Detection of Geobodies in 3D Seismic using Unsupervised Machine Learning by Quentin Corlay (post viva), Heriot-Watt GeoEnergy seminar talk, February 2023

Conference presentations

AI-GMM, Exeter / EAGE 2024

Uncertainty in AI-based Reservoir Modelling Workflows – an Overview, V. Demyanov

ECMOR 2022, the Hauge, September 2022

History Matching And Uncertainty Quantification Of Reservoir Performance With Generative Deep Learning And Graph Convolutions, G. Shishaev

IAMG 2022, Nancy, September 2022

Uncertainty Quantification of depositional and structural properties with Generative Deep Learning and Graph Convolutions – Gleb Shishaev, Vasily Demyanov, Dan Arnold

AAPG ICE 2022, March 2022

Agent-Based Modeling for Secondary Hydrocarbon Migration – A Wessex Basin Case Study,  by A. Kreiensiek*, Q. Corlay, B. Steffens, T. Wagner, V. Demyanov
Theme 1 – Petroleum Systems

EAGE 2021 Annual Conference talks:

GAN learning complex fluvial facies distribution from process-based modelling”, by Chao Sun, Tuesday, October 19th, Digitalization & AI: Seismic Interpretation I session

Comparison of popular Generative Adversarial Network flavours for fluvial reservoir modelling”, by Chao Sun, Thursday, October 21st, Digitalization & AI: Reservoir and Wells session,

“The Importance of Blending Different Data Types to Train Machine Learning Classifiers for Sedimentary Structure Detection”, by Athanasios Nathanail, Wednesday, October 20thDigitalization & AI: Quantitative Interpretation and Geology session

“Entropy-driven particle swarm optimization for reservoir modelling under geological uncertainty – application to a fractured reservoir”, by Bastian Steffens ,Thursday, October 21st, Static Geomodels session,

Can agent-based modelling help to update conceptual geological models? – A fractured reservoir example by Bastian Steffens at RING 2021 Annual meeting on Mon, 6th September, 2021

Open lectures

GeoDataScience group open on-line research dissemination day, Feb 2021

  • Turbidite fan interpretation in 3D seismic data by point cloud segmentation using Machine Learning, by Quentin Corlay
  • Machine Learning for sedimentary structure classification, by Athos Nathanail
  • Modeling variations of complex geological concepts with Generative Adversarial Network (GAN) learning from process modelling , by Chao Sun
  • How Generative Networks can ​help improve geological history matching, by Gleb Shishaev
  • A workflow with dynamic screening assisted, automated fractured reservoir modelling, by Bastian Steffens
  • Can agents model hydrocarbon migration? by Bastian Steffens & Quentin Corlay

Open Lectures

How can we use Al techniques to support decisions making in subsurface activities

a lecture by Prof. V. Demyanov for ReFine webinar series, Newcastle University, June 2020.

Reservoir predictions under uncertainty: confidence and optimisation

V. Demyanov’s invited lecture at Moscow School of Economics, April 2021 (recording in Russian)

Machine learning for spatial geoscience data

by V. Demyanov, a talk at Jagiellonski University, 2020

Academic Course Lectures