Climate change mitigation and CO2 Geological Storage with Machine Learning
CO2 geological storage plays an essential role in global decarbonization and the energy transition. Predicting the transport of CO2 in subsurface formations requires the numerical simulation of multiphase flow through porous media. However, such simulations are challenging at scale due to the high computational costs of existing numerical methods. As a result, the lack of efficient modeling approaches can lead to significant uncertainties in evaluating storage capacities and optimizing for safe and effective injection sites.
This talk introduces a general-purpose machine learning-based framework that can serve as an alternative to numerical simulation for modeling CO2 geological storage. We show that the machine learning approach provides several orders of magnitude speedups compared to simulators while maintaining comparable accuracy. Our framework enables unprecedented real-time modeling to support engineering decisions and reduce uncertainties in CO2 storage deployment.
Dr Gege Wen
Gege Wen is an assistant professor at Imperial College London, co-appointed by the Earth Science Engineering department and the I-X initiative on Artificial Intelligence. She obtained her Ph.D. in Energy Sciences and Engineering at Stanford University, advised by Professor Sally Benson. Prior to her Ph.D., she received her M.S. in Fluid Mechanics and Hydrology from Stanford University and her B.S. in Mineral Engineering from the University of Toronto. Her research interest is developing computational methods for Earth and environmental science problems to help fulfill society’s energy needs and transition toward a low-carbon future. She specializes in (1) multiphase flow and transport for CO2 geological storage, (2) sustainable subsurface energy storage, and (3) ML for scientific computing.
Date: Thursday 7 March
Location: In Person | I-X Conference Room | Level 5
Translation and Innovation Hub (I-HUB)
Imperial White City Campus
84 Wood Lane
London W12 0BZ