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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.
Session Chairpersons
Dingzhou Cao - Devon Energy Corporation
Hector Klie - DeepCast
  • 0830-0855 228077
    Machine Learning for Flow Rate and AICD Status Prediction Using Distributed Acoustic Sensing Data
    R.G. Hashish, B. Spivey, K. Walker, B. Campbell, B. Seabrook, ExxonMobil Upstream Research Co.
  • 0855-0920 227916
    Hybrid AI Models for Wet Gas Compressor Modelling
    N. Nguyen, G. Draskovic, B. Torkildsen, N. Butin, SLB
  • 0920-0945 227888
    Detecting Pipeline Leaks Using A Novel Data-driven Statistical Methodology With Earth Mover's Distance
    C. Johnston, Pipewise Technology Ltd.; A. Rajesh, Pipewise Technology Ltd
  • 1015-1040 227906
    Modular Framework Integrating Large Language Models With Drilling Hazard Detection Systems To Provide Operational Context-Informed Interpretations And Recommended Actions
    S. Suhail, T.S. Robinson, O.E. Revheim, P. Bekkeheien, Exebenus
  • 1040-1105 228135
    How To Leverage Agentic AI And Knowledge Graphs To Enhance Overall Equipment Efficiency (OEE)
    N. Sengar, A. Jain, R. Elsinga, P. Rai, A. Anand, Baker Hughes
  • 1105-1130 228268
    Deep Natural Language Processing for Automatic Root Cause Analysis of Non Productive Time Events in Drilling Reports
    M. Karnot, H. B. Abdulkhaleq, Rumaila Operating Organization; W. Al-Mudhafar, Basra Oil Company; A. Alibraheem, Rumaila Operating Organization; A. Alsubaih, University of Texas at Austin; U. Alshawi, Rumaila Operating Organisation
  • Alternate 228097
    Building "EnergyLLM": A Domain-Specific Large Language Model Trained on SPE Content
    J. Eckroth, J. Boden, L. Hough, H. Gatewood, S. Gipson, E. Schoen, B. Gunderson, i2k connect
Call for Paper Proposals is OPEN NOW

Call for Paper Proposals is Open Now

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