Integration Approach and Machine Learning for Hazards Identification and Safe Drilling Operation
Monday, 1 May
610
Technical / Poster Session
Safe drilling operation is very critical in oil and gas industry not only for cost saving but also for reducing negative environmental impact. The papers in this technical session will take the audience through integrated approaches and workflows from geosciences and geomechanics to the application of machine learning for hazards identification and safe well drilling with very good case studies confirmed by drilling results.
Sponsoring Society:
- American Association of Petroleum Geologists (AAPG)
- American Society of Mechanical Engineers (ASME)
- Society of Exploration Geophysicists (SEG)
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0930-0948 32447Real-Time Machine Learning Application for Formation Tops and Lithology Prediction
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0950-1008 32230Geohazards in Riserless Drilling for an Exploration Well in Deepwater GoM: Identification and Mitigations
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1010-1028 32169Leveraging Targeted Machine Learning for Early Warning and Prevention of Stuck Pipe, Tight Holes, Pack Offs, Hole Cleaning Issues and Other Potential Drilling Hazards
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1030-1048 32350Wellbore Casing Integrity Envelope for Deepwater Reservoirs - Workflow and Case Study
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1050-1108 32309A Robust Static Model for Geomechanical Characterization And Drilling Optimization in Offshore Niger Delta Basin, Nigeria
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1110-1128 32635Developing a Digital Twin for Offshore Wells using Physics-Rooted Models
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1130-1148 32210Pushing the Limits in Deep Water Data Acquisition for Accelerated Field Development: Industry Record Batch Well Testing
