ERE Campfire - Numerical and Machine Learning Modeling for Geological CO2 Sequestration at an Intermediate Scale
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Industrial deployment of Geological Carbon Storage (GCS) operates across megaton scales and tens of square kilometers over decades, while standard laboratory experiments are often confined to centimeter-scale cores and brief observation windows. This seminar explores how intermediate-scale experiments—coupled with modern numerical and machine learning approaches—bridge the gap between benchtop testing and full field-scale behavior.
Through results from a novel meter-scale laboratory experiment, our speaker will present a proof-of-concept modeling framework that integrates two-phase hydraulic flow simulations with machine learning. The talk will cover real-time characterization of effective permeability and tortuosity across reservoir–caprock layers, strategies for optimizing sensor placement to extract the most informative observations, and methods to prevent ML overfitting on synthetic multiphase flow datasets.
Conveners:
Energy, Resources and the Environment division
Speaker:
Dr. Dario Sciandra (Postdoctoral researcher at École Polytechnique Fédérale de Lausanne, now at ETH Zurich)
Need help?
If you have any questions about the campfire "ERE Campfire - Numerical and Machine Learning Modeling for Geological CO2 Sequestration at an Intermediate Scale", please contact us via ecs-ere@egu.eu.
- Campfire
- ERE Campfire - Numerical and Machine Learning Modeling for Geological CO2 Sequestration at an Intermediate Scale
- Start time
- Fri, 2 Oct 2026 16:00 CEST
- Duration
- ca. 1h 00m
- Contact
- ecs-ere@egu.eu