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Found 7 papers, 2 papers with code
Learning CO$_2$ plume migration in faulted reservoirs with Graph Neural Networks
Accurately capturing the impact of faults on CO$_2$ plume migration remains a challenge for many existing deep learning surrogate models based on Convolutional Neural Networks (CNNs) or Neural Operators.
Optimizing Carbon Storage Operations for Long-Term Safety
In this study, we address these issues by modeling the decision-making process for carbon storage operations as a partially observable Markov decision process (POMDP).
Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges
These applications have implications in areas ranging as widely as energy, agriculture, and finance.
Real-time high-resolution CO$_2$ geological storage prediction using nested Fourier neural operators
Carbon capture and storage (CCS) plays an essential role in global decarbonization.
U-FNO -- An enhanced Fourier neural operator-based deep-learning model for multiphase flow
Here we present U-FNO, a novel neural network architecture for solving multiphase flow problems with superior accuracy, speed, and data efficiency.
CCSNet: a deep learning modeling suite for CO$_2$ storage
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 nature, highly non-linear governing equations, inherent parameter uncertainties, and the need for high spatial resolutions to capture multi-scale heterogeneity.
Multiphase flow prediction with deep neural networks
This paper proposes a deep neural network approach for predicting multiphase flow in heterogeneous domains with high computational efficiency.
