Abstract:

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.

Key words: machine learning, shale mechanical properties, shale oil and gas, mechanical parameter prediction, model interpretability

摘要：

页岩油气开采过程面临诸多复杂地质力学问题，页岩的力学性质作为决定开采效率与安全性的核心要素，亟待深入的研究与探索。在此背景下，机器学习凭借强大的数据处理与模式识别能力，为页岩力学性质研究开辟了新路径。本文聚焦机器学习在页岩力学性质研究中的应用，系统阐述该领域的现状、挑战与展望。首先，详细梳理当前机器学习算法在页岩力学参数预测、破坏模式识别方面的应用成果，展示其相较于传统研究方法在处理复杂数据、挖掘潜在规律上的显著优势。随后，梳理了机器学习在页岩力学性质研究应用中面临的诸多挑战。页岩样本数据具有高维、小样本特性，容易导致模型出现过拟合现象；同时，多数机器学习模型内部运行机制难以解释，限制了其推广使用。此外，页岩地质条件复杂多变，不同地区页岩的矿物组成、孔隙结构差异巨大，现有模型在跨区域、跨地质条件应用时，普适性明显不足。最后，基于前沿技术发展趋势展望未来。机器学习在页岩力学性质研究领域前景广阔，通过融合地质、地球物理、测井等多源数据，能够为模型提供更丰富的信息，降低数据维度带来的负面影响；优化算法架构，结合迁移学习、集成学习等技术，可提高模型的泛化能力；构建基于物理约束的机器学习模型，既能增强模型的可解释性，又能提升其在复杂地质条件下的适应性。这些策略有望突破现有瓶颈，推动机器学习在页岩力学性质研究中的深度应用，为页岩油气资源的高效开发提供坚实的理论与技术支撑。

关键词: 机器学习, 页岩力学性质, 页岩油气, 力学参数预测, 模型可解释性

CLC Number: 

• P618.13

• TP18