Application of neural architecture search in lithology identification
Abstract Identifying rock types is the essential step in geological exploration because it guides reservoir description and development planning. Conventional methods that rely on empirical correlations or elementary machine learning approaches frequently produce suboptimal outcomes under complex, m...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | Journal of Petroleum Exploration and Production Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s13202-025-02039-y |
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| _version_ | 1849333707533451264 |
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| author | Yuhao Zhang Hanmin Xiao Meng Du Qingjie Liu Jingwei Tao Yongcheng Luo Li Peng Jianbo Tan |
| author_facet | Yuhao Zhang Hanmin Xiao Meng Du Qingjie Liu Jingwei Tao Yongcheng Luo Li Peng Jianbo Tan |
| author_sort | Yuhao Zhang |
| collection | DOAJ |
| description | Abstract Identifying rock types is the essential step in geological exploration because it guides reservoir description and development planning. Conventional methods that rely on empirical correlations or elementary machine learning approaches frequently produce suboptimal outcomes under complex, multidimensional subsurface conditions. Although recent advancements in Artificial Intelligence (AI) have introduced automated approaches, these often exhibit limited adaptability when confronted with intricate well-log data. To address these constraints, the present study proposes an enhanced Neural Architecture Search (NAS) framework featuring an expanded search space that incorporates advanced the deep learning constructs, including one-dimensional deep neural network (DNN), long short-term memory (LSTM), and Transformers. This comprehensive strategy facilitates the automated discovery of specialized network configurations specifically designed for lithology classification. Experimental findings indicate that the NAS-derived model achieves an accuracy of approximately 96% on lithology test data, underscoring its effectiveness in managing heterogeneity within complex formations. Furthermore, the incorporation of Shapley Additive Explanations (SHAP) enhances interpretability by quantifying the contribution of each logging parameter, thereby ensuring consistency with geological reasoning. These results highlight the potential of a geoscience-adaptive NAS methodology for lithology identification by delivering improved performance, greater adaptability, and reduced reliance on exhaustive manual tuning. |
| format | Article |
| id | doaj-art-9d2dfa77d3664c93ace1b320b5853e2c |
| institution | Kabale University |
| issn | 2190-0558 2190-0566 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Petroleum Exploration and Production Technology |
| spelling | doaj-art-9d2dfa77d3664c93ace1b320b5853e2c2025-08-20T03:45:47ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662025-07-0115811910.1007/s13202-025-02039-yApplication of neural architecture search in lithology identificationYuhao Zhang0Hanmin Xiao1Meng Du2Qingjie Liu3Jingwei Tao4Yongcheng Luo5Li Peng6Jianbo Tan7University of Chinese Academy of SciencesUniversity of Chinese Academy of SciencesUniversity of Chinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghai Pukka Information Tech LtdResearch Institute of Tsinghua University in ShenzhenBeijing Ashoka Technology Development Co.Beijing Ashoka Technology Development Co.Abstract Identifying rock types is the essential step in geological exploration because it guides reservoir description and development planning. Conventional methods that rely on empirical correlations or elementary machine learning approaches frequently produce suboptimal outcomes under complex, multidimensional subsurface conditions. Although recent advancements in Artificial Intelligence (AI) have introduced automated approaches, these often exhibit limited adaptability when confronted with intricate well-log data. To address these constraints, the present study proposes an enhanced Neural Architecture Search (NAS) framework featuring an expanded search space that incorporates advanced the deep learning constructs, including one-dimensional deep neural network (DNN), long short-term memory (LSTM), and Transformers. This comprehensive strategy facilitates the automated discovery of specialized network configurations specifically designed for lithology classification. Experimental findings indicate that the NAS-derived model achieves an accuracy of approximately 96% on lithology test data, underscoring its effectiveness in managing heterogeneity within complex formations. Furthermore, the incorporation of Shapley Additive Explanations (SHAP) enhances interpretability by quantifying the contribution of each logging parameter, thereby ensuring consistency with geological reasoning. These results highlight the potential of a geoscience-adaptive NAS methodology for lithology identification by delivering improved performance, greater adaptability, and reduced reliance on exhaustive manual tuning.https://doi.org/10.1007/s13202-025-02039-yNeural architecture searchLithology identificationTight sandstone reservoirsDeep learningSHAP model interpretation |
| spellingShingle | Yuhao Zhang Hanmin Xiao Meng Du Qingjie Liu Jingwei Tao Yongcheng Luo Li Peng Jianbo Tan Application of neural architecture search in lithology identification Journal of Petroleum Exploration and Production Technology Neural architecture search Lithology identification Tight sandstone reservoirs Deep learning SHAP model interpretation |
| title | Application of neural architecture search in lithology identification |
| title_full | Application of neural architecture search in lithology identification |
| title_fullStr | Application of neural architecture search in lithology identification |
| title_full_unstemmed | Application of neural architecture search in lithology identification |
| title_short | Application of neural architecture search in lithology identification |
| title_sort | application of neural architecture search in lithology identification |
| topic | Neural architecture search Lithology identification Tight sandstone reservoirs Deep learning SHAP model interpretation |
| url | https://doi.org/10.1007/s13202-025-02039-y |
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