IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i4p821-d1336192.html
   My bibliography  Save this article

A New Model for Predicting Permeability of Chang 7 Tight Sandstone Based on Fractal Characteristics from High-Pressure Mercury Injection

Author

Listed:
  • Yuxuan Yang

    (Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China
    College of Resources and Environment, Yangtze University, Wuhan 430100, China)

  • Zhigang Wen

    (Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China
    College of Resources and Environment, Yangtze University, Wuhan 430100, China)

  • Weichao Tian

    (Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China
    College of Resources and Environment, Yangtze University, Wuhan 430100, China)

  • Yunpeng Fan

    (Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China
    College of Resources and Environment, Yangtze University, Wuhan 430100, China)

  • Heting Gao

    (Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China
    College of Resources and Environment, Yangtze University, Wuhan 430100, China)

Abstract

Accurately predicting permeability is important to elucidate the fluid mobility and development potential of tight reservoirs. However, for tight sandstones with the same porosity, permeability can change by nearly three orders of magnitude, which greatly increases the difficulty of permeability prediction. In this paper, we performed casting thin section, scanning electron microscopy and high-pressure mercury injection experiments to analyze the influence of pore structure parameters and fractal dimensions on the permeability of Chang 7 tight sandstones. Furthermore, the key parameters affecting the permeability were optimized, and a new permeability prediction model was established. The results show that the pore throat structure of Chang 7 tight sandstone exhibits three-stage fractal characteristics. Thus, the pore throat structure was divided into large pore throat, medium pore throat and small pore throat. The large pore throat reflects the microfracture system, whose fractal dimension was distributed above 2.99, indicating that the heterogeneity of the large pore throat was the strongest. The medium pore throat is dominated by the conventional pore throat system, and its fractal dimension ranged from 2.378 to 2.997. Small pore throats are mainly composed of the tree-shaped pore throat system, and its fractal dimension varied from 2.652 to 2.870. The medium pore throat volume and its fractal dimension were key factors affecting the permeability of Chang 7 tight sandstones. A new permeability prediction model was established based on the medium pore throat volume and its fractal dimension. Compared to other models, the prediction results of the new model are the best according to the analysis of root mean square value, average absolute percentage error and correlation coefficient. These results indicate that the permeability of tight sandstones can be accurately predicted using mesopore throat volume and fractal dimension.

Suggested Citation

  • Yuxuan Yang & Zhigang Wen & Weichao Tian & Yunpeng Fan & Heting Gao, 2024. "A New Model for Predicting Permeability of Chang 7 Tight Sandstone Based on Fractal Characteristics from High-Pressure Mercury Injection," Energies, MDPI, vol. 17(4), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:821-:d:1336192
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/4/821/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/4/821/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    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. Wensong Huang & Ping Wang & Gang Hui & Xiangwen Kong & Yuepeng Jia & Lei Huang & Yufei Bai & Zhiyang Pi & Ye Li & Fuyu Yao & Penghu Bao & Yujie Zhang, 2024. "Unconventional Fracture Networks Simulation and Shale Gas Production Prediction by Integration of Petrophysics, Geomechanics and Fracture Characterization," Energies, MDPI, vol. 17(20), pages 1-16, October.
    2. Zou, Xiaojing & He, Changyu & Guan, Wei & Zhou, Yan & Zhao, Hongyang & Cai, Mingyu, 2023. "Reservoir tortuosity prediction: Coupling stochastic generation of porous media and machine learning," Energy, Elsevier, vol. 285(C).
    3. Niu, Wente & Lu, Jialiang & Sun, Yuping & Zhang, Xiaowei & Li, Qiaojing & Cao, Xu & Liang, Pingping & Zhan, Hongming, 2024. "Techno-economic integration evaluation in shale gas development based on ensemble learning," Applied Energy, Elsevier, vol. 357(C).
    4. 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).
    5. Hui, Gang & Chen, Zhangxin & Schultz, Ryan & Chen, Shengnan & Song, Zhaojie & Zhang, Zhaochen & Song, Yilei & Wang, Hai & Wang, Muming & Gu, Fei, 2023. "Intricate unconventional fracture networks provide fluid diffusion pathways to reactivate pre-existing faults in unconventional reservoirs," Energy, Elsevier, vol. 282(C).
    6. Gang Hui & Fei Gu & Junqi Gan & Erfan Saber & Li Liu, 2023. "An Integrated Approach to Reservoir Characterization for Evaluating Shale Productivity of Duvernary Shale: Insights from Multiple Linear Regression," Energies, MDPI, vol. 16(4), pages 1-18, February.
    7. Yi, Jun & Qi, ZhongLi & Li, XiangChengZhen & Liu, Hong & Zhou, Wei, 2024. "Spatial correlation-based machine learning framework for evaluating shale gas production potential: A case study in southern Sichuan Basin, China," Applied Energy, Elsevier, vol. 357(C).
    8. Priya Bijalwan & Ashulekha Gupta & Anubhav Mendiratta & Amar Johri & Mohammad Asif, 2024. "Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms," Economies, MDPI, vol. 12(1), pages 1-19, January.
    9. Xingzhi Liu & Songhang Zhang & Yongkang Xie & Tao Wang, 2024. "Three-Dimensional Heterogeneity of the Pore and Fracture Development and Acoustic Emission Response Characteristics of Coal Rocks in the Yunnan Laochang Block," Energies, MDPI, vol. 17(5), pages 1-19, March.
    10. Shakouri, Sina & Mohammadzadeh-Shirazi, Maysam, 2023. "Modeling of asphaltic sludge formation during acidizing process of oil well reservoir using machine learning methods," Energy, Elsevier, vol. 285(C).

    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:17:y:2024:i:4:p:821-:d:1336192. 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.