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The Effect of Oil Properties on the Supercritical CO 2 Diffusion Coefficient under Tight Reservoir Conditions

Author

Listed:
  • Chao Zhang

    (College of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China
    These authors contributed equally to this work.)

  • Chenyu Qiao

    (College of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China
    These authors contributed equally to this work.)

  • Songyan Li

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

  • Zhaomin Li

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

Abstract

In this paper, a generalized methodology has been developed to determine the diffusion coefficient of supercritical CO 2 in cores that are saturated with different oil samples, under reservoir conditions. In theory, a mathematical model that combines Fick’s diffusion equation and the Peng-Robinson equation of state has been established to describe the mass transfer process. In experiments, the pressure decay method has been employed, and the CO 2 diffusion coefficient can be determined once the experimental data match the computational result of the theoretical model. Six oil samples with different compositions (oil samples A to F) are introduced in this study, and the results show that the supercritical CO 2 diffusion coefficient decreases gradually from oil samples A to F. The changing properties of oil can account for the decrease in the CO 2 diffusion coefficient in two aspects. First, the increasing viscosity of oil slows down the speed of the mass transfer process. Second, the increase in the proportion of heavy components in oil enlarges the mass transfer resistance. According to the results of this work, a lower viscosity and lighter components of oil can facilitate the mass transfer process.

Suggested Citation

  • Chao Zhang & Chenyu Qiao & Songyan Li & Zhaomin Li, 2018. "The Effect of Oil Properties on the Supercritical CO 2 Diffusion Coefficient under Tight Reservoir Conditions," Energies, MDPI, vol. 11(6), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1495-:d:151326
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    References listed on IDEAS

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    1. Zhang, Liang & Li, Xin & Zhang, Yin & Cui, Guodong & Tan, Chunyang & Ren, Shaoran, 2017. "CO2 injection for geothermal development associated with EGR and geological storage in depleted high-temperature gas reservoirs," Energy, Elsevier, vol. 123(C), pages 139-148.
    2. Zhang, Zhien & Cai, Jianchao & Chen, Feng & Li, Hao & Zhang, Wenxiang & Qi, Wenjie, 2018. "Progress in enhancement of CO2 absorption by nanofluids: A mini review of mechanisms and current status," Renewable Energy, Elsevier, vol. 118(C), pages 527-535.
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    Cited by:

    1. Liu, Huang & Yao, Desong & Yang, Bowen & Li, Huashi & Guo, Ping & Du, Jianfen & Wang, Jian & Yang, Shuokong & Wen, Lianhui, 2022. "Experimental investigation on the mechanism of low permeability natural gas extraction accompanied by carbon dioxide sequestration," Energy, Elsevier, vol. 253(C).
    2. Jianchao Cai & Shuyu Sun & Ali Habibi & Zhien Zhang, 2019. "Emerging Advances in Petrophysics: Porous Media Characterization and Modeling of Multiphase Flow," Energies, MDPI, vol. 12(2), pages 1-5, January.
    3. Chen, Hao & Wang, Yu & Zuo, Mingsheng & Zhang, Chao & Jia, Ninghong & Liu, Xiliang & Yang, Shenglai, 2022. "A new prediction model of CO2 diffusion coefficient in crude oil under reservoir conditions based on BP neural network," Energy, Elsevier, vol. 239(PC).

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