Talk Title
Machine learning for climate change mitigation and CO2 geological storage modelling

Talk Summary
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. In this seminar, we will discuss how machine learning can help address this challenge, support engineering decisions, and reduce uncertainties in CO2 storage deployment.

Speaker Bio – Dr Gege Wen
Gege Wen is an Assistant Professor at Imperial College London, co-appointed by the Earth Science Engineering department and the newly launched 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 fulfil 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.

Time: 14.00 – 15.00
Date: Tuesday 14 May
Location: Hybrid Event | Online and in I-X Conference Room, Level 5
Translation and Innovation Hub (I-HUB)
Imperial White City Campus
84 Wood Lane
W12 0BZ

Link to join online via Teams.

Any questions, please contact Andreas Joergensen (a.joergensen@imperial.ac.uk).