Optimizing Drilling and Monitoring Through Innovations in Digitization, AI, and Machine Learning
Monday, 5 May
606
Technical Session
This technical session will focus on innovative technologies in digitization, artificial intelligence, and machine learning aimed at improving drilling efficiency and monitoring operations. Key presentations will cover advanced techniques such as data-driven approaches for detecting energy loss from surface measurements and for classifying operations. Attendees will learn about automated systems for generating risk profiles and mud reports. The session will also explore machine learning methods for predicting rate of penetration and identifying carbonate reservoirs.
Session Chairperson(s)
Sponsoring Society:
- Institute of Electrical and Electronics Engineers, Oceanic and Engineering Society (IEEE-OES)
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1030-1048 35607Large Language Model-based Workflow For Optimizing Offset Well Data Analysis And Generating Well Design Risk Profiles
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1050-1108 35513Identification Of Porous Systems In Complex Carbonates Using Artificial Intelligence
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1110-1128 35929Optimizing Drilling Efficiency: A Fast Fourier Transform Approach To Energy Loss Detection From Surface Measurements
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1130-1148 35660Depth-based ROP Optimization Using A Physics-informed Machine Learning Model: A Near Real-time Approach
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1150-1208 35572Developing A Data-driven Operation Classifier And Stand Counter Tool To Improve Drilling Operations
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1210-1228 35625Automatic Daily Drilling Mud Report Processing Using Generative AI To Maximize The Operational Efficiency
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Alternate 35965Embedded System For Cavings Detection In Well Drilling Operations Using Computer Visiontechniques To Enhance Real Time Well Stability Analysis
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Alternate 35742Revolutionizing Real-time Drilling: Leveraging LLM's And RAG For Enhanced Operational Efficiency
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Alternate 35771A Hybrid Deep Learning Approach For Rate Of Penetration Prediction In Deepwater Drilling
