Facies modeling using GANs
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Updated
Dec 16, 2022 - Jupyter Notebook
Facies modeling using GANs
The code snippets and repositories are for generating embedded markov model to establish facies changes in the stratigraphic succession
Lithofacies classification using well log data from the Hugoton and Panoma Fields dataset. This project implements various machine learning algorithms including Support Vector Machines, Random Forest, Neural Networks, and others to predict facies groups. The study focuses on improving facies classification accuracy using well log data from 9 well.
Calculate each facies proportion for each well in a field and plot them as bubble map distribution
Analysis notebooks for the geolink well log dataset
Calculate facies percentage within specific intervals
Codes related to the publication Gaussian mixture Markov chain Monte Carlo method for linear seismic inversion
Supervised classification to predict rock facies and a T-test flow to evaluate the prediction performance.
Python package for Exploratory Lithology Analysis
The repository includes PyTorch code, and the data, to reproduce the results for our paper titled "A Machine Learning Benchmark for Facies Classification" (published in the SEG Interpretation Journal, August 2019).
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