IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v275y2023ics0360544223007004.html
   My bibliography  Save this article

A fluid discrimination method based on Gassmann-Brie-Patchy Equation full waveform simulations and time-frequency analysis

Author

Listed:
  • Guo, Yuhang
  • Pan, Baozhi
  • Zhang, Lihua
  • Lai, Qiang
  • Wu, Yuyu
  • A, Ruhan
  • Wang, Xinru
  • Zhang, Pengji
  • Zhang, Naiyu
  • Li, Yan

Abstract

In recent years, igneous reservoirs have been the focus of natural gas exploration and development, which can help solve the energy demand, and in addition, when oil and gas reservoirs are depleted, volcanic reservoirs provide good storage space, transport channels and storage capacity, and this is very beneficial for CO2 storage. For reservoir prediction and CO2 storage monitoring, fluid discrimination techniques are very important. For igneous rock, the lithology and mineral composition, porosity, and pore structural characteristics are known to all affect the distribution patterns and saturation of fluid. The presence of high resistivity water layers and low resistivity gas layers will affect the accuracy of fluid discrimination using resistivity data. In this study, based on core experiments, the G-B-P (Gassmann-Brie-Patchy)equation was adopted to describe the relationships between the acoustic velocity and fluid saturation values. Then, finite element acoustic logging numerical simulations of wells were carried out to obtain the array logging full waveforms of the different lithology, porosity, and saturation. Finally, the ICEEMDAN(Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) mode decomposition method and SPWVD were used to analyze the time-frequency characteristics of the simulated waveforms, and a fluid discrimination chart of gas layer was established. The results of the synthetic and field data experiments using the proposed method demonstrated that it was a good approach for fluid discrimination processes in igneous gas reservoirs.

Suggested Citation

  • Guo, Yuhang & Pan, Baozhi & Zhang, Lihua & Lai, Qiang & Wu, Yuyu & A, Ruhan & Wang, Xinru & Zhang, Pengji & Zhang, Naiyu & Li, Yan, 2023. "A fluid discrimination method based on Gassmann-Brie-Patchy Equation full waveform simulations and time-frequency analysis," Energy, Elsevier, vol. 275(C).
    • Handle: RePEc:eee:energy:v:275:y:2023:i:c:s0360544223007004
    DOI: 10.1016/j.energy.2023.127306
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223007004
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127306?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Niu, Hongli & Xu, Kunliang & Liu, Cheng, 2021. "A decomposition-ensemble model with regrouping method and attention-based gated recurrent unit network for energy price prediction," Energy, Elsevier, vol. 231(C).
    2. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    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. Fuster-Palop, Enrique & Prades-Gil, Carlos & Masip, X. & Viana-Fons, Joan D. & Payá, Jorge, 2021. "Innovative regression-based methodology to assess the techno-economic performance of photovoltaic installations in urban areas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    2. Zhang, Shuo & Song, Shengyuan & Zhang, Wen & Zhao, Jinmin & Cao, Dongfang & Ma, Wenliang & Chen, Zijian & Hu, Ying, 2023. "Research on the inherent mechanism of rock mass deformation of oil shale in-situ mining under the condition of thermal-fluid-solid coupling," Energy, Elsevier, vol. 280(C).
    3. Raza, Muhammad Yousaf & Lin, Boqiang, 2023. "Future outlook and influencing factors analysis of natural gas consumption in Bangladesh: An economic and policy perspectives," Energy Policy, Elsevier, vol. 173(C).
    4. Golberg, Alexander, 2015. "Environmental exergonomics for sustainable design and analysis of energy systems," Energy, Elsevier, vol. 88(C), pages 314-321.
    5. Jiang, Ping & Liu, Zhenkun & Wang, Jianzhou & Zhang, Lifang, 2021. "Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm," Resources Policy, Elsevier, vol. 73(C).
    6. Yue, Xiufeng & Deane, J.P. & O'Gallachoir, Brian & Rogan, Fionn, 2020. "Identifying decarbonisation opportunities using marginal abatement cost curves and energy system scenario ensembles," Applied Energy, Elsevier, vol. 276(C).
    7. Mohamad Taghvaee, Vahid & Saboori, Behnaz & Soretz, Susanne & Magazzino, Cosimo & Tatar, Moosa, 2024. "Renewable energy, energy efficiency, and economic complexity in the middle East and North Africa: A panel data analysis," Energy, Elsevier, vol. 311(C).
    8. Park, Nyun-Bae & Park, Sang Yong & Kim, Jong-Jin & Choi, Dong Gu & Yun, Bo Yeong & Hong, Jong Chul, 2017. "Technical and economic potential of highly efficient boiler technologies in the Korean industrial sector," Energy, Elsevier, vol. 121(C), pages 884-891.
    9. Rafiee, A. & Dias, E. & Koomen, E., 2019. "Analysing the impact of spatial context on the heat consumption of individual households," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 461-470.
    10. Wen, Kai & Jiao, Jianfeng & Zhao, Kang & Yin, Xiong & Liu, Yuan & Gong, Jing & Li, Cuicui & Hong, Bingyuan, 2023. "Rapid transient operation control method of natural gas pipeline networks based on user demand prediction," Energy, Elsevier, vol. 264(C).
    11. Gabrielli, Paolo & Wüthrich, Moritz & Blume, Steffen & Sansavini, Giovanni, 2022. "Data-driven modeling for long-term electricity price forecasting," Energy, Elsevier, vol. 244(PB).
    12. Kong, Xiangyu & Lu, Wenqi & Wu, Jianzhong & Wang, Chengshan & Zhao, Xv & Hu, Wei & Shen, Yu, 2023. "Real-time pricing method for VPP demand response based on PER-DDPG algorithm," Energy, Elsevier, vol. 271(C).
    13. Tian, Feng & Wang, Junlei & Xu, Zhenhua & Xiong, Fansheng & Xia, Peng, 2023. "A nonlinear model of multifractured horizontal wells in heterogeneous gas reservoirs considering the effect of stress sensitivity," Energy, Elsevier, vol. 263(PD).
    14. Hu, Ting & Huang, Xin, 2019. "A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 240(C), pages 778-792.
    15. Xu, Kunliang & Wang, Weiqing, 2023. "Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?," International Review of Financial Analysis, Elsevier, vol. 87(C).
    16. 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).
    17. Liu, Hao & Su, Guandong & Okere, Chinedu J. & Li, Guozhang & Wang, Xiangchun & Cai, Yuzhe & Wu, Tong & Zheng, Lihui, 2022. "Working fluid-induced formation damage evaluation for commingled production of multi-layer natural gas reservoirs with flow rate method," Energy, Elsevier, vol. 239(PB).
    18. Huang, Zhiwen & Li, Tong & Huang, Kexin & Ke, Hanbing & Lin, Mei & Wang, Qiuwang, 2022. "Predictions of flow and temperature fields in a T-junction based on dynamic mode decomposition and deep learning," Energy, Elsevier, vol. 261(PA).
    19. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Chen, Zuoqi & Liu, Rui & Li, Linyi & Wu, Jianping, 2016. "Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis," Applied Energy, Elsevier, vol. 168(C), pages 523-533.
    20. Qin, Lei & Wang, Ping & Lin, Haifei & Li, Shugang & Zhou, Bin & Bai, Yang & Yan, Dongjie & Ma, Chao, 2023. "Quantitative characterization of the pore volume fractal dimensions for three kinds of liquid nitrogen frozen coal and its enlightenment to coalbed methane exploitation," Energy, Elsevier, vol. 263(PA).

    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:275:y:2023:i:c:s0360544223007004. 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.