Scaling up highly heterogeneous aperture distributions of fractures into equivalent permeability tensors enables a substantial reduction in the computational cost of simulating fluid flow in fractured porous media by allowing the employment of coarser grids while keeping the accuracy of an explicit model. This work proposes the adaptation and application of conditional generative adversarial networks (CGAN) for upscaling the permeability of single fractures. Three different types of aperture distributions are used as input in this work: layered media, Zinn & Harvey transformations and self-affine fractals. As output, the model predicts the pressure inside the fracture which is used for calculation of the equivalent permeability tensor. Our results show that the framework employing CGAN provides equivalent tensors that can capture accurately both the permeability angle and anisotropy of discrete fractures, with a substantial reduction of the computational time when compared to traditional frameworks that rely on the numerical simulations.

将裂缝的高度异质孔径分布放大为等效渗透率张量，通过允许使用更粗的网格同时保持显式模型的准确性，可以显着降低模拟裂缝多孔介质中流体流动的计算成本。这项工作提出了条件生成对抗网络（CGAN）的适应和应用，以提高单个裂缝的渗透率。在这项工作中使用三种不同类型的孔径分布作为输入：分层介质、Zinn & Harvey 变换和自仿射分形。作为输出，该模型预测裂缝内的压力，用于计算等效渗透率张量。