IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v322y2025ics0360544225013374.html
   My bibliography  Save this article

Microleakage localization method for subsea production manifold based on transient pressure wave

Author

Listed:
  • Liu, Xuelin
  • Cai, Baoping
  • Jiang, Yi
  • Ji, Guowei
  • Wu, Kaizheng
  • Li, Qingping
  • Shao, Xiaoyan
  • Wang, Xintong

Abstract

Subsea production manifold is the center of collection and distribution of subsea oil and gas production. Microleakage will occur on long-term service manifold, if not timely intervention, will evolve into environmental pollution and huge economic losses. In this paper, a microleakage localization method for subsea production manifold is proposed. Through cross-validation of multi-scale data and model information, the complex aliasing strong noise modes can be reasonably filtered and effectively separated. The leakage pipeline identification model based on local linear fitting is established. A microleakage localization based on the boundary-enhanced dynamic time warping matrix is established to analyze the pressure time series of the microleakage. The arrival time difference is obtained by combining the distance of the greatest similarity with the signal sampling frequency. In combination with the dynamic pressure wave propagation velocity in the manifold, the microleakage localization is realized. The effectiveness and accuracy of the method are verified by the microleakage simulation experiment of the prototype of the subsea production manifold principle. The average identification accuracy of the proposed microleakage localization method is 90.48 %, the average localization accuracy is 98.94 %, and the average localization error is 12.83 cm. The changing law of localization error at different pressure is also analyzed.

Suggested Citation

  • Liu, Xuelin & Cai, Baoping & Jiang, Yi & Ji, Guowei & Wu, Kaizheng & Li, Qingping & Shao, Xiaoyan & Wang, Xintong, 2025. "Microleakage localization method for subsea production manifold based on transient pressure wave," Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:energy:v:322:y:2025:i:c:s0360544225013374
    DOI: 10.1016/j.energy.2025.135695
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225013374
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135695?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Xu, Ziqiang & Li, Cheng & Mu, Lianbo & Wang, Suilin & Lu, Junhui & Lan, Yuncheng, 2024. "Leakage detection method of underground heating pipeline based on improved wavelet threshold function," Energy, Elsevier, vol. 295(C).
    2. Bi, Yubo & Wu, Qiulan & Wang, Shilu & Shi, Jihao & Cong, Haiyong & Ye, Lili & Gao, Wei & Bi, Mingshu, 2023. "Hydrogen leakage location prediction at hydrogen refueling stations based on deep learning," Energy, Elsevier, vol. 284(C).
    3. Zhu, Jianlu & Wang, Sailei & Pan, Jun & Lv, Hao & Zhang, Yixiang & Han, Hui & Liu, Cuiwei & Duo, Zhili & Li, Yuxing, 2024. "Experimental study on leakage temperature field of hydrogen blending into natural gas buried pipeline," Applied Energy, Elsevier, vol. 359(C).
    4. Zhao, Guangcai & Kang, Yongzhe & Huang, Peng & Duan, Bin & Zhang, Chenghui, 2023. "Battery health prognostic using efficient and robust aging trajectory matching with ensemble deep transfer learning," Energy, Elsevier, vol. 282(C).
    5. Li, Pengyu & Wang, Xiufang & Jiang, Chunlei & Bi, Hongbo & Liu, Yongzhi & Yan, Wendi & Zhang, Cong & Dong, Taiji & Sun, Yu, 2024. "Advanced transformer model for simultaneous leakage aperture recognition and localization in gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Yao, Lizhong & Zhang, Yu & He, Tiantian & Luo, Haijun, 2023. "Natural gas pipeline leak detection based on acoustic signal analysis and feature reconstruction," Applied Energy, Elsevier, vol. 352(C).
    7. Shao, Xiaoyan & Cai, Baoping & Gao, Lei & Zhang, Yanping & Yang, Chao & Gao, Chuntan, 2024. "Data-model-linked remaining useful life prediction method with small sample data: A case of subsea valve," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    8. Chen, Xin & Ye, Xiaoling & Xiong, Xiong & Zhang, Yingchao & Li, Yuanlu, 2024. "Improving the accuracy of wind speed spatial interpolation: A pre-processing algorithm for wind speed dynamic time warping interpolation," Energy, Elsevier, vol. 295(C).
    9. Du, Ying & Jiang, Jinbao & Yu, Zijian & Liu, Ziwei & Pan, Yingyang & Xiong, Kangni, 2024. "A knowledge guided deep learning framework for underground natural gas micro-leaks detection from hyperspectral imagery," Energy, Elsevier, vol. 294(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. Ye, Lin & Wang, Chengyou & Zhou, Xiao & Jiang, Baocheng & Yu, Changsong & Qin, Zhiliang, 2025. "Natural gas pipeline weak leakage detection based on negative pressure wave decomposition and feature enhancement," Reliability Engineering and System Safety, Elsevier, vol. 257(PB).
    2. Wang, Lin & Guo, Wannian & Guo, Junyu & Zheng, Shaocong & Wang, Zhiyuan & Kang, Hooi Siang & Li, He, 2025. "An integrated deep learning model for intelligent recognition of long-distance natural gas pipeline features," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    3. Tan, Qiong & Fu, Ming & Wang, Zhengxing & Yuan, Hongyong & Sun, Jinhua, 2024. "A real-time early warning classification method for natural gas leakage based on random forest," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    4. Chen, Xin & Ye, Xiaoling & Shi, Jian & Zhang, Yingchao & Xiong, Xiong, 2024. "A spatial transfer-based hybrid model for wind speed forecasting," Energy, Elsevier, vol. 313(C).
    5. Gao, Xianhui & Wang, Sheng & Sun, Ying & Zhai, Junyi & Chen, Nan & Zhang, Xiao-Ping, 2024. "Low-carbon energy scheduling for integrated energy systems considering offshore wind power hydrogen production and dynamic hydrogen doping strategy," Applied Energy, Elsevier, vol. 376(PA).
    6. Fan Zhang & Xiao Zheng & Zixuan Xing & Minghu Wu, 2024. "Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features," Energies, MDPI, vol. 17(7), pages 1-21, March.
    7. Jun He & Xinyu Liu & Wentao Huang & Bohan Zhang & Zuoming Zhang & Zirui Shao & Zimu Mao, 2024. "Health State Assessment of Lithium-Ion Batteries Based on Multi-Health Feature Fusion and Improved Informer Modeling," Energies, MDPI, vol. 17(9), pages 1-18, April.
    8. Hu, Xuguang & Ma, Jilong & Zhao, Haoran & Ma, Dazhong, 2024. "A weak leak location method based on signal attenuation mechanism for energy transportation system," Energy, Elsevier, vol. 307(C).
    9. Jing, Qi & Song, Xingwang & Sun, Bingcai & Li, Yuntao & Zhang, Laibin, 2025. "Efficient estimation of natural gas leakage source terms using physical information and improved particle filtering," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    10. Ji, Shanling & Zhang, Zhisheng & Stein, Helge S. & Zhu, Jianxiong, 2025. "Flexible health prognosis of battery nonlinear aging using temporal transfer learning," Applied Energy, Elsevier, vol. 377(PD).
    11. Yin, Linfei & Ju, Linyi, 2025. "ShuffleTransformerMulti-headAttentionNet network for user load forecasting," Energy, Elsevier, vol. 322(C).
    12. Zhang, Yanping & Cai, Baoping & Ahmed, Salim & Wang, Chengyushu & Li, Qingping & Gao, Lei, 2025. "A resilience-driven emergency maintenance operation scheme optimization method based on risk," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    13. Leiqi Zhang & Qiliang Wu & Min Liu & Hao Chen & Dianji Wang & Xuefang Li & Qingxin Ba, 2025. "Hydrogen Leakage Location Prediction in a Fuel Cell System of Skid-Mounted Hydrogen Refueling Stations," Energies, MDPI, vol. 18(2), pages 1-17, January.
    14. Giovane Ronei Sylvestrin & Joylan Nunes Maciel & Marcio Luís Munhoz Amorim & João Paulo Carmo & José A. Afonso & Sérgio F. Lopes & Oswaldo Hideo Ando Junior, 2025. "State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review," Energies, MDPI, vol. 18(3), pages 1-77, February.
    15. Haoxin, Dong & Qiyuan, Deng & Chaojie, Li & Nian, Liu & Zhang, Wenzuo & Hu, Mingyue & Xu, Chuanbo, 2025. "A comprehensive review on renewable power-to-green hydrogen-to-power systems: Green hydrogen production, transportation, storage, re-electrification and safety," Applied Energy, Elsevier, vol. 390(C).
    16. Kumar, Roushan & Das, Kaushik & Krishna, Anurup, 2024. "Comparative analysis of data-driven electric vehicle battery health models across different operating conditions," Energy, Elsevier, vol. 309(C).
    17. Hussain, Muhammad & Zhang, Tieling, 2025. "Machine learning-based outlier detection for pipeline in-line inspection data," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    18. Chen, Xiang & Wang, Xingxing & Deng, Yelin, 2025. "Federated learning-based prediction of electric vehicle battery pack capacity using time-domain and frequency-domain feature extraction," Energy, Elsevier, vol. 319(C).
    19. Cheraghi, Morteza & Taghipour, Sharareh, 2025. "Safety Integrity Level (SIL) evaluation of safety instrumented systems considering competing failure modes and subsystem priorities," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    20. Sun, Yang & Tian, Zhirui, 2025. "Solving few-shot problem in wind speed prediction: A novel transfer strategy based on decomposition and learning ensemble," Applied Energy, Elsevier, vol. 377(PD).

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:energy:v:322:y:2025:i:c:s0360544225013374. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.