IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v236y2021ics0360544221017618.html
   My bibliography  Save this article

A novel Monte Carlo simulation on gas flow in fractal shale reservoir

Author

Listed:
  • Yang, Jinghua
  • Wang, Min
  • Wu, Lei
  • Liu, Yanwei
  • Qiu, Shuxia
  • Xu, Peng

Abstract

The apparent gas permeability (AGP) is one of the key parameters in shale gas exploitation, which is difficult to determine due to the mutliscale structure of shale formation. In this paper, a fractal probability law and Monte Carlo technique are used to predict the rarefied gas flow through shale reservoir, since from the adsorption-desorption experiments it is known that the distribution of pore size follows the fractal scaling law. The results indicate that the AGP increases with the increment of Knudsen number, and it grows linearly with Knudsen number when Knudsen number is larger than 0.1. When the porosity is fixed, increased pore fractal dimension reduces the AGP and the intrinsic permeability, but enhances the permeability ratio (ratio of AGP to intrinsic permeability). However, when the pore size limit is fixed, the permeability ratio can be reduced by increasing pore fractal dimension. While the increment of tortuosity fractal dimension can lower both AGP and intrinsic permeability, it has marginal effect on the permeability ratio. The proposed fractal Monte Carlo model is an efficient and economic method to predict the AGP, which bridges the microscale structures and macroscale gas flow properties of shale reservoir.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:236:y:2021:i:c:s0360544221017618
    DOI: 10.1016/j.energy.2021.121513
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221017618
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2021.121513?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Wang, Hui & Chen, Li & Qu, Zhiguo & Yin, Ying & Kang, Qinjun & Yu, Bo & Tao, Wen-Quan, 2020. "Modeling of multi-scale transport phenomena in shale gas production — A critical review," Applied Energy, Elsevier, vol. 262(C).
    3. Solarin, Sakiru Adebola & Gil-Alana, Luis A. & Lafuente, Carmen, 2020. "An investigation of long range reliance on shale oil and shale gas production in the U.S. market," Energy, Elsevier, vol. 195(C).
    4. Yang, Xu & Zhou, Wenning & Liu, Xunliang & Yan, Yuying, 2020. "A multiscale approach for simulation of shale gas transport in organic nanopores," Energy, Elsevier, vol. 210(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. El-Dib, Yusry O. & Elgazery, Nasser S., 2022. "A novel pattern in a class of fractal models with the non-perturbative approach," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    2. Kasala, Erasto E. & Wang, Jinjie & Lwazi, Hussein M. & Nyakilla, Edwin E. & Kibonye, John S., 2024. "The influence of hydraulic fracture and reservoir parameters on the storage of CO2 and enhancing CH4 recovery in Yanchang formation," Energy, Elsevier, vol. 296(C).
    3. Zheng, Yangfeng & Zhai, Cheng & Chen, Aikun & Yu, Xu & Xu, Jizhao & Sun, Yong & Cong, Yuzhou & Tang, Wei & Zhu, Xinyu & Li, Yujie, 2023. "Microstructure evolution of bituminite and anthracite modified by different fracturing fluids," Energy, Elsevier, vol. 263(PB).
    4. Gang Liu & Duo Chen & Bo Li & Changjun Li, 2023. "Primary Growth Behavior of Sulfur Particles through the Throttle Valve in the Transmission System of High Sulfur Content Natural Gas," Energies, MDPI, vol. 16(7), pages 1-31, March.
    5. Tian, Feng & Wang, Junlei & Xu, Zhenhua & Xiong, Fansheng & Xia, Peng, 2023. "A nonlinear model of multifractured horizontal wells in heterogeneous gas reservoirs considering the effect of stress sensitivity," Energy, Elsevier, vol. 263(PD).
    6. Qin, Lei & Wang, Ping & Lin, Haifei & Li, Shugang & Zhou, Bin & Bai, Yang & Yan, Dongjie & Ma, Chao, 2023. "Quantitative characterization of the pore volume fractal dimensions for three kinds of liquid nitrogen frozen coal and its enlightenment to coalbed methane exploitation," Energy, Elsevier, vol. 263(PA).
    7. Tian, Xin & Duan, Xianggang & Sun, Mengdi & Mohammadian, Erfan & Hu, Qinhong & Ostadhassan, Mehdi & Liu, Bo & Ke, Yubin & Pan, Zhejun, 2024. "Evolution of fractal characteristics in shales with increasing thermal maturity: Evidence from neutron scattering, N2 physisorption, and FE-SEM imaging," Energy, Elsevier, vol. 298(C).
    8. Li, Jing & Xie, Yetong & Liu, Huimin & Zhang, Xuecai & Li, Chuanhua & Zhang, Lisong, 2023. "Combining macro and micro experiments to reveal the real-time evolution of permeability of shale," Energy, Elsevier, vol. 262(PB).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shan, Baochao & Wang, Runxi & Guo, Zhaoli & Wang, Peng, 2021. "Contribution quantification of nanoscale gas transport in shale based on strongly inhomogeneous kinetic model," Energy, Elsevier, vol. 228(C).
    2. Cao, Gaohui & Jiang, Wenbin & Lin, Mian & Ji, Lili & Xu, Zhipeng & Zheng, Siping & Hao, Fang, 2021. "Mortar dynamic coupled model for calculating interface gas exchange between organic and inorganic matters of shale," Energy, Elsevier, vol. 236(C).
    3. Li, Dafang & Sun, Weifu & Luo, Zhenmin, 2023. "Methane deflagration promoted by enhancing ignition efficiency via hydrogen doping, with a view to fracturing shales," Energy, Elsevier, vol. 282(C).
    4. Yan, Min & Zhou, Ming & Li, Shugang & Lin, Haifei & Zhang, Kunyin & Zhang, Binbin & Shu, Chi-Min, 2021. "Numerical investigation on the influence of micropore structure characteristics on gas seepage in coal with lattice Boltzmann method," Energy, Elsevier, vol. 230(C).
    5. Shuguang Liu & Jiayi Wang & Yin Long, 2023. "Research into the Spatiotemporal Characteristics and Influencing Factors of Technological Innovation in China’s Natural Gas Industry from the Perspective of Energy Transition," Sustainability, MDPI, vol. 15(9), pages 1-34, April.
    6. Xuhua Gao & Junhong Yu & Xinchun Shang & Weiyao Zhu, 2023. "Investigation on Nonlinear Behaviors of Seepage in Deep Shale Gas Reservoir with Viscoelasticity," Energies, MDPI, vol. 16(17), pages 1-23, August.
    7. 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).
    8. Guo, Dan & Cao, Xuewen & Ding, Gaoya & Zhang, Pan & Liu, Yang & Bian, Jiang, 2022. "Crystallization and nucleation mechanism of heavy hydrocarbons in natural gas," Energy, Elsevier, vol. 239(PB).
    9. Wenchao Liu & Yuejie Yang & Chengcheng Qiao & Chen Liu & Boyu Lian & Qingwang Yuan, 2023. "Progress of Seepage Law and Development Technologies for Shale Condensate Gas Reservoirs," Energies, MDPI, vol. 16(5), pages 1-30, March.
    10. Gou, Qiyang & Xu, Shang & Hao, Fang & Yang, Feng & Shu, Zhiguo & Liu, Rui, 2021. "The effect of tectonic deformation and preservation condition on the shale pore structure using adsorption-based textural quantification and 3D image observation," Energy, Elsevier, vol. 219(C).
    11. Yang, Xu & Zhou, Wenning & Liu, Xunliang & Yan, Yuying, 2020. "A multiscale approach for simulation of shale gas transport in organic nanopores," Energy, Elsevier, vol. 210(C).
    12. Li, Jing & Xie, Yetong & Liu, Huimin & Zhang, Xuecai & Li, Chuanhua & Zhang, Lisong, 2023. "Combining macro and micro experiments to reveal the real-time evolution of permeability of shale," Energy, Elsevier, vol. 262(PB).
    13. Golsanami, Naser & Jayasuriya, Madusanka N. & Yan, Weichao & Fernando, Shanilka G. & Liu, Xuefeng & Cui, Likai & Zhang, Xuepeng & Yasin, Qamar & Dong, Huaimin & Dong, Xu, 2022. "Characterizing clay textures and their impact on the reservoir using deep learning and Lattice-Boltzmann simulation applied to SEM images," Energy, Elsevier, vol. 240(C).
    14. Bowen Ling & Hasan J. Khan & Jennifer L. Druhan & Ilenia Battiato, 2020. "Multi-Scale Microfluidics for Transport in Shale Fabric," Energies, MDPI, vol. 14(1), pages 1-23, December.
    15. Fargalla, Mandella Ali M. & Yan, Wei & Deng, Jingen & Wu, Tao & Kiyingi, Wyclif & Li, Guangcong & Zhang, Wei, 2024. "TimeNet: Time2Vec attention-based CNN-BiGRU neural network for predicting production in shale and sandstone gas reservoirs," Energy, Elsevier, vol. 290(C).
    16. Yintao Dong & Laiming Song & Fengpeng Lai & Qianhui Zhao & Chuan Lu & Guanzhong Chen & Qinwan Chong & Shuo Yang & Junjie Wang, 2024. "Characterization of the Macroscopic Impact of Diverse Microscale Transport Mechanisms of Gas in Micro-Nano Pores and Fractures," Energies, MDPI, vol. 17(5), pages 1-23, February.
    17. Wu, Jian & Gan, Yixiang & Shi, Zhang & Huang, Pengyu & Shen, Luming, 2023. "Pore-scale lattice Boltzmann simulation of CO2-CH4 displacement in shale matrix," Energy, Elsevier, vol. 278(PB).
    18. Tian, Feng & Wang, Junlei & Xu, Zhenhua & Xiong, Fansheng & Xia, Peng, 2023. "A nonlinear model of multifractured horizontal wells in heterogeneous gas reservoirs considering the effect of stress sensitivity," Energy, Elsevier, vol. 263(PD).
    19. 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).
    20. Xiaoping Li & Shudong Liu & Ji Li & Xiaohua Tan & Yilong Li & Feng Wu, 2020. "Apparent Permeability Model for Gas Transport in Multiscale Shale Matrix Coupling Multiple Mechanisms," Energies, MDPI, vol. 13(23), pages 1-24, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:236:y:2021:i:c:s0360544221017618. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.