Machine Learning Approach for Predictive Maintenance of the Electrical Submersible Pumps (ESPs).

Area of Science:

• Petroleum Engineering
• Data Science
• Machine Learning

Background:

• Electrical submersible pumps (ESPs) are crucial for artificial lift in the petroleum industry.
• Reactive maintenance of ESPs is resource-intensive and relies on analyzing multivariate sensor data.
• Predictive maintenance can optimize ESP operations and reduce associated costs.

Purpose of the Study:

• To develop and present a methodology for predictive maintenance of ESPs using real-time sensor data.
• To enhance operational efficiency by predicting ESP failures before they occur.
• To reduce downtime and associated economic losses in petroleum production.

Main Methods:

• Implementation of Principal Component Analysis (PCA) as an unsupervised learning technique.
• Pipelining PCA output with Extreme Gradient Boosting trees (XGBoosting) for failure prediction.
• Analysis of real-time and historical multivariate sensor data from ESPs.

Main Results:

• The proposed model identifies deeper functional relationships and long-term trends in historical data.
• The predictive model successfully signaled potential ESP failures up to 7 days in advance.
• Achieved an F1-score exceeding 0.71 on the test set, demonstrating high predictive accuracy.

Conclusions:

• The integrated PCA and XGBoosting approach offers a robust solution for ESP predictive maintenance.
• Real-time alarming systems based on this methodology can significantly increase production efficiencies.
• Proactive identification of ESP failures minimizes deferment losses and optimizes resource allocation.