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Technical Communities

Artificial Intelligence

Technical Communities

Artificial Intelligence

The EAGE Artificial Intelligence (A.I.) Community is the network of members interested in sharing knowledge and developing skills among geoscientists and engineers exploring A.I. and machine learning solutions, as well as data scientists working on techniques that can be applied to the fields of geoscience and engineering.

This Community is open to members at all stages of their career.

Join in Linkedin


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The A.I. Community is coordinated by a Committee that serves for a period of two years. The current Committee is serving the 2021 – 2023 term.

NameCompany / InstitutionCountry
Ashley RussellEquinorNorway
Jan H. van de MortelIndependentNetherlands
George GhonCapgeminiNorway
Cédric M. JohnQueen Mary University of LondonUnited Kingdom
Lukas Mosser Earth Science AnalyticsAustria
Oleg OvcharenkoNVIDIAUnited Arab Emirates
Nicole GrobysWintershall DeaGermany
Robert FergusonUniversity of CalgaryCanada
Ruslan MiftakhovGeoplatUnited Kingdom
Surender ManralSchlumbergerNorway

Would you like to take an active role within the Association? Discover all our volunteering opportunities!


Update your affiliations

Connecting with professionals who share similar interests – such as the members of the EAGE AI Community – is key in promoting innovation and technical progress. That’s why we invite you to update your EAGE affiliations to Local Chapters, Circles, Technical and Special Interest Communities. This will help you stay connected with fellow members and ensure our offerings meet your needs.

Update now

Other questions or ideas?

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Siddharth Misra


Prof Dr Siddharth Misra’s research focuses on improving subsurface characterization and prospect evaluation for the exploration of hydrocarbons, minerals and water resources.

His major contribution is in the theory of electromagnetic responses of geological formations to various charge polarization phenomena. The theory has enabled him to introduce a multi-frequency electromagnetic log-inversion technique to remove dielectric effects for improved estimation of hydrocarbon pore volume.


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