JOURNAL ARTICLE
The experimental study of shale laminae influence on the mechanical properties and brittle failure of shale oil reservoirs.
Published In: Physics of Fluids, 2025, v. 37, n. 5. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Wang, Xiaoqiong; Zhong, Yi; Hou, Shuoyang; Wan, Youyu; Ge, Hongkui 3 of 3
Abstract
This article investigates the influence of laminae—thin, high-frequency sedimentary layers—on the mechanical properties, fracture behavior, and hydraulic fracturing strategies of shale oil reservoirs, focusing on laminated shale and layered shale from the Lower Ganchaigou Formation in the Chaidam Basin, China. Experimental results demonstrate that rock fabric (laminated versus layered structure) has a greater impact than lithology on shale’s mechanical properties, with laminated shale exhibiting lower compressive strength, cohesive force, and internal friction angle but more developed natural micro-cracks, higher anisotropy, and greater pressure sensitivity. Fracture propagation in both shale types tends to follow laminae or bedding planes, but laminated shale forms a more complex network of micro-cracks with smaller apertures, which enhances specific surface area and permeability, potentially benefiting oil recovery. Based on these findings and field hydraulic fracturing data, the study recommends a composite stimulation strategy using high-viscosity gel plus slippery water for laminated shale to support micro-cracks and increase fracture complexity, while suggesting slippery water fracturing for layered shale to economically create main fractures.
Additional Information
- Source:Physics of Fluids. 2025/05, Vol. 37, Issue 5, p1
- Document Type:Article
- Subject Area:Power and Energy
- Publication Date:2025
- ISSN:1070-6631
- DOI:10.1063/5.0268210
- Accession Number:185593669
- Copyright Statement:Copyright of Physics of Fluids is the property of American Institute of Physics 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.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.