Predicting porosity, permeability, and tortuosity of porous media from images by deep learning

Abstract Convolutional neural networks (CNN) are utilized to encode the relation between initial configurations of obstacles and three fundamental quantities in porous media: porosity ( $$\varphi$$ φ ), permeability (k), and tortuosity (T). The two-dimensional systems with obstacles are considered....

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Autores principales:	Krzysztof M. Graczyk, Maciej Matyka
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/44c73faaec22405489053f3f380e5ee8
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id	oai:doaj.org-article:44c73faaec22405489053f3f380e5ee8
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spelling	oai:doaj.org-article:44c73faaec22405489053f3f380e5ee82021-12-02T11:43:43ZPredicting porosity, permeability, and tortuosity of porous media from images by deep learning10.1038/s41598-020-78415-x2045-2322https://doaj.org/article/44c73faaec22405489053f3f380e5ee82020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78415-xhttps://doaj.org/toc/2045-2322Abstract Convolutional neural networks (CNN) are utilized to encode the relation between initial configurations of obstacles and three fundamental quantities in porous media: porosity ( $$\varphi$$ φ ), permeability (k), and tortuosity (T). The two-dimensional systems with obstacles are considered. The fluid flow through a porous medium is simulated with the lattice Boltzmann method. The analysis has been performed for the systems with $$\varphi \in (0.37,0.99)$$ φ ∈ ( 0.37 , 0.99 ) which covers five orders of magnitude a span for permeability $$k \in (0.78, 2.1\times 10^5)$$ k ∈ ( 0.78 , 2.1 × 10 5 ) and tortuosity $$T \in (1.03,2.74)$$ T ∈ ( 1.03 , 2.74 ) . It is shown that the CNNs can be used to predict the porosity, permeability, and tortuosity with good accuracy. With the usage of the CNN models, the relation between T and $$\varphi$$ φ has been obtained and compared with the empirical estimate.Krzysztof M. GraczykMaciej MatykaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Krzysztof M. Graczyk Maciej Matyka Predicting porosity, permeability, and tortuosity of porous media from images by deep learning
description	Abstract Convolutional neural networks (CNN) are utilized to encode the relation between initial configurations of obstacles and three fundamental quantities in porous media: porosity ( $$\varphi$$ φ ), permeability (k), and tortuosity (T). The two-dimensional systems with obstacles are considered. The fluid flow through a porous medium is simulated with the lattice Boltzmann method. The analysis has been performed for the systems with $$\varphi \in (0.37,0.99)$$ φ ∈ ( 0.37 , 0.99 ) which covers five orders of magnitude a span for permeability $$k \in (0.78, 2.1\times 10^5)$$ k ∈ ( 0.78 , 2.1 × 10 5 ) and tortuosity $$T \in (1.03,2.74)$$ T ∈ ( 1.03 , 2.74 ) . It is shown that the CNNs can be used to predict the porosity, permeability, and tortuosity with good accuracy. With the usage of the CNN models, the relation between T and $$\varphi$$ φ has been obtained and compared with the empirical estimate.
format	article
author	Krzysztof M. Graczyk Maciej Matyka
author_facet	Krzysztof M. Graczyk Maciej Matyka
author_sort	Krzysztof M. Graczyk
title	Predicting porosity, permeability, and tortuosity of porous media from images by deep learning
title_short	Predicting porosity, permeability, and tortuosity of porous media from images by deep learning
title_full	Predicting porosity, permeability, and tortuosity of porous media from images by deep learning
title_fullStr	Predicting porosity, permeability, and tortuosity of porous media from images by deep learning
title_full_unstemmed	Predicting porosity, permeability, and tortuosity of porous media from images by deep learning
title_sort	predicting porosity, permeability, and tortuosity of porous media from images by deep learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/44c73faaec22405489053f3f380e5ee8
work_keys_str_mv	AT krzysztofmgraczyk predictingporositypermeabilityandtortuosityofporousmediafromimagesbydeeplearning AT maciejmatyka predictingporositypermeabilityandtortuosityofporousmediafromimagesbydeeplearning
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Predicting porosity, permeability, and tortuosity of porous media from images by deep learning

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