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Technology Transformation in Digital Energy

Wednesday, 18 October
Room 214 B
Technical Session
Advanced machine learning algorithms such as deep learning and natural language processing have opened new horizons for performance prediction and well analysis. This session discusses novel ideas for reservoir simulation surrogate modeling, data-driven production forecasting and AI-assisted well data management utilizing advanced analytics.
Session Chairperson(s)
Satomi Suzuki - ExxonMobil Technology & Engineering Co
He Zhang - Halliburton Far East Pte Ltd
  • 0830-0855 215103
    A Novel Surrogate Model For Reservoir Simulations Using Fourier Neural Operators
    M. Kazemi, Slippery Rock University; A. Takbiri-Borujeni, Amazon Web Services; H. Nouroizeh, Parex Resources; A. Kazemi, Shiraz Univeristy; S. Takbiri, University of Isfahan; C. Wallrich, Slippery Rock University
  • 0855-0920 214889
    Deep Learning Based Upscaling Of Geomechanical Constitutive Behavior for Lithological Heterogeneities
    Z. Ma, Los Alamos National Laboratory; B. Zhang, University of Alberta
  • 0920-0945 214881
    A Comparative Study Of Deep Learning Models And Traditional Methods In Forecasting Oil Production In The Volve Field
    Z. Al-Ali, R. Horne, Stanford University
  • 1015-1040 215056
    Shale Gas Production Forecasting With Well Interference Based On Spatial-Temporal Graph Convolutional Network
    Z. Xu, J.Y. Leung, University of Alberta
  • 1040-1105 214888
    Answering Natural Language Questions with Open AI's GPT in the Petroleum Industry
    J. Eckroth, M. Gipson, i2k Connect; J. Boden, Society of Petroleum Engineers; L. Hough, J. Elliott, i2k Connect; J. Quintana, Society of Petroleum Engineers
  • 1105-1130 214817
    Optimizing ESP Well Analysis Using Natural Language Processing And Machine Learning Techniques
    A. Alquraini, M.S. Al-Kadem, M. Bori, I. Bukhamseen, Saudi Aramco
  • Alternate 214769
    Multiple Production Time Series Forecasting Using Deepar And Probabilistic Forecasting
    J. Han, L. Xue, University of Petroleum China Beijing
  • Alternate 215091
    A Novel Shale Well Production Forecast Model Achieves >95% Accuracy Using Only 1.5 Years Of Production Data
    S.T. Haider, King Abdullah University of Science and Technology, Sinopec Tech Middle East; W. Saputra, University of Texas; T. Patzek, King Abdullah University of Science & Tech
  • Alternate 214818
    AI-Powered, Lightning-Fast Production Modeling Of Multi-well And Multi-bench Unconventional Development
    D. Gala, G. Becker, K. Kaul, M. DallAqua, Exxonmobil; A. Hegde, S. Moisselin, C. Fuda, Noble.AI; S. Doraiswamy, V. Verma, X. Wu, ExxonMobil

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.


Moderator: Elena Melchert | Podcast Host and Consultant | Oil and Gas Global Network