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Fluctuation of fracturing curves indicates in-situ brittleness and reservoir fracturing characteristics in unconventional energy exploitation

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  • Sun, Fuqiang
  • Du, Shuheng
  • Zhao, Ya-Pu

Abstract

Reservoir brittleness is a key index to evaluate the fracability and to predict the fracturing characteristics in unconventional energy exploitation. Traditional brittleness indices could only be derived from laboratory experiments, which are far from the field conditions and may lead to deviations. In this paper, a fracturing-curve based in-situ brittleness evaluation method is proposed and the in-situ brittleness of Woodford Shale is derived under field stress and temperature conditions. Based on the analysis of real-time micro-seismic data, it is confirmed that the real-time fracturing characteristics can be known by observing the fluctuations in the fracturing curves. Results showed that the in-situ brittleness is much lower than that derived in laboratory experiments. About 78% of micro-seismic events happen at the extreme value and the fastest fluctuation of the fracturing curve. These provide guidance for accurate formulation and real-time optimization of exploitation scheme.

Suggested Citation

  • Sun, Fuqiang & Du, Shuheng & Zhao, Ya-Pu, 2022. "Fluctuation of fracturing curves indicates in-situ brittleness and reservoir fracturing characteristics in unconventional energy exploitation," Energy, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:energy:v:252:y:2022:i:c:s036054422200946x
    DOI: 10.1016/j.energy.2022.124043
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    References listed on IDEAS

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    1. Du, Shuheng, 2020. "Profound connotations of parameters on the geometric anisotropy of pores in which oil store and flow: A new detailed case study which aimed to dissect, conclude and improve the theoretical meaning and," Energy, Elsevier, vol. 211(C).
    2. Lin, Kui & Zhao, Ya-Pu, 2021. "Entropy and enthalpy changes during adsorption and displacement of shale gas," Energy, Elsevier, vol. 221(C).
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    Cited by:

    1. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).

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