Shi Xiangyun, Misch David, Zak Stanislav, Cordill Megan, Kiener Daniel
Chair of Energy Geosciences, Department of Applied Geosciences and Geophysics, Montanuniversität Leoben, Peter-Tunner-Straße 5, 8700 Leoben, Austria.
Erich Schmid Institute of Materials Science, Austrian Academy of Sciences, Jahnstraße 12, 8700 Leoben, Austria.
Geomech Geophys Geo Energy Ge Resour. 2024;10(1):139. doi: 10.1007/s40948-024-00864-9. Epub 2024 Aug 14.
Mudstones and shales serve as natural barrier rocks in various geoenergy applications. Although many studies have investigated their mechanical properties, characterizing these parameters at the microscale remains challenging due to their fine-grained nature and susceptibility to microstructural damage introduced during sample preparation. This study aims to investigate the micromechanical properties of clay matrix composite in mudstones by combining high-speed nanoindentation mapping and machine learning data analysis. The nanoindentation approach effectively captured the heterogeneity in high-resolution mechanical property maps. Utilizing machine learning-based -means clustering, the mechanical characteristics of matrix clay, brittle minerals, as well as measurements on grain boundaries and structural discontinuities (e.g., cracks) were successfully distinguished. The classification results were validated through correlation with broad ion beam-scanning electron microscopy images. The resulting average reduced elastic modulus ( ) and hardness () values for the clay matrix were determined to be 16.2 ± 6.2 and 0.5 ± 0.5 GPa, respectively, showing consistency across different test settings and indenter tips. Furthermore, the sensitivity of indentation measurements to various factors was investigated, revealing limited sensitivity to indentation depth and tip geometry (when comparing Cube corner and Berkovich tip in a small range of indentation depth variations), but decreased stability at lower loading rates. Box counting and bootstrapping methods were applied to assess the representativeness of parameters determined for the clay matrix. A relatively small dataset (indentation number = 60) is needed to achieve representativeness, while the main challenges is to cover a representative mapping area for clay matrix characterization. Overall, this study demonstrates the feasibility of high-speed nanoindentation mapping combined with data analysis for micromechanical characterization of the clay matrix in mudstones, paving the way for efficient analysis of similar fine-grained sedimentary rocks.
The online version contains supplementary material available at 10.1007/s40948-024-00864-9.
泥岩和页岩在各种地热能应用中作为天然屏障岩石。尽管许多研究已经调查了它们的力学性能，但由于其细粒性质以及在样品制备过程中引入的微观结构损伤敏感性，在微观尺度上表征这些参数仍然具有挑战性。本研究旨在通过结合高速纳米压痕映射和机器学习数据分析来研究泥岩中粘土基质复合材料的微观力学性能。纳米压痕方法有效地捕捉了高分辨率力学性能图中的非均质性。利用基于机器学习的 - 均值聚类，成功区分了基质粘土、脆性矿物的力学特征，以及对晶界和结构不连续性（如裂缝）的测量。通过与宽离子束扫描电子显微镜图像的相关性验证了分类结果。确定粘土基质的平均约化弹性模量（ ）和硬度（ ）值分别为16.2±6.2和0.5±0.5 GPa，表明在不同测试设置和压头尖端下具有一致性。此外，研究了压痕测量对各种因素的敏感性，结果表明对压痕深度和尖端几何形状的敏感性有限（在小范围的压痕深度变化中比较立方角和贝氏压头尖端时），但在较低加载速率下稳定性降低。应用盒计数和自举方法来评估为粘土基质确定的参数的代表性。需要相对较小的数据集（压痕数 = 60）来实现代表性，而主要挑战是覆盖用于粘土基质表征的代表性映射区域。总体而言，本研究证明了高速纳米压痕映射结合数据分析用于泥岩中粘土基质微观力学表征的可行性，为类似细粒沉积岩的高效分析铺平了道路。
在线版本包含可在10.1007/s40948-024-00864-9获取的补充材料。
