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Lithofacies prediction from well log data based on Deep learning

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pylitho

Lithofacies prediction from well log data based on Deep learning

If you want to use a pre-trained model for training, 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.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Lithofacies prediction from well log data based on Deep learning

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