Application of machine learning in the study of shale mechanical properties: Current situation, challenges and prospects

The extraction of shale oil and gas is confronted with numerous complex geomechanical issues. As a core factor determining extraction efficiency and safety, the mechanical properties of shale urgently require in-depth research and exploration. Against this backdrop, machine learning, with its powerful capabilities in data processing and pattern recognition, has opened up new avenues for research on shale mechanical properties. This paper focuses on the application of machine learning in the study of shale mechanical properties, systematically elaborating on the current status, challenges, and prospects in this field. Firstly, it details the application achievements of current machine learning algorithms in the prediction of shale mechanical parameters and the recognition of failure modes, demonstrating their significant advantages over traditional research methods in processing complex data and mining potential patterns. Subsequently, it summarizes the multiple challenges faced in the application of machine learning to the study of shale mechanical properties. Shale sample data exhibits high-dimensional and small-sample characteristics, which easily lead to overfitting in models. Meanwhile, the internal operating mechanisms of most machine learning models are difficult to interpret, restricting their popularization and application. In addition, the geological conditions of shale are complex and variable, with significant differences in mineral composition and pore structure of shale in different regions. The existing models show obviously insufficient universality when applied across regions and geological conditions. Finally, the future is prospected based on the development trends of cutting-edge technologies. Machine learning has broad prospects in the field of shale mechanical properties research. By integrating multi-source data such as geological, geophysical, and logging data, it can provide more abundant information for models and reduce the negative impact caused by data dimensionality. Optimizing algorithm architectures and combining technologies such as transfer learning and ensemble learning can improve the generalization ability of models. Constructing physics-constrained machine learning models can not only enhance the interpretability of models but also improve their adaptability under complex geological conditions. These strategies are expected to break through existing bottlenecks, promote the in-depth application of machine learning in the study of shale mechanical properties, and provide solid theoretical and technical support for the efficient development of shale oil and gas resources.