Prediction of heavy oil viscosity using a radial basis function neural network

References

• Barati-Harooni, A., Najafi-Marghmaleki, A., Tatar, A., and Mohammadi, A. H. (2016a). Experimental and modeling studies on adsorption of a nonionic surfactant on sandstone minerals in enhanced oil recovery process with surfactant flooding. J. Mol. Liq. 220:1022–1032.
   Web of Science ®Google Scholar
• Barati-Harooni, A., Soleymanzadeh, A., Tatar, A., Najafi-Marghmaleki, A., Samadi, S.-J., Yari, A., Roushani, B., and Mohammadi, A. H. (2016b). Experimental and modeling studies on the effects of temperature, pressure and brine salinity on interfacial tension in live oil-brine systems. J. Mol. Liq. 219:985–993.
   Web of Science ®Google Scholar
• Butler, R. M. (1991). Thermal recovery of oil and bitumen. Englewood Cliffs, NJ: Prentice-Hall.
   Google Scholar
• Chang, C.-L., and Fogler, H. S. (1994). Stabilization of asphaltenes in aliphatic solvents using alkylbenzene-derived amphiphiles. 1. Effect of the chemical structure of amphiphiles on asphaltene stabilization. Langmuir 10:1749–1757.
   Web of Science ®Google Scholar
• Chang, F.-J., and Chang, Y.-T. (2006). Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv. Water Resour. 29:1–10.
   Web of Science ®Google Scholar
• Cholet, H. (2008). Well production practical handbook. Paris, France: Editions Technip.
   Google Scholar
• Du, K.-L., and Swamy, M. N. (2006). Neural networks in a softcomputing framework. New York, NY: Springer Science & Business Media.
   Google Scholar
• Elsharkawy, A., and Alikhan, A. (1999). Models for predicting the viscosity of Middle East crude oils. Fuel 78:891–903.
   Web of Science ®Google Scholar
• Fayazi, A., Arabloo, M., Shokrollahi, A., Zargari, M. H., and Ghazanfari, M. H. (2013). State-of-the-art least square support vector machine application for accurate determination of natural gas viscosity. Ind. Eng. Chem. Res. 53:945–958.
   Web of Science ®Google Scholar
• Hashemi-Kiasari, H., Hemmati-Sarapardeh, A., Mighani, S., Mohammadi, A. H., and Sedaee-Sola, B. (2014). Effect of operational parameters on SAGD performance in a dip heterogeneous fractured reservoir. Fuel 122:82–93.
   Web of Science ®Google Scholar
• Hemmati-Sarapardeh, A., Khishvand, M., Naseri, A., and Mohammadi, A. H. (2013). Toward reservoir oil viscosity correlation. Chem. Eng. Sci. 90:53–68.
   Web of Science ®Google Scholar
• Hemmati-Sarapardeh, A., Mahmoudi, B., and Mohammadi, A. H. (2014a). Experimental measurement and modeling of saturated reservoir oil viscosity. Korean J. Chem. Eng. 31:1253–1264.
   Web of Science ®Google Scholar
• Hemmati-Sarapardeh, A., Mohagheghian, E., Fathinasab, M., and Mohammadi, A. H. (2016). Determination of minimum miscibility pressure in N 2–crude oil system: A robust compositional model. Fuel 182:402–410.
   Web of Science ®Google Scholar
• Hemmati-Sarapardeh, A., Shokrollahi, A., Tatar, A., Gharagheizi, F., Mohammadi, A. H., and Naseri, A. (2014b). Reservoir oil viscosity determination using a rigorous approach. Fuel 116:39–48.
   Web of Science ®Google Scholar
• Hu, Y.-F., Chen, G.-J., Yang, J.-T., and Guo, T.-M. (2000). A study on the application of scaling equation for asphaltene precipitation. Fluid Phase Equilib. 171:181–195.
   Web of Science ®Google Scholar
• Isehunwa, O., Olamigoke, O., and Makinde, A. (2006). A correlation to predict the viscosity of light crude oils. SPE 105983, Nigeria Annual International Conference and Exhibition, Abuja, Nigeria, July 31 to August 2.
   Google Scholar
• Karimpouli, S., Fathianpour, N., and Roohi, J. (2010). A new approach to improve neural networks' algorithm in permeability prediction of petroleum reservoirs using supervised committee machine neural network (SCMNN). J. Pet. Sci. Eng. 73:227–232.
   Web of Science ®Google Scholar
• Khajeh, A., Modarress, H., and Rezaee, B. (2009). Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers. Expxert Systm Appl. 36:5728–5732.
   Web of Science ®Google Scholar
• Martinius, A. W., Hegner, J., Kaas, I., Bejarano, C., Mathieu, X., and Mjøs, R. (2012). Sedimentology and depositional model for the Early Miocene Oficina Formation in the Petrocedeño Field (Orinoco heavy-oil belt, Venezuela). Marine Pet. Geol. 35:354–380.
   Web of Science ®Google Scholar
• Najafi-Marghmaleki, A., Khosravi-Nikou, M. R., and Barati-Harooni, A. (2016). A new model for prediction of binary mixture of ionic liquids+ water density using artificial neural network. J. Mol. Liq. 220:232–237.
   Web of Science ®Google Scholar
• Naseri, A., Nikazar, M., and Dehghani, S. M. (2005). A correlation approach for prediction of crude oil viscosities. J. Pet. Sci. Eng. 47:163–174.
   Web of Science ®Google Scholar
• Poggio, T., and Girosi, F. (1990). Networks for approximation and learning. Proc. IEEE 78:1481–1497.
   Web of Science ®Google Scholar
• Powell, M. J. (1987). Radial basis functions for multivariable interpolation: a review. In: Algorithms for Approximation, Mason, J. C., and Cox, M. G. (Eds). New York, NY: Clarendon Press, pp. 143–167.
   Google Scholar
• Safari, H., Nekoeian, S., Shirdel, M. R., Ahmadi, H., Bahadori, A., and Zendehboudi, S. (2014). Assessing the dynamic viscosity of Na–K–Ca–Cl–H2O aqueous solutions at high-pressure and high-temperature conditions. Ind. Eng. Chem. Res. 53:11488–11500.
   Web of Science ®Google Scholar
• Santos, R. B., Ruppb, M., Bonzi, S. J., and Filetia, A. (2013). Comparison between multilayer feedforward neural networks and a radial basis function network to detect and locate leaks in pipelines transporting gas. Chem. Eng. Trans. 32:e1380.
   Google Scholar
• Shokrollahi, A., Tatar, A., and Safari, H. (2015). On accurate determination of PVT properties in crude oil systems: Committee machine intelligent system modeling approach. J. Taiwan Institute Chem. Eng. 55:17–26.
   Web of Science ®Google Scholar
• Tanoumand, N., Hemmati-Sarapardeh, A., Bahadori, A. (2015). A CSA-LSSVM model to estimate diluted heavy oil viscosity in the presence of kerosene. Pet. Sci. Technol. 33:1085–1092.
   Web of Science ®Google Scholar
• Tatar, A., Barati-Harooni, A., Partovi, M., Najafi-Marghmaleki, A., and Mohammadi, A. H. (2016a). An accurate model for predictions of vaporization enthalpies of hydrocarbons and petroleum fractions. J. Mol. Liq. 220:192–199.
   Web of Science ®Google Scholar
• Tatar, A., Barati, A., Yarahmadi, A., Najafi, A., Lee, M., and Bahadori, A. (2016b). Prediction of carbon dioxide solubility in aqueous mixture of methyldiethanolamine and N-methylpyrrolidone using intelligent models. Int. J. Greenhouse Gas Control 47:122–136.
   Web of Science ®Google Scholar
• Tatar, A., Najafi-Marghmaleki, A., Barati-Harooni, A., Gholami, A., Ansari, H., Bahadori, M., Kashiwao, T., Lee, M., and Bahadori, A. (2016c). Implementing radial basis function neural networks for prediction of saturation pressure of crude oils. Pet. Sci. Technol. 34:454–463.
   Web of Science ®Google Scholar
• Tatar, A., Naseri, S., Bahadori, M., Hezave, A. Z., Kashiwao, T., Bahadori, A., and Darvish, H. (2016d). Prediction of carbon dioxide solubility in ionic liquids using MLP and radial basis function (RBF) neural networks. J. Taiwan Institute Chem. Eng. 60:151–164.
   Web of Science ®Google Scholar
• Tatar, A., Naseri, S., Sirach, N., Lee, M., and Bahadori, A. (2015). Prediction of reservoir brine properties using radial basis function (RBF) neural network. Petroleum 1:349–357.
   Google Scholar
• Tatar, A., Nasery, S., Bahadori, M., Bahadori, A., Bahadori, M., Barati-Harooni, A., and Najafi-Marghmaleki, A. (2016e). Prediction of water removal rate in a natural gas dehydration system using radial basis function neural network. Pet. Sci. Technol. 34:951–960.
   Web of Science ®Google Scholar
• Tatar, A., Shokrollahi, A., Mesbah, M., Rashid, S., Arabloo, M., and Bahadori, A. (2013). Implementing radial basis function networks for modeling CO 2-reservoir oil minimum miscibility pressure. J. Nat. Gas Sci. Eng. 15:82–92.
   Web of Science ®Google Scholar
• Tatar, A., Yassin, M. R., Rezaee, M., Aghajafari, A. H., and Shokrollahi, A. (2014). Applying a robust solution based on expert systems and GA evolutionary algorithm for prognosticating residual gas saturation in water drive gas reservoirs. J. Nat. Gas Sci. Eng. 21:79–94.
   Web of Science ®Google Scholar
• Tohidi-Hosseini, S.-M., Hajirezaie, S., Hashemi-Doulatabadi, M., Hemmati-Sarapardeh, A., and Mohammadi, A. H. (2016). Toward prediction of petroleum reservoir fluids properties: A rigorous model for estimation of solution gas-oil ratio. J. Nat. Gas Sci. Eng. 29:506–516.
   Web of Science ®Google Scholar
• Xu, D.-H., and Khurana, A. (1996). A simple and efficient approach for improving the prediction of reservoir fluid viscosity. SPE 37011, SPE Asia-Pacific Oil and Gas Conference, Adelaide, Australia, October 28–31.
   Google Scholar
• Yu, H., Xie, T., Paszczyñski, S., and Wilamowski, B. M. (2011). Advantages of radial basis function networks for dynamic system design. IEEE Trans. Ind. Electron. 58:5438–5450.
   Web of Science ®Google Scholar