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2024 Vol. 30, No. 4
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LIU Shengxin, FU Huiqi, FENG Xingqiang, HAN Xiaoxiang, WANG Bingqian. 2024. Fracture network complexity of tight sandstone and its influencing factors. Journal of Geomechanics, 30(4): 563-578. doi: 10.12090/j.issn.1006-6616.2023128
Citation: LIU Shengxin, FU Huiqi, FENG Xingqiang, HAN Xiaoxiang, WANG Bingqian. 2024. Fracture network complexity of tight sandstone and its influencing factors. Journal of Geomechanics, 30(4): 563-578. doi: 10.12090/j.issn.1006-6616.2023128
shu

Fracture network complexity of tight sandstone and its influencing factors

  • 1.

    Institue of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China

  • 2.

    Key Laboratory of Petroleum Geomechanics, China Geological Survey, Beijing 100081, China

  • 3.

    The Second Survey of Jilin Geological mineral, Jilin 132001, Jilin, China

  • 4.

    School of Mechanics and Civil Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China

    • Fund Project: This research is financially supported by the Geological Survey Projects of the China Geological Survey (Grant No. DD20221660) and National Natural Science Foundation of China (Grant No. 42277167).
More Information
Received Date:  03 August 2023
Revised Date:  18 December 2023
Accepted Date:  04 January 2024
Available Online:  04 January 2024
Publish Date:  28 August 2024
    Abstract
  • Objective

    Fracture network analysis plays an important role in oil and gas exploration and development. However, complexity analysis of tight sandstone fracture networks and their control factors is relatively lagging. Based on an experimental study of the dynamic evolution of the complex fracture network in tight sandstone, the fractal and multifractal spectral characteristics of the fracture network were defined, and the complexity and main controlling factors of the fracture network were analyzed. Fracture network complexity analysis of tight sandstone plays an important role in hydraulic fracturing optimization, fracture network prediction, and fracture modeling.

    Methods

    Rock mechanics and X-ray computed tomography scan experiments determined the characteristics of rock mechanics and fracture networks . The microstructure and fracture network fractal characteristics of tight sandstone were quantitatively characterized by SEM and fracture network fractal analysis.

    Results

    The results showed that the quartz content of tight sandstone ranges from 28.08 to 52.88%, clay content ranges from 11.54 to 25.45%, particle size ranges from 61.18 to 184.55 μm, and porosity ranges from 8.125 to 10.296%. Uniaxial compressive strength ranges from 69.09 to 188.33 MPa, and the elastic modulus ranges from 31.69 to 92.76 GPa. The fractal dimension (DB) ranges from 1.28 to 2.35 and average spectral width (Δα) ranges from 1.0851 to 1.3638.

    Conclusion

    The initiation and propagation of fractures extend through the entire stress–strain process. The complexity of the fracture network of tight sandstone is mainly controlled by microscopic fabric characteristics, and has obvious confining pressure as well as scale effects. The DB of the three-dimensional fracture network and average Δα of the multifractal spectrum represents the complexity and heterogeneity of the fracture spatial distribution, respectively, and are relatively independent. As the content of quartz, feldspar, and other brittle minerals in sandstone increases, the porosity of the reservoir increases, particle size of the sandstone decreases, DB of the fracture network increases, and average Δα decreases. In the absence of confining pressure, the complexity of the sample fracture network is mainly controlled by the microscopic fabric characteristics, and the complexity increases with increase of axial pressure. When present confining pressure plays a leading role; the higher it is, the lower the DB value, and the higher the mean Δα value. Clay minerals are unconducive to complex fractures formation. The mean values of DB and Δα of small-scale samples are greater than those of large-scale samples. The elastic modulus and compressive strength of sandstone are positively correlated with DB and mean Δα.
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