Predictive Corresponding-States Viscosity Model for the Entire Fluid Region: n-Alkanes

2013 ◽  
Vol 52 (20) ◽  
pp. 6841-6847 ◽  
Author(s):  
Lawrence T. Novak
Keyword(s):  
Corresponding States ◽  
Viscosity Model

The Corresponding States Viscosity Model Applied to Heavy Oil Systems

2003 ◽  
Author(s):  
N. Lindeloff ◽  
K. Schou Pedersen ◽  
H.P. Ronningsen ◽  
J. Milter
Keyword(s):  
Heavy Oil ◽  
Corresponding States ◽  
Viscosity Model

A corresponding states predictive viscosity model based on a new scaling parameter: application to hydrocarbons, halocarbons and mixtures

2001 ◽  
Vol 26 (1) ◽  
pp. 1-26 ◽  
Author(s):  
G. Scalabrin ◽  
G. Cristofoli ◽  
M. Grigiante
Keyword(s):  
Corresponding States ◽  
Viscosity Model ◽  
Scaling Parameter ◽  
Model Based

scholarly journals Sensitivity Analysis, Calibration and Uncertainty Quantification for Heavy Oil Viscosity Estimates with a Corresponding States Model

2020 ◽  
Author(s):  
Diego Volpatto ◽  
Lucas V. A. Oliveira ◽  
Sofia P. Bittencourt ◽  
Danilo Silva ◽  
Edson T. M. Manoel
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Approach ◽  
Heavy Oil ◽  
Corresponding States ◽  
Viscosity Model ◽  
Least Squares Regression ◽  
Oil Viscosity ◽  
Bayesian Calibration ◽  
Tuning Parameters ◽  
Sensitivity Assessment

The main goal of this work is to assess heavy oil viscosity estimates by a Corresponding States Principle (CSP) model using a Bayesian approach in an efficient way. To determine and select relevant parameters for model calibration, an enhanced Elementary Effects method is used to evaluate sensitivity measures of CSP tuning parameters. With the combination of sensitivity analysis and Bayesian calibration, a unified procedure to automatically tune CSP viscosity model while reducing the number of tuning parameters is devised. Moreover, the Bayesian approach provides additional information on CSP model uncertainties and credible regions inherited from experimental data. To evaluate such uncertainties in CSP viscosity model, it was used five heavy oil samples available in the literature. The viscosity curves constructed by 50th-percentile from Monte Carlo realizations for the CSP calibration show good agreement when compared with classical Least-Squares regression (deterministic), demonstrating the potential of the sensitivity assessment for both Bayesian and deterministic approaches. However, when Bayesian calibration is used, limitations of CSP viscosity estimates are detected through violation of credible regions, suggesting that heavy oil viscosity estimates for relatively low pressure conditions can be insufficiently accurate for the CSP model considered in this study

Sensitivity Analysis, Calibration and Uncertainty Quantification for Heavy Oil Viscosity Estimates with a Corresponding States Model

2020 ◽  
Author(s):  
Diego Volpatto ◽  
Lucas V. A. Oliveira ◽  
Sofia P. Bittencourt ◽  
Danilo Silva ◽  
Edson T. M. Manoel
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Approach ◽  
Heavy Oil ◽  
Corresponding States ◽  
Viscosity Model ◽  
Least Squares Regression ◽  
Oil Viscosity ◽  
Bayesian Calibration ◽  
Tuning Parameters ◽  
Sensitivity Assessment

The main goal of this work is to assess heavy oil viscosity estimates by a Corresponding States Principle (CSP) model using a Bayesian approach in an efficient way. To determine and select relevant parameters for model calibration, an enhanced Elementary Effects method is used to evaluate sensitivity measures of CSP tuning parameters. With the combination of sensitivity analysis and Bayesian calibration, a unified procedure to automatically tune CSP viscosity model while reducing the number of tuning parameters is devised. Moreover, the Bayesian approach provides additional information on CSP model uncertainties and credible regions inherited from experimental data. To evaluate such uncertainties in CSP viscosity model, it was used five heavy oil samples available in the literature. The viscosity curves constructed by 50th-percentile from Monte Carlo realizations for the CSP calibration show good agreement when compared with classical Least-Squares regression (deterministic), demonstrating the potential of the sensitivity assessment for both Bayesian and deterministic approaches. However, when Bayesian calibration is used, limitations of CSP viscosity estimates are detected through violation of credible regions, suggesting that heavy oil viscosity estimates for relatively low pressure conditions can be insufficiently accurate for the CSP model considered in this study

The Corresponding States Viscosity Model Applied to Heavy Oil Systems

2004 ◽  
Vol 43 (09) ◽  
Author(s):  
N. Lindeloff ◽  
K.S. Pedersen ◽  
H.P. Ronningsen ◽  
J. Milter
Keyword(s):  
Heavy Oil ◽  
Corresponding States ◽  
Viscosity Model

Correction to “Predictive Corresponding-States Viscosity Model for the Entire Fluid Region: n-Alkanes”

2013 ◽  
Vol 52 (38) ◽  
pp. 13886-13886 ◽  
Author(s):  
Lawrence T. Novak
Keyword(s):  
Corresponding States ◽  
Viscosity Model

Corresponding states theory of mixtures of Lennard-Jones 6:12 liquids

1972 ◽  
Vol 37 (7) ◽  
pp. 2141-2148
Author(s):  
K. Hlavatý
Keyword(s):  
Corresponding States ◽  
Lennard Jones ◽  
Theory Of Mixtures

Corresponding states, complex mixtures and mixture models

1983 ◽  
Vol 14 ◽  
pp. 79-90 ◽  
Author(s):  
D.S.H. Wong ◽  
S.I. Sandler ◽  
A.S. Teja
Keyword(s):  
Mixture Models ◽  
Corresponding States ◽  
Complex Mixtures

Relative Viscosity Model for Oil/Water Stable Emulsion Flow within Electrical Submersible Pumps

2021 ◽  
pp. 116827
Author(s):  
Natan Augusto Vieira Bulgarelli ◽  
Jorge Luiz Biazussi ◽  
William Monte Verde ◽  
Carlos Eduardo Perles ◽  
Marcelo Souza de Castro ◽  
...  
Keyword(s):  
Relative Viscosity ◽  
Viscosity Model ◽  
Stable Emulsion ◽  
Electrical Submersible Pumps ◽  
Emulsion Flow ◽  
Oil Water
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