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Geoscience for the Energy Transition and Supporting Innovation

2022 Technical Program


Geoscience for the Energy Transition and Supporting Innovation

Tuesday, 3 May
Technical / Poster Session
The energy industry is increasingly embracing the opportunities brought by the world's transition to a carbon-neutral economy while optimizing clean and safe HC extraction. Geophysical techniques are a key enabler: geophysicists already contribute to identification and monitoring of CCUS and hydrogen storage systems, geotechnical windfarm support, geothermal and lithium prospecting - and this list will keep growing in coming years. In this session, we showcase examples of successful integrated geophysical project support as well as promising emerging technologies.
Martin Terrell - Exxon Mobil Corporation
Stephan Gelinsky - Shell International E&P Co.
Sponsoring Societies:
  • American Association of Petroleum Geologists (AAPG)
  • American Society of Mechanical Engineers (ASME)
  • Society of Exploration Geophysicists (SEG)
  • Society of Petroleum Engineers (SPE)
  • 1400-1422 31743
    New Ultrasensitive Techniques For Reservoir Characterization And Monitoring Of CO2 Sequestration Sites Offshore
    R. Schrynemeeckers, Amplified Geochemical Imaging LLC
  • 1422-1444 31788
    Cloud-based Array Electromagnetics Contributing To Zero Carbon Footprint
    K.M. Strack, Y. Martinez, KMS Technologies; H. Passalacqua, Red Tree Consulting LLC; X. Xu, KMS Technologies - Houston, TX
  • 1444-1506 32003
    Experimental Investigation Of Wellbore Integrity Of Depleted Oil And Gas Reservoirs For Underground Hydrogen Storage
    A. Hussain, Texas Tech University; H.K. Al-Hadrami, Sultan Qaboos University; H. Emadi, F.S. Altawati, S. Thiyagarajan, M.C. Watson, Texas Tech University
  • 1506-1528 32073
    Quantitative Interpretation In Support Of The Energy Transition
    S. Gelinsky, Shell International E&P Co.
  • 1528-1550 32049
    High Precision Velocity Model Building Technology For Pore Pressure Prediction
    S. Li, J. Zheng, W. Wang, R. Han, B. Xue, Tianjin Branch of CNOOC Ltd; S. Gelinsky, Shell
  • 1550-1612 31781
    Machine Learning Applications to Improve Pore Pressure Prediction in Hazardous Drilling Environments
    R. Nye, H. Bui, N. De Nicolais, Enovate Upstream; J. Estrada, Enovate
  • 1612-1634 31937
    Simulation Of Subgouge Sand Deformations Using Robust Machine Learning Algorithms
    H. Azimi, H. Shiri, M. Mahdianpari, E. Malta, Memorial University of Newfoundland



  • AAPG Logo
  • AIChE Logo

  • AIME
  • ASCE

  • ASME
  • IBP

  • MTS
  • SEG
  • SME

  • SPE
  • TMS