A Small-Sample Borehole Fluvial Facies Identification Method Using Generative Adversarial Networks in the Context of Gas-Fired Power Generation, with the Hangjinqi Gas Field in the Ordos Basin as an Example
Natural gas power generation has the advantages of flexible operation, short start–stop times, and fast ramp rates. It has a strong peaking capacity and speed compared to coal power generation, and can greatly reduce emissions of harmful substances such as sulphur dioxide. However, in practice, the...
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MDPI AG
2023-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/3/1361 |
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| author | Yong Liu Qingjie Xu Xingrui Li Weiwen Zhan Jingkai Guo Jun Xiao |
| author_facet | Yong Liu Qingjie Xu Xingrui Li Weiwen Zhan Jingkai Guo Jun Xiao |
| author_sort | Yong Liu |
| collection | DOAJ |
| description | Natural gas power generation has the advantages of flexible operation, short start–stop times, and fast ramp rates. It has a strong peaking capacity and speed compared to coal power generation, and can greatly reduce emissions of harmful substances such as sulphur dioxide. However, in practice, the accurate identification of borehole fluvial facies in the exploration area is one of the most important conditions affecting the success of gas field exploration. An insufficient number of drilling points in the exploration area and the accurate identification of lithological data features are key to the correct identification of borehole fluvial facies, and understanding how to achieve accurate identification of borehole fluvial facies when there are insufficient training data is the focus and challenge of research within the field of natural gas energy exploration. This paper proposes a borehole fluvial facies identification method applicable to the sparse sample size of drilling points, using the Sulige gas field in the Ordos Basin of China as the research object, with the drilling lithology data in the field as the sample data and the data augmentation and classification of the images through generative adversarial networks. The trained model was then validated on the Hangjinqi gas field with the same geological properties. Finally, this paper compares the recognition accuracy of borehole fluvial facies with that of other deep learning algorithms. It was verified that this research method can be applied to oil and gas exploration areas where the number of wells drilled is small and there are limited data, and that this method achieves accurate identification of borehole fluvial facies in the exploration area, which can help to improve the efficiency of oil and gas resources drilling identification to ensure the healthy development of the power and energy industry. |
| first_indexed | 2024-03-11T09:46:33Z |
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| id | doaj.art-ef94db0fd214411cb959497c410a651b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T09:46:33Z |
| publishDate | 2023-01-01 |
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| record_format | Article |
| series | Energies |
| spelling | doaj.art-ef94db0fd214411cb959497c410a651b2023-11-16T16:36:24ZengMDPI AGEnergies1996-10732023-01-01163136110.3390/en16031361A Small-Sample Borehole Fluvial Facies Identification Method Using Generative Adversarial Networks in the Context of Gas-Fired Power Generation, with the Hangjinqi Gas Field in the Ordos Basin as an ExampleYong Liu0Qingjie Xu1Xingrui Li2Weiwen Zhan3Jingkai Guo4Jun Xiao5School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, ChinaCollege of Marine Science and Technology, China University of Geosciences (Wuhan), Wuhan 430074, ChinaNatural gas power generation has the advantages of flexible operation, short start–stop times, and fast ramp rates. It has a strong peaking capacity and speed compared to coal power generation, and can greatly reduce emissions of harmful substances such as sulphur dioxide. However, in practice, the accurate identification of borehole fluvial facies in the exploration area is one of the most important conditions affecting the success of gas field exploration. An insufficient number of drilling points in the exploration area and the accurate identification of lithological data features are key to the correct identification of borehole fluvial facies, and understanding how to achieve accurate identification of borehole fluvial facies when there are insufficient training data is the focus and challenge of research within the field of natural gas energy exploration. This paper proposes a borehole fluvial facies identification method applicable to the sparse sample size of drilling points, using the Sulige gas field in the Ordos Basin of China as the research object, with the drilling lithology data in the field as the sample data and the data augmentation and classification of the images through generative adversarial networks. The trained model was then validated on the Hangjinqi gas field with the same geological properties. Finally, this paper compares the recognition accuracy of borehole fluvial facies with that of other deep learning algorithms. It was verified that this research method can be applied to oil and gas exploration areas where the number of wells drilled is small and there are limited data, and that this method achieves accurate identification of borehole fluvial facies in the exploration area, which can help to improve the efficiency of oil and gas resources drilling identification to ensure the healthy development of the power and energy industry.https://www.mdpi.com/1996-1073/16/3/1361borehole fluvial faciesgenerative adversarial networkssulige gas fieldHangjinqi gas field |
| spellingShingle | Yong Liu Qingjie Xu Xingrui Li Weiwen Zhan Jingkai Guo Jun Xiao A Small-Sample Borehole Fluvial Facies Identification Method Using Generative Adversarial Networks in the Context of Gas-Fired Power Generation, with the Hangjinqi Gas Field in the Ordos Basin as an Example Energies borehole fluvial facies generative adversarial networks sulige gas field Hangjinqi gas field |
| title | A Small-Sample Borehole Fluvial Facies Identification Method Using Generative Adversarial Networks in the Context of Gas-Fired Power Generation, with the Hangjinqi Gas Field in the Ordos Basin as an Example |
| title_full | A Small-Sample Borehole Fluvial Facies Identification Method Using Generative Adversarial Networks in the Context of Gas-Fired Power Generation, with the Hangjinqi Gas Field in the Ordos Basin as an Example |
| title_fullStr | A Small-Sample Borehole Fluvial Facies Identification Method Using Generative Adversarial Networks in the Context of Gas-Fired Power Generation, with the Hangjinqi Gas Field in the Ordos Basin as an Example |
| title_full_unstemmed | A Small-Sample Borehole Fluvial Facies Identification Method Using Generative Adversarial Networks in the Context of Gas-Fired Power Generation, with the Hangjinqi Gas Field in the Ordos Basin as an Example |
| title_short | A Small-Sample Borehole Fluvial Facies Identification Method Using Generative Adversarial Networks in the Context of Gas-Fired Power Generation, with the Hangjinqi Gas Field in the Ordos Basin as an Example |
| title_sort | small sample borehole fluvial facies identification method using generative adversarial networks in the context of gas fired power generation with the hangjinqi gas field in the ordos basin as an example |
| topic | borehole fluvial facies generative adversarial networks sulige gas field Hangjinqi gas field |
| url | https://www.mdpi.com/1996-1073/16/3/1361 |
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