Machine Learning-Driven Micromechanical Characterization of Shale Rocks Leveraging High-Speed Nanoindentation Data

This study explores a rapid and precise method for micromechanical characterization and mapping of heterogeneous rocks, utilizing high-speed nanoindentation and mineral volume fractions. Traditional method for determination of mechanical properties of the different phases of a heterogeneous material require the combination of nanoindentation data and chemical analysis of the material. However, this results in increasing the cost, time, and complexity of the process. Hence, the proposed study explores different data mining techniques such as Uniform Manifold Approximation and Projection (UMAP) with k-means clustering, Dirichlet Process Mixture Model (DPMM) clustering, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), utilizing high speed nanoindentation data for an efficient and accurate evaluation of the micromechanical properties of heterogeneous shale rock. Comparison of the three techniques deduced that UMAP with k-means clustering technique provides appropriate micromechanical characterization and mapping results with a weighted error of about 13.40%. Even DPMM and DBSCAN performed reasonably well with slightly high weighted errors, therefore they can be adopted as a secondary clustering technique for validation of other clustering technique results. The results demonstrate the potential and efficiency of high-speed nanoindentation test in conjunction with data analytics for characterization and mapping of micromechanical properties of heterogeneous material.