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Mechanistic Modeling of Nanoparticles-Assisted Surfactant Flood

  • Research Article - Petroleum Engineering
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Abstract

High interfacial tension (IFT) between oil and water brings about high capillarity leading to high residual oil saturation. Surfactants are employed to reduce IFT or modify wettability and mobilize the trapped oil. This paper aims to investigate the interaction of sodium dodecyl sulfate as a surfactant and two types of silica nanoparticles in different particle sizes for the purpose of enhancing oil recovery. Accordingly, the effect of employed nanoparticles on the critical micelle concentration (CMC) of the surfactant was investigated by the use of electrical conductivity measurements. Phase behavior studies were also carried out to examine the solubilizing ability of the surfactant and nanoparticles assembly. Based on the analysis of solubilization curves, an ultra-low IFT chemical formulation for the target reservoir crude oil was identified and the stability of the optimum solutions was examined through visual observation, optical absorption, and zeta potential measurements. The oil recovery experiments were performed in a quarter five-spot transparent pore network model saturated with crude oil to observe the displacement behavior of the injectant and its influence on oil recovery. Phase behavior tests indicated that the silica nanoparticles smaller in size are more effective in terms of IFT reduction since they can achieve ultra-low IFT level, and the conductivity measurements showed they relatively reduce the CMC of the surfactant. The results of stability tests demonstrated the optimum solutions are stable for more than 1 week. The micromodel experiments displayed that oil recovery increased by 4% during nanoparticles-assisted surfactant flood in comparison with surfactant flood.

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Abbreviations

2D:

2-Dimensional

ASP:

Alkaline–surfactant–polymer flood

CSEL:

Lower effective salinity window

CSEU:

Upper effective salinity window

TEM:

Transmission electron microscope

UTCHEM:

The University of Texas at Austin Chemical Flood Simulator

HBNC70:

UTCHEM input parameter for surfactant model (intercept of maximum height of binodal curve at zero salinity [44])

HBNC71:

UTCHEM input parameter for surfactant model (intercept of maximum height of binodal curve at optimal salinity [44])

HBNC72:

UTCHEM input parameter for surfactant model (intercept of maximum height of binodal curve at twice optimal salinity [44])

CSEL7:

UTCHEM input parameter for surfactant model (lower critical salinity window [44])

CSEU7:

UTCHEM input parameter for surfactant model (upper critical salinity window [44])

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Acknowledgements

The work was financially sponsored by Eqbal Lahoori Institute of Higher Education, in Mashhad (Iran). We would like to thank Nanosany Corporation for providing the authors with some laboratory materials. The authors thank Research Institute of Food Science and Technology (RIFST), in Mashhad, and Petro Ahoura Company, in Tehran, for the provision laboratory facilities. We would like to thank ESSS for providing the post-processor software needed to complete this work.

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Authors and Affiliations

  1. Department of Petroleum Engineering, Eqbal Lahoori Institute of Higher Education, Mashhad, Iran

    Seyyed Shahram Khalilinezhad

  2. Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Sina Mobaraki & Mahdi Zakavi

  3. Department of Petroleum Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran

    Milad Omidvar Sorkhabadi

  4. Young Researchers and Elites Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran

    Goshtasp Cheraghian

  5. Institute of Petroleum Engineering, Heriot Watt University, EH14 2SQ, Edinburg, UK

    Khosro Jarrahian

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  1. Seyyed Shahram Khalilinezhad
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  2. Sina Mobaraki
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  5. Goshtasp Cheraghian
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Correspondence to Seyyed Shahram Khalilinezhad.

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Khalilinezhad, S.S., Mobaraki, S., Zakavi, M. et al. Mechanistic Modeling of Nanoparticles-Assisted Surfactant Flood. Arab J Sci Eng 43, 6609–6625 (2018). https://doi.org/10.1007/s13369-018-3415-8

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