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Thermal Conductivity Estimation Based on Well Logging

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  • Jie Hu

    (State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing 100029, China)

  • Guangzheng Jiang

    (State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing 100029, China)

  • Yibo Wang

    (State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing 100029, China)

  • Shengbiao Hu

    (State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing 100029, China)

Abstract

The thermal conductivity of a stratum is a key factor to study the deep temperature distribution and the thermal structure of the basin. A huge expense of core sampling from boreholes, especially in offshore areas, makes it expensive to directly test stratum samples. Therefore, the use of well logging (the gamma-ray, the neutron porosity, and the temperature) to estimate the thermal conductivity of the samples obtained from boreholes could be a good alternative. In this study, we measured the thermal conductivity of 72 samples obtained from an offshore area as references. When the stratum is considered to be a shale–sand–fluid model, the thermal conductivity can be calculated based on the mixing models (the geometric mean and the square root mean). The contents of the shale and the sand were derived from the natural gamma-ray logs, and the content of the fluid (porosity) was derived from the neutron porosity logs. The temperature corrections of the thermal conductivity were performed for the solid component and the fluid component separately. By comparing with the measured data, the thermal conductivity predicted based on the square root model showed good consistency. This technique is low-cost and has great potential to be used as an application method to obtain the thermal conductivity for geothermal research.

Suggested Citation

  • Jie Hu & Guangzheng Jiang & Yibo Wang & Shengbiao Hu, 2021. "Thermal Conductivity Estimation Based on Well Logging," Mathematics, MDPI, vol. 9(11), pages 1-11, May.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:11:p:1176-:d:560386
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    References listed on IDEAS

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    1. Lu, Shyi-Min, 2018. "A global review of enhanced geothermal system (EGS)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2902-2921.
    2. Fridleifsson, Ingvar B., 2001. "Geothermal energy for the benefit of the people," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(3), pages 299-312, September.
    3. Ellabban, Omar & Abu-Rub, Haitham & Blaabjerg, Frede, 2014. "Renewable energy resources: Current status, future prospects and their enabling technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 748-764.
    4. Olasolo, P. & Juárez, M.C. & Morales, M.P. & D´Amico, Sebastiano & Liarte, I.A., 2016. "Enhanced geothermal systems (EGS): A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 133-144.
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    Keywords

    thermal conductivity; well logs;

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