Numerical simulation of hydraulic fracture extension patterns at the interface of coal-measure composite rock mass with Cohesive Zone Model
Journal of Cleaner Production, ISSN: 0959-6526, Vol: 426, Page: 139001
2023
- 11Citations
- 5Captures
- 1Mentions
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Article Description
This study presents the development of a numerical computational model for hydraulic fracturing of coal-measure rock mass. The model is based on the B-K hybrid fracture energy criterion and is implemented using the extended finite element method (XFEM) and cohesive zone model (CZM) in the ABAQUS software. The study took into account the elastic-plastic damage properties of the coal-measure rock mass, as well as the combined effects of tensile and shear damage during the start and expansion of hydraulic fractures. The expansion trajectories and fracture morphology of hydraulic fractures at the interface of coal-measure rock mass under different lithological combinations (lithological strength differences) and ground stress conditions are revealed, and the main controlling factors affecting the expansion across the bedding are clarified. The study provides valuable insights into the management of hydraulic fracturing in the challenging context of hard roof coal mines, the stimulation of coal-measure gas co-production reservoirs, and the development of effective fracturing stimulation strategies for shale oil reservoirs interlayered with thin sandstone, and it can promote the development of industrial production technology for coal-measure gas and shale oil and gas resources, and is beneficial to increase the proportion of clean energy in China's energy consumption structure, and thus promoting a further reduction in the carbon emissions from energy consumption.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0959652623031591; http://dx.doi.org/10.1016/j.jclepro.2023.139001; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85173275962&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0959652623031591; https://dx.doi.org/10.1016/j.jclepro.2023.139001
Elsevier BV
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