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.

 

Source: anvaka.github.io/map-of-github/

 

 

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:

 

Sunday

Workshop: Geostatistics and its Latest Developments Using Machine Learning Methods

 

Tuesday:

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)

 

Wednesday:

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

 

Thursday:

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.

Name	Company / Institution	Country
Ashley Russell	Equinor	Norway
Jan H. van de Mortel	Independent	Netherlands
George Ghon	Capgemini	Norway
Cédric M. John	Queen Mary University of London	United Kingdom
Lukas Mosser	Earth Science Analytics	Austria
Oleg Ovcharenko	NVIDIA	United Arab Emirates
Nicole Grobys	Wintershall Dea	Germany
Robert Ferguson	University of Calgary	Canada
Ruslan Miftakhov	Geoplat	United Kingdom
Surender Manral	Schlumberger	Norway