Predictive Approach to Perform Fault Detection in Electrical Submersible Pump Systems

Area of Science:

• Petroleum Engineering
• Data Science
• Machine Learning

Background:

• Electrical submersible pumps (ESPs) are critical in oil and gas production.
• ESP failures incur significant costs and operational disruptions.
• Timely detection of abnormal ESP performance is a major industry challenge.

Purpose of the Study:

• To develop and validate a robust Principal Component Analysis (PCA) model for continuous fault detection in ESP systems.
• To enable early prediction of ESP failures and avoid costly downtime.
• To enhance real-time monitoring capabilities for ESP operations.

Main Methods:

• Implementation of a robust Principal Component Analysis (PCA) model for fault detection.
• Utilizing three-dimensional score plots of principal components to visualize system behavior.
• Testing the model with 47 actual failure events and 40 stable operating events.

Main Results:

• The robust PCA model successfully identified 20 failure events prior to their occurrence.
• The model accurately distinguished all 40 cases of stable operating wells.
• Demonstrated capability in recognizing dynamic changes indicative of developing failures.

Conclusions:

• Principal Component Analysis (PCA) shows significant potential as a monitoring platform for ESP systems.
• The developed PCA model can effectively predict developing ESP failures.
• This approach offers a proactive strategy to mitigate risks associated with ESP malfunctions.