A Continuous and Predictive Viscosity Model Coupled to a Microemulsion Equation-Of-State

Pooya Khodaparast, Russell T. Johns

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

Surfactant floods can attain high oil recovery if optimum conditions with ultra-low interfacial tensions (IFT) are achieved in the reservoir. A new equation-of-state (EoS) phase behavior model based on the hydrophilic-lipophilic difference (HLD-NAC) has been shown to fit and predict phase behavior data continuously throughout the Winsor I, II, III, and IV regions. The state-of-the-art for viscosity estimation, however, uses empirical non-predictive models based on fits to salinity scans, even though other parameters change, such as the phase number and compositions. In this paper, we develop the first-of-its-kind microemulsion viscosity model that gives continuous viscosity estimates in composition space. This model is coupled to our existing HLD-NAC phase behavior EoS. The results show that experimentally measured viscosities in all Winsor regions (two and three-phase) are a function of phase composition, temperature, pressure, salinity, and EACN. More specifically, microemulsion viscosities associated with the three-phase invariant point have an "M" shape as formulation variables change, such as from a salinity scan. The location and magnitude of viscosity peaks in the "M" are predicted from two percolation thresholds after tuning to viscosity data. These percolation thresholds as well as other model parameters change linearly with alkane chain length (EACN) and brine salinity. We also show that the minimum viscosity in the "M' shape correlates linearly with alkane chain length (EACN) or viscosity ratio. Other key parameters in the model are also shown to linearly correlate with EACN and brine salinity Based on these correlations, two and three-phase microemulsion viscosities are determined in five-component space (surfactant, two brine, and two oil components) independent of flash calculations. Phase compositions from the EoS flash calculations are input into the viscosity model. Fits to experimental data are excellent, as well as viscosity predictions for salinity scans not used in the fitting process.

Original languageEnglish (US)
StatePublished - Jan 1 2018
EventSPE Improved Oil Recovery Conference 2018 - Tulsa, United States
Duration: Apr 14 2018Apr 18 2018

Other

OtherSPE Improved Oil Recovery Conference 2018
CountryUnited States
CityTulsa
Period4/14/184/18/18

Fingerprint

Microemulsions
Equations of state
equation of state
viscosity
Viscosity
salinity
Phase behavior
brine
Chain length
Phase composition
alkane
Paraffins
surfactant
Surface active agents
oil
Chemical analysis
Surface tension
Tuning

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Geotechnical Engineering and Engineering Geology

Cite this

Khodaparast, P., & Johns, R. T. (2018). A Continuous and Predictive Viscosity Model Coupled to a Microemulsion Equation-Of-State. Paper presented at SPE Improved Oil Recovery Conference 2018, Tulsa, United States.
Khodaparast, Pooya ; Johns, Russell T. / A Continuous and Predictive Viscosity Model Coupled to a Microemulsion Equation-Of-State. Paper presented at SPE Improved Oil Recovery Conference 2018, Tulsa, United States.
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Khodaparast, P & Johns, RT 2018, 'A Continuous and Predictive Viscosity Model Coupled to a Microemulsion Equation-Of-State', Paper presented at SPE Improved Oil Recovery Conference 2018, Tulsa, United States, 4/14/18 - 4/18/18.

A Continuous and Predictive Viscosity Model Coupled to a Microemulsion Equation-Of-State. / Khodaparast, Pooya; Johns, Russell T.

2018. Paper presented at SPE Improved Oil Recovery Conference 2018, Tulsa, United States.

Research output: Contribution to conferencePaper

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AU - Johns, Russell T.

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N2 - Surfactant floods can attain high oil recovery if optimum conditions with ultra-low interfacial tensions (IFT) are achieved in the reservoir. A new equation-of-state (EoS) phase behavior model based on the hydrophilic-lipophilic difference (HLD-NAC) has been shown to fit and predict phase behavior data continuously throughout the Winsor I, II, III, and IV regions. The state-of-the-art for viscosity estimation, however, uses empirical non-predictive models based on fits to salinity scans, even though other parameters change, such as the phase number and compositions. In this paper, we develop the first-of-its-kind microemulsion viscosity model that gives continuous viscosity estimates in composition space. This model is coupled to our existing HLD-NAC phase behavior EoS. The results show that experimentally measured viscosities in all Winsor regions (two and three-phase) are a function of phase composition, temperature, pressure, salinity, and EACN. More specifically, microemulsion viscosities associated with the three-phase invariant point have an "M" shape as formulation variables change, such as from a salinity scan. The location and magnitude of viscosity peaks in the "M" are predicted from two percolation thresholds after tuning to viscosity data. These percolation thresholds as well as other model parameters change linearly with alkane chain length (EACN) and brine salinity. We also show that the minimum viscosity in the "M' shape correlates linearly with alkane chain length (EACN) or viscosity ratio. Other key parameters in the model are also shown to linearly correlate with EACN and brine salinity Based on these correlations, two and three-phase microemulsion viscosities are determined in five-component space (surfactant, two brine, and two oil components) independent of flash calculations. Phase compositions from the EoS flash calculations are input into the viscosity model. Fits to experimental data are excellent, as well as viscosity predictions for salinity scans not used in the fitting process.

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Khodaparast P, Johns RT. A Continuous and Predictive Viscosity Model Coupled to a Microemulsion Equation-Of-State. 2018. Paper presented at SPE Improved Oil Recovery Conference 2018, Tulsa, United States.