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).

Codes related to the publication Gaussian mixture Markov chain Monte Carlo method for linear seismic inversion

Python package for Exploratory Lithology Analysis

Supervised classification to predict rock facies and a T-test flow to evaluate the prediction performance.

Analysis notebooks for the geolink well log dataset

Facies modeling using GANs

The code snippets and repositories are for generating embedded markov model to establish facies changes in the stratigraphic succession

Calculate each facies proportion for each well in a field and plot them as bubble map distribution

Calculate facies percentage within specific intervals

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.