Application of sparry grain limestone petrographic analysis combining image processing and deep learning
Traditional carbonate rock slice identification is based on manual observation and description, which is highly subjective, mainly qualitative and difficult to quantify. In this paper, an intelligent rock thin-section image information mining model covering the process and technology is designed. Th...
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| Autor*in: |
Xiaolu YU [verfasserIn] Longlong LI [verfasserIn] Hong JIANG [verfasserIn] Longfei LU [verfasserIn] Chongjiao DU [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Chinesisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Shiyou shiyan dizhi - Editorial Office of Petroleum Geology and Experiment, 2024, 45(2023), 5, Seite 1026-1038 |
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| Übergeordnetes Werk: |
volume:45 ; year:2023 ; number:5 ; pages:1026-1038 |
| Links: |
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| DOI / URN: |
10.11781/sysydz2023051026 |
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| Katalog-ID: |
DOAJ095559388 |
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| 520 | |a Traditional carbonate rock slice identification is based on manual observation and description, which is highly subjective, mainly qualitative and difficult to quantify. In this paper, an intelligent rock thin-section image information mining model covering the process and technology is designed. The mapping relationship between lithofacies characteristics and thin slice images is constructed through a petrographic analysis framework. A full-flow feature extraction algorithm is also designed by combing image processing and deep learning. Qualitative recognition of grain type in structural component features is obtained by convolutional neural network, that is, the classification of grains (intraclasts, bioclasts, envelopes, spherulites and agglomerates) based on the improved ResNet50 model. Quantitative recognition of grain content, size, shape and contact mode in structural component features are obtained by digital image processing, that is, grain content is calculated based on threshold segmentation, grain morphology parameters are calculated based on minimum peripheral circle/minimum peripheral rectangle and by combining with area ratio/aspect ratio, intersection ratio (IoU) and other algorithms. And the qualitative and quantitative identification of calcite and other minerals in mineral component features are obtained by HSV colour space processing of the stained images. A thin section example from Shun X well is given as an example, and the validity of each feature extraction algorithm is verified through the complete image recognition process and comparison with the manual identification report. The results show that the petrographic analysis framework is effective in representing meaningful information in sparry grain limestone. Through the model of combing petrographic analysis framework with image analysis algorithm, a standard and intelligent identification of this type of carbonate rocks has been achieved, which can provide effective method support for the study of intelligent identification of rock slice images. | ||
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10.11781/sysydz2023051026 doi (DE-627)DOAJ095559388 (DE-599)DOAJ908ee6231d1e40458236966ef4fda8c8 DE-627 ger DE-627 rakwb chi QC801-809 QE1-996.5 Xiaolu YU verfasserin aut Application of sparry grain limestone petrographic analysis combining image processing and deep learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Traditional carbonate rock slice identification is based on manual observation and description, which is highly subjective, mainly qualitative and difficult to quantify. In this paper, an intelligent rock thin-section image information mining model covering the process and technology is designed. The mapping relationship between lithofacies characteristics and thin slice images is constructed through a petrographic analysis framework. A full-flow feature extraction algorithm is also designed by combing image processing and deep learning. Qualitative recognition of grain type in structural component features is obtained by convolutional neural network, that is, the classification of grains (intraclasts, bioclasts, envelopes, spherulites and agglomerates) based on the improved ResNet50 model. Quantitative recognition of grain content, size, shape and contact mode in structural component features are obtained by digital image processing, that is, grain content is calculated based on threshold segmentation, grain morphology parameters are calculated based on minimum peripheral circle/minimum peripheral rectangle and by combining with area ratio/aspect ratio, intersection ratio (IoU) and other algorithms. And the qualitative and quantitative identification of calcite and other minerals in mineral component features are obtained by HSV colour space processing of the stained images. A thin section example from Shun X well is given as an example, and the validity of each feature extraction algorithm is verified through the complete image recognition process and comparison with the manual identification report. The results show that the petrographic analysis framework is effective in representing meaningful information in sparry grain limestone. Through the model of combing petrographic analysis framework with image analysis algorithm, a standard and intelligent identification of this type of carbonate rocks has been achieved, which can provide effective method support for the study of intelligent identification of rock slice images. carbonate rock petrography convolutional neural network deep learning artificial intelligence Geophysics. Cosmic physics Geology Longlong LI verfasserin aut Hong JIANG verfasserin aut Longfei LU verfasserin aut Chongjiao DU verfasserin aut In Shiyou shiyan dizhi Editorial Office of Petroleum Geology and Experiment, 2024 45(2023), 5, Seite 1026-1038 (DE-627)1681607743 (DE-600)2999009-9 10016112 nnns volume:45 year:2023 number:5 pages:1026-1038 https://doi.org/10.11781/sysydz2023051026 kostenfrei https://doaj.org/article/908ee6231d1e40458236966ef4fda8c8 kostenfrei https://www.sysydz.net/cn/article/doi/10.11781/sysydz2023051026 kostenfrei https://doaj.org/toc/1001-6112 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 45 2023 5 1026-1038 |
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10.11781/sysydz2023051026 doi (DE-627)DOAJ095559388 (DE-599)DOAJ908ee6231d1e40458236966ef4fda8c8 DE-627 ger DE-627 rakwb chi QC801-809 QE1-996.5 Xiaolu YU verfasserin aut Application of sparry grain limestone petrographic analysis combining image processing and deep learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Traditional carbonate rock slice identification is based on manual observation and description, which is highly subjective, mainly qualitative and difficult to quantify. In this paper, an intelligent rock thin-section image information mining model covering the process and technology is designed. The mapping relationship between lithofacies characteristics and thin slice images is constructed through a petrographic analysis framework. A full-flow feature extraction algorithm is also designed by combing image processing and deep learning. Qualitative recognition of grain type in structural component features is obtained by convolutional neural network, that is, the classification of grains (intraclasts, bioclasts, envelopes, spherulites and agglomerates) based on the improved ResNet50 model. Quantitative recognition of grain content, size, shape and contact mode in structural component features are obtained by digital image processing, that is, grain content is calculated based on threshold segmentation, grain morphology parameters are calculated based on minimum peripheral circle/minimum peripheral rectangle and by combining with area ratio/aspect ratio, intersection ratio (IoU) and other algorithms. And the qualitative and quantitative identification of calcite and other minerals in mineral component features are obtained by HSV colour space processing of the stained images. A thin section example from Shun X well is given as an example, and the validity of each feature extraction algorithm is verified through the complete image recognition process and comparison with the manual identification report. The results show that the petrographic analysis framework is effective in representing meaningful information in sparry grain limestone. Through the model of combing petrographic analysis framework with image analysis algorithm, a standard and intelligent identification of this type of carbonate rocks has been achieved, which can provide effective method support for the study of intelligent identification of rock slice images. carbonate rock petrography convolutional neural network deep learning artificial intelligence Geophysics. Cosmic physics Geology Longlong LI verfasserin aut Hong JIANG verfasserin aut Longfei LU verfasserin aut Chongjiao DU verfasserin aut In Shiyou shiyan dizhi Editorial Office of Petroleum Geology and Experiment, 2024 45(2023), 5, Seite 1026-1038 (DE-627)1681607743 (DE-600)2999009-9 10016112 nnns volume:45 year:2023 number:5 pages:1026-1038 https://doi.org/10.11781/sysydz2023051026 kostenfrei https://doaj.org/article/908ee6231d1e40458236966ef4fda8c8 kostenfrei https://www.sysydz.net/cn/article/doi/10.11781/sysydz2023051026 kostenfrei https://doaj.org/toc/1001-6112 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 45 2023 5 1026-1038 |
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10.11781/sysydz2023051026 doi (DE-627)DOAJ095559388 (DE-599)DOAJ908ee6231d1e40458236966ef4fda8c8 DE-627 ger DE-627 rakwb chi QC801-809 QE1-996.5 Xiaolu YU verfasserin aut Application of sparry grain limestone petrographic analysis combining image processing and deep learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Traditional carbonate rock slice identification is based on manual observation and description, which is highly subjective, mainly qualitative and difficult to quantify. In this paper, an intelligent rock thin-section image information mining model covering the process and technology is designed. The mapping relationship between lithofacies characteristics and thin slice images is constructed through a petrographic analysis framework. A full-flow feature extraction algorithm is also designed by combing image processing and deep learning. Qualitative recognition of grain type in structural component features is obtained by convolutional neural network, that is, the classification of grains (intraclasts, bioclasts, envelopes, spherulites and agglomerates) based on the improved ResNet50 model. Quantitative recognition of grain content, size, shape and contact mode in structural component features are obtained by digital image processing, that is, grain content is calculated based on threshold segmentation, grain morphology parameters are calculated based on minimum peripheral circle/minimum peripheral rectangle and by combining with area ratio/aspect ratio, intersection ratio (IoU) and other algorithms. And the qualitative and quantitative identification of calcite and other minerals in mineral component features are obtained by HSV colour space processing of the stained images. A thin section example from Shun X well is given as an example, and the validity of each feature extraction algorithm is verified through the complete image recognition process and comparison with the manual identification report. The results show that the petrographic analysis framework is effective in representing meaningful information in sparry grain limestone. Through the model of combing petrographic analysis framework with image analysis algorithm, a standard and intelligent identification of this type of carbonate rocks has been achieved, which can provide effective method support for the study of intelligent identification of rock slice images. carbonate rock petrography convolutional neural network deep learning artificial intelligence Geophysics. Cosmic physics Geology Longlong LI verfasserin aut Hong JIANG verfasserin aut Longfei LU verfasserin aut Chongjiao DU verfasserin aut In Shiyou shiyan dizhi Editorial Office of Petroleum Geology and Experiment, 2024 45(2023), 5, Seite 1026-1038 (DE-627)1681607743 (DE-600)2999009-9 10016112 nnns volume:45 year:2023 number:5 pages:1026-1038 https://doi.org/10.11781/sysydz2023051026 kostenfrei https://doaj.org/article/908ee6231d1e40458236966ef4fda8c8 kostenfrei https://www.sysydz.net/cn/article/doi/10.11781/sysydz2023051026 kostenfrei https://doaj.org/toc/1001-6112 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 45 2023 5 1026-1038 |
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10.11781/sysydz2023051026 doi (DE-627)DOAJ095559388 (DE-599)DOAJ908ee6231d1e40458236966ef4fda8c8 DE-627 ger DE-627 rakwb chi QC801-809 QE1-996.5 Xiaolu YU verfasserin aut Application of sparry grain limestone petrographic analysis combining image processing and deep learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Traditional carbonate rock slice identification is based on manual observation and description, which is highly subjective, mainly qualitative and difficult to quantify. In this paper, an intelligent rock thin-section image information mining model covering the process and technology is designed. The mapping relationship between lithofacies characteristics and thin slice images is constructed through a petrographic analysis framework. A full-flow feature extraction algorithm is also designed by combing image processing and deep learning. Qualitative recognition of grain type in structural component features is obtained by convolutional neural network, that is, the classification of grains (intraclasts, bioclasts, envelopes, spherulites and agglomerates) based on the improved ResNet50 model. Quantitative recognition of grain content, size, shape and contact mode in structural component features are obtained by digital image processing, that is, grain content is calculated based on threshold segmentation, grain morphology parameters are calculated based on minimum peripheral circle/minimum peripheral rectangle and by combining with area ratio/aspect ratio, intersection ratio (IoU) and other algorithms. And the qualitative and quantitative identification of calcite and other minerals in mineral component features are obtained by HSV colour space processing of the stained images. A thin section example from Shun X well is given as an example, and the validity of each feature extraction algorithm is verified through the complete image recognition process and comparison with the manual identification report. The results show that the petrographic analysis framework is effective in representing meaningful information in sparry grain limestone. Through the model of combing petrographic analysis framework with image analysis algorithm, a standard and intelligent identification of this type of carbonate rocks has been achieved, which can provide effective method support for the study of intelligent identification of rock slice images. carbonate rock petrography convolutional neural network deep learning artificial intelligence Geophysics. Cosmic physics Geology Longlong LI verfasserin aut Hong JIANG verfasserin aut Longfei LU verfasserin aut Chongjiao DU verfasserin aut In Shiyou shiyan dizhi Editorial Office of Petroleum Geology and Experiment, 2024 45(2023), 5, Seite 1026-1038 (DE-627)1681607743 (DE-600)2999009-9 10016112 nnns volume:45 year:2023 number:5 pages:1026-1038 https://doi.org/10.11781/sysydz2023051026 kostenfrei https://doaj.org/article/908ee6231d1e40458236966ef4fda8c8 kostenfrei https://www.sysydz.net/cn/article/doi/10.11781/sysydz2023051026 kostenfrei https://doaj.org/toc/1001-6112 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 45 2023 5 1026-1038 |
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Quantitative recognition of grain content, size, shape and contact mode in structural component features are obtained by digital image processing, that is, grain content is calculated based on threshold segmentation, grain morphology parameters are calculated based on minimum peripheral circle/minimum peripheral rectangle and by combining with area ratio/aspect ratio, intersection ratio (IoU) and other algorithms. And the qualitative and quantitative identification of calcite and other minerals in mineral component features are obtained by HSV colour space processing of the stained images. A thin section example from Shun X well is given as an example, and the validity of each feature extraction algorithm is verified through the complete image recognition process and comparison with the manual identification report. The results show that the petrographic analysis framework is effective in representing meaningful information in sparry grain limestone. 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Application of sparry grain limestone petrographic analysis combining image processing and deep learning |
| abstract |
Traditional carbonate rock slice identification is based on manual observation and description, which is highly subjective, mainly qualitative and difficult to quantify. In this paper, an intelligent rock thin-section image information mining model covering the process and technology is designed. The mapping relationship between lithofacies characteristics and thin slice images is constructed through a petrographic analysis framework. A full-flow feature extraction algorithm is also designed by combing image processing and deep learning. Qualitative recognition of grain type in structural component features is obtained by convolutional neural network, that is, the classification of grains (intraclasts, bioclasts, envelopes, spherulites and agglomerates) based on the improved ResNet50 model. Quantitative recognition of grain content, size, shape and contact mode in structural component features are obtained by digital image processing, that is, grain content is calculated based on threshold segmentation, grain morphology parameters are calculated based on minimum peripheral circle/minimum peripheral rectangle and by combining with area ratio/aspect ratio, intersection ratio (IoU) and other algorithms. And the qualitative and quantitative identification of calcite and other minerals in mineral component features are obtained by HSV colour space processing of the stained images. A thin section example from Shun X well is given as an example, and the validity of each feature extraction algorithm is verified through the complete image recognition process and comparison with the manual identification report. The results show that the petrographic analysis framework is effective in representing meaningful information in sparry grain limestone. Through the model of combing petrographic analysis framework with image analysis algorithm, a standard and intelligent identification of this type of carbonate rocks has been achieved, which can provide effective method support for the study of intelligent identification of rock slice images. |
| abstractGer |
Traditional carbonate rock slice identification is based on manual observation and description, which is highly subjective, mainly qualitative and difficult to quantify. In this paper, an intelligent rock thin-section image information mining model covering the process and technology is designed. The mapping relationship between lithofacies characteristics and thin slice images is constructed through a petrographic analysis framework. A full-flow feature extraction algorithm is also designed by combing image processing and deep learning. Qualitative recognition of grain type in structural component features is obtained by convolutional neural network, that is, the classification of grains (intraclasts, bioclasts, envelopes, spherulites and agglomerates) based on the improved ResNet50 model. Quantitative recognition of grain content, size, shape and contact mode in structural component features are obtained by digital image processing, that is, grain content is calculated based on threshold segmentation, grain morphology parameters are calculated based on minimum peripheral circle/minimum peripheral rectangle and by combining with area ratio/aspect ratio, intersection ratio (IoU) and other algorithms. And the qualitative and quantitative identification of calcite and other minerals in mineral component features are obtained by HSV colour space processing of the stained images. A thin section example from Shun X well is given as an example, and the validity of each feature extraction algorithm is verified through the complete image recognition process and comparison with the manual identification report. The results show that the petrographic analysis framework is effective in representing meaningful information in sparry grain limestone. Through the model of combing petrographic analysis framework with image analysis algorithm, a standard and intelligent identification of this type of carbonate rocks has been achieved, which can provide effective method support for the study of intelligent identification of rock slice images. |
| abstract_unstemmed |
Traditional carbonate rock slice identification is based on manual observation and description, which is highly subjective, mainly qualitative and difficult to quantify. In this paper, an intelligent rock thin-section image information mining model covering the process and technology is designed. The mapping relationship between lithofacies characteristics and thin slice images is constructed through a petrographic analysis framework. A full-flow feature extraction algorithm is also designed by combing image processing and deep learning. Qualitative recognition of grain type in structural component features is obtained by convolutional neural network, that is, the classification of grains (intraclasts, bioclasts, envelopes, spherulites and agglomerates) based on the improved ResNet50 model. Quantitative recognition of grain content, size, shape and contact mode in structural component features are obtained by digital image processing, that is, grain content is calculated based on threshold segmentation, grain morphology parameters are calculated based on minimum peripheral circle/minimum peripheral rectangle and by combining with area ratio/aspect ratio, intersection ratio (IoU) and other algorithms. And the qualitative and quantitative identification of calcite and other minerals in mineral component features are obtained by HSV colour space processing of the stained images. A thin section example from Shun X well is given as an example, and the validity of each feature extraction algorithm is verified through the complete image recognition process and comparison with the manual identification report. The results show that the petrographic analysis framework is effective in representing meaningful information in sparry grain limestone. Through the model of combing petrographic analysis framework with image analysis algorithm, a standard and intelligent identification of this type of carbonate rocks has been achieved, which can provide effective method support for the study of intelligent identification of rock slice images. |
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