Machine learning assisted model based petrographic classification: a case study from Bokaro coal field

Abstract

This study applies machine learning techniques to improve petrographic classification in India's Bokaro coalfield's Barakar Formation, using conventional geophysical well logs from three wells. We analysed natural gamma ray, true resistivity, bulk density, neutron porosity, and photoelectric factor data using k-nearest neighbor (kNN), support vector machine (SVM) and random forest (RF) classifiers. A master well provided initial reference log measurement cut-off values for typical lithologies like shale, sandstone, carbonaceous shale, and coal, forming the basis of our training dataset. We assessed model accuracy using precision, recall, and F1-score metrics, finding the random forest model to be the most effective in litho-type discrimination. During the training phase, the computed overall accuracy of the predicted ML modes exceeded 89% and model accuracy hierarchy was RF>SVM>kNN. These classifiers were then applied to other well locations to predict lithological sequences, aiding in lithofacies sequence identification and potential fault extension detection. The study demonstrates the random forest model's superior precision and efficiency in lithological discrimination. Our findings enhance automated processes for identifying missing lithology during well correlation, offering valuable insights for geological interpretation in resource exploration and development. This machine learning-driven approach marks a significant advancement in subsurface geological studies.

Graphical abstract