Fabien Allo highlights his award-winning article, “Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks.”
In this episode with host Andrew Geary, Fabien shares the potential of deep neural networks (DNNs) in integrating seismic data for reservoir characterization. He explains why DNNs have yet to be widely utilized in the energy industry and why utilizing a training set was key to this study. Fabien also details why they did not include any original wells in the final training set and the advantages of neural networks over seismic inversion. He closes with how this method of training neural networks on synthetic data might be useful beyond the application to a geothermal study.
This episode is an exciting opportunity to hear directly from an award-winning author on some of today’s most cutting-edge geophysics tools.
- Fabien Allo, Jean-Philippe Coulon, Jean-Luc Formento, Romain Reboul, Laure Capar, Mathieu Darnet, Benoit Issautier, Stephane Marc, and Alexandre Stopin, (2021), “Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks,” The Leading Edge 40: 751–758.
Fabien Allo received his BSc in mathematics, physics, and chemistry with a biology option from the Lycée Chateaubriand, Rennes (France) in 2000 and his MSc and engineering degree in geology from the École Nationale Supérieure de Géologie, Nancy (France) in 2003.
Since joining CGG 20 years ago, he has held several roles in the UK, Brazil, and now Canada working on inventing, designing, and developing reservoir R&D workflows for seismic forward modeling and inversion with a specific focus on data integration through rock physics.
Fabien was recently promoted to the position of rock physics & reservoir expert within CGG’s TECH+ Reservoir R&D team. He has increasingly applied geoscience capabilities to energy transition areas, such as carbon capture & sequestration (CCS) and geothermal projects.
He received the SEG Award for Best Paper in The Leading Edge in 2021 for a CGG-BRGM co-authored paper published in October 2021: “Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks.”
Seismic Soundoff explores the depth and usefulness of geophysics for the scientific community and the public. If you want to be the first to know about the next episode, please follow or subscribe to the podcast wherever you listen to podcasts. Two of our favorites are Apple Podcasts and Spotify.
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Zach Bridges created original music for this show. This episode was hosted, edited, and produced by Andrew Geary at TreasureMint. The SEG podcast team is Jennifer Cobb, Kathy Gamble, and Ally McGinnis.