SPE Online Education
Practical Machine Learning Applications in Hydrocarbon Exploration & Production
Recorded On: 11/20/2023
This webinar presents popular machine learning applications in exploration, extraction, and recovery of subsurface energy resources, primarily in hydrocarbon exploration and production industry with potential applications in geothermal energy production and geological carbon storage. Machine learning has led to improvements in the efficiency and efficacy of subsurface engineering and characterization. Subsurface data ranges from nano-scale to kilometer-scale passive as well as active measurements in the form of physical fluid/solid samples, images, 3D scans, time-series data, waveforms, and depth-based multi-modal signals representing various physical phenomena, ranging from transport, chemical, mechanical, electrical, and thermal properties, to name a few. Integration of such varied multimodal, multipoint, time-varying data sources being acquired at varying scales, rates, resolutions, and volumes mandates robust machine learning methods to better characterize and engineer the subsurface earth.
This webinar is categorized under the Data Science and Engineering Analytics technical discipline.
All content contained within this webinar is copyrighted by Sid Misra and its use and/or reproduction outside the portal requires express permission from Sid Misra.
Dr. Sid Misra is an associate professor at Texas A&M University. He is a researcher and educator in the field of subsurface monitoring and forecasting for the exploration and production of subsurface earth resources. He has published two books and developed nine technologies related to machine learning and electromagnetic sensing for energy and earth resource exploration. Misra holds a bachelor's of technology degree in electrical engineering from the Indian Institute of Technology, Bombay, and a PhD degree in petroleum and geosystems engineering from The University of Texas at Austin.
James Omeke (Moderator)
James Omeke is currently a PhD candidate in Petroleum Engineering at Texas A&M University, holding both a master's and bachelor's degree in the same field. With years of hands-on reservoir engineering experience, he is adept at numerical simulations and understands the intricacies of advanced machine learning algorithms. James' current research centers on the synergistic application of data-driven algorithms and physics-based deep learning architectures, aiming to optimize field-scale simulation processes for subsurface Hydrogen Storage, as well as CO2 storage. He is under the supervision of Dr. Misra Siddharth.
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