煤炭断裂的聚类分形离散断裂网络模型

Fractals Pub Date : 2024-02-16 DOI:10.1142/s0218348x2450035x
XIN LIANG, PENG HOU, GUANNAN LIU, YI XUE, JIA LIU, FENG GAO, ZHIZHEN ZHANG
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摘要

断裂煤层中的裂缝网络是煤层瓦斯流动的主要通道。不仅是单条断裂的几何拓扑特性(如分形特征),断裂网络的连接拓扑特性(断裂之间的互连特征)对断裂网络中的流体流动也有重要影响。本研究首次基于复杂网络理论探讨了煤炭断裂网络的连接拓扑特性。分析了断裂节点度、聚类系数和平均路径长度等属性参数。结果表明,裂煤断裂网络的平均聚类系数较大,平均路径长度较小。煤炭断裂网络的连接特性呈现出典型的 "小世界 "聚类模型。此外,通过考虑单条断裂的分形特征和断裂网络的聚类特征,建立了改进的聚类分形离散断裂网络(DFN)模型。然后,基于晶格玻尔兹曼法,分析了生成的聚类分形离散断裂网络的渗透特性。结果表明,DFN 的渗透率与断裂网络的平均聚类系数呈正相关,与断裂的分形维数呈负相关。因此,在描述断裂网络中的流体流动时,不能忽视断裂网络的拓扑聚类特征和断裂的分形特征,我们的聚类分形 DFN 模型为指导 DFN 工程的优化设计提供了新思路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A CLUSTERED FRACTAL DISCRETE FRACTURE NETWORK MODEL FOR FRACTURED COAL

The fracture network in fractured coal is the main channel of coal seam gas flow. Not only the geometric topology properties (such as fractal characteristics) of a single fracture but also the connection topology properties (interconnection characteristics between fractures) of the fracture network have an important impact on the fluid flow in fracture networks. In this study, the connection topology properties of the fracture network in the fractured coal are explored based on the complex network theory for the first time. The property parameters such as the fracture node degree, the clustering coefficient, and the average path length are analyzed. It shows that the average clustering coefficient of the fracture network in fractured coal is larger, and the average path length is smaller. The connection property of the fracture network in the fractured coal presents a typical “small-world” clustering model. Further, by considering the fractal characteristics of the single fracture and the clustering characteristics of the fracture network, an improved clustered fractal discrete fracture network (DFN) model is developed. Then, based on the lattice Boltzmann method, the permeability properties of the generated clustered fractal DFNs are analyzed. The results show that the permeability of DFNs is positively correlated with the average clustering coefficient of fracture network, and negatively correlated with the fractal dimension of fracture. Therefore, the topological clustering characteristics of fracture networks and the fractal characteristics of fractures cannot be ignored in describing the fluid flow in the fracture network, and our clustered fractal DFN model provides a new idea for guiding the optimization design in DFN engineering.

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