Data-Driven Drilling: From Rig Automation to Rock Properties
Friday, 23 October
Room 370 CF
Technical Session
This session focuses on improving performance through drilling automation using advanced control algorithms, physics-based models, and downhole measurements, as demonstrated through case studies. Topics include auto-driller optimization, rig control system – BHA interactions, AI-driven geomechanics and formation evaluation, vibration-based rock property inference, and enhanced understanding for cuttings transport.
Session Chairpersons
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1400-1425 233801Automated Static and Dynamic Zeroing for Improved Weight-on-Bit Accuracy: A Multi-Well Field Validation
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1425-1450 234119Reducing Auto Driller Dysfunction and Tripping Risk at Scale with Reduced-Order Physical Modeling and Gain Scheduling
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1450-1515 233888Partners in Crime: Matching the Rig Control System and Bottom Hole Assembly to Maximize Performance and Minimize Failures
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1515-1540 233879A Scientific Machine Learning Model For Real-time Transient Prediction Of Cuttings Bed Height In Long Horizontal Laterals
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1540-1605 234089Inferring Formation Properties from Drilling-Induced Vibrations: A Physics-Informed Workflow Integrating Laboratory Measurements and Finite Element Modeling
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1605-1630 233927The Shape Effect: How Particle Sphericity Controls Cuttings Transport In Directional Wells
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Alternate 234135Drillbench: A Sequential Decision-Making Benchmark for Drilling Operations with Cross-Stage Agent Feedback
