盒数分形维数与裂缝网络性质的关系

Shaoqun Dong , Xiaohong Yu , Lianbo Zeng , Jing Ye , Leting Wang , Chunqiu Ji , Kaifeng Fu , Ruyi Wang
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引用次数: 0

摘要

由于能够量化裂缝网络的复杂性,分形维数(D)被广泛应用于分析裂缝网络相关问题,如连通性和渗透率。虽然D与裂缝网络单个属性之间的关系已被广泛研究,但D受到裂缝网络多个属性的综合影响。因此,考虑到裂缝网络的各种属性,即裂缝长度、数量和方向,本工作利用多变量分析建立了预测D的方程。采用蒙特卡罗模拟方法生成了大量的裂缝网络模型。在此基础上,推导了三种裂缝网络的分形维数(D)与各种性质的关系,分别为(1)不变裂缝长度和随机取向、(2)指数裂缝长度和随机取向、(3)指数裂缝长度和von-Mises取向。首先分析最简单的关系式,确定分数表达式的基本公式。然后将第一关系式中的固定参数替换为裂缝性质分布参数,得到第二关系式和第三关系式。预测D值与实际值之间的相关分析显示相关性非常高(>0.99)。为了验证建立的关系,利用地质露头获得的裂缝网络。结果证明了推导关系的有效性。这些方程的应用提高了从裂缝性质估计分形维数的效率、实用性和方便性。因此,裂缝网络相关问题的分析变得更加可行和容易。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Relationship between box-counting fractal dimension and properties of fracture networks

Relationship between box-counting fractal dimension and properties of fracture networks

Due to the capacity to quantify the complexity of a fracture network, fractal dimension (D) is widely applied in analyzing fracture network-related issues, such as connectivity and permeability. While the relationship between D and individual properties of a fracture network has been extensively studied, D is influenced by a combination of multiple attributes of the fracture network. Therefore, this work utilizes multivariate analysis to establish an equation for predicting D, taking into account various properties of the fracture network, namely fracture length, number, and orientation. Monte Carlo simulation is employed to generate a substantial number of fracture network models. Subsequently, relationships between the fractal dimension (D) and various properties are derived for three types of fracture networks with (1) invariant fracture length and random orientation, (2) exponential fracture length and random orientation, and (3) exponential fracture length and von-Mises orientation. The initial analysis focuses on the simplest relationship, wherein the fundamental formula of fractional expression is determined. Then the second and third relationships are obtained through replacing the fixed parameter in the first relationship with the distribution parameters of fracture properties. Correlation analyses between the predicted D and the actual values reveal a remarkably high correlation (>0.99). To validate the established relationships, a fracture network obtained from geological outcrops is utilized. The results demonstrate the validity of the derived relationships. The utilization of these equations enhances the efficiency, practicality, and convenience of estimating fractal dimensions from fracture properties. As a result, the analysis of fracture network-related issues becomes more feasible and accessible.

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