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
-
1.
The coal microstructure is precisely extracted using a combination method of μCT scanning and 3D U-Net.
-
2.
The evolution of coal 3D microstructure under LN2 cyclic fracturing is visualized and quantitatively analyzed.
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3.
LN2 cyclic fracturing has a higher efficiency on CT slices with small initial porosity.
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4.
The effect of fracture size on coal P-wave and mechanical properties was revealed.
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Data Availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Abbreviations
- τ :
-
Local thickness of fractures
- Ω:
-
Set of all fracture voxels
- \({\overrightarrow{p}}\) :
-
Arbitrary point within the fracture structure
- \(S({\overrightarrow{x}} ,r)\) :
-
Maximum sphere that contains the point \({\overrightarrow{p}}\)
- \({\overrightarrow{x}}\) :
-
Center of the sphere \(S({\overrightarrow{x}} ,r)\)
- r :
-
Radius of the sphere \(S({\overrightarrow{x}} ,r)\)
- P f :
-
Frost expansion force of single fracture (MPa)
- k i :
-
Volume expansion coefficient of water
- K i T :
-
Ice bulk modulus at temperature T (MPa)
- β :
-
Volume expansion coefficient of water–ice phase transition (°C−1)
- u T :
-
Freezing ratio at temperature T (%)
- ζ :
-
Water migration flux ratio
- E i T :
-
Elastic modulus of ice (MPa)
- v i T :
-
Poisson ratio of ice
- v s T :
-
Poisson ratio of coal
- G i T :
-
Shear modulus of coal (MPa)
- σ :
-
Tensile stress of micro-unit (MPa)
- E :
-
Elasticity modulus (MPa)
- ε :
-
Strain
- α :
-
Linear thermal expansion coefficient (°C−1)
- ΔT :
-
Variation of temperature (°C)
- \(\sigma^{\prime}\) :
-
Real tensile stress of micro-unit (MPa)
- \(\varepsilon^{\prime}\) :
-
Real strain
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Funding
This work was supported by the National Natural Science Foundation of China (Grant no. 51934007, 51874292), the Fundamental Research Funds for the Central Universities (Grant no. 2022XSCX20), and the Postgraduate Research and Practice Innovation Program of Jiangsu Province (Grant no. KYCX21_2379).
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Chen, S., Dou, L., Cai, W. et al. Evolution of Coal Microfracture by Cyclic Fracturing of Liquid Nitrogen Based on μCT and Convolutional Neural Networks. Rock Mech Rock Eng 57, 2103–2124 (2024). https://doi.org/10.1007/s00603-023-03649-w
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DOI: https://doi.org/10.1007/s00603-023-03649-w