Oil Mixture Viscosity Behavior: Use in Pipeline Design
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As the petroleum industry accesses more low gravity oil resources, modification of viscosity by blending lighter hydrocarbons has become a necessity in order to attain bulk properties that will flow though a pipeline. In the more conventional oil reservoirs, the need to estimate the viscosity of oil blends occurs when reservoir fluids are contaminated with oil base muds or when production streams from different reservoirs or fields are commingled in a single pipeline. Several methods have appeared in the literature for estimating blend viscosity. All of these methods require a measured viscosity for each component of the blend. The number of viscosity measurements is compounded when the viscosity of the blended mixture is required at several temperatures. The Bergman and Sutton method has the widest range of temperature and oil API gravity of the viscosity correlations published and has been consistently demonstrated to provide accurate results over these conditions. This method requires the component specific gravity, characterization factor (Watson K) and temperature to estimate viscosity. By using the proper mixing rules, an estimate of blend viscosity can be made with comparable or improved accuracy over the "best" published methods without the need for individual component viscosity measurements. A database of 2000 blend viscosity measurements from over 800 mixtures was created to compare the accuracy of the various methods. Viscosity measurements of the individual components in the blends studied exceeded 7600 datapoints. A diverse group of mixtures ranging from light alkane or aromatic pure components to bitumen, diesel, biodiesel, condensate, crude and assay fractions were included in this database. Blends were comprised of the typical binary mixtures but ranged up to a maximum of eight components in the mixture. Introduction The prediction of the blend viscosity resulting from the mixture of two or more oils has historically been considered a rather complex problem. As a result, numerous empirical methods have been published which are used to predict the viscosity of hydrocarbon mixtures. The viscosity of a blend typically does not vary linearly as a function of component concentration 1. This point is illustrated in Fig. 1 which depicts the behavior of the normalized change in viscosity with the volume fraction of the lighter component in the blend. Line 1 illustrates the behavior of an ideal mixture of the components where the mixture viscosity varies linearly with the light component fraction of the blend. However, this is not the case in real life so line 1 essentially provides only a point of reference. The correct relationship results in either a concave up or down response with the light component fraction. The more common trend for oil mixtures is concave up so the blend viscosity is impacted more by the less viscous component. As the viscosity ratio defined by Eqn. 1 increases, the relationship exhibits more curvature as indicated by curves 2–5 in Fig. 1. Equation (1) where µ2 is the viscosity of the solvent hydrocarbon (ie the component with the lower viscosity). This response is typical of mixtures with a similar chemical nature (Watson K factor). As the chemical nature of the components begins to differ, the complexity of the interaction grows and the resulting trend of normalized viscosity with blend fraction can result in a near linear or concave downward trend (curves 6–7). This behavior though is less common and the concave upward behavior is prevalent for most blends. Viscosity ratio also has a greater impact on the final relationship than Watson K factor as evidenced by the behavior of curve 8 in Fig. 1. This curve shares the same difference in Watson K factor as curves 6 and 7, but the higher viscosity ratio results in the typical concave upward behavior exhibited by most blends.
As the petroleum industry accesses more low gravity oil resources, modification of viscosity by blending lighter hydrocarbons has become a necessity in order to attain bulk properties that will flow though a pipeline. In the more conventional oil reservoirs, the need to estimate the viscosity of oil blends occurs when reservoir fluids are contaminated with oil base muds or when production streams from different reservoirs or fields are commingled in a single pipeline. Several methods have appeared in the literature for estimating blend viscosity. All of these methods require a measured viscosity for each component of the blend. The number of viscosity measurements is compounded when the viscosity of the blended mixture is required at several temperatures. The Bergman and Sutton method has the widest range of temperature and oil API gravity of the viscosity correlations published and has been consistently demonstrated to provide accurate results over these conditions. This method requires the component specific gravity, characterization factor (Watson K) and temperature to estimate viscosity. By using the proper mixing rules, an estimate of blend viscosity can be made with comparable or improved accuracy over the "best" published methods without the need for individual component viscosity measurements. A database of 2000 blend viscosity measurements from over 800 mixtures was created to compare the accuracy of the various methods. Viscosity measurements of the individual components in the blends studied exceeded 7600 datapoints. A diverse group of mixtures ranging from light alkane or aromatic pure components to bitumen, diesel, biodiesel, condensate, crude and assay fractions were included in this database. Blends were comprised of the typical binary mixtures but ranged up to a maximum of eight components in the mixture. Introduction The prediction of the blend viscosity resulting from the mixture of two or more oils has historically been considered a rather complex problem. As a result, numerous empirical methods have been published which are used to predict the viscosity of hydrocarbon mixtures. The viscosity of a blend typically does not vary linearly as a function of component concentration 1. This point is illustrated in Fig. 1 which depicts the behavior of the normalized change in viscosity with the volume fraction of the lighter component in the blend. Line 1 illustrates the behavior of an ideal mixture of the components where the mixture viscosity varies linearly with the light component fraction of the blend. However, this is not the case in real life so line 1 essentially provides only a point of reference. The correct relationship results in either a concave up or down response with the light component fraction. The more common trend for oil mixtures is concave up so the blend viscosity is impacted more by the less viscous component. As the viscosity ratio defined by Eqn. 1 increases, the relationship exhibits more curvature as indicated by curves 2–5 in Fig. 1. Equation (1) where µ2 is the viscosity of the solvent hydrocarbon (ie the component with the lower viscosity). This response is typical of mixtures with a similar chemical nature (Watson K factor). As the chemical nature of the components begins to differ, the complexity of the interaction grows and the resulting trend of normalized viscosity with blend fraction can result in a near linear or concave downward trend (curves 6–7). This behavior though is less common and the concave upward behavior is prevalent for most blends. Viscosity ratio also has a greater impact on the final relationship than Watson K factor as evidenced by the behavior of curve 8 in Fig. 1. This curve shares the same difference in Watson K factor as curves 6 and 7, but the higher viscosity ratio results in the typical concave upward behavior exhibited by most blends.
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