Presenting at Applications of Machine Leaning in Geoscience Conference in Nov 2025

Delighted to be invited to present at the GESGB “Innovating Geoscience through Machine Learning” conference with such a great line up of speakers. Link in the comments.

Prof. Cedric John John – Queen Mary University of London Towards Meaningful Applications of Al in the Geosciences

Nanzhe Wang – Stanford University
Deep Learning based Multi-Objective Markov Chain Monte Carlo (MOMCMC) for Multi-Source Data Assimilation in Geological Carbon Storage

Paul Cleverley – Robert Gordon University
Large Language Models in Geoscience

Session Chairs Howard Nicholls & David Psaila

Elias Ortis – Rock Flow Dynamics
Expanding a Dataset-Independent Framework for Uncertainty Quantification in ML-Based Electrofacies Prediction

Peter Evans – Repsol
Geoscience Al Pore Pressure and Geomechanics: Assessment of Formation Integrity/Leak-Off Tests Using Al/Machine Learning for Wellbore Stability in Operations Geology Central North Sea

Dan Cornford – IGI Ltd
The Reality of Machine Learning in Geochemistry: Working with Small, Imperfect Data Sets

David Wade – Equinor
Scampi – Revolutionizing Biostratigraphy

Session Chairs: Ceylan Gomez & Jonathan Watson

John Brittan – TGS
Accelerating Marine Seismic Processing with Machine Learning

Omar Aly (MSc I MBA) SLB
Leveraging Machine Learning Capabilities in Seismic Petrophysics for Quantitative Seismic Reservoir Characterisation

Thomas Bartholomew Grant – Cegal
Enhanced Fault Identification in 3D Seismic Data Through RANSAC-Based Segmentation

Daniel Holden – University of Aberdeen
Improved Interpretation of Sandstone Intrusions from Seismic Data Using Random Forest and Linear Regression Machine Learning

Session Chairs: Dr Gerald Stein & Aaron Lockwood

Istvan Szabo – MOL Group
Sequential Machine Learning Solution for Reservoir Characterisation from Legacy Well Data – A Case Study from a Hungarian Hydrocarbon Field

Ali Ismail – Viridien Group
Accelerating Energy Transition through Al-driven Remote Sensing and Foundation Models

Pavel Didenko – GeoSoftware
Machine Learning and Geoscience – a Perspective from Years of Experience

Bill Shea – Sharp Reflections

Register here: https://www.ges-gb.org.uk/events/applications-of-machine-learning-in-geoscience/