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Effective permeability of an immiscible fluid in porous media determined from its geometric state

Autor: Alzubaidi, Fatimah, Mostaghimi, Peyman, Niu, Yufu, Armstrong, Ryan T., Mohammadi, Gelareh, Berg, Steffen, McClure, James E.

Based on the phenomenological extension of Darcy's law, two-fluid flow is dependent on a relative permeability function of saturation only that is process/path dependent with an underlying dependency on pore structure. For applications, fuel cells to

Externí odkaz: http://arxiv.org/abs/2208.08027

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A comparative study of paired versus unpaired deep learning methods for physically enhancing digital rock image resolution

Autor: Niu, Yufu, Jackson, Samuel J., Alqahtani, Naif, Mostaghimi, Peyman, Armstrong, Ryan T.

X-ray micro-computed tomography (micro-CT) has been widely leveraged to characterise pore-scale geometry in subsurface porous rock. Recent developments in super resolution (SR) methods using deep learning allow the digital enhancement of low resoluti

Externí odkaz: http://arxiv.org/abs/2112.08644

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Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling

Autor: Jackson, Samuel J., Niu, Yufu, Manoorkar, Sojwal, Mostaghimi, Peyman, Armstrong, Ryan T.

Field-of-view and resolution trade-offs in X-Ray micro-computed tomography (micro-CT) imaging limit the characterization, analysis and model development of multi-scale porous systems. To this end, we developed an applied methodology utilising deep le

Externí odkaz: http://arxiv.org/abs/2111.01270

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