Gege Wen is Ph.D. candidate at the Energy Resources Engineering Department in the School of Earth, Energy and Environmental Sciences at Stanford University. She received her Master's degree in Fluid Mechanics and Hydrology from Civil and Environmental Engineering at Stanford University and has been working with Professor Sally Benson since 2016 on numerical simulation for carbon capture and storage. During her Ph.D., she focuses on machine learning approaches for carbon storage problems and published journal articles on this topic. She is currently an ExxonMobil Emerging Energy Fellow. She served as reviewer for academic journals and ICML, NeurIPS, and ICLR conference workshops. Prior to attending Stanford, she received her Bachelor's degree with honors from Lassonde Mineral Engineering at University of Toronto.
Gege Wen developed CCSNet.ai a deep learning modeling suite for CO2 storage (https://ccsnet.ai).
Education & Certifications
Ph.D., Stanford University, Energy Resource Engineering
M.S., Stanford University, Environmental Fluid Mechanics and Hydrology (2017)
B.A.Sc., University of Toronto, Lassonde Mineral Engineering (2016)
CCSNet.ai, Stanford Center for Carbon Storage
CCSNet.ai is a deep learning modeling suite to provide fast prediction for CO2 storage problems. It is developed by Gege Wen at Stanford University, advised by Prof. Sally M. Benson. CCSNet provides Synthetic Heterogeneous, Homogeneous, Purely layered, and User upload isotropic permeability maps. The isotropic cases are predicted with pre-trained convolutional neural network models. Anisotropic permeability maps are supported with the Synthetic Heterogeneous option, predicted with pre-trained enhanced Fourier neural operators.
For More Information:
- Criteria and workflow for selecting depleted hydrocarbon reservoirs for carbon storage APPLIED ENERGY 2022; 324
U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow
Advances in Water Resources
View details for DOI 10.1016/j.advwatres.2022.104180
- CCSNet: A deep learning modeling suite for CO2 storage ADVANCES IN WATER RESOURCES 2021; 155
Towards a predictor for CO2 plume migration using deep neural networks
INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL
View details for DOI 10.1016/j.ijggc.2020.103223
- CO2 plume migration and dissolution in layered reservoirs INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL 2019; 87: 66–79