Open Access
Issue
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 73, 2018
Article Number 4
Number of page(s) 17
DOI https://doi.org/10.2516/ogst/2017032
Published online 09 February 2018
  • Alvarado V., Manrique E. (2010) Enhanced oil recovery: An update review, Energies 3, 1529–1575. [CrossRef] [Google Scholar]
  • Ayirala S.C., Yousef A.A. (2015) A state-of-the-art review to develop injection-water-chemistry requirement guidelines for IOR/EOR projects, SPE Prod. Oper. 30, 26–42. [CrossRef] [Google Scholar]
  • Chen F., Jiang H., Bai X., Zheng W. (2013) Evaluation the performance of sodium metaborate as a novel alkali in alkali/surfactant/polymer flooding, J. Ind. Eng. Chem. 19, 450–457. [CrossRef] [Google Scholar]
  • Iglauer S., Paluszny A., Blunt M.J. (2013) Simultaneous oil recovery and residual gas storage: A pore-level analysis using in situ X-ray micro-tomography, Fuel 103, 905–914. [CrossRef] [Google Scholar]
  • Jang H.Y., Zhang K., Chon B.H., Choi H.J. (2015) Enhanced oil recovery performance and viscosity characteristics of polysaccharide xanthan gum solution, J. Ind. Eng. Chem. 21, 741–745. [CrossRef] [Google Scholar]
  • Santanna V.C., Curbelo F.D.S., Castro Dantas T.N., Dantas Neto A.A., Albuquerque H.S., Garnica A.I.C. (2009) Microemulsion flooding for enhanced oil recovery, J. Pet. Sci. Eng. 66, 117–120. [CrossRef] [Google Scholar]
  • Jeirani Z., Mohamed Jan B., Si Ali B., Noor I.M., See C.H., Saphanuchart W. (2013) Correlations between interfacial tension and cumulative tertiary oil recovery in a triglyceride microemulsion flooding, J. Ind. Eng. Chem. 19, 1310–1314. [CrossRef] [Google Scholar]
  • Bera A., Kumar T., Ojha K., Mandal A. (2014) Screening of microemulsion properties for application in enhanced oil recovery, Fuel 121, 198–207. [CrossRef] [Google Scholar]
  • Fathi Z., Ramirez W.F. (1984) Optimal injection policies for enhanced oil recovery: part 2-surfactant flooding, Soc. Petrol. Eng. J. 24, 333–341. [CrossRef] [Google Scholar]
  • Zerpa L.E., Queipo N.V., Pintos S., Salager J.L. (2005) An optimization methodology of alkaline-surfactant-polymer flooding processes using field scale numerical simulation and multiple surrogates, J. Pet. Sci. Eng. 47, 197–208. [CrossRef] [Google Scholar]
  • Carrero E., Queipo N.V., Pintos S., Zerpa L.E. (2007) Global sensitivity analysis of Alkali-Surfactant-Polymer enhanced oil recovery processes, J. Pet. Sci. Eng. 58, 30–42. [CrossRef] [Google Scholar]
  • Poettmann, F.H., Hause, W.R. (1978) Micellar-Polymer Screening Criteria And Design, SPE Paper 7068, in: Presented at SPE Symposium on Improved Methods of Oil Recovery, 16–17 April, Tulsa, Oklahoma. [Google Scholar]
  • Wu W., Vaskas A., Delshad M., Pope G.A., Sepehrnoori K. (1996) Design and optimization of low-cost chemical flooding, SPE Paper 35355, in: Presented at SPE/DOE Improved Oil Recovery Symposium, Tulsa, Oklahoma. [Google Scholar]
  • Anderson G.A., Delshad M., King C.B., Mohammadi H., Pope G.A. (2006) Optimization of chemical flooding in a mixed-wet dolomite reservoir, SPE Paper 100082, in: Presented at SPE/DOE Symposium on Improved Oil Recovery, Tulsa, Oklahoma. [Google Scholar]
  • Prasanphanich J., Kalaei M.H., Delshad M., Sepehrnoori K. (2012) Chemical flooding optimisation using the experimental design approach and response surface methodology, Int. J. Oil Gas Coal. Tech. 5, 368–384. [CrossRef] [Google Scholar]
  • Douarche F., Da Veiga S., Feraille M., Enchéry G., Touzani S., Barsalou R. (2014) Sensitivity analysis and optimization of surfactant-polymer flooding under uncertainties, Oil Gas Sci. Technol. − Rev. IFP 69, 603–617. [CrossRef] [Google Scholar]
  • AlSofi A.M., Blunt M.J. (2014) Polymer flooding design and optimization under economic uncertainty, J. Pet. Sci. Eng. 124, 46–59. [CrossRef] [Google Scholar]
  • Ebbesen S., Elbert P., Guzzella L. (2013) Engine downsizing and electric hybridization under consideration of cost and drivability, Oil Gas Sci. Technol. − Rev. IFP 68, 109–116. [CrossRef] [EDP Sciences] [Google Scholar]
  • Ahmadi M.H., Ahmadi M.A., Feidt M. (2016) Performance optimization of a solar-driven multi-step irreversible brayton cycle based on a multi-objective genetic algorithm, Oil Gas Sci. Technol. − Rev. IFP 71, 1–14. [CrossRef] [EDP Sciences] [Google Scholar]
  • Zhang H., Dong M., Zhao S. (2010) Which one is more important in chemical flooding for enhanced court heavy oil recovery, lowering interfacial tension or reducing water mobility? Energy & Fuels 24, 1829–1836. [CrossRef] [Google Scholar]
  • Liu R., Pu W., Wang L., Chen Q., Li Z., Li Y., Li B. (2015) Solution properties and phase behavior of a combination flooding system consisting of hydrophobically amphoteric polyacrylamide, alkyl polyglycoside and n-alcohol at high salinities, RSC Adv. 5, 69980–69989. [CrossRef] [Google Scholar]
  • Iglauer S., Wu Y., Shuler P., Tang Y., Goddard Iii W.A. (2010) New surfactant classes for enhanced oil recovery and their tertiary oil recovery potential, J. Pet. Sci. Eng. 71, 23–29. [CrossRef] [Google Scholar]
  • Guo Y.j., Liu J.x., Zhang X.m., Feng R.s., Li H.b., Zhang J., Lv X., Luo P.y. (2012) Solution property investigation of combination flooding systems consisting of gemini–non-ionic mixed surfactant and hydrophobically associating polyacrylamide for enhanced oil recovery, Energy & Fuels 26, 2116–2123. [CrossRef] [Google Scholar]
  • Marcus J., Wolfrum S., Touraud D., Kunz W. (2015) Influence of high intensity sweeteners and sugar alcohols on a beverage microemulsion, J. Colloid Interf. Sci. 460, 105–112. [CrossRef] [Google Scholar]
  • Bardhan S., Kundu K., Saha S.K., Paul B.K. (2013) Physicochemical studies of mixed surfactant microemulsions with isopropyl myristate as oil, J. Colloid Interf. Sci. 402, 180–189. [CrossRef] [Google Scholar]
  • McClements D.J. (2012) Nanoemulsions versus microemulsions: terminology, differences, and similarities, Soft Matter 8, 1719–1729. [CrossRef] [Google Scholar]
  • Mason T.G., Wilking J.N., Meleson K., Chang C.B., Graves S.M. (2006) Nanoemulsions: formation, structure, and physical properties, J. Phys. Condens. Matter 18, 635–666. [CrossRef] [Google Scholar]
  • Karambeigi M.S., Nasiri M., Haghighi Asl A., Emadi M.A. (2016) Enhanced oil recovery in high temperature carbonates using microemulsions formulated with a new hydrophobic component, J. Ind. Eng. Chem. 39, 136–148. [CrossRef] [Google Scholar]
  • Esfandian H., Samadi-Maybodi A., Parvini M., Khoshandam B. (2016) Development of a novel method for the removal of diazinon pesticide from aqueous solution and modeling by artificial neural networks (ANN), J. Ind. Eng. Chem. 35, 295–308. [CrossRef] [Google Scholar]
  • Bezerra M.A., Santelli R.E., Oliveira E.P., Villar L.S., Escaleira L.A. (2008) Response surface methodology (RSM) as a tool for optimization in analytical chemistry, Talanta 76, 965–977. [CrossRef] [PubMed] [Google Scholar]
  • Rahimi K., Towfighi J., Sedighi M., Masoumi S., Kooshki Z. (2016) The effects of SiO2/Al2O3 and H2O/Al2O3 molar ratios on SAPO-34 catalysts in methanol to olefins (MTO) process using experimental design, J. Ind. Eng. Chem. 35, 123–131. [CrossRef] [Google Scholar]
  • Jeirani Z., Mohamed Jan B., Si Ali B., Mohd Noor I., See C.H., Saphanuchart W. (2013) Prediction of water and oil percolation thresholds of a microemulsion by modeling of dynamic viscosity using response surface methodology, J. Ind. Eng. Chem. 19, 554–560. [CrossRef] [Google Scholar]
  • Khuri A.I., Mukhopadhyay S. (2010) Response surface methodology, Wiley Interdiscip. Rev. Comput. Stat. 2, 128–149. [CrossRef] [Google Scholar]
  • Chapoy A., Mohammadi A.H., Richon D. (2007) Predicting the hydrate stability zones of natural gases using artificial neural networks, Oil Gas Sci. Technol − Rev. IFP 62, 701–706. [CrossRef] [Google Scholar]
  • Guilherme I.R., Marana A.N., Papa J.P., Chiachia G., Afonso L.C.S., Miura K., Ferreira M.V.D., Torres F. (2011) Petroleum well drilling monitoring through cutting image analysis and artificial intelligence techniques, Eng. Appl. Artif. Intell. 24, 201–207. [CrossRef] [Google Scholar]
  • Li X., Chan C.W. (2010) Application of an enhanced decision tree learning approach for prediction of petroleum production, Eng. Appl. Artif. Intell. 23, 102–109. [CrossRef] [Google Scholar]
  • Mohaghegh S. (2000) Virtual-intelligence applications in petroleum engineering: Part I − Artificial neural networks, J. Petrol. Technol. 52, 64–73. [CrossRef] [Google Scholar]
  • Al-Dousari M.M., Garrouch A.A. (2013) An artificial neural network model for predicting the recovery performance of surfactant polymer floods, J. Pet. Sci. Eng. 109, 51–62. [CrossRef] [Google Scholar]
  • Fathinasab M., Ayatollahi S., Hemmati-Sarapardeh A. (2015) A rigorous approach to predict nitrogen-crude oil minimum miscibility pressure of pure and nitrogen mixtures, Fluid Phase Equilib. 399, 30–39. [CrossRef] [Google Scholar]
  • Salahshoor K., Zakeri S., Haghighat Sefat M. (2013) Stabilization of gas-lift oil wells by a nonlinear model predictive control scheme based on adaptive neural network models, Eng. Appl. Artif. Intell. 26, 1902–1910. [CrossRef] [Google Scholar]
  • Karambeigi M.S., Zabihi R., Hekmat Z. (2011) Neuro-simulation modeling of chemical flooding, J. Pet. Sci. Eng. 78, 208–219. [CrossRef] [Google Scholar]
  • Kennedy J., Eberhart R. (1995) Particle swarm optimization, in: IEEE International Conference on Neural Networks, pp. 1942–1948. [Google Scholar]
  • Nezamabadi-Pour H. (2015) A quantum-inspired gravitational search algorithm for binary encoded optimization problems, Eng. Appl. Artif. Intell. 40, 62–75. [CrossRef] [Google Scholar]
  • Forouzanfar M., Forghani N., Teshnehlab M. (2010) Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation, Eng. Appl. Artif. Intell. 23, 160–168. [CrossRef] [Google Scholar]
  • Reche López P., Jurado F., Ruiz Reyes N., García Galán S., Gómez M. (2008) Particle swarm optimization for biomass-fuelled systems with technical constraints, Eng. Appl. Artif. Intell. 21, 1389–1396. [CrossRef] [Google Scholar]
  • Neyestani M., Farsangi M.M., Nezamabadi-Pour H. (2010) A modified particle swarm optimization for economic dispatch with non-smooth cost functions, Eng. Appl. Artif. Intell. 23, 1121–1126. [CrossRef] [Google Scholar]
  • Zadeh L.A. (1965) Fuzzy sets, Inform. Control. 8, 338–353. [CrossRef] [MathSciNet] [Google Scholar]
  • Ja'fari A., Kadkhodaie-Ilkhchi A., Sharghi Y., Ghaedi M. (2014) Integration of adaptive neuro-fuzzy inference system, neural networks and geostatistical methods for fracture density modeling, Oil Gas Sci. Technol. − Rev. IFP 69, 1143–1154. [CrossRef] [Google Scholar]
  • Ghatee M., Hashemi S.M. (2009) Optimal network design and storage management in petroleum distribution network under uncertainty, Eng. Appl. Artif. Intell. 22, 806–817. [CrossRef] [Google Scholar]
  • Hu Z., Chan C.W. (2015) In-situ bioremediation for petroleum contamination: a fuzzy rule-based model predictive control system, Eng. Appl. Artif. Intell. 38, 70–78. [CrossRef] [Google Scholar]
  • Liao R.F., Chan C.W., Hromek J., Huang G.H., He L. (2008) Fuzzy logic control for a petroleum separation process, Eng. Appl. Artif. Intell. 21, 835–845. [CrossRef] [Google Scholar]
  • Mohaghegh S. (2000) Virtual-intelligence applications in petroleum engineering: Part 3-Fuzzy logic, J. Petrol. Technol. 52, 82–87. [CrossRef] [Google Scholar]
  • Khatami H.R., Ranjbar M., Schaffie M., Emadi M.A. (2008) Prediction of calcium carbonate precipitation in oilfields based on a fuzzy solubility model, Oil Gas-Eur. Mag. 34, 78–83. [Google Scholar]
  • Nashawi I.S., Malallah A. (2009) Improved electrofacies characterization and permeability predictions in sandstone reservoirs using a data mining and expert system approach, Petrophysics 50, 250–268. [Google Scholar]
  • Nowroozi S., Ranjbar M., Hashemipour H., Schaffie M. (2009) Development of a neural fuzzy system for advanced prediction of dew point pressure in gas condensate reservoirs, Fuel Process. Technol. 90, 452–457. [CrossRef] [Google Scholar]
  • Jeirani Z., Mohamed Jan B., Si Ali B., Noor I.M., See C.H., Saphanuchart W. (2013) Formulation, optimization and application of triglyceride microemulsion in enhanced oil recovery, Ind. Crops Prod. 43, 6–14. [CrossRef] [Google Scholar]
  • Jeirani Z., Mohamed Jan B., Si Ali B., Noor I.M., See C.H., Saphanuchart W. (2013) Prediction of the optimum aqueous phase composition of a triglyceride microemulsion using response surface methodology, J. Ind. Eng. Chem. 19, 1304–1309. [CrossRef] [Google Scholar]
  • Jeirani Z., Mohamed Jan B., Si Ali B., Noor I.M., See C.H., Saphanuchart W. (2013) Formulation and phase behavior study of a nonionic triglyceride microemulsion to increase hydrocarbon production, Ind. Crops Prod. 43, 15–24. [CrossRef] [Google Scholar]
  • Anderson M.J., Whitcomb P.J. (2004) RSM Simplified: Optimizing Processes using Response Surface Methods for Design of Experiments, Productivity Press. [Google Scholar]
  • Zahedzadeh M., Karambeigi M.S., Roayaei E., Emadi M.A., Radmehr M., Gholamianpour H., Ashoori S., Shokrollahzadeh S. (2014) Comprehensive management of mineral scale deposition in carbonate oil fields − a case study, Chem. Eng. Res. Des. 92, 2264–2272. [CrossRef] [Google Scholar]
  • Van Den Bergh F., Engelbrecht A.P. (2006) A study of particle swarm optimization particle trajectories, Inform. Sci. 176, 937–971. [CrossRef] [Google Scholar]
  • Raphael B., Smith I.F.C. (2003) A direct stochastic algorithm for global search, Appl. Math. Comput. 146, 729–758. [CrossRef] [Google Scholar]
  • Mathews B.P., Diamantopoulos A. (1994) Towards a taxonomy of forecast error measures a factor-comparative investigation of forecast error dimensions, J. Forecast. 13, 409–416. [CrossRef] [Google Scholar]
  • Shcherbakov M.V., Brebels A., Shcherbakova N.L., Tyukov A.P., Janovsky T.A., Kamaev V.A. (2013) A survey of forecast error measures, World Appl. Sci. J. 24, 171–176. [Google Scholar]
  • Chai T., Draxler R.R. (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature, Geosci. Model. Dev. 7, 1247–1250. [Google Scholar]
  • Goodwin P., Lawton R. (1999) On the asymmetry of the symmetric MAPE, Int. J. Forcast. 15, 405–408. [CrossRef] [Google Scholar]
  • Makridakis S., Hibon M. (2000) The M3-competition: Results, conclusions and implications, Int. J. Forcast. 16, 451–476. [CrossRef] [Google Scholar]
  • Tadros T.F. (2013) Emulsion Formation, Stability, and Rheology, In: Emulsion Formation and Stability, Wiley, Weinheim, Germany, pp. 1–75. [Google Scholar]
  • Deng S., Wang Y., Hu Y., Ge X., He X. (2013) Integrated petrophysical log characterization for tight carbonate reservoir effectiveness: A case study from the Longgang area, Sichuan Basin, China, Pet. Sci. 10, 336–346. [CrossRef] [Google Scholar]
  • Gundogar A.S., Ross C.M., Akin S., Kovscek A.R. (2015) Multiscale pore structure characterization of Middle East carbonates, J. Pet. Sci. Eng., in press, DOI:17.10.1016/ j.petrol.2016.1007.1018. [Google Scholar]
  • Zhao H., Ning Z., Wang Q., Zhang R., Zhao T., Niu T., Zeng Y. (2015) Petrophysical characterization of tight oil reservoirs using pressure-controlled porosimetry combined with rate-controlled porosimetry, Fuel 154, 233–242. [CrossRef] [Google Scholar]
  • Øren P.E., Bakke S. (2003) Reconstruction of Berea sandstone and pore-scale modelling of wettability effects, J. Pet. Sci. Eng. 39, 177–199. [CrossRef] [Google Scholar]
  • Etris E.L., Brumfield D.S., Ehrlich R., Crabtree S.J. (1988) Relations between pores, throats and permeability: a petrographic/physical analysis of some carbonate grainstones and packstones, Carbonates Evaporites 3, 17–32. [CrossRef] [Google Scholar]
  • Ausbrooks R., Hurley N.F., May A., Neese D.G. (1999) Pore-size distributions in vuggy carbonates from core images, NMR, and capillary pressure. SPE Paper 56506, Presented at SPE Annual Technical Conference and Exhibition, Houston, Texas. [Google Scholar]

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