Correlation of Static and Dynamic Mechanical Properties of Australian Sedimentary Rocks

This paper presents a comprehensive analysis of the correlation between static and dynamic mechanical properties of sedimentary rocks from Australian basins. By leveraging both empirical and machine learning techniques, we provide valuable insights into the predictive capabilities of different methodologies and identify the most effective approaches for capturing the intricate relationships within the dataset. The results of our study reveal that several machine learning methods consistently outperform empirical approaches, yielding lower error values and providing more accurate predictions of the correlation between static and dynamic properties. Furthermore, we rank these machine learning methods based on their respective error values, offering insights into the relative performance of each algorithm. Our findings not only advance our understanding of sedimentary rock mechanics but also offer practical recommendations for improving reservoir characterization, enhancing geotechnical assessments, and optimizing engineering design.