Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/180728
Title: | Dynamic change in dominant factor controls the injection-induced slip behaviors of rock fractures | Authors: | Fang, Zhou Wu, Wei |
Keywords: | Engineering | Issue Date: | 2024 | Source: | Fang, Z. & Wu, W. (2024). Dynamic change in dominant factor controls the injection-induced slip behaviors of rock fractures. International Journal of Rock Mechanics and Mining Sciences, 183, 105887-. https://dx.doi.org/10.1016/j.ijrmms.2024.105887 | Project: | RG143/23 | Journal: | International Journal of Rock Mechanics and Mining Sciences | Abstract: | In the geo-energy industry, fluid injection induces different slip behaviors of a rock fracture, from aseismic creep to dynamic slip. The transition from aseismic creep to dynamic slip is explained by the ratio of the stiffness of surrounding rock and the critical stiffness of the fracture. However, numerous studies suggest multiple controls affecting the slip behaviors, and their joint influences on the slip transition remain unclear. Here we trained a dual-stage attention-based recurrent neural network model using fluid injection experimental data to explore the dominant factor controlling the slip behaviors. Our results showed that the dominant factor changes during fluid injection, and the attention to shear stress dominates the occurrence of dynamic slip. We found that high fluctuations of the attentions to normal stress, shear stress, and water pressure gradient promote the slip transition. Our model was applied to explore the competing process between water pressure front and aseismic creep front while gradually increasing the injection pressure and to reveal the dynamic change in the dominant factor during the growth of cumulative moment release. | URI: | https://hdl.handle.net/10356/180728 | ISSN: | 1365-1609 | DOI: | 10.1016/j.ijrmms.2024.105887 | Schools: | School of Civil and Environmental Engineering | Rights: | © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | CEE Journal Articles |
SCOPUSTM
Citations
50
1
Updated on Mar 12, 2025
Page view(s)
64
Updated on Mar 17, 2025
Google ScholarTM
Check
Altmetric
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.