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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.
  • 0830-0855 215074
    Digital Twin For Development & Subsurface Integration - A Window Into The Future
    T. Gan, Shell Exploration & Production Co; E. Sumadh, Shell Trinidad Ltd.; B. Sagram, Shell
  • 0855-0920 215161
    Duplicate Detection Of Technical Documents In Petroleum Context Based On Near-duplicate Image Search
    E. Maze, TotalEnergies; A. Dirhoussi, Y. Vernaz, Expert Data Science on Freelance; R. Farahbakhsh, TotalEnergies
  • 0920-0945 215011
    Optimizing Well Trajectory Navigation And Advanced Geo-steering Using Deep-reinforcement Learning
    N. Vishnumolakala, V. Kesireddy, Texas A&M University; S. Dey, Teale Engineering; E. Gildin, Texas A&M University; E.Z. Losoya, Teale Engineering
  • 1015-1040 215167
    Large Language Models (LLMs) for Natural Language Processing (NLP) Of Oil and Gas Drilling Data
    P. Kumar, S. Kathuria, Oil & Natural Gas Corp. Ltd.
  • 1040-1105 215028
    Reducing NPT Using A Novel Approach To Real-time Drilling Data Analysis
    J. Wang, University of Tulsa; S. Kareepadath Sajeev, BP; E. Ozbayoglu, S. Baldino, Y. Liu, H. Jing, University of Tulsa
  • 1105-1130 215085
    Physics Based Machine Learning Models for Real Time Prediction of Rheological and Filtration Properties for KCI Polymer Mud
    A. Abdulwarith, B. Dindoruk, University of Houston; S. Livescu, University of Texas at Austin
  • Alternate 214944
    An Explainable Artificial Intelligence Model For Top-of-Cement Identification
    T. Hou, A.L. Jan, Z. Snovida, M. Blyth, SLB
  • Alternate 215020
    Modernizing Data Management by Developing a Data Mesh Knowledge Layer with OSDU
    M. Haven, CGI Groupe; V. Thakur, CGI

Prepare for an Unforgettable Opening Session!

Through an insightful discussion, we aim to provide a comprehensive understanding of the past, present, and future of innovation within the Oil & Gas industry, inspiring a new era of energy professionals committed to shaping a resilient and sustainable energy landscape.

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