Minimize downtime
Reduced costs
Increased profitability
Driving revenue growth
A leading global energy company needed a way to minimize downtime by predicting equipment failures before they occurred. The remote locations of oil wells and the depth of pumps made quick repairs challenging. Traditional maintenance—based on scheduled inspections—was inefficient, often leading to unnecessary downtime or unexpected failures that disrupted operations and profitability.
Key challenges
Minimizing ESP failures to reduce costly delays and downtime
Predictive maintenance cut shutdowns, boosted ROI
The solution
Failure analysis and prediction
Identified ESP failure patterns via root cause analysis
Used DNN models with historical sensor data
Built a predictive model for early failure detection
Optimization and deployment
Applied domain expertise and physics-based analysis
Improved accuracy with real-time machine insights
Deployed on Azure for scalable, efficient monitoring
1
Data processing training
Analyzed data using Databricks
Trained models with Azure Machine Learning
Managed data with Azure Data Lake
2
Deployment and integration
Integrated models into operations
Enabled real-time monitoring and alerts
Optimized scheduling to reduce downtime
3
Improvement and scaling
Refined models with new data
Expanded to more ESP systems
Ensured scalability with cloud deployment
Immediate benefits
Cut unplanned downtime
Boosted revenue and efficiency
Predicted failures two weeks early for timely fixes
Long-term value
Improved ROI with more failure data
Minimized downtime with proactive repairs
Reduced false positives for better maintenance
Operational efficiency
Enhanced predictive accuracy
Maximized pump uptime and productivity
Secured long-term cost savings and growth
AI-driven enhancements
Leverage advanced AI to improve predictive accuracy
Scalable deployment
Expand the solution across more sites for greater impact
Continuous optimization
Refine models with real-time data for sustained efficiency
