README.md
This repository contains the code and figures associated with the paper:
Chu, A.K.; Benson, S.M.; Wen, G. Deep-Learning-Based Flow Prediction for CO2 Storage in Shale–Sandstone Formations. Energies 2023, 16, 246. https://doi.org/10.3390/en16010246
Run_FNORUNet3_dP_4layer.py: trains dP model. Can pass arguments specifying parameters such as the training/validation data set size, error type, learning rate, modes, etc.
Run_FNORUNet3_SG_5layer.py: trains SG model.
Run_FNORUNet4_dP_4layer_0rerr.py: trains dP model, with a loss function excluding the r-error.
Run_FNORUNet4_SG_5layer_0rerr.py: trains SG model, with a loss function excluding the r-error.
FNORUNet_4layer_model.py: model architecture for RU-FNO with 4 ResNet layers.
FNORUNet_5layer_model.py: model architecture for RU-FNO with 5 ResNet layers.
analysis.ipynb: plots for analysis of shale case studies.
calculateErr.ipynb: calculate R2 scores and mean errors of models.
dataExample.ipynb: plots for examples from training data.
dataGenerationExample.ipynb: plots illustrating data generation methodology.
plotResults.ipynb: plots for model prediction results.
R2plots.ipynb: R2 histograms and scatter plots (Fig 2)
R2training.ipynb: plots of R2 score over training process (Fig 2)
sleipnerSim.ipynb: model prediction for Sleipner-like reservoir (Fig 11)
speedup.ipynb: calculation of model speedup (Table 3)
The .npy data and PyTorch model files referenced in the code are available here.
.png files for figures are located in the Figures directory.