Advanced Machine Learning Framework for Enhanced Lithology Classification and Identification

Abstract

Abstract In this comprehensive study, machine learning (ML) techniques are employed to revolutionize lithology classification within the geosciences, emphasizing the transformative impact of ML on traditional practices. The research encapsulates ML's integration into well-log data analysis, enhancing prediction accuracy and efficiency in lithology identification—a crucial aspect of subsurface exploration. The methodology adopted includes systematic data preprocessing, feature extraction, and the deployment of advanced ML algorithms such as Support Vector Machines and Random Forest for lithology classification. Models are trained and validated against well-log data from the Teapot Dome Reservoir and the Force 2020 Dataset, with the latter representing a collaborative and competitive environment aimed at advancing ML applications in geoscience. Results reveal a marked increase in predictive accuracy when incorporating a wider array of logs, as evidenced by Models A1 and A2 for the Teapot Dome Reservoir, and Models B1 and B2 for the Force 2020 Dataset. The research highlights the critical role of ML in achieving high accuracies in lithology prediction, with improved generalization capabilities across different geological settings. The workflow emphasizes the potential of ML algorithms to enhance well-log interpretation, streamline geological analyses, and reduce the time required for data processing. The study suggests future work focusing on expanding lithology types, normalizing log data, and broadening geographical coverage to further refine ML models for lithology classification. This effort underscores the convergence of ML with geoscience, promising a future where digital technologies create a more interconnected system for subsurface exploration.