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AI and Machine Learning

Wednesday, 5 March
Room 2
Technical Session

This session will explore the latest advancements in AI and machine learning applied to drilling operations. Presentations will cover automated assessments to prevent casing run failures and fleet-wide algorithms for detecting operational anomalies. The session will also discuss the generation of automated drilling control functions with built-in validation, as well as machine learning systems for optimizing drilling rates while addressing issues like hole cleaning and stick-slip. Other topics include improving procedural reviews for better risk management and using haptic feedback to enhance driller situational awareness.

The e-poster session will feature tools for real-time risk management, enhanced automation of drilling processes, and predictive maintenance systems utilizing AI and haptic feedback.

Chairperson
Kriti Singh - CORVA
Gregory Payette - Exxon Mobil Corporation
  • 1045-1110 223706
    Casing Run Failure Avoidance Through Automated Assessment of Borehole Quality
    A.C. Montes, P. Ashok, E. van Oort, The University of Texas At Austin; B. Les, Equinor ASA; M.A. Rossi, S.R. Limaye, Shell
  • 1110-1135 223719
    Impacts On Tare Quality and Best Practices from Fleet-wide Breakover Detection Algorithm
    A. Groh, M.E. Kaya, D. Dunbar, Patterson-UTI Drilling Company LLC
  • 1135-1200 223716
    Code Generation of Automatic Drilling Control System Functions with Embedded Verification and Validation Functionalities
    E. Cayeux, R. Mihai, NORCE Norwegian Research Centre AS; R. Herikstad, btwn AS; K. Olsen, K. Antosz, Halliburton; M. Pham, Aker BP
  • 1200-1225 223713
    System For Real-time Rate Of Penetration Optimization Using Machine Learning With Integrated Preventive Safeguards Against Hole Cleaning Issues And Stick-slip
    T. Robinson, Exebenus AS; P.M. Arshad, O.E. Revheim, Exebenus; M. Regan, P. Bekkeheien, Exebenus AS
  • Alternate 223726
    Increasing Driller's Situational Awareness through Haptic Feedback
    A. Pavlov, Norwegian University of Science and Technology; J. Visser, W. Maas, Eindhoven University of Technology; A. Hashemi, Norwegian University of Science and Technology; O. Ødegård, Siemens Energy AS; E. Steur, Eindhoven University of Technology; L. Imsland, Norwegian University of Science and Technology