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Production data analysis of shale gas using fractal model and fuzzy theory: Evaluating fracturing heterogeneity

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  • You, Xu-Tao
  • Liu, Jian-Yi
  • Jia, Chun-Sheng
  • Li, Jun
  • Liao, Xin-Yi
  • Zheng, Ai-Wei

Abstract

The development of shale-gas reservoirs has been dramatically accelerated with the technical breakthroughs made in the fields of horizontal well drilling and multistage hydraulic fracturing. After being restructured by hydraulic fracturing, the shale reservoir would have a complicated fracture network featuring great randomness. The accurate evaluation of the fracture network structure and the quantitative calculation of the fracturing fluid flowback are keys to the efficient development of shale-gas fields. In this paper, a new fractal seepage model of single fracturing section was established based on the conversion relationship of gas-liquid flow fractal dimension. Then, the sensitivity of model parameters and the flow characteristics of single fracture section were analyzed to determine the reason that gas production “rises first, and then drops down” at the initial production phase. Finally, the mathematical description of the heterogeneity of fracture networks was further proposed based on Fuzzy theory. Case studies in Fuling shale-gas field were performed by means of the Extended Monte Carlo Simulation and the optimal fracturing heterogeneity factors of typical wells are 0.25, 0.10 and 0.15. Compared with the homogeneous model, the accuracies of gas production and water production calculated by heterogeneous model are increased by up to 15% and 20%, respectively.

Suggested Citation

  • You, Xu-Tao & Liu, Jian-Yi & Jia, Chun-Sheng & Li, Jun & Liao, Xin-Yi & Zheng, Ai-Wei, 2019. "Production data analysis of shale gas using fractal model and fuzzy theory: Evaluating fracturing heterogeneity," Applied Energy, Elsevier, vol. 250(C), pages 1246-1259.
    • Handle: RePEc:eee:appene:v:250:y:2019:i:c:p:1246-1259
    DOI: 10.1016/j.apenergy.2019.05.049
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    References listed on IDEAS

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    1. Hammond, Geoffrey P. & O’Grady, Áine, 2017. "Indicative energy technology assessment of UK shale gas extraction," Applied Energy, Elsevier, vol. 185(P2), pages 1907-1918.
    2. Wang, Ke & Li, Haitao & Wang, Junchao & Jiang, Beibei & Bu, Chengzhong & Zhang, Qing & Luo, Wei, 2017. "Predicting production and estimated ultimate recoveries for shale gas wells: A new methodology approach," Applied Energy, Elsevier, vol. 206(C), pages 1416-1431.
    3. Gascard, Eric & Simeu-Abazi, Zineb, 2018. "Quantitative Analysis of Dynamic Fault Trees by means of Monte Carlo Simulations: Event-Driven Simulation Approach," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 487-504.
    4. X.-H. Tan & C.-Y. Liu & X.-P. Li & H.-Q. Wang & H. Deng, 2018. "A stress sensitivity model for the permeability of porous media based on bi-dispersed fractal theory," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 29(02), pages 1-12, February.
    5. Yuan, Jiehui & Luo, Dongkun & Xia, Liangyu & Feng, Lianyong, 2015. "Policy recommendations to promote shale gas development in China based on a technical and economic evaluation," Energy Policy, Elsevier, vol. 85(C), pages 194-206.
    6. Chang, Yuan & Huang, Runze & Ries, Robert J. & Masanet, Eric, 2014. "Shale-to-well energy use and air pollutant emissions of shale gas production in China," Applied Energy, Elsevier, vol. 125(C), pages 147-157.
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    5. Zhang, Xian-min & Chen, Bai-yan-yue & Zheng, Zhuang-zhuang & Feng, Qi-hong & Fan, Bin, 2023. "New methods of coalbed methane production analysis based on the generalized gamma distribution and field applications," Applied Energy, Elsevier, vol. 350(C).

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