Advertisement

Range Estimation of Interfacial Tension and Emulsifying Properties of Surfactants in Imbibition

  • Yi Zhang
  • Jian-Guo Li
  • Hai-Hui Chen
  • Hong-Yan Cai
  • Hongfu Fan
  • Yun Sun
Conference paper
Part of the Springer Series in Geomechanics and Geoengineering book series (SSGG)

Abstract

Interfacial tension and emulsion stability are two of the most important factors in imbibition agent properties which influence the imbibition efficiency. Through a large number of oil sand and core laboratory experiments, the core experimental model which is obtained by the method of curve-fitting was studied. The regression analysis of physical simulation experiment results was carried out by using curve-fitting method and binary regression method. The constant term of interfacial tension regression equation model has a significant effect at the level of α = 0.05 by using binary regression method. And the coefficient term and equation passed the inspection of significance at the level of α = 0.01 which can explain more than 80% of the experimental points. The constant term of the model obtained by analyzing the results of oil sand experiment passed the inspection of significance at the level of α = 0.05, meanwhile the coefficient term and equation passed the inspection of significance at the level of α = 0.0025 and α = 0.02. By synthetically analyzing the consequence of oil sand and core experiment, the most suitable range of two attribute parameters in ultra-/extra-low permeability reservoir was worked out. The range of interfacial tension is from 3 × 10−4 to 2 × 10−1mN/m, and emulsion stability is from 1.4 × 10−1% to 9.0%. The results reflected that pore of low permeability reservoir rock had a limiting effect to imbibition agents. It can also provide an important experimental basis for further study of optimizing and evaluation of imbibition agents.

Keywords

Binary regression method SPSS Ultra-low/extra-low permeability Emulsion stability Interfacial tension 

Notes

Acknowledgements

Supported by Advanced Research Project of RIPED of CNPC (Grant No. 2015yj-03) and the Coalbed Gas Joint Research Foundation of Shanxi Province (Grant No. 2013012003).

References

  1. 1.
    Aronofskfsky J, Masse L, Natanson SG (1958) A model for the mechanism of oil recovery from the porous matrix due to water invasion in fractured reservoir. Trans. AIME 213:17–19Google Scholar
  2. 2.
    Zhang Y, Wu K, Rao H et al (2014) Imbibition oil recovery theory and influencing factors: a review, ICEEP 2014. Adv Mater Res 962–965:P429–P436CrossRefGoogle Scholar
  3. 3.
    Li Z, Lin C, Dong B et al (2012) Factors influencing the development of water injection in low permeability reservoirs and improvement measures. J Geol Miner Resour 19(2):171–175 (in Chinese)Google Scholar
  4. 4.
    Zhang Y, Fu C, Zhu Y et al (2013) Pearson weight analysis for main factors of binary oil flooding efficiency. In: Environmental engineering [MSEE 2013], Wuhan, Hubei, China, 17–18 Aug 2013Google Scholar
  5. 5.
    Zhang Y, Fu C, Zhu Y et al (2013) Nonlinear regression analysis of binary flooding recovery influence factors (ICCMME 2013), Zhuhai, Guangdong, ChinaGoogle Scholar
  6. 6.
    Xie L, Song Z, He X (2013) SPSS statistical analysis using the tutorial, 2nd edn. People Post PressGoogle Scholar
  7. 7.
    Zhang Y, Zhu Y, Wu J et al (2012) Evaluation of emulsification stability of crude oil, ChinaGoogle Scholar
  8. 8.
    Liu H, Zhang Y, Li Y et al (2016) Influence on emulsification in binary flooding of oil displacement effect. J Dispers Sci Technol 37:89–96CrossRefGoogle Scholar
  9. 9.
    Zhang Y, Wang D, Lin Q et al (2012) Improvement on determination method for emulsifying ability of emulsifier used in oil field. Chem Ind Eng Progr 31(8):1852–1856Google Scholar
  10. 10.
    Zhang Y, Liu H, Zhu Y et al (2014) A comprehensive quantitative evaluation method of emulsifying properties. In: International conference on experimental and applied mechanics (EAM 2014), , Miami, USA, 20–21 Jan 2014CrossRefGoogle Scholar
  11. 11.
    Zhang Y, Han D, Ma D et al (2011) A kind of self-absorption, ChinaGoogle Scholar
  12. 12.
    Zhang Y, Fan J, Zhu Y et al (2011) Infiltration instrument, ChinaGoogle Scholar
  13. 13.
    Zhang Y, Ma D, Wu K et al (2014) An experimental oil sands preparation device, ChinaGoogle Scholar
  14. 14.
    Zhang Y, Ma D, Liu H et al (2013) Quantitative evaluation method of seepage oil production efficiency, ChinaGoogle Scholar
  15. 15.
    Wang D (2010) Multivariate statistical analysis and SPSS application. East China University of Science and Technology Press, ChinaGoogle Scholar
  16. 16.
    Gao H (2011) Application of multivariate statistical analysis. Peking University Press, BeijingGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yi Zhang
    • 1
  • Jian-Guo Li
    • 1
  • Hai-Hui Chen
    • 2
  • Hong-Yan Cai
    • 1
  • Hongfu Fan
    • 2
  • Yun Sun
    • 2
  1. 1.State Key Laboratory of EOR, Research Institute of Petroleum Exploration & Development, EOR DepartmentCNPCBeijingChina
  2. 2.School of EnergyChina University of Geosciences (Beijing)BeijingChina

Personalised recommendations