Intelligent Microfluidics Research on Relative Permeability Measurement and Prediction of Two-Phase Flow in Micropores
Relative permeability is a key index in resource exploitation, energy development, environmental monitoring, and other fields. However, the current determination methods of relative permeability are inefficient and invisible without considering wetting order and pore structure characteristics either...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2021/1194186 |
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| _version_ | 1849305276812886016 |
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| author | Hongqing Song Changchun Liu Junming Lao Jiulong Wang Shuyi Du Mingxu Yu |
| author_facet | Hongqing Song Changchun Liu Junming Lao Jiulong Wang Shuyi Du Mingxu Yu |
| author_sort | Hongqing Song |
| collection | DOAJ |
| description | Relative permeability is a key index in resource exploitation, energy development, environmental monitoring, and other fields. However, the current determination methods of relative permeability are inefficient and invisible without considering wetting order and pore structure characteristics either. In this study, microfluidic experiments were designed for figuring out key factors impacting on the two-phase relative permeability. The optimized intelligent image recognition was established for saturation extraction. The deep learning was conducted for the prediction of two-phase permeability based on the inputs from microfluidic experiments and image recognition and optimized. Results revealed that phase saturation, wetting order, and pore topology were the key factors influencing the two-phase relative permeability, with the importance of 38.22%, 34.84%, and 26.94%, respectively. The deep learning-based relative permeability model performed well, with MSE<0.05 and operational efficiency of 3 ms/epoch. Aiming at relative permeability model optimization, on the one hand, the dividing ratio of training set and testing set for flooding phase relative permeability prediction achieved the highest prediction accuracy at 7 : 3, while that for displaced phase was 6 : 4. On the other hand, tanh() activation function performed 40% more accurate than the sigmoid() activation function. |
| format | Article |
| id | doaj-art-09db06e6b3ff42fcbc5702d29f50e854 |
| institution | Kabale University |
| issn | 1468-8123 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-09db06e6b3ff42fcbc5702d29f50e8542025-08-20T03:55:29ZengWileyGeofluids1468-81232021-01-01202110.1155/2021/1194186Intelligent Microfluidics Research on Relative Permeability Measurement and Prediction of Two-Phase Flow in MicroporesHongqing Song0Changchun Liu1Junming Lao2Jiulong Wang3Shuyi Du4Mingxu Yu5School of Civil and Resource EngineeringSchool of Civil and Resource EngineeringSchool of Civil and Resource EngineeringNational and Local Joint Engineering Laboratory of Big Data Analysis and Computing TechnologySchool of Civil and Resource EngineeringBeilong Zeda (Beijing) Data Technology Co.Relative permeability is a key index in resource exploitation, energy development, environmental monitoring, and other fields. However, the current determination methods of relative permeability are inefficient and invisible without considering wetting order and pore structure characteristics either. In this study, microfluidic experiments were designed for figuring out key factors impacting on the two-phase relative permeability. The optimized intelligent image recognition was established for saturation extraction. The deep learning was conducted for the prediction of two-phase permeability based on the inputs from microfluidic experiments and image recognition and optimized. Results revealed that phase saturation, wetting order, and pore topology were the key factors influencing the two-phase relative permeability, with the importance of 38.22%, 34.84%, and 26.94%, respectively. The deep learning-based relative permeability model performed well, with MSE<0.05 and operational efficiency of 3 ms/epoch. Aiming at relative permeability model optimization, on the one hand, the dividing ratio of training set and testing set for flooding phase relative permeability prediction achieved the highest prediction accuracy at 7 : 3, while that for displaced phase was 6 : 4. On the other hand, tanh() activation function performed 40% more accurate than the sigmoid() activation function.http://dx.doi.org/10.1155/2021/1194186 |
| spellingShingle | Hongqing Song Changchun Liu Junming Lao Jiulong Wang Shuyi Du Mingxu Yu Intelligent Microfluidics Research on Relative Permeability Measurement and Prediction of Two-Phase Flow in Micropores Geofluids |
| title | Intelligent Microfluidics Research on Relative Permeability Measurement and Prediction of Two-Phase Flow in Micropores |
| title_full | Intelligent Microfluidics Research on Relative Permeability Measurement and Prediction of Two-Phase Flow in Micropores |
| title_fullStr | Intelligent Microfluidics Research on Relative Permeability Measurement and Prediction of Two-Phase Flow in Micropores |
| title_full_unstemmed | Intelligent Microfluidics Research on Relative Permeability Measurement and Prediction of Two-Phase Flow in Micropores |
| title_short | Intelligent Microfluidics Research on Relative Permeability Measurement and Prediction of Two-Phase Flow in Micropores |
| title_sort | intelligent microfluidics research on relative permeability measurement and prediction of two phase flow in micropores |
| url | http://dx.doi.org/10.1155/2021/1194186 |
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