Abstract: The 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 break-downs. However, such workflows are reactive in nature, i.e., action is taken after tripping or failure. 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.
Type:
Grant
Filed:
March 16, 2017
Date of Patent:
August 3, 2021
Assignees:
University of Houston System, Frontender Corporation, Halliburton
Inventors:
Supriya Gupta, Michael Nikolaou, Luigi Saputelli, Cesar Bravo
Abstract: The 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 break-downs. However, such workflows are reactive in nature, i.e., action is taken after tripping or failure. 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.
Type:
Application
Filed:
March 16, 2017
Publication date:
October 15, 2020
Applicants:
University of Houston System, Frontender Corporation, Halliburton
Inventors:
Supriya Gupta, Michael Nikolaou, Luigi Saputelli, Cesar Bravo