Electrical submersible pump (ESP) prognostic health management (PHM)
Challenge
- ESP pumps are critical for lifting fluids in oil, gas, and water production.
- These pumps operate under demanding conditions and performance is highly sensitive to operational and environmental factors.
- Unexpected failures in ESPs have resulted in production halts with significant cost implications, stemming from mechanical, electrical, or operational issues.
- Underperformance of ESPs leads to production interruptions, significantly affecting overall operations.
Solution
This machine learning (ML) solution is designed to predict ESP failure probabilities by analyzing anomalies in operating parameters and correlating them with historical failure patterns.
Improved reliability: Trained ML models leverage multiple input parameters to deliver the highest possible prediction accuracy, enabling proactive maintenance and reducing downtime.
Enhanced operational efficiency: Automated anomaly detection and mapping allow timely identification of high-risk pumps, minimizing production interruptions and cost implications.
Results
Early detection of anomalies in parameters affecting pump performance.
Prediction of failure probabilities with confidence intervals.
Reliable models trained on multiple input parameters for highest possible prediction accuracy.
Better understanding of pump performance and early failure risk detection.
