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Practical Machine Learning Applications in Hydrocarbon Exploration & Production

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Course Credit: 0.15 CEU, 1.5 PDH

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.

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.

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Course Chapters

  • 1Practical Machine Learning Applications in Hydrocarbon Exploration & Production - Chapter 1
    Media Type: Video


Earn credits by completing this course0.15 CEU credit1.5 PDH credits


James Omeke
Siddharth Misra