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Machine Learning Applications in Geomodeling and Formation Evaluations

Tuesday, 17 October
Room 214 D
Technical Session
This session highlights state-of-the-art machine learning (ML) applications in geomodeling and formation evaluation domains. It covers not only the latest developments of GAN application in subsurface modeling but also some successful ML uses cases such as seismic facies classification, CT rock image enhancement and rock properties prediction.
Session Chairperson(s)
Dingzhou Cao - Devon Energy Corporation
Wael Ziadat, Vice President Middle East - Corva.ai
  • 1400-1425 215099
    Attention Mechanism Neural Network For Seismic Facies Classification
    C. Ang, M. Sajid, PETRONAS Research Sdn Bhd; A.M. El Sheikh, H. Al Salmi, Heriot-Watt University
  • 1425-1450 214985
    Efficient Subsurface Modeling with Sequential Patch Generative Adversarial Neural Networks
    W. Pan, J. Chen, Shell; M. Sidahmed, Shell Global Solutions International BV; H. Jo, J.E. Santos, M.J. Pyrcz, The University of Texas At Austin
  • 1450-1515 215065
    Machine Learning Based Automatic Marker Clustering
    A.L. Katole, A. Abubakar, E. Hoekstra, S. Ryali, T. Zhao, SLB
  • 1545-1610 214866
    A Data Analytics And Machine Learning Study On Site Screening Of CO2 Geological Storage In Depleted Oil And Gas Reservoirs In The Gulf Of Mexico
    J. Leng, H. Wang, S. Hosseini, University of Texas at Austin
  • 1610-1635 214883
    Enhancing The Resolution Of Micro CT Images Of Rock Samples Via Unsupervised Machine Learning Based On A Diffusion Model
    Z. Ma, S. Sun, B. Yan, King Abdullah University of Science & Tech; H.T. Kwak, J. Gao, Saudi Aramco PE&D
  • 1635-1700 215117
    A Comparative Study Of Deep Learning Models For Fracture And Pore Space Segmentation In Synthetic Fractured Digital Rocks
    H. Wang, Bureau of Economic Geology; R. Guo, Stevens Institute of Technology; J. Leng, S. Hosseini, Bureau of Economic Geology; M. Fan, Oak Ridge National Laboratory
  • Alternate 214831
    A Data-driven Approach For Stylolite Detection
    J. Cheng, B. He, R. Horne, Stanford University

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