Abstract
Quantitatively assessing the morphology of fractures by hydraulic stimulation is crucial for evaluating the effectiveness of fracturing operations. Herein, a series of laboratory tests are conducted to induce fractures originating from a borehole situated within cylindrical-shaped granite specimens. A total of 13 specimens are pressurized with various viscosities of fracturing fluids and injection rates. For each fractured specimen, 3D X-ray computed tomography imaging is performed. To accurately identify and segment the various morphologies of fractures, a convolutional neural network (CNN) model is employed. A nested U-Net model, based on an encoder–decoder structure, is designed to obtain a more sophisticated segmentation of fractures compared with conventional image processing methods. The results indicate that the complicated fracture patterns, such as the hair-like thin fracture, fracture junction, and complex fracture network, are successfully segmented. For the 3D reconstructed binary fractures, the morphological parameters are quantitatively computed. The increase in fracturing fluid viscosity and injection rates, which in turn led to an increase in breakdown pressure, results in the increase in fracture volume and aperture, whereas the tortuosity and surface roughness tend to decrease. Thus, the CNN-based model enables the accurate segmentation of fractures induced by hydraulic stimulation and helps clarify the relationship between morphological consequences and pressurization conditions.
Highlights
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The proposed method of hydraulically stimulated fracture segmentation enables accurate quantitative morphological analysis.
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The nested U-Net-based CNN model can successfully segment various morphologies of fractures from 3D X-ray CT images.
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The 3D reconstructed fracture surface reveals that hydraulic fracture morphology is well correlated with injection conditions.
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A higher breakdown pressure increases the aperture and volume of fractures while decreasing the fracture undulation.
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This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (Nos. 2023R1A2C2003534, NRF-2021R1A5A1032433).
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JP: Methodology; Investigation; Validation; and Writing—original draft. YK: Validation; and Writing—review and editing. SSK: Investigation; and Validation. KYK: Resources; and Writing. TSY: Supervision; and Writing—review and editing.
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Park, J., Kim, Y., Kim, S.S. et al. Effect of Injection Rate and Viscosity on Stimulated Fracture in Granite: Extraction of Fracture by Convolutional Neural Network and Morphological Analysis. Rock Mech Rock Eng 57, 2159–2174 (2024). https://doi.org/10.1007/s00603-023-03678-5
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DOI: https://doi.org/10.1007/s00603-023-03678-5