Heavy oil viscosity and density prediction at normal and elevated temperatures
Viscosity and density are important physical properties of crude oil. However, no practical theory exists for the calculation of these properties for heavy oil at elevated temperatures. The principal objective of this paper is to obtain exact models that can successfully predict these two important...
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Veröffentlicht in: | Journal of petroleum exploration and production technology 2016-06, Vol.6 (2), p.253-263 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Viscosity and density are important physical properties of crude oil. However, no practical theory exists for the calculation of these properties for heavy oil at elevated temperatures. The principal objective of this paper is to obtain exact models that can successfully predict these two important fluid properties covering a wide range of temperatures. In this study, heavy oil density was predicted from API and temperature, and then the predicted values of the densities were used in the second step to develop the viscosity correlation. A total of 30 heavy oil samples of different API gravities ranging from 11.7 to 18.8 were tested. Viscosity and density were measured in the temperature range from 20 to 160 °C. The accuracy of the experimental density data was determined using Standing and Katz method. Published correlations were also used to evaluate the experimental viscosity data. The comparison between the experimental data and the predicted values indicated that the proposed model successfully predicted the experimental data with an average absolute relative error of less than 8 % and correlation coefficients (
R
2
) of 0.97 and 0.92 at normal and high temperatures, respectively. The proposed model and the literature models were tested on heavy oil samples. It was found that it is not possible to generalize a correlation for the heavy oil viscosity using only API and temperature. However, the proposed model significantly minimizes the relative error and increases the correlation between the predicted and experimental data compared with other published methods. |
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ISSN: | 2190-0558 2190-0566 |
DOI: | 10.1007/s13202-015-0184-8 |