Lithofacies (Facies) Classification Using Various ML Algorithms.

Facies (Lithofacies) classification is a critical task for geoscientists in development and exploration projects. Sedimentary facies represent the specific physical, chemical, and biological conditions present during the sedimentation process. Analyzing these facies typically requires rock samples. In this study, various machine learning algorithms are employed to predict facies from well log data. The dataset used originates from the Hugoton and Panoma Fields in North America and was part of a class exercise at The University of Kansas (Dubois et al., 2007). It comprises well log data from nine wells. These logs are used to train supervised classifiers to predict distinct facies groups. The implementation utilizes scikit-learn libraries, and the algorithms used include:

1. Support Vector Machines (SVM)
2. Gaussian Process Classification (GPC)
3. Random Forest Classifier (RFC)
4. Multi-layer Perceptron Classifier (Neural Network Classifier, NNC)
5. K-Nearest Neighbors Classifier (KNN)
6. Decision Tree Classifier (DT)
7. Logistic Regression (LR) "# Lithofacies-Classification-using-ML-Algorithms"