IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i12p4254-d835089.html
   My bibliography  Save this article

Lattice Boltzmann Modeling of Spontaneous Imbibition in Variable-Diameter Capillaries

Author

Listed:
  • Rundong Gong

    (Unconventional Petroleum Research Institute, China University of Petroleum-Beijing, Beijing 102249, China)

  • Xiukun Wang

    (Unconventional Petroleum Research Institute, China University of Petroleum-Beijing, Beijing 102249, China)

  • Lei Li

    (College of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Kaikai Li

    (The No. 6 Oil Production Plant, Petrochina Changqing Oilfield Company, Xi’an 710018, China)

  • Ran An

    (The No. 6 Oil Production Plant, Petrochina Changqing Oilfield Company, Xi’an 710018, China)

  • Chenggang Xian

    (Unconventional Petroleum Research Institute, China University of Petroleum-Beijing, Beijing 102249, China)

Abstract

Previous micro-scale studies of the effect of pore structure on spontaneous imbibition are mainly limited to invariable-diameter capillaries. However, in real oil and gas reservoir formations, the capillary diameters are changing and interconnected. Applying the lattice Boltzmann color gradient two-phase flow model and the parallel computation of CPUs, we simulated the spontaneous imbibition in variable-diameter capillaries. We explored the reasons for the nonwetting phase snap-off and systematically studied the critical conditions for the snap-off in spontaneous imbibition. The effects of pore-throat aspect ratio, throat diameter, and the pore-throat tortuosity of the capillary on spontaneous imbibition were studied. Through analyzing the simulated results, we found that the variation in the capillary diameter produces an additional resistance, which increases with the increase in the pore-throat ratio and the pore-throat tortuosity of a capillary. Under the action of this additional resistance, the snap-off phenomenon sometimes occurs in the spontaneous imbibition, which makes the recovery efficiency of the non-wetting phase extremely low. In addition, the main factors affecting this phenomenon are the pore-throat ratio and the pore-throat tortuosity, which is different from the conventional concept of tortuosity. When the snap-off does not occur, the spontaneous imbibition velocity increases when the throat diameter increases and the pore-throat aspect ratio is fixed, and when the period increases, i.e., the diameter changing rate decreases, the spontaneous imbibition velocity also increases. In addition, when the capillary throat diameter is fixed, a bigger pore diameter and a smaller period of sine function both inhibit the speed of spontaneous imbibition.

Suggested Citation

  • Rundong Gong & Xiukun Wang & Lei Li & Kaikai Li & Ran An & Chenggang Xian, 2022. "Lattice Boltzmann Modeling of Spontaneous Imbibition in Variable-Diameter Capillaries," Energies, MDPI, vol. 15(12), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4254-:d:835089
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/12/4254/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/12/4254/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiukun Wang & James J. Sheng, 2020. "Dynamic Pore-Scale Network Modeling of Spontaneous Water Imbibition in Shale and Tight Reservoirs," Energies, MDPI, vol. 13(18), pages 1-15, September.
    2. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    3. Nan Wei & Changjun Li & Jiehao Duan & Jinyuan Liu & Fanhua Zeng, 2019. "Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model," Energies, MDPI, vol. 12(2), pages 1-15, January.
    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, Jian & Zheng, Jianqin & Liang, Yongtu & Lu, Xinyi & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Shahzad, Khurram & Rashid, Muhammad Imtiaz & Ali, Arshid Mahmood & Liao, Qi & Wang, Bohong, 2022. "A hybrid deep learning framework for predicting daily natural gas consumption," Energy, Elsevier, vol. 257(C).
    2. Tomasz Cieślik & Piotr Narloch & Adam Szurlej & Krzysztof Kogut, 2022. "Indirect Impact of the COVID-19 Pandemic on Natural Gas Consumption by Commercial Consumers in a Selected City in Poland," Energies, MDPI, vol. 15(4), pages 1-18, February.
    3. Qiao, Weibiao & Liu, Wei & Liu, Enbin, 2021. "A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of U.S," Energy, Elsevier, vol. 235(C).
    4. Wei, Nan & Yin, Lihua & Li, Chao & Wang, Wei & Qiao, Weibiao & Li, Changjun & Zeng, Fanhua & Fu, Lingdi, 2022. "Short-term load forecasting using detrend singular spectrum fluctuation analysis," Energy, Elsevier, vol. 256(C).
    5. Wei, Nan & Yin, Lihua & Li, Chao & Li, Changjun & Chan, Christine & Zeng, Fanhua, 2021. "Forecasting the daily natural gas consumption with an accurate white-box model," Energy, Elsevier, vol. 232(C).
    6. 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).
    7. Yong Wang & Nan Wei & Dejun Wan & Shouxi Wang & Zongming Yuan, 2019. "Numerical Simulation for Preheating New Submarine Hot Oil Pipelines," Energies, MDPI, vol. 12(18), pages 1-26, September.
    8. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    9. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    10. Fang, Lei & He, Bin, 2023. "A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting," Applied Energy, Elsevier, vol. 348(C).
    11. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM," Energy, Elsevier, vol. 263(PE).
    12. Jinyuan Liu & Shouxi Wang & Nan Wei & Yi Yang & Yihao Lv & Xu Wang & Fanhua Zeng, 2023. "An Enhancement Method Based on Long Short-Term Memory Neural Network for Short-Term Natural Gas Consumption Forecasting," Energies, MDPI, vol. 16(3), pages 1-14, January.
    13. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Chatzis, Georgios V. & Biskas, Pandelis N. & Keranidis, Stratos D., 2021. "Minimization of natural gas consumption of domestic boilers with convolutional, long-short term memory neural networks and genetic algorithm," Applied Energy, Elsevier, vol. 299(C).
    14. Reza Rezaee, 2022. "Editorial on Special Issues of Development of Unconventional Reservoirs," Energies, MDPI, vol. 15(7), pages 1-9, April.
    15. Ahmad, Tanveer & Huanxin, Chen & Zhang, Dongdong & Zhang, Hongcai, 2020. "Smart energy forecasting strategy with four machine learning models for climate-sensitive and non-climate sensitive conditions," Energy, Elsevier, vol. 198(C).
    16. Yun Bai & Nejc Bezak & Klaudija Sapač & Mateja Klun & Jin Zhang, 2019. "Short-Term Streamflow Forecasting Using the Feature-Enhanced Regression Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4783-4797, November.
    17. Bartłomiej Gaweł & Andrzej Paliński, 2021. "Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree," Energies, MDPI, vol. 14(16), pages 1-26, August.
    18. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
    19. Rehman, Aniqa & Zhu, Jun-Jie & Segovia, Javier & Anderson, Paul R., 2022. "Assessment of deep learning and classical statistical methods on forecasting hourly natural gas demand at multiple sites in Spain," Energy, Elsevier, vol. 244(PA).
    20. Song, Jiancai & Zhang, Liyi & Jiang, Qingling & Ma, Yunpeng & Zhang, Xinxin & Xue, Guixiang & Shen, Xingliang & Wu, Xiangdong, 2022. "Estimate the daily consumption of natural gas in district heating system based on a hybrid seasonal decomposition and temporal convolutional network model," Applied Energy, Elsevier, vol. 309(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:15:y:2022:i:12:p:4254-:d:835089. 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.