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Learning and Hybrid Modeling Approaches for Operational Excellence

Tuesday, 24 September
215 - 216
Technical Session
This session highlights the latest in machine learning and hybrid modeling, focusing on applications like seismic inversion, flare detection, drilling efficiency, and pipeline safety. It offers insights into innovative solutions that drive operational excellence and informed decision-making within the energy sector, making it crucial for industry professionals eager to explore the latest machine learning innovations and their practical applications in improving operational efficiency, safety, and decision-making processes.
Session Chairpersons
He Zhang - Halliburton Far East Pte Ltd
Yingwei Yu - Amazon Web Services
  • 1400-1425 220762
    Determination Of Dispersion Coefficient Of Solvent In Heavy Oil/Bitumen Under Reservoir Conditions
    W. Zhao, S. Yang, D. Yang, University of Regina
  • 1425-1450 220898
    A Field Application Of Machine Learning For Sonic Log Prediction And In-situ Stress Estimation In Natural Buttes, Unita Basin, Utah
    C. Kim, Dominion Energy Inc.; A. Morales, Dominion Energy Wexpro
  • 1450-1515 220773
    A Novel Approach For Gas Delivery Optimization Of Complex Pipeline Network System Using Hybrid Physics-machine Learning Modeling
    A. Ahmed, Schlumberger Technology Corporation
  • 1545-1610 220857
    Real Time Application Of Deep Learning Based Flare Smoke Detection
    P. Chatterjee, ExxonMobil Services & Technology Pvt Ltd; K. Gupta, ExxonMobil
  • 1610-1635 221074
    Enhancing Real-time Drilling Efficiency: Mechanism Of Rop Prediction Models And Novel Optimization Strategies In Chinese Oilfields
    Z. Zhu, China University of Petroleum, Beijing; R. Zhang, China University of Petroleum (Beijing); X. Song, G. Li, University of Petroleum China Beijing; Q. Li, China University of Petroleum (Beijing)
  • 1635-1700 221070
    Estimating Time-varying Wavelets Using Deep Learning For Seismic Inversion
    A. Abd Rahman, PETRONAS
  • Alternate 220931
    Novel Ml Modeling Approach For Fatigue Failure Of Hydrogen-transporting Pipelines
    N. Ahmed, University of Oklahoma MPGE; R. Ahmed, C. Teodoriu, University of Oklahoma
  • Alternate 220714
    Automated Well And Reservoir Management Using Hybrid Physics And Data-driven Models - North Malay Basin Case Study
    M. Ravuri, Xecta Digital Labs; A. Mustafa, Hess Corporation; C. Heah, Hess E&P Malaysia BV; D. Rodriguez, Hess Corporation; S. Sankaran, V. Sabharwal, Xecta Digital Labs
  • Alternate 220745
    Towards Robust And Automated Contamination Estimation During Downhole Fluid Sampling.
    M. Berkane, Saudi Aramco PE&D; A. Ali, Schlumberger Saudi Arabia; M. Kristensen, Schlumberger Information Solutions

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.