期刊
ADVANCES IN WATER RESOURCES
卷 166, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.advwatres.2022.104264
关键词
Fracturedporousmedia; Permeabilitytensor; Permeabilityanisotropy; Reducedordermodelling
资金
- Danish Offshore Technology Centre (DTU Offshore)
- DTU Offshore
- US Department of Energy Office of Fossil Energy and Carbon Management
- U.S. Department of Energy's National Nuclear Security Administration [DE-NA-0003525]
- Sibley School of Mechanical and Aerospace Engineering, Cornell University
- Science-Informed Machine Learning to Accelerate Real Time Decisions-Carbon Storage (SMART-CS) initiative
This study proposes the adaptation and application of conditional generative adversarial networks (CGAN) for upscaling the permeability of single fractures. The results show that the framework employing CGAN provides accurate capture of both the permeability angle and anisotropy of discrete fractures, with a substantial reduction in computational time.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据