IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i16p5811-d884826.html
   My bibliography  Save this article

3D Fracture Propagation Simulation and Pressure Decline Analysis Research for I-Shaped Fracture of Coalbed

Author

Listed:
  • Chengwang Wang

    (PetroChina Coalbed Methane Company Limited, Chaoyang, Beijing 100028, China)

  • Zixi Guo

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

  • Lifeng Zhang

    (PetroChina Xinjiang Oilfield Company Development Company, Karamay 834000, China)

  • Yunwei Kang

    (School of Sciences, Southwest Petroleum University, Chengdu 610500, China)

  • Zhenjiang You

    (Centre for Sustainable Energy and Resources, Edith Cowan University, Joondalup, WA 6027, Australia
    School of Chemical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
    Centre for Natural Gas, The University of Queensland, Brisbane, QLD 4072, Australia)

  • Shuguang Li

    (PetroChina Coalbed Methane Company Limited, Chaoyang, Beijing 100028, China)

  • Yubin Wang

    (PetroChina Coalbed Methane Company Limited, Chaoyang, Beijing 100028, China)

  • Huaibin Zhen

    (PetroChina Coalbed Methane Company Limited, Chaoyang, Beijing 100028, China)

Abstract

After hydraulic fracturing, some treatments intended for production enhancement fail to yield predetermined effects. The main reason is the insufficient research about the fracture propagation mechanism. There is compelling evidence that I-shaped fracture, two horizontal fractures at the junction of coalbed and cover/bottom layer, and one vertical fracture in the coalbed have formed in part of the coalbed after hydraulic fracturing. Therefore, this paper aims at I-shaped fracture propagation simulation. A novel propagation model is derived on the basis of a three-dimensional (3D) model, and the coupling conditions of vertical fracture and horizontal fractures are established based on the flow rate distribution and the bottom-hole pressure equality, respectively. Moreover, an associated PDA (pressure decline analysis of post-fracturing) model is established. Both models complement with each other and work together to guide fracturing treatment. Finally, a field case is studied to show that the proposed models can effectively investigate and simulate fracture initiation/propagation and pressure decline.

Suggested Citation

  • Chengwang Wang & Zixi Guo & Lifeng Zhang & Yunwei Kang & Zhenjiang You & Shuguang Li & Yubin Wang & Huaibin Zhen, 2022. "3D Fracture Propagation Simulation and Pressure Decline Analysis Research for I-Shaped Fracture of Coalbed," Energies, MDPI, vol. 15(16), pages 1-20, August.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5811-:d:884826
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/16/5811/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/16/5811/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guo, Zixi & Zhao, Jinzhou & You, Zhenjiang & Li, Yongming & Zhang, Shu & Chen, Yiyu, 2021. "Prediction of coalbed methane production based on deep learning," Energy, Elsevier, vol. 230(C).
    Full references (including those not matched with items on IDEAS)

    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. Du, Shuyi & Wang, Meizhu & Yang, Jiaosheng & Zhao, Yang & Wang, Jiulong & Yue, Ming & Xie, Chiyu & Song, Hongqing, 2023. "An enhanced prediction framework for coalbed methane production incorporating deep learning and transfer learning," Energy, Elsevier, vol. 282(C).
    2. Nassabeh, Mehdi & You, Zhenjiang & Keshavarz, Alireza & Iglauer, Stefan, 2024. "Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection," Energy, Elsevier, vol. 305(C).
    3. Zhang, Baoxin & Deng, Ze & Fu, Xuehai & Yu, Kun & Zeng, Fanhua (Bill), 2023. "An experimental study on the effects of acidization on coal permeability: Implications for the enhancement of coalbed methane production," Energy, Elsevier, vol. 280(C).
    4. Li, Qixian & Xu, Jiang & Shu, Longyong & Yan, Fazhi & Pang, Bo & Peng, Shoujian, 2023. "Exploration of the induced fluid-disturbance effect in CBM co-production in a superimposed pressure system," Energy, Elsevier, vol. 265(C).
    5. Min, Chao & Wen, Guoquan & Gou, Liangjie & Li, Xiaogang & Yang, Zhaozhong, 2023. "Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing," Energy, Elsevier, vol. 285(C).
    6. Fan, Lurong & Ma, Ning & Zhang, Wen, 2023. "Multi-stakeholder equilibrium-based subsidy allocation mechanism for promoting coalbed methane scale extraction-utilization," Energy, Elsevier, vol. 277(C).
    7. Wen, Hu & Yan, Li & Jin, Yongfei & Wang, Zhipeng & Guo, Jun & Deng, Jun, 2023. "Coalbed methane concentration prediction and early-warning in fully mechanized mining face based on deep learning," Energy, Elsevier, vol. 264(C).
    8. Zhou, Lijun & Zhou, Xihua & Fan, Chaojun & Bai, Gang & Yang, Lei & Wang, Yiqi, 2023. "Modelling of flue gas injection promoted coal seam gas extraction incorporating heat-fluid-solid interactions," Energy, Elsevier, vol. 268(C).
    9. Zhou, H.W. & Liu, Z.L. & Zhong, J.C. & Chen, B.C. & Zhao, J.W. & Xue, D.J., 2022. "NMRI online observation of coal fracture and pore structure evolution under confining pressure and axial compressive loads: A novel approach," Energy, Elsevier, vol. 261(PA).
    10. Wang, Kai & Gong, Haoran & Wang, Gongda & Yang, Xin & Xue, Haiteng & Du, Feng & Wang, Zhie, 2024. "N2 injection to enhance gas drainage in low-permeability coal seam: A field test and the application of deep learning algorithms," Energy, Elsevier, vol. 290(C).
    11. Wu, Han & Liang, Yan & Gao, Xiao-Zhi, 2023. "Left-right brain interaction inspired bionic deep network for forecasting significant wave height," Energy, Elsevier, vol. 278(PB).
    12. Kang, Yili & Ma, Chenglin & Xu, Chengyuan & You, Lijun & You, Zhenjiang, 2023. "Prediction of drilling fluid lost-circulation zone based on deep learning," Energy, Elsevier, vol. 276(C).
    13. Du, Shuyi & Wang, Jiulong & Wang, Meizhu & Yang, Jiaosheng & Zhang, Cong & Zhao, Yang & Song, Hongqing, 2023. "A systematic data-driven approach for production forecasting of coalbed methane incorporating deep learning and ensemble learning adapted to complex production patterns," Energy, Elsevier, vol. 263(PE).
    14. Liu, Jinyuan & Wang, Shouxi & Wei, Nan & Qiao, Weibiao & Li, Ze & Zeng, Fanhua, 2023. "A clustering-based feature enhancement method for short-term natural gas consumption forecasting," Energy, Elsevier, vol. 278(PB).

    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:gam:jeners:v:15:y:2022:i:16:p:5811-:d:884826. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.