Open Access
Numéro
Oil & Gas Science and Technology - Rev. IFP Energies nouvelles
Volume 73, 2018
Numéro d'article 22
Nombre de pages 17
DOI https://doi.org/10.2516/ogst/2018006
Publié en ligne 25 juin 2018
  • Ait‐Kadi A., Carreau P., Chauveteau G. (1987) Rheological properties of partially hydrolyzed polyacrylamide solutions, J. Rheol. (1978-present) 31, 537–561 [CrossRef] [Google Scholar]
  • Sheng J. (2010) Modern chemical enhanced oil recovery: theory and practice, Gulf Professional Publishing, Houston, Texas, United States [Google Scholar]
  • Hashmet M.R., Onur M., Tan I.M. (2014) Empirical correlations for viscosity of polyacrylamide solutions with the effects of concentration, molecular weight and degree of hydrolysis of polymer, J. Appl. Sci. 14, 1000 [CrossRef] [Google Scholar]
  • Lee K.E., Khan I., Morad N., Teng T.T., Poh B.T. (2012) Physicochemical and rheological properties of novel magnesium salt-polyacrylamide composite polymers, J. Dispers. Sci. Technol. 33, 1284–1291 [CrossRef] [Google Scholar]
  • Hashmet M.R., Onur M., Tan I.M. (2014) Empirical correlations for viscosity of Polyacrylamide solutions with the effects of salinity and hardness, J. Dispers. Sci. Technol. 35, 510–517 [CrossRef] [Google Scholar]
  • Garrouch A.A., Gharbi R.B. (1999) An empirical investigation of polymer flow in porous media, Ind. Eng. Chem. Res. 38, 3564–3571 [CrossRef] [Google Scholar]
  • Levitt D., Pope G.A. (2008) Selection and screening of polymers for enhanced-oil recovery, in: SPE Symposium on Improved Oil Recovery, Society of Petroleum Engineers [Google Scholar]
  • Yen H.-Y., Yang M.-H. (2003) The effect of metal ions additives on the rheological behavior of polyacrylamide solution, Polym. Test. 22, 389–393 [CrossRef] [Google Scholar]
  • Niu Y., Jian O., Zhu Z., Wang G., Sun G. (2001) Research on hydrophobically associating water-soluble polymer used for EOR, in: SPE International Symposium on Oilfield Chemistry, Society of Petroleum Engineers [Google Scholar]
  • Sorbie K.S. (1991) Polymer-Improved Oil Recovery, Blackie and Son Ltd, Glasgow and London [Google Scholar]
  • Seright R., Henrici B. (1990) Xanthan stability at elevated temperatures, SPE Reserv. Eng. 5, 52–60. [CrossRef] [Google Scholar]
  • Ghoniem S., Chauveteau G., Moan M., Wolff C. (1981) Mechanical degradation of semi‐dilute polymer solutions in laminar flows, Can. J. Chem. Eng. 59, 450–454 [CrossRef] [Google Scholar]
  • Reed R., Healy R., Shah D., Schechter R. (1977) Improved Oil Recovery by Surfactant and Polymer Flooding, in: Shah D.O., Schecther R.S. (eds), Academic Press Inc., New York, 383 p [CrossRef] [Google Scholar]
  • Willhite G.P., Dominguez J.G. (1977) Mechanisms of polymer retention in porous media, in: Schechter R.S. (ed), Improved Oil Recovery by Surfactant and Polymer Flooding, Academic Press, Cambridge, Massachusetts, United States pp. 511–554 [CrossRef] [Google Scholar]
  • Fuoss R.M. (1948) Viscosity function for polyelectrolytes, J. Polym. Sci. 3, 603–604 [Google Scholar]
  • Doe P.H., Moradi-Araghi A., Shaw J.E., Stahl G.A. (1987) Development and evaluation of EOR polymers suitable for hostile environments part 1: Copolymers of vinylpyrrolidone and acrylamide, SPE Reserv.Eng. 2, 461–467 [Google Scholar]
  • Gao C.H. (2011) Scientific research and field applications of polymer flooding in heavy oil recovery, J. Pet. Explor. Prod. Technol. 1, 65–70 [CrossRef] [Google Scholar]
  • Seright R.S., Fan T., Wavrik K., Balaban R.D.C. (2011) New insights into polymer rheology in porous media, SPE J. 16, 35–42 [CrossRef] [Google Scholar]
  • Ward J., Martin F.D. (1981) Prediction of viscosity for partially hydrolyzed polyacrylamide solutions in the presence of calcium and magnesium ions, Soc. Pet. Eng. J. 21, 623–631 [CrossRef] [Google Scholar]
  • Rashidi M., Blokhus A.M., Skauge A. (2011) Viscosity and retention of sulfonated polyacrylamide polymers at high temperature, J. Appl. Polym. Sci. 119, 3623–3629 [CrossRef] [Google Scholar]
  • Hashmet M.R., Onur M., Tan I.M. (2014) Empirical correlations for viscosity of polyacrylamide solutions with the effects of temperature and shear rate. II, J. Dispers. Sci. Technol. 35, 1685–1690 [CrossRef] [Google Scholar]
  • Gao C. (2013) Viscosity of partially hydrolyzed polyacrylamide under shearing and heat, J. Pet. Explor. Prod. Technol. 3, 203–206 [CrossRef] [Google Scholar]
  • Gao C.H. (2014) Comprehensive correlations to calculate viscosity of partially hydrolyzed polyacrylamide, in: SPE EOR Conference at Oil and Gas West Asia, Society of Petroleum Engineers [Google Scholar]
  • Zhang P., Wang Y., Yang Y., Chen W., Bai S. (2015) Effective viscosity in porous media and applicable limitations for polymer flooding of an associative polymer, Oil Gas Sci. Technol. − Rev. IFP Energ. Nouv. 70, 931–939 [Google Scholar]
  • Haiyang Y., Yefei W., Jian Z., Peng L., Shenglong S. (2015) Dynamic gelation of HPAM/Cr(III) under shear in an agitator and porous media, Oil Gas Sci. Technol. − Rev. IFP Energ. Nouv. 70, 941–949 [Google Scholar]
  • Hinch E. (1977) Mechanical models of dilute polymer solutions in strong flows, Phys. Fluids (1958–1988) 20, S22–S30 [CrossRef] [Google Scholar]
  • Gordon R., Schowalter W. (1972) Anisotropic fluid theory: a different approach to the dumbbell theory of dilute polymer solutions, Trans. Soc. Rheol. (1957–1977) 16, 79–97 [CrossRef] [Google Scholar]
  • Bird R., Dotson P., Johnson N. (1980) Polymer solution rheology based on a finitely extensible bead—spring chain model, J. Non-Newton. Fluid Mech. 7, 213–235 [CrossRef] [Google Scholar]
  • De Gennes P. (1974) Coil‐stretch transition of dilute flexible polymers under ultrahigh velocity gradients, J. Chem. Phys. 60, 5030–5042 [CrossRef] [Google Scholar]
  • Rostami A., Masoudi M., Ghaderi-Ardakani A., Arabloo M., Amani M. (2016) Effective thermal conductivity modeling of sandstones: SVM framework analysis, Int. J. Thermophys. 37, 1–15 [CrossRef] [Google Scholar]
  • Meybodi M.K., Daryasafar A., Karimi M. (2016) Determination of hydrocarbon-water interfacial tension using a new empirical correlation, Fluid Phase Equilib. 415, 42–50 [CrossRef] [Google Scholar]
  • Shokrollahi A., Arabloo M., Gharagheizi F., Mohammadi A.H. (2013) Intelligent model for prediction of CO 2-reservoir oil minimum miscibility pressure, Fuel 112, 375–384 [CrossRef] [Google Scholar]
  • Mesbah M., Soroush E., Azari V., Lee M., Bahadori A., Habibnia S. (2015) Vapor liquid equilibrium prediction of carbon dioxide and hydrocarbon systems using LSSVM algorithm, J. Supercrit. Fluids 97, 256–267 [CrossRef] [Google Scholar]
  • Mesbah M., Soroush E., Shokrollahi A., Bahadori A. (2014) Prediction of phase equilibrium of CO2/cyclic compound binary mixtures using a rigorous modeling approach, J. Supercrit. Fluids 90, 110–125 [CrossRef] [Google Scholar]
  • Rafiee-Taghanaki S., Arabloo M., Chamkalani A., Amani M., Zargari M.H., Adelzadeh M.R. (2013) Implementation of SVM framework to estimate PVT properties of reservoir oil, Fluid Phase Equilib. 346, 25–32 [CrossRef] [Google Scholar]
  • Arabloo M., Shokrollahi A., Gharagheizi F., Mohammadi A.H. (2013) Toward a predictive model for estimating dew point pressure in gas condensate systems, Fuel Process. Technol. 116, 317–324 [CrossRef] [Google Scholar]
  • Tatar A., Barati-Harooni A., Najafi-Marghmaleki A., Mohebbi A., Ghiasi M.M., Mohammadi A.H., Hajinezhad A. (2016) Comparison of two soft computing approaches for predicting CO2 solubility in aqueous solution of piperazine, Int. J. Greenh. Gas Control. 53, 85–97 [CrossRef] [Google Scholar]
  • Esmaeili-Jaghdan Z., Shariati A., Nikou M.R.K. (2016) A hybrid smart modeling approach for estimation of pure ionic liquids viscosity, J. Mol. Liq. 222, 14–27 [CrossRef] [Google Scholar]
  • Ghiasi M.M., Arabloo M., Mohammadi A.H., Barghi T. (2016) Application of ANFIS soft computing technique in modeling the CO2 capture with MEA, DEA, and TEA aqueous solutions, Int. J. Greenh. Gas Control. 49, 47–54 [CrossRef] [Google Scholar]
  • Dadkhah M.R., Tatar A., Mohebbi A., Barati-Harooni A., Najafi-Marghmaleki A., Ghiasi M.M., Mohammadi A.H., Pourfayaz F. (2017) Prediction of solubility of solid compounds in supercritical CO2 using a connectionist smart technique, J. Supercrit. Fluids 120, 181–190 [CrossRef] [Google Scholar]
  • Tatar A., Barati A., Yarahmadi A., Najafi A., Lee M., Bahadori A. (2016) Prediction of carbon dioxide solubility in aqueous mixture of methyldiethanolamine and N-methylpyrrolidone using intelligent models, Int. J. Greenh. Gas Control. 47, 122–136 [CrossRef] [Google Scholar]
  • Meybodi M.K., Shokrollahi A., Safari H., Lee M., Bahadori A. (2015) A computational intelligence scheme for prediction of interfacial tension between pure hydrocarbons and water, Chem. Eng. Res. Des. 95, 79–92 [CrossRef] [Google Scholar]
  • Taghvaei H., Amooie M.A., Hemmati-Sarapardeh A., Taghvaei H. (2016) A comprehensive study of phase equilibria in binary mixtures of carbon dioxide + alcohols: Application of a hybrid intelligent model (CSA-LSSVM), J. Mol. Liq. Part A 224, 745–756 [CrossRef] [Google Scholar]
  • Atashrouz S., Mirshekar H., Hemmati-Sarapardeh A. (2017) A soft-computing technique for prediction of water activity in PEG solutions, Colloid Polym. Sci. 295, 421–432 [CrossRef] [Google Scholar]
  • Haifeng W., Dejin H. (2005) Comparison of SVM and LS-SVM for regression, in: 2005 International Conference on Neural Networks and Brain, Vol. 1, ICNN & B'05. IEEE, pp. 279–283) [CrossRef] [Google Scholar]
  • Suykens J.A.K., Vandewalle J. (1999) Least squares support vector machine classifiers, Neural Process. Lett. 9, 293–300 [Google Scholar]
  • Suykens J.A., Van Gestel T., De Brabanter J., De Moor B., Vandewalle J., Suykens J., Van Gestel T. (2002) Least Squares Support Vector Machines, World Scientific, Singapore. [CrossRef] [Google Scholar]
  • Jang J.S.R., Chuen-Tsai S. (1995) Neuro-fuzzy modeling and control, Proc. IEEE 83, 378–406 [Google Scholar]
  • Takagi T., Sugeno M. (1985) Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Syst. Man Cybern. 15, 116–132 [Google Scholar]
  • Ziaee H., Hosseini S.M., Sharafpoor A., Fazavi M., Ghiasi M.M., Bahadori A. (2015) Prediction of solubility of carbon dioxide in different polymers using support vector machine algorithm, J. Taiwan Inst. Chem. Eng. 46, 205–213 [CrossRef] [Google Scholar]
  • Rostami A., Arabloo M., Esmaeilzadeh S., Mohammadi A.H. (2018) On modeling of bitumen/n-tetradecane mixture viscosity: Application in solvent-assisted recovery method, Asia-Pac. J. Chem. Eng. 13, e2152 https://doi.org/2110.1002/apj.2152 [Google Scholar]
  • Haykin S., Network N. (2004) A comprehensive foundation, Neural Netw. 2, 41 [Google Scholar]
  • Sayahi T., Tatar A., Bahrami M. (2016) A RBF model for predicting the pool boiling behavior of nanofluids over a horizontal rod heater, Int. J. Therm. Sci. 99, 180–194 [CrossRef] [Google Scholar]
  • Wilamowski B.M., Jaeger R.C. (1996) Implementation of RBF type networks by MLP networks, in: Neural Networks, 1996, IEEE International Conference on IEEE, pp. 1670–1675 [Google Scholar]
  • Tatar A., Shokrollahi A., Mesbah M., Rashid S., Arabloo M., 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 [CrossRef] [Google Scholar]
  • Nilsson N.J. (1965) Learning Machines: Foundations of Trainable Pattern-Classifying Systems, McGraw-Hill, United States [Google Scholar]
  • Haykin S.S. (2001) Neural Networks: A Comprehensive Foundation, Tsinghua University Press, Beijing, China [Google Scholar]
  • Sharkey A.J.C. (1996) On combining artificial neural nets, Connect. Sci. 8, 299–314 [CrossRef] [Google Scholar]
  • Genest C., Zidek J.V. (1986) Combining probability distributions: A critique and an annotated bibliography, Stat. Sci. 1, 114–135 [CrossRef] [Google Scholar]
  • Xu L., Krzyzak A., Suen C.Y. (1992) Methods of combining multiple classifiers and their applications to handwriting recognition, IEEE Trans. Syst. Man Cybern. 22, 418–435 [CrossRef] [Google Scholar]
  • Jacobs R.A. (1995) Methods for combining experts' probability assessments, Neural Comput. 7, 867–888 [CrossRef] [Google Scholar]
  • Perrone M.P., Cooper L.N. (1992) When networks disagree: Ensemble methods for hybrid neural networks, in: DTIC Document [Google Scholar]
  • Hashem S. (1993) Approximating a function and its derivatives using MSE-optimal linear combinations of trained feedforward neural networks, in: Proc. 1993 World Congress on Neural Networks, 1, pp. 617–620 [Google Scholar]
  • Rostami A., Arabloo M., Lee M., Bahadori A. (2018) Applying SVM framework for modeling of CO2 solubility in oil during CO2 flooding, Fuel 214, 73–87 [CrossRef] [Google Scholar]
  • Rostami A., Ebadi H., Arabloo M., Meybodi M.K., Bahadori A. (2017) Toward genetic programming (GP) approach for estimation of hydrocarbon/water interfacial tension, J. Mol. Liq. 230, 175–189 [CrossRef] [Google Scholar]
  • Rostami A., Arabloo M., Ebadi H. (2017) Genetic programming (GP) approach for prediction of supercritical CO 2 thermal conductivity, Chem. Eng. Res. Des. 122, 164–175 [CrossRef] [Google Scholar]
  • Rostami A., Arabloo M., Joonaki E., Ghanaatian S., Youzband A.H. (2017) Fast estimation of supercritical co2 thermal conductivity by a supervised learning machine-implications for EOR, in: 79th EAGE Conference and Exhibition 2017 [Google Scholar]
  • Rostami A., Anbaz M.A., Gahrooei H.R.E., Arabloo M., Bahadori A. (2017) Accurate estimation of CO 2 adsorption on activated carbon with multi-layer feed-forward neural network (MLFNN) algorithm, Egypt. J. Petrol. doi:10.1016/j.ejpe.2017.01.003 (in press) [Google Scholar]
  • Kamari A., Pournik M., Rostami A., Amirlatifi A., Mohammadi A.H. (2017) Characterizing the CO2-brine interfacial tension (IFT) using robust modeling approaches: A comparative study, J. Mol. Liq. 246, 32–38 [CrossRef] [Google Scholar]
  • Rostami A., Arabloo M., Kamari A., Mohammadi A.H. (2017) Modeling of CO2 solubility in crude oil during carbon dioxide enhanced oil recovery using gene expression programming, Fuel 210, 768–782 [CrossRef] [Google Scholar]
  • Rostami A., Ebadi H. (2017) Toward gene expression programming for accurate prognostication of the critical oil flow rate through the choke: Correlation development, Asia-Pac. J. Chem. Eng. 12, 884–893 [Google Scholar]
  • Rostami A., Shokrollahi A. (2017) Accurate prediction of water dewpoint temperature in natural gas dehydrators using gene expression programming approach, J. Mol. Liq. 243, 196–204 [CrossRef] [Google Scholar]
  • Bahari M., Rostami A., Joonaki E., Ali M. (2014) Investigation of a novel technique for decline curve analysis in comparison with the conventional models, Int. J. Comput. Appl. 98, 1–11 [Google Scholar]
  • Rostami A., Baghban A. (2018) Application of a supervised learning machine for accurate prognostication of higher heating values of solid wastes, Energy Sources, Part A: Recovery, Util. Environ. Eff. 40, 558–564 [CrossRef] [Google Scholar]
  • Karkevandi-Talkhooncheh A., Rostami A., Hemmati-Sarapardeh A., Ahmadi M., Husein M.M., Dabir B. (2018) Modeling minimum miscibility pressure during pure and impure CO2 flooding using hybrid of radial basis function neural network and evolutionary techniques, Fuel 220, 270–282 [CrossRef] [Google Scholar]
  • Gao C. (2014) Empirical correlations for viscosity of partially hydrolyzed Polyacrylamide, J. Pet. Explor. Prod. Technol. 4, 209–213 [CrossRef] [Google Scholar]
  • Pelckmans K., Suykens J., Gestel T., Brabanter J., Lukas L., Hamers B., Moor B., Vandewalle J. (2002) A Matlab/c toolbox for least square support vector machines, in: ESAT-SCD-SISTA Technical Report, 02-145 [Google Scholar]
  • Suykens J.A., De Brabanter J., Lukas L., Vandewalle J. (2002) Weighted least squares support vector machines: Robustness and sparse approximation, Neurocomputing 48, 85–105 [CrossRef] [Google Scholar]
  • Hemmati-Sarapardeh A., Shokrollahi A., Tatar A., Gharagheizi F., Mohammadi A.H., Naseri A. (2014) Reservoir oil viscosity determination using a rigorous approach, Fuel 116, 39–48 [CrossRef] [Google Scholar]
  • Cybenko G. (1989) Approximation by superpositions of a sigmoidal function, Math. Control. Signals Syst. 2, 303–314 [CrossRef] [Google Scholar]
  • Hemmati-Sarapardeh A., Ameli F., Dabir B., Ahmadi M., Mohammadi A.H. (2016) On the evaluation of asphaltene precipitation titration data: Modeling and data assessment, Fluid Phase Equilib. 415, 88–100 [CrossRef] [Google Scholar]
  • Gramatica P. (2007) Principles of QSAR models validation: Internal and external, QSAR Comb. Sci. 26, 694–701 [CrossRef] [Google Scholar]
  • Goodall C.R. (1993) Computation using the QR decomposition, in: Handbook of Statistics, Elsevier, Amsterdam, Netherland, pp. 467–508 [CrossRef] [Google Scholar]
  • Eslamimanesh A., Gharagheizi F., Mohammadi A.H., Richon D. (2011) Phase equilibrium modeling of structure H clathrate hydrates of methane + water “insoluble” hydrocarbon promoter using QSPR molecular approach, J. Chem. Eng. Data 56, 3775–3793 [CrossRef] [Google Scholar]
  • Chen G., Fu K., Liang Z., Sema T., Li C., Tontiwachwuthikul P., Idem R. (2014) The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process, Fuel 126, 202–212 [CrossRef] [Google Scholar]

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