Estimating permeability of 3D micro-CT images by physics-informed CNNs based on DNS DOI Creative Commons
Stephan Gärttner, Faruk O. Alpak, Andreas Meier

и другие.

Computational Geosciences, Год журнала: 2023, Номер 27(2), С. 245 - 262

Опубликована: Янв. 31, 2023

Abstract In recent years, convolutional neural networks (CNNs) have experienced an increasing interest in their ability to perform a fast approximation of effective hydrodynamic parameters porous media research and applications. This paper presents novel methodology for permeability prediction from micro-CT scans geological rock samples. The training data set CNNs dedicated consists labels that are typically generated by classical lattice Boltzmann methods (LBM) simulate the flow through pore space segmented image data. We instead direct numerical simulation (DNS) solving stationary Stokes equation efficient distributed-parallel manner. As such, we circumvent convergence issues LBM frequently observed on complex geometries, therefore, improve generality accuracy our set. Using DNS-computed permeabilities, physics-informed CNN (PhyCNN) is trained additionally providing tailored characteristic quantity space. More precisely, exploiting connection problems graph representation space, additional information about confined structures provided network terms maximum value, which key innovative component workflow. robustness this approach reflected very high accuracy, variety sandstone samples archetypal formations.

Язык: Английский

Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning DOI Creative Commons
Ying Da Wang, Quentin Meyer, Kunning Tang

и другие.

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Фев. 14, 2023

Abstract Proton exchange membrane fuel cells, consuming hydrogen and oxygen to generate clean electricity water, suffer acute liquid water challenges. Accurate modelling is inherently challenging due the multi-phase, multi-component, reactive dynamics within multi-scale, multi-layered porous media. In addition, currently inadequate imaging capabilities are limiting simulations small areas (<1 mm 2 ) or simplified architectures. Herein, an advancement in achieved using X-ray micro-computed tomography, deep learned super-resolution, multi-label segmentation, direct multi-phase simulation. The resulting image most resolved domain (16 with 700 nm voxel resolution) largest flow simulation of a cell. This generalisable approach unveils multi-scale clustering transport mechanisms over large dry flooded gas diffusion layer fields, paving way for next generation proton cells optimised structures wettabilities.

Язык: Английский

Процитировано

91

Pore-scale simulation of multiphase flow and reactive transport processes involved in geologic carbon sequestration DOI
Wendong Wang,

Qiuheng Xie,

Senyou An

и другие.

Earth-Science Reviews, Год журнала: 2023, Номер 247, С. 104602 - 104602

Опубликована: Окт. 24, 2023

Язык: Английский

Процитировано

56

Pore network characterization of shale reservoirs through state-of-the-art X-ray computed tomography: A review DOI
Qing Liu, Mengdi Sun, Xianda Sun

и другие.

Gas Science and Engineering, Год журнала: 2023, Номер 113, С. 204967 - 204967

Опубликована: Апрель 7, 2023

Язык: Английский

Процитировано

48

Conceptualizing future groundwater models through a ternary framework of multisource data, human expertise, and machine intelligence DOI
Chuanjun Zhan, Zhenxue Dai, Shangxian Yin

и другие.

Water Research, Год журнала: 2024, Номер 257, С. 121679 - 121679

Опубликована: Апрель 26, 2024

Язык: Английский

Процитировано

36

Deep learning-based pore network generation: Numerical insights into pore geometry effects on microstructural fluid flow behaviors of unconventional resources DOI

Bei-Er Guo,

Nan Xiao, Dmitriy A. Martyushev

и другие.

Energy, Год журнала: 2024, Номер 294, С. 130990 - 130990

Опубликована: Март 14, 2024

Язык: Английский

Процитировано

19

Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images DOI
Ying Da Wang, Mehdi Shabaninejad, Ryan T. Armstrong

и другие.

Applied Soft Computing, Год журнала: 2021, Номер 104, С. 107185 - 107185

Опубликована: Фев. 17, 2021

Язык: Английский

Процитировано

99

Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media DOI Creative Commons
Javier E. Santos, Ying Yin, Honggeun Jo

и другие.

Transport in Porous Media, Год журнала: 2021, Номер 140(1), С. 241 - 272

Опубликована: Май 29, 2021

Abstract The permeability of complex porous materials is interest to many engineering disciplines. This quantity can be obtained via direct flow simulation, which provides the most accurate results, but very computationally expensive. In particular, simulation convergence time scales poorly as domains become less or more heterogeneous. Semi-analytical models that rely on averaged structural properties (i.e., porosity and tortuosity) have been proposed, these features only partly summarize domain, resulting in limited applicability. On other hand, data-driven machine learning approaches shown great promise for building general by virtue accounting spatial arrangement domains’ solid boundaries. However, prior convolutional neural network (ConvNet) literature concerning 2D image recognition problems do not scale well large 3D required obtain a representative elementary volume (REV). As such, work focused homogeneous samples, where small REV entails global nature fluid could mostly neglected, accordingly, memory bottleneck addressing with ConvNets was side-stepped. Therefore, important geometries such fractures vuggy modeled properly. this work, we address limitation multiscale deep model able learn from media data. By using coupled set networks view domain different scales, enable evaluation ( $$>512^3$$ > 512 3 ) images approximately one second single graphics processing unit. architecture opens up possibility modeling sizes would feasible traditional tools desktop computer. We validate our method laminar case samples fractures. result viewing entire at once, perform prediction exhibiting degree heterogeneity. expect methodology applicable transport play central role.

Язык: Английский

Процитировано

67

An upscaling approach to predict mine water inflow from roof sandstone aquifers DOI
Lulu Xu, Meifeng Cai, Shuning Dong

и другие.

Journal of Hydrology, Год журнала: 2022, Номер 612, С. 128314 - 128314

Опубликована: Авг. 12, 2022

Язык: Английский

Процитировано

60

Pore-network modeling of flow in shale nanopores: Network structure, flow principles, and computational algorithms DOI
Ronghao Cui, S. Majid Hassanizadeh, Shuyu Sun

и другие.

Earth-Science Reviews, Год журнала: 2022, Номер 234, С. 104203 - 104203

Опубликована: Окт. 5, 2022

Язык: Английский

Процитировано

57

Multi-scale reconstruction of porous media from low-resolution core images using conditional generative adversarial networks DOI
Yongfei Yang, Fugui Liu, Jun Yao

и другие.

Journal of Natural Gas Science and Engineering, Год журнала: 2022, Номер 99, С. 104411 - 104411

Опубликована: Янв. 6, 2022

Язык: Английский

Процитировано

46