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A quantification method for shale fracability based on analytic hierarchy process

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Listed:
  • Sui, Lili
  • Ju, Yang
  • Yang, Yongming
  • Yang, Yong
  • Li, Aishan

Abstract

A quantification method for evaluation of the fracability of shale is essential for optimizing hydraulic fracturing of shale gas reservoirs and enhancing shale gas recovery. To quantitatively evaluate the fracability, seven sets of shale cores are drilled from the reservoirs at different depths of an oilfield in the east of China. The influences of six fracability-related mechanical and physical characteristics of shale, i.e. brittleness, brittle mineral content, clay mineral content, cohesion, angle of internal friction, and unconfined compressive strength, are analyzed. A mathematical model taking account of significance of the influencing factors is proposed based on analytic hierarchy process (AHP) to evaluate the fracability according to their different effects on shale fracability. The analysis indicates that the fracability decreases with the increase of reservoir depths. The hydraulic fracturing tests of the shale cores are conducted to verify the accuracy of the quantification method. The fractal geometry is used to characterize the fracture degree of the shale. It is shown that a larger fractal dimension of the fracture network corresponds to a better fracability of shale. The more complex the fractures are, the larger the fracability of shale is. The experimental data coincide with the results of the proposed evaluation model.

Suggested Citation

  • Sui, Lili & Ju, Yang & Yang, Yongming & Yang, Yong & Li, Aishan, 2016. "A quantification method for shale fracability based on analytic hierarchy process," Energy, Elsevier, vol. 115(P1), pages 637-645.
    • Handle: RePEc:eee:energy:v:115:y:2016:i:p1:p:637-645
    DOI: 10.1016/j.energy.2016.09.035
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    References listed on IDEAS

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    1. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
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    Cited by:

    1. Luo, Chao & Ju, Yanbing & Santibanez Gonzalez, Ernesto D.R. & Dong, Peiwu & Wang, Aihua, 2020. "The waste-to-energy incineration plant site selection based on hesitant fuzzy linguistic Best-Worst method ANP and double parameters TOPSIS approach: A case study in China," Energy, Elsevier, vol. 211(C).
    2. Hui, Gang & Chen, Zhangxin & Wang, Youjing & Zhang, Dongmei & Gu, Fei, 2023. "An integrated machine learning-based approach to identifying controlling factors of unconventional shale productivity," Energy, Elsevier, vol. 266(C).
    3. Salvia, Amanda Lange & Brandli, Luciana Londero & Leal Filho, Walter & Locatelli Kalil, Rosa Maria, 2019. "An analysis of the applications of Analytic Hierarchy Process (AHP) for selection of energy efficiency practices in public lighting in a sample of Brazilian cities," Energy Policy, Elsevier, vol. 132(C), pages 854-864.
    4. Jiang, Xingwen & Chen, Mian & Li, Qinghui & Liang, Lihao & Zhong, Zhen & Yu, Bo & Wen, Hang, 2022. "Study on the feasibility of the heat treatment after shale gas reservoir hydration fracturing," Energy, Elsevier, vol. 254(PB).
    5. Yang, Jinghua & Wang, Min & Wu, Lei & Liu, Yanwei & Qiu, Shuxia & Xu, Peng, 2021. "A novel Monte Carlo simulation on gas flow in fractal shale reservoir," Energy, Elsevier, vol. 236(C).

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