From Sensors to Semantics: GenAI and Data Science Driving Production and Facilities Innovation
Tuesday, 21 October
Room 340
Technical Session
This session highlights how the convergence of domain-specific large language models (LLMs), machine learning, and sensor-driven analytics is reshaping production and facilities workflows across the energy sector. Presentations will showcase applied innovations that accelerate access to engineering knowledge, optimize asset performance, and streamline decision-making from the field to the control room. Topics will include GenAI-powered systems for technical document interpretation, predictive maintenance, workflow automation, and operational intelligence in upstream and midstream environments.
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0830-0855 228077Flow Rate And AICD Status Prediction From Distributed Acoustic Sensing Data Using Machine Learning
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0855-0920 227916Hybrid AI Models For Wet Gas Compressor Modelling
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0920-0945 227888Detecting Pipeline Leaks Using A Novel Data-driven Statistical Methodology With Earth Mover's Distance
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1015-1040 227906Modular Framework Integrating Large Language Models With Drilling Hazard Detection Systems To Provide Operational Context-Informed Interpretations And Recommended Actions
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1040-1105 228135How To Leverage Agentic Ai And Knowledge Graphs To Enhance Overall Equipment Efficiency (oee)
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1105-1130 227885Ai-driven Geology: Vision Rags Decoding Legacy Report Using Colpali
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Alternate 227990Machine Learning Application For Accelerating Fracture Network Analysis: A Geothermal Use Case
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Alternate 228268Deep Natural Language Processing For Automatic Root Cause Analysis Of Non-productive Time Events In Drilling Reports
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Alternate 228097Building "Energy LLM": A Domain-specific Large Language Model Trained On SPE Content