158 citations · 451 across the 8 of their papers we have counts for
8 papers
Learning CO plume migration in faulted reservoirs with Graph Neural Networks
Xin Ju, François P. Hamon, Gege Wen +3
Deep-learning-based surrogate models provide an efficient complement to numerical simulations for subsurface flow problems such as CO geological storage. Accurately capturing t…
Optimizing Carbon Storage Operations for Long-Term Safety
Yizheng Wang, Markus Zechner, Gege Wen +4
To combat global warming and mitigate the risks associated with climate change, carbon capture and storage (CCS) has emerged as a crucial technology. However, safely sequestering C…
Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges
Feras A. Batarseh, Priya L. Donti, Ján Drgoňa +14
Climate change is one of the most pressing challenges of our time, requiring rapid action across society. As artificial intelligence tools (AI) are rapidly deployed, it is therefor…
Real-time high-resolution CO geological storage prediction using nested Fourier neural operators
Gege Wen, Zongyi Li, Qirui Long +3
Carbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoi…
U-FNO -- An enhanced Fourier neural operator-based deep-learning model for multiphase flow
Gege Wen, Zongyi Li, Kamyar Azizzadenesheli +2
Numerical simulation of multiphase flow in porous media is essential for many geoscience applications. Machine learning models trained with numerical simulation data can provide a…
CCSNet: a deep learning modeling suite for CO storage
Gege Wen, Catherine Hay, Sally M. Benson
Numerical simulation is an essential tool for many applications involving subsurface flow and transport, yet often suffers from computational challenges due to the multi-physics na…
Multiphase flow prediction with deep neural networks
Gege Wen, Meng Tang, Sally M. Benson
This paper proposes a deep neural network approach for predicting multiphase flow in heterogeneous domains with high computational efficiency. The deep neural network model is able…
Fast uncertainty quantification of reservoir simulation with variational U-Net
Larry Jin, Hannah Lu, Gege Wen
Quantification of uncertainty in production/injection forecasting is an important aspect of reservoir simulation studies. Conventional approaches include intrusive Galerkin-based m…