Skip to main content

Hybrid Reservoir Modeling and Evaluation Merging Physics and Data-Driven Technology

Monday, 23 September
220 - 222
Technical Session
This session is focused on integration of advanced machine learning technologies into existing workflows for reservoir modeling and evaluation. Highlights includes using data analytics to analyze large datasets and extract meaningful insights and using hybrid approaches for well performance analysis, completion design, connectivity analysis, and enhancement of reservoir modeling and simulation.
Session Chairpersons
Dingzhou Cao - Devon Energy Corporation
Pedro Vivas Sanchez - Sensia
  • 1400-1425 220875
    Analyzing Impact Of Fracturing Revolution On Shale Oil Well Performance In Permian Basin: A Review From Over 10,000 Wells
    X. Xia, Ryder Scott Company, L.P.; K. Zhang, OGRE Systems; Q. Lu, J. Li, Sinopec Petroleum Exploration and Production Research Institute; F. Wang, S. Gao, Sinopec Exploration and Production Department; D. Olds, H. Zhang, Ryder Scott Company, L.P.
  • 1425-1450 220989
    A New Algorithm For Automated ISIP Interpretation
    A. Garbino, D. Espinoza, The University of Texas At Austin; A. Savitski, Shell Exploration & Production Co
  • 1450-1515 220933
    Physics-Informed Machine Learning Approach For Closed-Loop Reservoir Management Using RGNet
    Z. Guo, S. Sankaran, Xecta Digital Labs
  • 1545-1610 220978
    Transfer Learning In Flow Surrogate Model With Physics-guided Neural Network
    H. Cheng, Shenyang Institute of Automation, Chinese Academy of Sciences
  • 1610-1635 220906
    Application Of Artificial Intelligence To Model Stresses And Failure Parameters In Anisotropic Formations
    W. Yousuf, texas a&m university; J. Kim, Texas A & M U (PE Dept Po's )
  • 1635-1700 221029
    Fast Evaluation Of Reservoir Connectivity By Use Of A Newdeep Learning Approach - Attention-guided Fusion Model
    A.A. Saihood, M. Al-Shaher, University of Thi-Qar; T. Saihood, Z. Zargar, University of Huston
  • Alternate 220982
    Physics Informed Production Forecast Using Long Short-Term Memory With Attention
    R. Manasipov, D. Didenko, D.S. Nikolaev, R. Abdalla, Datagration Solutions Inc.
  • Alternate 220838
    Enhanced 3d Pore Segmentation And Multi-model Pore-scale Simulation By Deep Learning
    H. Li, B. Yan, M. Mowafi, B. Aslam, S. Sun, King Abdullah University of Science & Tech
  • Alternate 221028
    Enhancing Reservoir Model History Matching With Ai Surrogate And Ensemble Iterative Algorithms
    K. Hammad, A. Alturki, Saudi Aramco; S. Sudirman, Saudi Aramco PE&D; Z. Sawlan, King Fahd University of Petroleum and Minerals

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.