Anil Godumagadda

Patterson-UTI Drilling Company operates nearly 90 rigs and has spent the last six years building and deploying edge and cloud platforms that historize more than 6,000 sub-second data points across four rig types and control systems. Through a disciplined systems architecture approach and custom connectors across equipment, the company has established an end to end rig-to-cloud-to-edge pipeline that trains ML models in the cloud and deploys them at the edge to improve performance, enhance equipment reliability, and reduce downtime across the fleet.

This case study highlights how Patterson-UTI Drilling Company:

• Built a unified data repository by standardizing sensors and connectivity across multiple control systems, enabling a scalable end-to-end rig-to-cloud-to-edge pipeline for intelligent products that improve equipment reliability and performance
• Applied ML models to identify unnecessary fuel consumption and monitor critical parameters, delivering real-time setpoint guidance that improves efficiency and reliability
• Enabled earlier intervention through an edge- and cloud-based alerting system that detects leading indicators of failure, escalates inaction, and reinforces reliability-focused operating behaviors