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Uncertainty Analysis of Factors Influencing Stimulated Fracture Volume in Layered Formation

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  • Jingxuan Zhang

    (State Key Laboratory of Oil and Gas Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Xiangjun Liu

    (State Key Laboratory of Oil and Gas Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Xiaochen Wei

    (State Key Laboratory of Oil and Gas Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Lixi Liang

    (State Key Laboratory of Oil and Gas Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Jian Xiong

    (State Key Laboratory of Oil and Gas Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Wei Li

    (State Key Laboratory of Oil and Gas Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu 610500, China)

Abstract

Hydraulic fracture dimension is one of the key parameters affecting stimulated porous media. In actual fracturing, plentiful uncertain parameters increase the difficulty of fracture dimension prediction, resulting in the difficulty in the monitoring of reservoir productivity. In this paper, we established a three-dimensional model to analyze the key factors on the stimulated reservoir volume (SRV), with the response surface method (RSM). Considering the rock properties and fracturing parameters, we established a multivariate quadratic prediction equation. Simulation results show that the interactions of injection rate ( Q ), Young’s modulus ( E ) and permeability coefficient ( K ), and Poisson’s ratio ( μ ) play a relatively significant role on SRV. The reservoir with a high Young’s modulus typically generates high pressure, leading to longer fractures and larger SRV. SRV reaches the maximum value when E 1 and E 2 are high. SRV is negatively correlated with K 1. Moreover, maintaining a high injection rate in this layered formation with high E 1 and E 2, relatively low K 1, and μ 1 at about 0.25 would be beneficial to form a larger SRV. These results offer new perceptions on the optimization of SRV, helping to improve the productivity in hydraulic fracturing.

Suggested Citation

  • Jingxuan Zhang & Xiangjun Liu & Xiaochen Wei & Lixi Liang & Jian Xiong & Wei Li, 2019. "Uncertainty Analysis of Factors Influencing Stimulated Fracture Volume in Layered Formation," Energies, MDPI, vol. 12(23), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4444-:d:289814
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    References listed on IDEAS

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    2. Qian Li & Yiyu Lu & Zhaolong Ge & Zhe Zhou & Jingwei Zheng & Songqiang Xiao, 2017. "A New Tree-Type Fracturing Method for Stimulating Coal Seam Gas Reservoirs," Energies, MDPI, vol. 10(9), pages 1-14, September.
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    4. Chen Chen & Lin Yang & Rui Jia & Youhong Sun & Wei Guo & Yong Chen & Xitong Li, 2017. "Simulation Study on the Effect of Fracturing Technology on the Production Efficiency of Natural Gas Hydrate," Energies, MDPI, vol. 10(8), pages 1-16, August.
    5. Deliang Zhang & Yu Dai & Xinhua Ma & Liehui Zhang & Bing Zhong & Jianfa Wu & Zhengwu Tao, 2018. "An Analysis for the Influences of Fracture Network System on Multi-Stage Fractured Horizontal Well Productivity in Shale Gas Reservoirs," Energies, MDPI, vol. 11(2), pages 1-19, February.
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    Cited by:

    1. M. S. Liew & Kamaluddeen Usman Danyaro & Noor Amila Wan Abdullah Zawawi, 2020. "A Comprehensive Guide to Different Fracturing Technologies: A Review," Energies, MDPI, vol. 13(13), pages 1-20, June.

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