Modeling of viscosity for mixtures of Athabasca bitumen and heavy n-alkane with LSSVM algorithm

References

• Ahmadi, M. A., and Baghban, A. (2015). Evolving simple-to-apply models for estimating thermal conductivity of supercritical CO2. Int. J. Ambient Energy. doi: 10.1080/01430750.2015.1086682.
   Google Scholar
• Amedi, H. R., Baghban, A., and Ahmadi, M. A. (2016). Evolving machine learning models to predict hydrogen sulfide solubility in the presence of various ionic liquids. J. Mol. Liq., 216:411–422.
   Web of Science ®Google Scholar
• Arrhenius, S. (1887). Über die Dissociation der in Wasser gelösten Stoffe. Leipzig, Germany: Verlag von Wilhelm Engelmann.
   Google Scholar
• Baghban, A., Ahmadi, M. A., Pouladi, B., and Amanna, B. (2015a). Phase equilibrium modeling of semi-clathrate hydrates of seven commonly gases in the presence of TBAB ionic liquid promoter based on a low parameter connectionist technique. J. Supercrit. Fluids, 101:184–192.
   Web of Science ®Google Scholar
• Baghban, A., Ahmadi, M. A., and Shahraki, B. H. (2015b). Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches. J. Supercrit. Fluids, 98:50–64.
   Web of Science ®Google Scholar
• Baghban, A., Bahadori, M., Ahmad, Z., Kashiwao, T., and Bahadori, A. (2016a). Modeling of true vapor pressure of petroleum products using ANFIS algorithm. Pet. Sci. Technol., 34:933–939.
   Web of Science ®Google Scholar
• Baghban, A., Bahadori, M., Kashiwao, T., and Bahadori, A. (2016b). Modelling of gas to hydrate conversion for promoting CO2 capture processes in the oil and gas industry. Pet. Sci. Technol., 34:642–651.
   Web of Science ®Google Scholar
• Baghban, A., Bahadori, M., Rozyn, J., Abbas, A., Bahadori, A., and Rahimali, A. (2015c). Estimation of air dew point temperature using computational intelligence schemes. Appl. Therm. Eng., 93:1043–1052.
   Web of Science ®Google Scholar
• Baghban, A., Kashiwao, T., Bahadori, M., Ahmad, Z., and Bahadori, A. (2016c). Estimation of natural gases water content using adaptive neuro-fuzzy inference system. Pet. Sci. Technol., 34:891–897. doi: 10.1080/10916466.2016.1176039.
   Web of Science ®Google Scholar
• Bahadori, A., Baghban, A., Bahadori, M., Kashiwao, T., and Vafaee Ayouri, M. (2016a). Estimation of emission of hydrocarbons and filling losses in storage containers using intelligent models. Pet. Sci. Technol., 34:145–152.
   Web of Science ®Google Scholar
• Bahadori, A., Baghban, A., Bahadori, M., Lee, M., Ahmad, Z., Zare, M., and Abdollahi, E. (2016b). Computational intelligent strategies to predict energy conservation benefits in excess air controlled gas-fired systems. Appl. Therm. Eng., 102:432–446.
   Web of Science ®Google Scholar
• Bellows, L. A., and Bohme, V. E. (1963). Athabasca oil sands. J. Pet. Technol., 15:479–483.
   Google Scholar
• Boser, B. E., Guyon, I. M., and Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. Proc. Fifth Annual Workshop Comput. Learning Theory 144–152.
   Google Scholar
• Carrigy, M. A. (1983). Thermal recovery from tar sands. J. Pet. Technol., 35:2149–2157.
   Google Scholar
• Cragoe, C. S. (1933). Changes in the viscosity of liquids with temperature, pressure and composition. WPC-201. 1st World Petroleum Congress, London, England, July 18–24.
   Google Scholar
• Diedro, F., Bryan, J., Kryuchkov, S., and Kantzas, A. (2015). Evaluation of diffusion of light hydrocarbon solvents in bitumen. SPE 174424-MS. SPE Canada Heavy Oil Technical Conference, Calgary, Canada, June 9–11.
   Google Scholar
• Govier, G. W. (1965). The Athabasca oil sands. SPE 1077-MS. Annual Meeting of the American Institute of Mining, Metallurgical, and Petroleum Engineers, Chicago, Illinois, February 14–18.
   Google Scholar
• Hayashitani, M., Bennion, D. W., Donnelly, J. K., and Moore, R. G. (1978). Thermal cracking models for Athabasca oil sands oil. SPE 7549-MS. SPE Annual Fall Technical Conference and Exhibition, Houston, Texas, October 1–3.
   Google Scholar
• Hernández, O. E., and Ali, S. M. (1972). Oil recovery from Athabasca tar sand by miscible-thermal methods. PETSOC 7249. Annual Technical Meeting, Calgary, Alberta, May 16–19.
   Google Scholar
• Jossy, C., Frauenfeld, T., and Rajan, V. (2009). Partitioning of bitumen-solvent systems into multiple liquid phases. J. Can. Pet. Technol., 48:16–20.
   Web of Science ®Google Scholar
• Kariznovi, M., Nourozieh, H., and Abedi, J. (2012). Phase behavior and viscosity measurements of heavy crude oil with methane and ethane at high-temperature conditions. SPE 152321. SPE Western Regional Meeting, Bakersfield, California, March 21–23.
   Google Scholar
• Kariznovi, M., Nourozieh, H., and Abedi, J. (2014). Measurement and correlation of viscosity and density for compressed athabasca bitumen at temperatures up to 200°C. J. Can. Pet. Technol., 53:330–338.
   Web of Science ®Google Scholar
• Kariznovi, M., Nourozieh, H., Guo, J., Guan, J., and Abedi, J. (2013). Measurement and modeling of density and viscosity for mixtures of Athabasca bitumen and heavy n-alkane. Fuel, 112:83–95.
   Web of Science ®Google Scholar
• Kendall, J., and Potter Monroe, K. (1917). The viscosity of liquids. ii. the viscosity-composition curve for ideal liquid mixtures. 1. J. Am. Chem. Soc., 39:1787–1802.
   Google Scholar
• Luhning, R. W., Anand, A., Blackmore, T., and Lawson, D. S. (2002). Pipeline transportation of emerging partially upgraded bitumen. PETSOC-2002–205. Canadian International Petroleum Conference, Calgary, Alberta, June 11–13.
   Google Scholar
• Miadonye, A., Doyle, N. L., Britten, A., Latour, N., and Puttagunta, V. R. (2001). Modelling viscosity and mass fraction of bitumen-diluent mixtures. J. Can. Pet. Technol., 40:1–6.
   Web of Science ®Google Scholar
• Motahhari, H. R., Schoeggl, F., Satyro, M., and Yarranton, H. (2013). Viscosity prediction for solvent-diluted live bitumen and heavy oil at temperatures up to 175-deg-C. J. Can. Pet. Technol., 52:376–390.
   Web of Science ®Google Scholar
• Motahhari, H. R., Schoeggl, F., Satyro, M. A., and Yarranton, H. W. (2011). Prediction of the viscosity of solvent diluted live bitumen at temperatures up to 175°C. SPE 149405. Canadian Unconventional Resources Conference, Calgary, Alberta, November 15–17.
   Google Scholar
• Nourozieh, H., Kariznovi, M., and Abedi, J. (2015a). Density and viscosity of Athabasca bitumen samples at temperatures up to 200C and pressures up to 10 MPa. SPE Res. Eval. Eng., 18:375–386.
   Web of Science ®Google Scholar
• Nourozieh, H., Kariznovi, M., and Abedi, J. (2015b). Viscosity measurement and modeling for mixtures of Athabasca bitumen/n-pentane at temperatures up to 200 C. SPE J., 20:226–238.
   Web of Science ®Google Scholar
• Nourozieh, H., Kariznovi, M., and Abedi, J. (2016a). Measurement and correlation of solubility and physical properties for gas-saturated Athabasca bitumen. SPE Prod. Oper., 31:207–218.
   Google Scholar
• Nourozieh, H., Kariznovi, M., and Abedi, J. (2016b). Measurement and modeling of solubility and saturated-liquid density and viscosity for methane/Athabasca-bitumen mixtures. SPE J., 21:180–189.
   Web of Science ®Google Scholar
• Shafiei, A., Ahmadi, M. A., Zaheri, S. H., Baghban, A., Amirfakhrian, A., and Soleimani, R. (2014). Estimating hydrogen sulfide solubility in ionic liquids using a machine learning approach. J. Supercrit. Fluids, 95:525–534.
   Web of Science ®Google Scholar
• Shu, W. R. (1984). A viscosity correlation for mixtures of heavy oil, bitumen, and petroleum fractions. SPE J., 24:277–282.
   Google Scholar
• Suykens, J. A. K., Vandewalle, J., and De Moor, B. (2001). Optimal control by least squares support vector machines. Neural Networks, 14:23–35.
   PubMed Web of Science ®Google Scholar