Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-02T20:01:18.699Z Has data issue: false hasContentIssue false
  • English
  • Français

Nanoscience Applied to Oil Recovery and Mitigation: A Multiscale Computational Approach

Published online by Cambridge University Press:  16 January 2017

Raphael S. Alvim ,
Vladivostok Suxo ,
Oscar A. Babilonia ,
Yuri M. Celaschi  and
Caetano R. Miranda
Raphael S. Alvim
Affiliation:
DFMT, Instituto de Física, Universidade de São Paulo, São Paulo, SP, 05508-090, Brazil.
Vladivostok Suxo
Affiliation:
DFMT, Instituto de Física, Universidade de São Paulo, São Paulo, SP, 05508-090, Brazil.
Oscar A. Babilonia
Affiliation:
DFMT, Instituto de Física, Universidade de São Paulo, São Paulo, SP, 05508-090, Brazil.
Yuri M. Celaschi
Affiliation:
PG-NMA, Universidade Federal do ABC, Santo André, SP, 09210-580, Brazil.
Caetano R. Miranda*
Affiliation:
DFMT, Instituto de Física, Universidade de São Paulo, São Paulo, SP, 05508-090, Brazil.
*
*(Email: [email protected])
Get access

Abstract

With emergence of nanotechnology, it is possible to control interfaces and flow at nanoscale. This is of particular interest in the Oil and Gas industry (O&G), where nanoscience can be applied on processes such as Enhance Oil Recovery (EOR) and oil mitigation. On this direction, one of potential strategies is the so called Nano-EOR based on surface drive flow, where mobilization of hydrocarbons trapped at the pore scale can be favored by controlling by the chemical environment through “wettability modifiers”, such as functionalized nanoparticles (NP) and surfactants. The challenge consists then to search for optimal functionalized NP for oil recovery and mitigation at the harsh conditions found in oil reservoirs. Here, we introduce a hierarchical computational protocol based on the role of NP interfacial and wetting properties within oil/brine/rock interfaces to the fluid displacement in pore network models (PNMs). This integrated multiscale computational protocol ranges from first principles calculations, to determine and benchmark interatomic potentials, which are coupled with molecular dynamics (MD) to characterize the descriptors (interfacial properties and viscosity). The MD results are then mapped into Lattice Boltzmann method (LBM) simulation parameters to model the oil displacement process in PNMs at the microscale. Here, we show that this multiscale protocol coupled with Machine Learning techniques can be a resourceful tool to explore the potentialities of chemical additives, such as NP and surfactants, for the oil recovery process and investigate the effects of interfacial tension and wetting properties on the fluid behavior at both nano and microscales.

Keywords

nanostructuresimulationelectronic structure
Type
Articles
Information
MRS Advances , Volume 2 , Issue 9: Theory, Characterization and Modeling , 2017 , pp. 477 - 482
Copyright
Copyright © Materials Research Society 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Hou, B.-f.,, Wang, Y.-f. and Huang, Y., Appl. Surf. Sci. 330, 56 (2015).Google Scholar
Sodeifian, G., Daroughegi, R. and Aalaie, J., Korean J. Chem. Eng. 32, 2484 (2015).Google Scholar
Yu, X. et al. , RSC Adv. 5, 62752 (2015).Google Scholar
Alvim, R. S. and Miranda, C. R., J. Phys. Chem. C. 120, 13503 (2016).CrossRefGoogle Scholar
Sánchez, V. M. and Miranda, C. R., J. Phys. Chem. C. 118, 19180 (2014).Google Scholar
Rigo, V. A., S de Lara, L. and Miranda, C. R., App Surf Sci 292, 742 (2014).CrossRefGoogle Scholar
7.Miranda CR, de Lara LS, Tonetto BC SPE 157033-MS (2012).Google Scholar
de Lara, L. S., Michelon, M. F. and Miranda, C. R., J. Phys. Chem. B 116, 14667 (2012).Google Scholar
de Lara, L. S., Michelon, M. F., Metin, C. O., Nguyen, Q. P. and Miranda, C. R., J. Chem. Phys. 136, 164702 (2012).CrossRefGoogle Scholar
Pereira, A. O., De Lara, L.S. and Miranda, C. R., Microfluid. Nanofluid. 20, 36 (2016).Google Scholar
Coon, E. T., Porter, M. L. and Kang, Q., Computat. Geosci. 18, 17 (2014).Google Scholar
Porter, M. L., Coon, E. T., Kang, Q., Moulton, J. D. and Carey, J. W., Phys. Rev. E, 86, 036701 (2012).Google Scholar
Pedregosa, F., G. et al, J. Mach. Learn. Res. 12, 2825 (2011).Google Scholar
Forgy, E. W., Biometrics. 21, 768 (1965).Google Scholar
Hartigan, J. A., In Clustering algorithms. (John Wiley & Sons Inc., 1975)Google Scholar
De Lara, L. S., Voltatoni, T., Rodrigues, M. C. and Miranda, C. R., Colloids Surf. A 469, 42 (2015).CrossRefGoogle Scholar
Kunieda, M., et al. , J. Am. Chem. Soc. 132, 18281 (2010).Google Scholar
de Lara, L. S., Rigo, V. A. and Miranda, C. R. J. Phys. Chem. C 120, 6787 (2016).CrossRefGoogle Scholar