Workshop 3

Maximizing Subsurface Recovery: From Hydrocarbons to Energy Storage for a Sustainable Energy Future Sunday, June 7th|

Convenors

  • Sonia Lopez Kovacs (Repsol)

Description

This workshop will explore how integrated subsurface characterization and engineering strategies can improve recovery and performance across diverse applications-including hydrocarbons, enhanced oil recovery (EOR), geothermal energy, and subsurface energy storage (e.g., hydrogen and CO2). With a focus on efficiency, sustainability, and cross-disciplinary innovation, the session will highlight advancements in geoscience, reservoir modeling, monitoring, and data integration. Participants will gain insights into best practices and novel approaches that bridge traditional and emerging energy systems, supporting the transition toward a low-carbon and resilient energy future. Contributions from both industry and academia will be featured through case studies and technical discussions.

Sub-Topics that will be covered in the workshop:

  • Geoscience
  • Geoenergy
  • Subsurface
  • Energy
  • Cross-disciplinary
  • Data integration
  • EOR
  • Oil and Gas
  • Storage
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Participant Profile

Geologists, geophysicists, petrophysicists, geoscientists, reservoir engineers, production engineers, drilling engineers 

Workshop Programme

Coming Soon!

TimeActivity
09:00Workshop Introduction: Welcome by Co-Chairs and Workshop
09:05Session 1 Introduction - Viridien
09:10Session 1a: Seismic processing, Velocity Model building and UQ
Distributed Acoustic Sensing Instrumental Noise removal: Machine Learning Approaches, Olivia Collet (Curtin University)
Seismic processing and machine learning: what is my data space distribution?, Laurent Lemaistre (TotalEnergies)
The Contribution of Active Learning for Seismic QC, Guillaume Poulain (Viridien)
AI-Driven Automatic Quality Control and Parameterization in Deep Water Seismic Processing, Harry Rynja (Shell)
Accelerating Seismic time processing and Model Building with Machine Learning, Alejandro Valenciano (TGS)
10:50Session 1a: Q&A
11:00 Coffee Break
11:15 Session 1b: Seismic processing, Velocity Model building and UQ
Using generative models for seismic processing and velocity model building, Tariq Alkhalifah (KAUST)
Seismic Image Super-Resolution with Sparse Wells, Aria Abubakar (SLB)
Development and Evolution of Machine Learning Workflows for Seismic Interpretation, Maisha Amaru (Chevron)
Generative AI for seismic imaging: uncertainty quantification and inference, Yuke XIE (Mines Paris)
12:35 Session 1b: Q&A
12:45 Lunch
13:45Session 2 Introduction: TotalEnergies
13:50Session 2a: Predict rock properties and hydrocarbon distribution: Integration of seismic together with well logs, and other data, log prediction. Comparisons with Seismic inversion, AVO Analysis
Deep Learning Seismic Inversion: Quantifying Uncertainty, Konstantin Osypov (Halliburton)
Integrating QI automation and ML workflows for accelerating subsurface rock-properties prediction, Andrea Murineddu (SLB)
Unravelling the Unknown: Advancing Seismic Reservoir Characterization with Machine Learning, Tanya Colwell (Geosoftware)
Foundation Models and Physics- Informed Neural Networks in Reservoir Characterization, Dimitrios Oikonomou (ESA)
15:10Session 2a: Q&A
15:20Coffee Break
15:35Session 2b: Predict rock properties and hydrocarbon distribution: Integration of seismic together with well logs, and other data, log prediction. - Comparisons with Seismic inversion, AVO Analysis
AI-Driven Elastic and Reservoir Property Estimation: Bridging Deep Learning and Physics-Based Approaches, Asmund Heir (RagnaRock)
Adaptation of Deep Neural Network flows for quantitative interpretation of subsurface reservoirs, Jean-Luc Formento (Viridien)
Maximizing field development through rock physics informed hi- res ML seismic estimates of reservoir properties, Benjamin Roure (Parex)
16:35Session 2b: Q&A
16:45Q&A and way forward
17:35End of the Workshop