IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i9p1779-d230058.html
   My bibliography  Save this article

An Evaluation Method of Brittleness Characteristics of Shale Based on the Unloading Experiment

Author

Listed:
  • Xiaogui Zhou

    (Yunnan Key Laboratory of Disaster Reduction in Civil Engineering, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650504, China)

  • Haiming Liu

    (Yunnan Key Laboratory of Disaster Reduction in Civil Engineering, Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650504, China)

  • Yintong Guo

    (State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China)

  • Lei Wang

    (State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China)

  • Zhenkun Hou

    (Guangzhou Institute of Building Science Co., Ltd., Guangzhou 510440, China)

  • Peng Deng

    (State Key Laboratory for Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China)

Abstract

Shale reservoir has an initial unloading effect during the natural uplift and erosion process, which causes the shale brittleness to change, affecting the design of the fracturing scheme. To consider this, the axial compression loading and confining pressure unloading experiment of shale is carried out, and then the influence of unloading rate on the mechanical parameters, failure characteristics, and the brittleness of rock are analyzed. What is more, a new evaluation method of brittleness characteristics that take the unloading effect into consideration is proposed. The conclusions are as follows: (1) The unloading rate has a weakening effect on the mechanical parameters, such as the destructive confining pressure and the residual strength of the samples. (2) The failure characteristics of shale specimens are a single shear failure in an oblique section under low unloading rate, and multiple shear zones accompanied with bedding fracture under high unloading rate. (3) The brittleness of shale samples is well verified by the brittleness index B d 1 and B d 2 during the loading path; nevertheless, it has shortage at the unloading path. This paper proposes a new brittleness evaluation method which can consider the influence of the different unloading rates and unloading points. Furthermore, there is a nice characterization between the brittleness damage and this method.

Suggested Citation

  • Xiaogui Zhou & Haiming Liu & Yintong Guo & Lei Wang & Zhenkun Hou & Peng Deng, 2019. "An Evaluation Method of Brittleness Characteristics of Shale Based on the Unloading Experiment," Energies, MDPI, vol. 12(9), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1779-:d:230058
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/9/1779/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/9/1779/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kyoungsu Kim & Jonggeun Choe, 2019. "Hydraulic Fracture Design with a Proxy Model for Unconventional Shale Gas Reservoir with Considering Feasibility Study," Energies, MDPI, vol. 12(2), pages 1-12, January.
    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. Lianchong Li & Mingyang Zhai & Liaoyuan Zhang & Zilin Zhang & Bo Huang & Aishan Li & Jiaqiang Zuo & Quansheng Zhang, 2019. "Brittleness Evaluation of Glutenite Based On Energy Balance and Damage Evolution," Energies, MDPI, vol. 12(18), pages 1-28, September.
    2. Lei Wang & Bohang Liu & Hanzhi Yang & Yintong Guo & Jing Li & Hejuan Liu, 2022. "Experimental Study on the Compressive and Shear Mechanical Properties of Cement–Formation Interface Considering Surface Roughness and Drilling Mud Contamination," Energies, MDPI, vol. 15(17), pages 1-17, September.

    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. Dongkwon Han & Sunil Kwon, 2021. "Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs," Energies, MDPI, vol. 14(12), pages 1-24, June.

    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:12:y:2019:i:9:p:1779-:d:230058. 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.