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Evolution of Coal Microfracture by Cyclic Fracturing of Liquid Nitrogen Based on μCT and Convolutional Neural Networks.

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Title: Evolution of Coal Microfracture by Cyclic Fracturing of Liquid Nitrogen Based on μCT and Convolutional Neural Networks.
Authors: Chen, Shuai1,2 (AUTHOR), Dou, Linming1,2 (AUTHOR) lmdou@126.com, Cai, Wu1,2 (AUTHOR) caiwu@cumt.edu.cn, Zhang, Lei1,2 (AUTHOR), Tian, Miaomiao3 (AUTHOR), Han, Zepeng2 (AUTHOR)
Source: Rock Mechanics & Rock Engineering. Mar2024, Vol. 57 Issue 3, p2103-2124. 22p.
Subjects: Convolutional neural networks, Greenhouse gas mitigation, Liquid nitrogen, Coal mining safety, Geometric distribution
Abstract: Coalbed methane (CBM) is an important unconventional fuel source, and its efficient extraction is of great significance in reducing greenhouse gas emissions, energy requirements, and coal mine safety. Liquid nitrogen (LN2) fracturing is a popular super-cryogenic waterless fracturing method used for enhancing CBM recovery processes. Understanding the behavior of coal fractures under LN2 cyclic fracturing is crucial for revealing the fracture mechanism. In this study, we applied micro-computed tomography (μCT) and the deep learning method to quantify the evolution of volume size, spatial distribution, connectivity, and thickness of 3D fractures in coal under LN2 cyclic fracturing. In addition, the spatial topological and geometric distribution of 3D fractures were further characterized by the pore network model (PNM). A coal microfracture segmentation method from a 2D U-Net model to a 3D U-Net model was proposed to segment fractures automatically and accurately with an average Dice coefficient of 0.942. The results show that LN2 treatment can effectively damage the coal sample and promote the expansion and formation of fractures. The porosity, fracture connectivity, and thickness increase with the number of LN2 cycles, and the increase in the first cycle is significantly higher than in the subsequent cycles. The PNM analysis indicates that the number and equivalent diameter of pores and throats, as well as the coordination numbers, increase with the cycles while the average throat length decreases. Furthermore, the increase in the size of fractures and the formation of large fractures would greatly reduce the P-wave velocity and weaken the uniaxial compressive strength, which decreases by 26.5% and 73.5% after four LN2 fracturing cycles, respectively. Finally, the mechanism of LN2 cyclic fracturing is discussed based on experimental results. The findings of this study provide a deeper understanding of the application of LN2 cyclic fracturing in CBM reservoir recovery. Highlights: The coal microstructure is precisely extracted using a combination method of μCT scanning and 3D U-Net. The evolution of coal 3D microstructure under LN2 cyclic fracturing is visualized and quantitatively analyzed. LN2 cyclic fracturing has a higher efficiency on CT slices with small initial porosity. The effect of fracture size on coal P-wave and mechanical properties was revealed. [ABSTRACT FROM AUTHOR]
Copyright of Rock Mechanics & Rock Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Evolution of Coal Microfracture by Cyclic Fracturing of Liquid Nitrogen Based on μCT and Convolutional Neural Networks.
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  Data: <searchLink fieldCode="JN" term="%22Rock+Mechanics+%26+Rock+Engineering%22">Rock Mechanics & Rock Engineering</searchLink>. Mar2024, Vol. 57 Issue 3, p2103-2124. 22p.
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  Data: Coalbed methane (CBM) is an important unconventional fuel source, and its efficient extraction is of great significance in reducing greenhouse gas emissions, energy requirements, and coal mine safety. Liquid nitrogen (LN2) fracturing is a popular super-cryogenic waterless fracturing method used for enhancing CBM recovery processes. Understanding the behavior of coal fractures under LN2 cyclic fracturing is crucial for revealing the fracture mechanism. In this study, we applied micro-computed tomography (μCT) and the deep learning method to quantify the evolution of volume size, spatial distribution, connectivity, and thickness of 3D fractures in coal under LN2 cyclic fracturing. In addition, the spatial topological and geometric distribution of 3D fractures were further characterized by the pore network model (PNM). A coal microfracture segmentation method from a 2D U-Net model to a 3D U-Net model was proposed to segment fractures automatically and accurately with an average Dice coefficient of 0.942. The results show that LN2 treatment can effectively damage the coal sample and promote the expansion and formation of fractures. The porosity, fracture connectivity, and thickness increase with the number of LN2 cycles, and the increase in the first cycle is significantly higher than in the subsequent cycles. The PNM analysis indicates that the number and equivalent diameter of pores and throats, as well as the coordination numbers, increase with the cycles while the average throat length decreases. Furthermore, the increase in the size of fractures and the formation of large fractures would greatly reduce the P-wave velocity and weaken the uniaxial compressive strength, which decreases by 26.5% and 73.5% after four LN2 fracturing cycles, respectively. Finally, the mechanism of LN2 cyclic fracturing is discussed based on experimental results. The findings of this study provide a deeper understanding of the application of LN2 cyclic fracturing in CBM reservoir recovery. Highlights: The coal microstructure is precisely extracted using a combination method of μCT scanning and 3D U-Net. The evolution of coal 3D microstructure under LN2 cyclic fracturing is visualized and quantitatively analyzed. LN2 cyclic fracturing has a higher efficiency on CT slices with small initial porosity. The effect of fracture size on coal P-wave and mechanical properties was revealed. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of Rock Mechanics & Rock Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00603-023-03649-w
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        Text: English
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        PageCount: 22
        StartPage: 2103
    Subjects:
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Greenhouse gas mitigation
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      – SubjectFull: Liquid nitrogen
        Type: general
      – SubjectFull: Coal mining safety
        Type: general
      – SubjectFull: Geometric distribution
        Type: general
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      – TitleFull: Evolution of Coal Microfracture by Cyclic Fracturing of Liquid Nitrogen Based on μCT and Convolutional Neural Networks.
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              M: 03
              Text: Mar2024
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