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Reservoir Monitoring for CO2 Storage and Utilization

Tuesday, 4 October
361 A-C
Technical Session
This section presents papers that use machine learning and traditional surveillance methods to explore the CO2 plume migration for CO2 storage and surveillance for CO2 enhanced oil recovery.
Session Chairperson(s)
Olaoluwa Adepoju - Chevron North America E&P
Yashesh Panchal - Advantek Waste Management Services LLC
  • 1400-1425 210262
    Technology Screening & Selection For Site Specific, Adaptive And Cost Effective MMV Planning & Design
    D.P. Das, P.K. Tiwari, P.A. Patil, P. Chidambaram, R. Jordan Leite, M. B Ebining Amir, R.D. Tewari, S. Baharuddin, M. Abdul Hamid, PETRONAS
  • 1425-1450 210309
    An Efficient Deep Learning-based Workflow For CO2 Plume Imaging Using Distributed Pressure And Temperature Measurements
    M. Nagao, Texas A&M University; C. Yao, Texas A & M University; T. Onishi, Chevron Corporation; H. Chen, A. Datta-gupta, Texas A&M University
  • 1450-1515 209974
    Surface-to-borehole Electromagnetics Using An Array System: A Case Study For Co2 Monitoring And The Energy Transition
    K.M. Strack, KMS Technologies; C. Barajas-Olalde, University of North Dakota - Energy & Environmental Research Center; S. Davydycheva, Y. Martinez, KMS Technologies; P. Soupios, King Fahd University of Petroleum & Minerals
  • 1545-1610 209959
    Deep Learning Model For CO2 Leakage Detection Using Pressure Measurements
    Z. Zhang, X. He, King Abdullah University of Science and Technology; M.M. Alsinan, Saudi Aramco PE&D; Y. Li, King Abdullah University of Science and Technology; H.T. Kwak, Saudi Aramco PE&D; H. Hoteit, King Abdullah University of Science and Technology
  • 1610-1635 210345
    A Probability Evaluation Of Seismicity Risks Associated With Co2 Injection Into Arbuckle Formation
    K.I. Ochie, R.I. Moghanloo, J. Daneshfar, University of Oklahoma; J. Burghardt, Pacific NW Natl Laboratory
  • 1635-1700 210200
    Life-cycle Production Optimization Of The Co2-water-alternating-gas Injection Process Using Least-squares Support-vector Regression (LS-SVR) Proxy
    A. Almasov, M. Onur, University of Tulsa

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