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Prediction of Thermal Breakthrough and Parameter Optimization in Geothermal Reinjection Systems Based on Deep Neural Networks: A Case Study of the Qihe Geothermal Field.

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Title: Prediction of Thermal Breakthrough and Parameter Optimization in Geothermal Reinjection Systems Based on Deep Neural Networks: A Case Study of the Qihe Geothermal Field.
Authors: Du, Li1,2, Li, Kefu2,3, likefu553@163.com, Liu, Fuchun1,2,3, Cui, Long4, Jia, Yanyu1,2, Zhu, Chuanqing2,3,4, Zheng, Fuhao3, Zhang, Ze3,4
Source: Applied Sciences (2076-3417); Jul2026, Vol. 16 Issue 13, p6291, 20p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 195445516
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Prediction of Thermal Breakthrough and Parameter Optimization in Geothermal Reinjection Systems Based on Deep Neural Networks: A Case Study of the Qihe Geothermal Field.
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.3390/app16136291
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      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 6291
    Titles:
      – TitleFull: Prediction of Thermal Breakthrough and Parameter Optimization in Geothermal Reinjection Systems Based on Deep Neural Networks: A Case Study of the Qihe Geothermal Field.
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            NameFull: Du, Li
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            NameFull: Li, Kefu
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            NameFull: Liu, Fuchun
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            NameFull: Cui, Long
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            NameFull: Jia, Yanyu
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            NameFull: Zhu, Chuanqing
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            NameFull: Zheng, Fuhao
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            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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              Value: 16
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              Value: 13
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            – TitleFull: Applied Sciences (2076-3417)
              Type: main
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