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Found 4 papers, 1 papers with code
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.
