IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i18p3518-d266786.html
   My bibliography  Save this article

Numerical Simulation for Preheating New Submarine Hot Oil Pipelines

Author

Listed:
  • Yong Wang

    (College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, Shaanxi, China
    Shaanxi Key Laboratory of Advanced Stimulation Technology for Oil & Gas Reservoirs, Xi’an Shiyou University, Xi’an 710065, Shaanxi, China)

  • Nan Wei

    (School of Petroleum and Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, Sichuan, China)

  • Dejun Wan

    (Sinopec Petroleum Engineering Cooperation, Dongying 257088, Shandong, China)

  • Shouxi Wang

    (College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, Shaanxi, China)

  • Zongming Yuan

    (School of Petroleum and Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, Sichuan, China)

Abstract

For new submarine hot oil pipelines, accurate simulation of preheating is difficult owing to complex transient flow and coupled heat transfer happening. Using quasi-steady equations to simulate preheating is inadequate as the hydraulic transient phenomenon is neglected. Considering this fact, this paper constructs an unsteady flow and heat transfer coupled mathematical model for the preheating process. By combining the double method of characteristics (DMOC) and finite element method (FEM), a numerical methodology is proposed, namely, DMOC-FEM. Its accuracy is validated by field data collected from the Bohai sea, China, showing the mean absolute percentage error (MAPE) of 4.27%. Simulation results demonstrate that the preheating medium mainly warms submarine pipe walls rather than the surrounding subsea mud. Furthermore, during the preheating process, the equivalent overall heat transfer coefficients deduced performs more applicably than the inverse-calculation method in presenting the unsteady propagation of fluid temperature with time and distance. Finally, according to the comparison results of 11 preheating plans, subject to a rated heat power and maximum flow, the preheating parameter at a lower fluid temperature combined with a higher flow rate will produce a better preheating effect.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3518-:d:266786
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/18/3518/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/18/3518/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qinglin Cheng & Yifan Gan & Wenkun Su & Yang Liu & Wei Sun & Ying Xu, 2017. "Research on Exergy Flow Composition and Exergy Loss Mechanisms for Waxy Crude Oil Pipeline Transport Processes," Energies, MDPI, vol. 10(12), pages 1-20, November.
    2. Wuyi Wan & Boran Zhang & Xiaoyi Chen & Jijian Lian, 2019. "Water Hammer Control Analysis of an Intelligent Surge Tank with Spring Self-Adaptive Auxiliary Control System," Energies, MDPI, vol. 12(13), pages 1-19, July.
    3. Li Ding & Jiasheng Zhang & Aiguo Lin, 2019. "A Deep-Sea Pipeline Skin Effect Electric Heat Tracing System," Energies, MDPI, vol. 12(13), pages 1-20, June.
    4. Wuyi Wan & Boran Zhang & Xiaoyi Chen, 2018. "Investigation on Water Hammer Control of Centrifugal Pumps in Water Supply Pipeline Systems," Energies, MDPI, vol. 12(1), pages 1-20, December.
    5. 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.
    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. 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).
    2. 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).
    3. 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.
    4. 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).
    5. 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).
    6. 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).
    7. 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).
    8. Yang, Ruiyue & Hong, Chunyang & Huang, Zhongwei & Song, Xianzhi & Zhang, Shikun & Wen, Haitao, 2019. "Coal breakage using abrasive liquid nitrogen jet and its implications for coalbed methane recovery," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    9. Dario Friso & Lucia Bortolini & Federica Tono, 2020. "Exergetic Analysis and Exergy Loss Reduction in the Milk Pasteurization for Italian Cheese Production," Energies, MDPI, vol. 13(3), pages 1-16, February.
    10. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
    11. Zhou, Dengji & Jia, Xingyun & Ma, Shixi & Shao, Tiemin & Huang, Dawen & Hao, Jiarui & Li, Taotao, 2022. "Dynamic simulation of natural gas pipeline network based on interpretable machine learning model," Energy, Elsevier, vol. 253(C).
    12. Ding, Jia & Zhao, Yuxuan & Jin, Junyang, 2023. "Forecasting natural gas consumption with multiple seasonal patterns," Applied Energy, Elsevier, vol. 337(C).
    13. Zhou, Weijie & Wu, Xiaoli & Ding, Song & Pan, Jiao, 2020. "Application of a novel discrete grey model for forecasting natural gas consumption: A case study of Jiangsu Province in China," Energy, Elsevier, vol. 200(C).
    14. Chuang Yin & Nan Wei & Jinghang Wu & Chuhong Ruan & Xi Luo & Fanhua Zeng, 2024. "An Empirical Mode Decomposition-Based Hybrid Model for Sub-Hourly Load Forecasting," Energies, MDPI, vol. 17(2), pages 1-17, January.
    15. Hu, Huanling & Wang, Lin & Peng, Lu & Zeng, Yu-Rong, 2020. "Effective energy consumption forecasting using enhanced bagged echo state network," Energy, Elsevier, vol. 193(C).
    16. Yang, Zhaoming & Liu, Zhe & Zhou, Jing & Song, Chaofan & Xiang, Qi & He, Qian & Hu, Jingjing & Faber, Michael H. & Zio, Enrico & Li, Zhenlin & Su, Huai & Zhang, Jinjun, 2023. "A graph neural network (GNN) method for assigning gas calorific values to natural gas pipeline networks," Energy, Elsevier, vol. 278(C).
    17. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    18. Michał Kubrak & Agnieszka Malesińska & Apoloniusz Kodura & Kamil Urbanowicz & Michał Stosiak, 2021. "Hydraulic Transients in Viscoelastic Pipeline System with Sudden Cross-Section Changes," Energies, MDPI, vol. 14(14), pages 1-12, July.
    19. Chen, Ying & Xu, Xiuqin & Koch, Thorsten, 2020. "Day-ahead high-resolution forecasting of natural gas demand and supply in Germany with a hybrid model," Applied Energy, Elsevier, vol. 262(C).
    20. Qiao, Weibiao & Fu, Zonghua & Du, Mingjun & Nan, Wei & Liu, Enbin, 2023. "Seasonal peak load prediction of underground gas storage using a novel two-stage model combining improved complete ensemble empirical mode decomposition and long short-term memory with a sparrow searc," Energy, Elsevier, vol. 274(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:12:y:2019:i:18:p:3518-:d:266786. 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.