Lithofacies prediction from well log data based on Deep learning
If you want to use a pre-trained model for prediction, use the command:
predict.py -m filemodel -d datafile
filemodel can be RESNET or CNN to use different models for prediction. datafile is the data to be predicted, in npy format.
For example in our code: python3 predict.py -m RESNET -d DATA_lianghe_mulclas.npy
datafile has following numpy matrix formart with 6 columns: Well name, Depth, NR, GG, GR, labels
Output will be save as in out dir.
If you want to train a new model, use the trainCNN.ipynb, trainRESNET.ipynb and trainRF.ipynb.
Shi, Y., Liao, J., Gan, L., & Tang, R. (2024). Lithofacies prediction from well log data based on deep learning: a case study from Southern Sichuan, China. Applied Sciences, 14(18), 8195.
This project is licensed under the MIT License. See the LICENSE file for more details.