The control effect of the pore and fracture characteristics of tectonic coal assisted by the triaxial stress permeability system on permeability
Abstract This study systematically investigates the pore and fracture characteristics of tectonic coal and their controlling effects on permeability, which are critical for advancing coalbed methane (CBM) development and geological engineering applications. To achieve this, a triaxial stress permeab...
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SpringerOpen
2025-04-01
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| Series: | Journal of Petroleum Exploration and Production Technology |
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| Online Access: | https://doi.org/10.1007/s13202-025-01967-z |
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| author | Shi wenfang Ru zhongliang Zhao binbin Lu guoju |
| author_facet | Shi wenfang Ru zhongliang Zhao binbin Lu guoju |
| author_sort | Shi wenfang |
| collection | DOAJ |
| description | Abstract This study systematically investigates the pore and fracture characteristics of tectonic coal and their controlling effects on permeability, which are critical for advancing coalbed methane (CBM) development and geological engineering applications. To achieve this, a triaxial stress permeability system and deep learning (DL) techniques are employed to conduct experiments on tectonic coal samples, enabling the measurement of permeability variations under different stress conditions. Furthermore, high-resolution fracture images are collected, and DL-based methods are utilized for fracture identification and feature extraction. Based on these findings, a permeability prediction model is developed to assess the applicability of DL in predicting the permeability of tectonic coal. The experimental results indicate that, under constant pore pressure, apparent permeability decreases with increasing effective stress. Specifically, when the deviatoric stress is maintained at 50 MPa and the pore pressure at 1 MPa, the apparent permeability declines from 14.846 × 10⁻¹⁸ m² to 8.326 × 10⁻¹⁸ m² as the effective stress increases from 29 MPa to 44 MPa. Moreover, the proposed ShuffleNet V2-Bidirectional Gated Recurrent Unit-Graph Attention Mechanism model demonstrates superior performance compared to existing models, achieving an accuracy of 95.62%, a root mean square error of 4.19, and a mean absolute error of 8.13. These results underscore the model’s effectiveness in accurately predicting the permeability characteristics of tectonic coal, thereby providing valuable technical support for CBM extraction and geological engineering applications. The novelty of this study lies in the integration of the triaxial stress permeability system with DL techniques, which enhances the accuracy of permeability predictions. Additionally, the study offers a novel perspective on fracture characterization in tectonic coal by leveraging the advanced recognition capabilities of DL models, surpassing previous methodologies in the literature. |
| format | Article |
| id | doaj-art-c540c42043e945cc835420b7d958b2d9 |
| institution | DOAJ |
| issn | 2190-0558 2190-0566 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Petroleum Exploration and Production Technology |
| spelling | doaj-art-c540c42043e945cc835420b7d958b2d92025-08-20T03:16:46ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662025-04-0115511810.1007/s13202-025-01967-zThe control effect of the pore and fracture characteristics of tectonic coal assisted by the triaxial stress permeability system on permeabilityShi wenfang0Ru zhongliang1Zhao binbin2Lu guoju3Taiyuan University of Science and Technology, Materials Science and EngineeringTaiyuan University of Science and Technology, Materials Science and EngineeringShanxi Institute of Science and TechnologyShanxi Institute of EnergyAbstract This study systematically investigates the pore and fracture characteristics of tectonic coal and their controlling effects on permeability, which are critical for advancing coalbed methane (CBM) development and geological engineering applications. To achieve this, a triaxial stress permeability system and deep learning (DL) techniques are employed to conduct experiments on tectonic coal samples, enabling the measurement of permeability variations under different stress conditions. Furthermore, high-resolution fracture images are collected, and DL-based methods are utilized for fracture identification and feature extraction. Based on these findings, a permeability prediction model is developed to assess the applicability of DL in predicting the permeability of tectonic coal. The experimental results indicate that, under constant pore pressure, apparent permeability decreases with increasing effective stress. Specifically, when the deviatoric stress is maintained at 50 MPa and the pore pressure at 1 MPa, the apparent permeability declines from 14.846 × 10⁻¹⁸ m² to 8.326 × 10⁻¹⁸ m² as the effective stress increases from 29 MPa to 44 MPa. Moreover, the proposed ShuffleNet V2-Bidirectional Gated Recurrent Unit-Graph Attention Mechanism model demonstrates superior performance compared to existing models, achieving an accuracy of 95.62%, a root mean square error of 4.19, and a mean absolute error of 8.13. These results underscore the model’s effectiveness in accurately predicting the permeability characteristics of tectonic coal, thereby providing valuable technical support for CBM extraction and geological engineering applications. The novelty of this study lies in the integration of the triaxial stress permeability system with DL techniques, which enhances the accuracy of permeability predictions. Additionally, the study offers a novel perspective on fracture characterization in tectonic coal by leveraging the advanced recognition capabilities of DL models, surpassing previous methodologies in the literature.https://doi.org/10.1007/s13202-025-01967-zTectonic coalTriaxial stress permeability systemDeep learningPore and fracture characteristicsPermeability prediction |
| spellingShingle | Shi wenfang Ru zhongliang Zhao binbin Lu guoju The control effect of the pore and fracture characteristics of tectonic coal assisted by the triaxial stress permeability system on permeability Journal of Petroleum Exploration and Production Technology Tectonic coal Triaxial stress permeability system Deep learning Pore and fracture characteristics Permeability prediction |
| title | The control effect of the pore and fracture characteristics of tectonic coal assisted by the triaxial stress permeability system on permeability |
| title_full | The control effect of the pore and fracture characteristics of tectonic coal assisted by the triaxial stress permeability system on permeability |
| title_fullStr | The control effect of the pore and fracture characteristics of tectonic coal assisted by the triaxial stress permeability system on permeability |
| title_full_unstemmed | The control effect of the pore and fracture characteristics of tectonic coal assisted by the triaxial stress permeability system on permeability |
| title_short | The control effect of the pore and fracture characteristics of tectonic coal assisted by the triaxial stress permeability system on permeability |
| title_sort | control effect of the pore and fracture characteristics of tectonic coal assisted by the triaxial stress permeability system on permeability |
| topic | Tectonic coal Triaxial stress permeability system Deep learning Pore and fracture characteristics Permeability prediction |
| url | https://doi.org/10.1007/s13202-025-01967-z |
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