Unlocking Drilling Operations and Asset Management Potential with AI, ML, and Digital Twins
Tuesday, 17 October
Room 214 B
Technical Session
Drilling operations generate significant amounts of real-time and static data and rely on quick and accurate interpretation. Another critical need throughout the well’s life cycle is data and document management and making sense of unstructured data while avoiding redundancy. For drilling and well placement engineers, this session presents advanced models in trajectory optimization, NPT reduction and mud properties prediction. For data management and digital transformation practitioners, this session presents natural language processing approaches using images and large language models in addition to advanced use case of digital twins.
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0830-0855 215074Digital Twin For Development & Subsurface Integration - A Window Into The Future
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0855-0920 215161Duplicate Detection Of Technical Documents In Petroleum Context Based On Near-duplicate Image Search
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0920-0945 215011Optimizing Well Trajectory Navigation And Advanced Geo-steering Using Deep-reinforcement Learning
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1015-1040 215167Large Language Models (LLMs) for Natural Language Processing (NLP) Of Oil and Gas Drilling Data
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1040-1105 215028Reducing NPT Using A Novel Approach To Real-time Drilling Data Analysis
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1105-1130 215085Physics Based Machine Learning Models for Real Time Prediction of Rheological and Filtration Properties for KCI Polymer Mud
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Alternate 214944An Explainable Artificial Intelligence Model For Top-of-Cement Identification
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Alternate 215020Modernizing Data Management by Developing a Data Mesh Knowledge Layer with OSDU