Mathematical model for the prediction of the dead heavy crude oil viscosity produced in Monagas State, Venezuela

Authors

  • Tomás Darío Marín Velásquez Universidad de Oriente

DOI:

https://doi.org/10.29019/enfoqueute.v8n3.164

Keywords:

viscosity, heavy oil, regression, mathematical model

Abstract

Viscosity is the property of fluids to oppose movement when a cutting effort is applied on them to convey them from one point to another. Heavy oil has a high viscosity greater than 1000 cP, which makes it difficult to transport. The present work shows a mathematical model for the prediction of the viscosity of dead heavy oils produced in the fields of Monagas State, Venezuela. For the development of the work, 25 samples of oil were collected and the viscosity was measured at 5 temperatures, in addition to the API gravity and the percentage of Asphaltenes. The data were introduced in the Statgraphics Centurion XVI statistical package and through multiple regression analysis two mathematical models were obtained, 1) linear multiple and 2) multiple nonlinear; The best model being divided according to its coefficient of determination R2 and the average relative error (ARE). The selected model was compared with the Glaso, Bennison and Naseri models. The nonlinear multiple model with R2 of 0.9792 and ARE of 5.05% was obtained as the best model, surpassing the models of Glaso (35.5% ARR, Bennison (107.5% ARE) and Naseri (61.7% ARE).

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References

American Psychological Association. (2012). Publication manual of the American Psychological Association. Washington, DC: American Psychological Assoc.
Alboudwarej, H., Badry, R., Baker, A., Beshry, M., Brown, G., Calvo, R., Hathcock, R., Hughes, T., Kundu, D., López, J., y West, C. (2006). La importancia del petróleo pesado. Oilfield Review, 18(2), 38–58. Recuperado de https://www.slb.com/
Alomair O., Jumaa M., Alkoriem A. y Hamed M. (2016). Heavy oil viscosity and density prediction at normal and elevated temperatures. J Petrol Explor Prod Techno, 6(1), 253 – 263. doi: 10.1007/s13202-015-0184-8.
Argillier J., Barré L., Brucy F., Dournaux J., Hènaut I. y Bouchard R. (2001). Influence of Asphaltenes Content and Dilution on Heavy Oil Rheology. SPE 69711, 1 – 8.
ASTM D287.(2012). Standard Test Method for API Gravity of Crude Petroleum and Petroleum Products (Hydrometer Method), USA: ASTM International, West Conshohocken, PA.
ASTM D2196. (2015). Standard Test Methods for Rheological Properties of Non-Newtonian Materials by Rotational Viscometer, USA: ASTM International, West Conshohocken, PA.
ASTM D6560 (2012). Standard Test Method for Determination of Asphaltenes (Heptane Insolubles) in Crude Petroleum and Petroleum Products, USA: ASTM International, West Conshohocken, PA.
Beldjazia, A. y Alatou, D. (2016). Precipitation variability on the massif Forest of Mahouna (North Eastern-Algeria) from 1986 to 2010. International Journal of Management Sciences and Business Research. 5(3), 2226 – 8235.
Bennison, T. (1998). Prediction of heavy oil viscosity. AEA Technology plc. Presented at the IBC Heavy Oil Field Development Conference, London.
Evdokimov, I. (2010). The Importance of Asphaltene Content in Petroleum II—Multi-peak Viscosity Correlations. Petroleum Science and Technology, 28(9), 920 – 924. doi: 10.1080/10916460902937018.
Glaso O (1980) Generalized pressure–volume–temperature correlation for crude oil system. J Pet Technol, 2(1), 785–795.
Jaramillo, O. (2007). Notas de Físico-Química. Estados de la Materia; Líquidos. Universidad Nacional Autónoma de México, Centro de Investigación en Energía. Temixco, Morelos, México.
Knežević, D. y Savić, V. (2006). Mathematical modeling of changing of dynamic viscosity, as a function of temperature and pressure, of mineral oils for hydraulic systems. Facta Universitatis Series: Mechanical Engineering, 4(1), 27 – 34.
Márquez, G., Alejandre, F. y Bencomo, M. (2006). Influencia de asfaltenos y resinas en la viscosidad de petróleos bituminosos utilizables como pinturas asfálticas de imprimación. Revista Materiales de construcción, 56(281). 41 – 49.
Monterrosa, J. (2014). Statgraphics Centurion XVI.I. Universidad Nacional de Colombia, Facultad de Ciencias Económicas. Bogotá, Colombia. Recuperado de http://www.fce.unal.edu.co
Naseri, A., Nikazar, M. y Mousavi, S. (2005). A correlation approach for prediction of crude oil viscosities. Petroleum Science and Engineering, 47, 163 – 174. Recuperado de http://www.sciencedirect.com
Naji, H. (2011). The Dead Oil Viscosity Correlations. A C-Sharp Simulation Approach. JKAU: Eng. Sci., 22(2), 61 – 87. doi: 10.4197 / Eng. 22-2.4
Ng, J. y Egbogah, E. (1983). An Improved Temperature-Viscosity Correlation For Crude Oil Systems. Annual Technical Meeting, doi: 0.2118/83-34-32
Sattarin, M., Modarresi, H., Bayat, M. y Teymori, M. (2007). New viscosity correlations for dead crude oils. Petroleum & Coa, 49(2), 33 – 39. Recuperado de http://www.vurup.sk/pc
Swissoil. (2012). Grados de Viscosidad ISO . SO Boletín 05. Recuperado de http://www.swissoil.com.ec

Published

2017-06-30

How to Cite

Marín Velásquez, T. D. (2017). Mathematical model for the prediction of the dead heavy crude oil viscosity produced in Monagas State, Venezuela. Enfoque UTE, 8(3), pp. 16 - 27. https://doi.org/10.29019/enfoqueute.v8n3.164

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Miscellaneous