Evaluation of machine learning methods for lithology classification using geophysical data

Abstract

Specific computational tools assist geologists in identifying and sorting lithologies in well surveys and reducing operational costs and practical working time. This allows for the management of professional output, the efficient interpretation of data, and completion of scientific research on data collected in geologically distinct regions. Machine learning methods and applications integrate large sets of information with the goal of efficient pattern recognition and the capability of leveraging accurate decision making. The objective of this study is to apply machine learning methods to the supervised classification of lithologies using multivariate log parameter data from offshore wells from the International Ocean Discovery Program (IODP). According to the analysis of the lithologies proposed in the IODP Expeditions and for the application of our methods, the lithologies were divided into four groups. The IODP Expeditions were organized into four templates for better results in analyzing the set of expeditions and practical application of the methods. The templates were submitted to training, validation, and testing by multilayer perceptron (MLP), decision tree, random forest, and support vector machine (SVM) methods. The evaluation was randomly divided into training (70%), validation (10%), and testing (20%) using the classification methods as an evaluation of the results. In the results, it was observed that Template1 (IODP Expedition 362) obtained better results with the MLP method, Template2 (IODP Expeditions 354, 355, and 359) and Template3 (IODP Expeditions 354, 355, 359, and 362) obtained better results with the random forest method with greater than 80.00% accuracy. For cross-validation, the random forest method performed well in all scenarios. In the practical template, the G2 group obtained a better result with the MLP method with an average accuracy above 85.00%. It is expected that machine learning methods can help improve the study of geology with accurate and rapid answers related to interpreting collected data in different study regions.