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EAGE AI Committee - Newsletter on AI, ML and all things Digitalization #5/2023

June 2023

As a group of EAGE members and volunteers, the EAGE A.I. Committee is dedicated to helping you navigate the digital world and finding the bits that are most relevant to geoscientists.

You are welcome to join EAGE or renew your membership to support the work of the EAGE A.I. Community and access all the benefits offered by the Association.

EAGE Membership Benefits: Join or Renew


Curious to know all EAGE is doing for the digital transformation? 

Visit the EAGE Digitalization Hub



All Earth Scientists Love Maps

Here is one of GitHub: Map of GitHub

This website provides a map of GitHub that shows different main topics, subtopics, and projects and how are all linked together. Each dot is a GitHub project. Two dots within the same cluster are usually close to each other if multiple users frequently gave stars to both projects. The size of the dot indicates the number of stars the project has received.





What to Do at the EAGE Annual 2023?

And where to learn the most about case studies and new developments of AI and ML in Earth Sciences and Engineering?

This year the EAGE Conference is fully packed with interesting sessions about these topics. The AI Committee will be present with multiple activities, starting with the hackathon on Sunday and Monday, which will focus on the topic of Natural Language Processing (NLP). The winners will show their results during the Dedicated Session Going Big – Scaling Machine Learning Applications in Geoscience and Engineering on Thursday.


Further insightful presentations/workshops/discussions on ML and AI are listed below:



Workshop: Geostatistics and its Latest Developments Using Machine Learning Methods



Machine Learning for Structural Interpretation

Machine Learning for Lithology Prediction

ML for Noise Attenuation

Machine Learning, AI, and Digitisation for More Efficient Operations (Joint EAGE/SPE)

Advances in Digital Rock Physics (Dedicated Session)



ML and Processing

Machine Learning for Inversion

ML – Case studies 1

ML – Case Studies 2

Machine Learning, AI, and Digitisation for More Efficient Operations I (SPE)

ML – New Technologies



Poster: Machine Learning for Seismic Interpretation

Going Big – Scaling Machine Learning Applications in Geoscience and Engineering (Dedicated Session)

Machine Learning, AI, and Digitisation for More Efficient Operations II (SPE)

Interpretation Terminator: Rise of the Machines?



To the EAGE Annual


This newsletter is edited by the EAGE A.I. Committee.

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

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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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