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1–3 March 2023
Bangkok, Thailand

2024 Technical Program

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144 New Frontiers for Advance Energy Evaluation

Wednesday, 14 February
Room 11
  • 1600-1620 23568
    Predicting Wettability Of Saudi Arabian Basalt/CO2/brine Using Machine Learning Models: Significance For Geo-storage Of Co2
    M. Khan, SLB; Z. Tariq, KAUST
  • 1620-1640 23500
    Rapid Assessment Of CO2 Cooling Effect During Geological Carbon Sequestration Using Machine Learning Approach
    B. Aslam, Z. Tariq, B. Yan, King Abdullah University of Science & Tech
  • 1640-1700 23508
    Shallow Hazard And Facies Characterization For Ultra-high Resolution Wind Farm Seismic Using A Data-centric, Multiclass Deep Learning Approach
    S. Salamoff, Bluware Inc
  • 1700-1720 23509
    A New Model To Predict The Dielectric Properties For Unconventional Shales By Employing Artificial Intelligence Techniques
    A. Hassan, M. Mahmoud, King Fahd University of Petroleum & Minerals; A. Oshaish, King Fahd University of Petroleum and Minerals (KFUPM)
  • Alternate 23520
    Machine Learning For Predictive Analysis Of Carbon Dioxide Storage And Oil Recovery In A Tight Oil Reservoir
    W.A. Khan, T. Hu, Z. Rui, Y. Liu, F. Zhang, Y. Zhao, China University of Petroleum Beijing
  • Alternate 23594
    Geochemical Mapping Of Light Hydrocarbons Through High Dimensional Data Mining And Machine Learning Techniques
    A. Alharbi, S. Sinan, R. Khaldi, M. Rowaie, Saudi Aramco PE&D
  • Alternate 23532
    Anomaly Detection In Gas Measurements Of Hydrocarbon Wells
    R. Alkheliwi, Y. Al-Ghamdi, Saudi Aramco PE&D
  • Alternate 23578
    Shale Gas Production Prediction In The Southern Montney Play Using Machine Learning Approaches
    G. Hui, H. WANG, M. Wang, University of Calgary; F. Gu, Research Institute of Petroleum Exploration and Development, CNPC; F. Yao, China University of Petroleum Beijing
  • Alternate 23488
    Enhancing Production Forecast Of Unconventional Bakken Oil Wells Through Integration Of Machine Learning Techniques And Time Series Analysis
    A. Laalam, University of North Dakota
  • Alternate 23529
    Multilayer Perceptron Modeling For The Prediction Of Gas Condensate Composition Changes During Pressure Depletion
    A. Embaireeg, SLB

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