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

Yong Liu, Qingjie Xu, Xingrui Li, Weiwen Zhan, Jingkai Guo and Jun Xiao ()
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Yong Liu: School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, China
Qingjie Xu: School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, China
Xingrui Li: School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, China
Weiwen Zhan: School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, China
Jingkai Guo: School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, China
Jun Xiao: College of Marine Science and Technology, China University of Geosciences (Wuhan), Wuhan 430074, China

Energies, 2023, vol. 16, issue 3, 1-15

Abstract: 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.

Keywords: borehole fluvial facies; generative adversarial networks; sulige gas field; Hangjinqi gas field (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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