future artificial lift projects. Being able to draw conclusions about the expect runtime can be used to drawn insight on find areas where efforts should be focused to improve ESP reliability, find where ESPs can be best utilized to improve field performance, and identify opportunities to reduce workover cost. Similar analysis can be done to visualize ESP runlife improvement over time, compare different ESP technologies, and find expected runtimes by completion design or producing formation. The values of insights gained from statistical analysis can be gotten from any field of ESPs to aid in making better oilfield business decisions.

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Wed 9:00AM - 9:50AM, Room 110
Thu 11:00AM - 11:50AM, Room 110

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