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Numerical estimation and prediction of stress-dependent permeability tensor for fractured rock masses

机译:裂隙岩体应力相关渗透率张量的数值估计和预测

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The numerical experiment based on the Composite Element Method (CEM) is extended to study the stress-dependent permeability tensor for fractured rock masses. The CEM allows rock meshing regardless of fractures, and the fractures are automatically formulated with an explicit representation. An equivalent filled fracture model is introduced into the CEM to realize a hydro-mechanical coupling simulation for the numerical experiment. This model assumes the hydraulic properties both along and normal to the fracture plane dependent on the fracture normal stress with an exponential relation. Large numbers of rock specimens with different sizes and orientations are modeled and their hydro-mechanical coupling behaviors under various stress states are simulated by the CEM. From the simulated results, the stress-dependent permeability tensors are calculated from the flow rates through the rock specimen boundaries. In order to predict the permeability tensor for any in situ stress state, a load/permeability database is developed using the Artificial Neural Network (ANN). The database establishes the mapping from the stress state to the permeability tensor, based on the powerful data modeling capacity of the ANN. Inputting any in situ stress state into the database, the database output is the relevant permeability tensor predicted. The above algorithms are verified by comparison with an analytical solution and successfully used in a stochastic fracture system.
机译:扩展了基于复合单元法(CEM)的数值实验,以研究裂隙岩体的应力相关渗透率张量。 CEM允许不考虑裂缝而对岩石进行网格划分,并且裂缝以明确的表示自动进行公式化。将等效的填充裂缝模型引入到CEM中,以实现数值实验的水力耦合模拟。该模型假设沿裂缝平面和垂直于裂缝平面的水力特性取决于裂缝的法向应力,并且具有指数关系。 CEM对大量具有不同大小和方向的岩石样本进行了建模,并模拟了它们在各种应力​​状态下的水力耦合行为。根据模拟结果,根据穿过岩石样本边界的流速计算应力相关的渗透率张量。为了预测任何原位应力状态的渗透率张量,使用人工神经网络(ANN)开发了载荷/渗透率数据库。该数据库基于ANN强大的数据建模能力,建立了从应力状态到渗透率张量的映射。将任何现场应力状态输入数据库后,数据库输出即为预测的相关渗透率张量。通过与解析解决方案进行比较,对以上算法进行了验证,并将其成功地用于随机裂缝系统中。

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