Drilling Automation / RTOC / AI / ML
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 233801Optimizing Drilling Operations With Auto-zeroed Wob: Insights From Case Studies
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1425-1450 234119Improving Auto Driller Control With Region-based Gain Schedules: A Scalable Field-ready Methodology
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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 233985Looking Backwards, Steering Forward - AI‑Based Real‑Time Geomechanics And Porosity Measurements At The Drill‑Bit
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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 233879A Scientific Machine Learning Model For Real-time Transient Prediction Of Cuttings Bed Height In Long Horizontal Laterals
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Alternate 234078Memory Based Machine Learning For Rotary Steerable Systems Health Predictions
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Alternate 234135Drillbench: A Sequential Decision-making Benchmark For Drilling Operations With Cross-stage Agent Feedback