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Machine Learning Applications to Reservoir Engineering

Tuesday, 24 September
217 - 219
Technical Session
This session highlights various applications of machine learning methods for reservoir engineering related problems, from optimization of well location and reservoir production, to estimation of petrophysical properties, including automation of history matching and productivity analysis.
Session Chairpersons
Guohua Gao - Shell
Keith Boyle - Chevron Australia Pty Ltd
  • 0830-0855 220783
    Embed-to-control-based Deep-learning Surrogate For Robust Nonlinearly Constrained Life-cycle Reservoir Production Optimization
    Q.M. Nguyen, M. Onur, University of Tulsa
  • 0855-0920 221087
    Dual Continuum Coarse Grid Network Model: A Data-driven Reduced Order Model Of Fractured Reservoir Simulation
    B. Aslam, King Abdullah University of Science & Technology; B. Yan, King Abdullah University of Science & Tech
  • 0920-0945 220992
    Estimate Rock Typing In Uncored Wells Using Machine Learning Techniques To Improve The Simulation Model Of The Brazilian Pre-salt Carbonate Reservoir
    M. AlLahham, V.E. Botechia, Center for Petroleum Studies; D. Schiozer, A. Davolio, Universidade Estadual De Campinas
  • 1015-1040 220847
    Estimating Petrophysical Properties Directly From Seismic: A Deep Learning Application To Carbonate Field For CO2 Storage Potential
    C.L. Lew, M. Ahmad Fuad, M. Jaya, A. Trianto, PETRONAS; C. Macbeth, Heriot-Watt University
  • 1040-1105 220876
    Efficacy Gain From A Deep Neural Network-based History-matching Model
    B. Yan, Y. Zhang, King Abdullah University of Science and Technology
  • 1105-1130 220754
    Deep Learning-driven Acceleration Of Stochastic Gradient Methods For Well Location Optimization Under Uncertainty
    E. Eltahan, K. Sepehrnoori, The University of Texas At Austin; F. Alpak, Shell International E&P Co.
  • Alternate 220964
    Developing A Novel Petrophysical Rock Typing (PRT) Classification Using Machine Learning Applied In The Majnoon Oil And Gas Field In Southern Iraq
    M.A. Abbas, Lukoil; W. Al-Mudhafer, The University of Texas At Austin
  • Alternate 220715
    Machine-learning Workflow For Fracture Geometry Characterization And Production Performance Evaluation Using High-resolution Distributed Strain Sensing
    W. Ma, K. Wu, Texas A&M University; G. Jin, Colorado School of Mines

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