Abstract
This study aims to put a supervised learning method for automatically classifying lithofacies in well-logging dataset, where several machine learning algorithms were compared in this study that took place in the Tarakan Basin, Indonesia. The predicted lithofacies in this study including shale, shaly sandstone, sandstone, and coal, where coal is considered as the unique lithofacies in the study area. As training and testing datasets, we used two separate well log datasets from the Tarakan Basin. The first well, named Omnicron, was used to train the model, while the second well, named Kay, was used to test it. Random Forest and Gradient Boosting outperformed the other models in the experiment, with the accuracy of 87.49% and 87.01%, respectively. When it came to classifying coal, however, both approaches had issues. The Pr-Recall curve revealed that the coal score was under average precision in each facies, with values of roughly 0.52 and 0.38, respectively, which explaining why, even with high accuracy, the machine learning algorithm predicted poorly in one lithofacies class. In order to evaluate this coal misclassification, we used rock physics to analyze the machine learning prediction in this report. As result, we found that each facies is well-differentiated by physical properties, and the predicted lithofacies have a distribution that is close to the original facies however, coal may be potentially misclassified as other lithofacies as some of the coals have similar rock physical properties with the surrounding lithology (e.g. coal with a mixture of shale may have similar DT and GR responses). Based on this research, the use of machine learning in the Tarakan Basin effectively provides lithofacies data with a high degree of precision and accuracy in a much shorter time.
NSTL