A Real-Time Oil Content Analysis Method of Cuttings Based on Deep Learning
In the oil exploration site, logging technology is usually used to obtain geological data, and the obtained cuttings are analyzed by manual identification. Due to the different experience of logging personnel and their different cognition of the results, only qualitative analysis can be conducted. I...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9989377/ |
| _version_ | 1797978279325466624 |
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| author | Yongzhuang Zhang Baoxi Yuan Yuqian Wang Feng Wang Jianxin Guo |
| author_facet | Yongzhuang Zhang Baoxi Yuan Yuqian Wang Feng Wang Jianxin Guo |
| author_sort | Yongzhuang Zhang |
| collection | DOAJ |
| description | In the oil exploration site, logging technology is usually used to obtain geological data, and the obtained cuttings are analyzed by manual identification. Due to the different experience of logging personnel and their different cognition of the results, only qualitative analysis can be conducted. In this paper, a real-time analysis method of oil content in cuttings based on deep learning is proposed. Firstly, the cuttings image is collected under the fluorescent lamp, and then the semantic segmentation network MobileNetV3 UNet (M3-UNet) is used to segment the oil-bearing region of cuttings image automatically. Finally, the real-time quantitative analysis of oil content in cuttings is realized. In order to make the proposed algorithm be applied to the oil exploration site, the proposed algorithm is deployed on the Jetson edge calculation equipment to realize the portable oil content in cuttings analysis equipment. In the coding stage of the proposed M3-UNet, MobileNetV3 is used as the backbone network to extract the features from the input cuttings image, which can reduce the requirements on the performance of logging equipment while maintaining the segmentation accuracy. The experimental results show that the Intersection over Union (IoU), F1-score and Pixel Accuracy (PA) of M3-UNet segmentation in the test dataset are all more than 9% higher than U-Net and MobileNetV2 UNet (M2-UNet). The segmentation speed of the cuttings image on Jetson equipment reaches 21FPS, meeting the requirements of real-time analysis of oil content of the cuttings image on the logging site. |
| first_indexed | 2024-04-11T05:21:33Z |
| format | Article |
| id | doaj.art-4a641938bb4640b29255a284c46d950b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T05:21:33Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4a641938bb4640b29255a284c46d950b2022-12-24T00:00:47ZengIEEEIEEE Access2169-35362022-01-011013208313209410.1109/ACCESS.2022.32297609989377A Real-Time Oil Content Analysis Method of Cuttings Based on Deep LearningYongzhuang Zhang0https://orcid.org/0000-0001-8274-6313Baoxi Yuan1https://orcid.org/0000-0002-5220-879XYuqian Wang2https://orcid.org/0000-0003-0992-6531Feng Wang3Jianxin Guo4School of Electronic Information, Xijing University, Xi’an, ChinaSchool of Electronic Information, Xijing University, Xi’an, ChinaGraduate Office, Xijing University, Xi’an, ChinaSchool of Electronic Information, Xijing University, Xi’an, ChinaSchool of Electronic Information, Xijing University, Xi’an, ChinaIn the oil exploration site, logging technology is usually used to obtain geological data, and the obtained cuttings are analyzed by manual identification. Due to the different experience of logging personnel and their different cognition of the results, only qualitative analysis can be conducted. In this paper, a real-time analysis method of oil content in cuttings based on deep learning is proposed. Firstly, the cuttings image is collected under the fluorescent lamp, and then the semantic segmentation network MobileNetV3 UNet (M3-UNet) is used to segment the oil-bearing region of cuttings image automatically. Finally, the real-time quantitative analysis of oil content in cuttings is realized. In order to make the proposed algorithm be applied to the oil exploration site, the proposed algorithm is deployed on the Jetson edge calculation equipment to realize the portable oil content in cuttings analysis equipment. In the coding stage of the proposed M3-UNet, MobileNetV3 is used as the backbone network to extract the features from the input cuttings image, which can reduce the requirements on the performance of logging equipment while maintaining the segmentation accuracy. The experimental results show that the Intersection over Union (IoU), F1-score and Pixel Accuracy (PA) of M3-UNet segmentation in the test dataset are all more than 9% higher than U-Net and MobileNetV2 UNet (M2-UNet). The segmentation speed of the cuttings image on Jetson equipment reaches 21FPS, meeting the requirements of real-time analysis of oil content of the cuttings image on the logging site.https://ieeexplore.ieee.org/document/9989377/Deep learningoil explorationU-Netcuttings imageimage segmentation |
| spellingShingle | Yongzhuang Zhang Baoxi Yuan Yuqian Wang Feng Wang Jianxin Guo A Real-Time Oil Content Analysis Method of Cuttings Based on Deep Learning IEEE Access Deep learning oil exploration U-Net cuttings image image segmentation |
| title | A Real-Time Oil Content Analysis Method of Cuttings Based on Deep Learning |
| title_full | A Real-Time Oil Content Analysis Method of Cuttings Based on Deep Learning |
| title_fullStr | A Real-Time Oil Content Analysis Method of Cuttings Based on Deep Learning |
| title_full_unstemmed | A Real-Time Oil Content Analysis Method of Cuttings Based on Deep Learning |
| title_short | A Real-Time Oil Content Analysis Method of Cuttings Based on Deep Learning |
| title_sort | real time oil content analysis method of cuttings based on deep learning |
| topic | Deep learning oil exploration U-Net cuttings image image segmentation |
| url | https://ieeexplore.ieee.org/document/9989377/ |
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