Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks
The permeability of porous materials determines the fluid flow rate and aids in the prediction of their mechanical properties. This study developed a novel approach that combines the discrete cosine transform (DCT) and artificial neural networks (ANN) for permeability analysis and prediction in digi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/16/13/4668 |
| _version_ | 1797591349682241536 |
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| author | Dongshuang Li Shaohua You Qinzhuo Liao Gang Lei Xu Liu Weiqing Chen Huijian Li Bo Liu Xiaoxi Guo |
| author_facet | Dongshuang Li Shaohua You Qinzhuo Liao Gang Lei Xu Liu Weiqing Chen Huijian Li Bo Liu Xiaoxi Guo |
| author_sort | Dongshuang Li |
| collection | DOAJ |
| description | The permeability of porous materials determines the fluid flow rate and aids in the prediction of their mechanical properties. This study developed a novel approach that combines the discrete cosine transform (DCT) and artificial neural networks (ANN) for permeability analysis and prediction in digital rock images, focusing on nanoscale porous materials in shale formations. The DCT effectively captured the morphology and spatial distribution of material structure at the nanoscale and enhanced the computational efficiency, which was crucial for handling the complexity and high dimensionality of the digital rock images. The ANN model, trained using the Levenberg–Marquardt algorithm, preserved essential features and demonstrated exceptional accuracy for permeability prediction from the DCT-processed rock images. Our approach offers versatility and efficiency in handling diverse rock samples, from nanoscale shale to microscale sandstone. This work contributes to the comprehension and exploitation of unconventional resources, especially those preserved in nanoscale pore structures. |
| first_indexed | 2024-03-11T01:36:11Z |
| format | Article |
| id | doaj.art-6dff5b484f4c4913b10cd411015e1589 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-11T01:36:11Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-6dff5b484f4c4913b10cd411015e15892023-11-18T16:58:07ZengMDPI AGMaterials1996-19442023-06-011613466810.3390/ma16134668Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural NetworksDongshuang Li0Shaohua You1Qinzhuo Liao2Gang Lei3Xu Liu4Weiqing Chen5Huijian Li6Bo Liu7Xiaoxi Guo8College of Petroleum Engineering, China University of Petroleum-Beijing, Beijing 102249, ChinaCollege of Petroleum Engineering, China University of Petroleum-Beijing, Beijing 102249, ChinaCollege of Petroleum Engineering, China University of Petroleum-Beijing, Beijing 102249, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaCollege of Petroleum Engineering & Geosciences, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaCollege of Petroleum Engineering & Geosciences, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaCollege of Petroleum Engineering & Geosciences, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaElectrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaState Grid Information & Telecommunication Branch, Beijing 100761, ChinaThe permeability of porous materials determines the fluid flow rate and aids in the prediction of their mechanical properties. This study developed a novel approach that combines the discrete cosine transform (DCT) and artificial neural networks (ANN) for permeability analysis and prediction in digital rock images, focusing on nanoscale porous materials in shale formations. The DCT effectively captured the morphology and spatial distribution of material structure at the nanoscale and enhanced the computational efficiency, which was crucial for handling the complexity and high dimensionality of the digital rock images. The ANN model, trained using the Levenberg–Marquardt algorithm, preserved essential features and demonstrated exceptional accuracy for permeability prediction from the DCT-processed rock images. Our approach offers versatility and efficiency in handling diverse rock samples, from nanoscale shale to microscale sandstone. This work contributes to the comprehension and exploitation of unconventional resources, especially those preserved in nanoscale pore structures.https://www.mdpi.com/1996-1944/16/13/4668permeabilitynanoscaleporous materialdiscrete cosine transformartificial neural network |
| spellingShingle | Dongshuang Li Shaohua You Qinzhuo Liao Gang Lei Xu Liu Weiqing Chen Huijian Li Bo Liu Xiaoxi Guo Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks Materials permeability nanoscale porous material discrete cosine transform artificial neural network |
| title | Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks |
| title_full | Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks |
| title_fullStr | Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks |
| title_full_unstemmed | Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks |
| title_short | Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks |
| title_sort | permeability prediction of nanoscale porous materials using discrete cosine transform based artificial neural networks |
| topic | permeability nanoscale porous material discrete cosine transform artificial neural network |
| url | https://www.mdpi.com/1996-1944/16/13/4668 |
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