Open Access
Issue
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 74, 2019
Article Number 62
Number of page(s) 10
DOI https://doi.org/10.2516/ogst/2019032
Published online 02 July 2019
  • Zabihi R., Schaffie M., Nezamabadi-pour H., Ranjbar M. (2011) Artificial neural network for permeability damage prediction due to sulfate scaling, J. Pet. Sci. Eng. 78, 575–581. [Google Scholar]
  • Moghadasi J., Jamialahmadi M., Müller-Steinhagen H., Sharif A., Ghalambor A., Izadpanah M.R., Motaie E. (2003) Scale formation in Iranian oil reservoir and production equipment during water injection, in: International Symposium on Oilfield Scale, Society of Petroleum Engineers, Aberdeen, United Kingdom. [Google Scholar]
  • Lindlof J.C., Stoffer K.G. (1983) A case study of seawater injection incompatibility 35, 1256–1262. [Google Scholar]
  • Aliaga D.A., Wu G., Sharma M.M., Lake L.W. (1992) Barium and calcium sulfate precipitation and migration inside sandpacks, SPE-19765-PA, SPE Formation Evaluation, 7, 1, 79–86. [CrossRef] [Google Scholar]
  • Boon J.A., Hamilton T., Holloway L., Wiwchar B. (1983) Reaction between rock matrix and injected fluids in cold lake oil sand – spotential for formation damage, J. Can. Pet. Technol. 22, 4, 55–66. [Google Scholar]
  • Cusack F., Brown D.R., Costerton J.W., Clementz D.M. (1987) Field and laboratory studies of microbial/fines plugging of water injection wells: Mechanism, diagnosis and removal, J. Pet. Sci. Eng. 1, 39–50. [Google Scholar]
  • El-Hattab M.I. (1985) Scale deposition in surface and subsurface production equipment in the Gulf of Suez, J. Pet. Technol. 37, 9, 1640–1652. [CrossRef] [Google Scholar]
  • Bayona H.J. (1993) A review of well injectivity performance in Saudi Arabia’s Ghawar field seawater injection program, in: Middle East Oil Show, Society of Petroleum Engineers, Bahrain. [Google Scholar]
  • Stalker R., Collins I.R., Graham G.M. (2003) The impact of chemical incompatibilities in commingled fluids on the efficiency of a produced water reinjection system: A North Sea example, in: International Symposium on Oilfield Chemistry, Society of Petroleum Engineers, Houston, Texas. [Google Scholar]
  • Bedrikovetsky P., Marchesin D., Shecaira F., Serra A.L., Marchesin A., Rezende E., Hime G. (2001) Well impairment during sea/produced water flooding: Treatment of laboratory data, in: SPE Latin American and Caribbean Petroleum Engineering Conference, Society of Petroleum Engineers, Buenos Aires, Argentina. [Google Scholar]
  • Ahmed S.J. (2004) Laboratory study on precipitation of calcium sulphate in berea sandstone cores, Doctoral dissertation, King Fahd University of Petroleum & Minerals, Saudi Arabia. [Google Scholar]
  • Gunn D.J., Murthy M.S. (1972) Kinetics and mechanisms of precipitations, Chem. Eng. Sci. 27, 1293–1313. [Google Scholar]
  • Liu S.-T., Nancollas G.H. (1975) The crystal growth and dissolution of barium sulfate in the presence of additives, J. Coll. Interf. Sci. 52, 582–592. [CrossRef] [Google Scholar]
  • Walton A.G., Füredi H., Elving P.J., Kolthoff I.M. (1967) The formation and properties of precipitates, Vol. 23, Interscience Publishers, New York, pp. 36–38. [Google Scholar]
  • Nancollas G.H., Eralp A.E., Gill J.S. (1978) Calcium sulfate scale formation: A kinetic approach, Soc. Pet. Eng. J. 18, 133–138. [CrossRef] [Google Scholar]
  • Nancollas G.H., Purdie N. (1963) Crystallization of barium sulphate in aqueous solution, Trans. Faraday Soc. 59, 735–740. [CrossRef] [Google Scholar]
  • Mitchell R.W., Grist D.M., Boyle M.J. (1980) Chemical treatments associated with North Sea projects, J. Pet. Technol. 32, 904–912. [CrossRef] [Google Scholar]
  • Yuan M. (1989) Prediction of sulphate scaling tendency and investigation of barium and strontium sulphate solid solution scale formation, Doctoral dissertation, Heriot-Watt University, Edinburgh. [Google Scholar]
  • Safari H., Jamialahmadi M. (2014) Thermodynamics, kinetics, and hydrodynamics of mixed salt precipitation in porous media: Model development and parameter estimation, Transp. Porous Media 101, 477–505. [Google Scholar]
  • Safari H., Jamialahmadi M. (2014) Estimating the kinetic parameters regarding barium sulfate deposition in porous media: A genetic algorithm approach, Asia-Pacific J. Chem. Eng. 9, 256–264. [CrossRef] [Google Scholar]
  • Vitthal S., Sharma M.M. (1992) A Stokesian dynamics model for particle deposition and bridging in granular media, J. Coll. Interf. Sci. 153, 314–336. [CrossRef] [Google Scholar]
  • Andersen K.I., Halvorsen E., Sælensminde T., Østbye N.O. (2000) Water management in a closed loop – Problems and solutions at brage field, in: SPE European Petroleum Conference, Society of Petroleum Engineers, Paris, France. [Google Scholar]
  • Paulo J., Mackay E.J., Menzies N., Poynton N. (2001) Implications of brine mixing in the reservoir for scale management in the Alba field, in: International Symposium on Oilfield Scale, Society of Petroleum Engineers, Aberdeen, United Kingdom. [Google Scholar]
  • Mackay E. (2003) Predicting in situ sulphate scale deposition and the impact on produced ion concentrations, Chem. Eng. Res. Des. 81, 326–332. [Google Scholar]
  • McElhiney J.E., Sydansk R.D., Lintelmann K.A., Benzel W.M., Davidson K.B. (2001) Determination of in-situ precipitation of barium sulphate during coreflooding, in: International Symposium on Oilfield Scale, Society of Petroleum Engineers, Aberdeen, United Kingdom. [Google Scholar]
  • Weintritt D.J., Cowan J.C. (1967) Unique characteristics of barium sulfate scale deposition, J. Pet. Technol. 19, 1381–1394. [CrossRef] [Google Scholar]
  • Read P.A., Ringen J.K. (1982) The use of laboratory tests to evaluate scaling problems during water injection, in: SPE Oilfield and Geothermal Chemistry Symposium, Society of Petroleum Engineers, Dallas, Texas. [Google Scholar]
  • Moghadasi J., Jamialahmadi M., Müller-Steinhagen H., Sharif A. (2004) Formation damage due to scale formation in porous media resulting from water injection, in: SPE International Symposium and Exhibition on Formation Damage Control, Society of Petroleum Engineers, Lafayette, Louisiana. [Google Scholar]
  • Chang F., Civan F. (1991) Modeling of formation damage due to physical and chemical interactions between fluids and reservoir rocks, in: SPE Annual Technical Conference and Exhibition, Society of Petroleum Engineers, Dallas, Texas. [Google Scholar]
  • Yeboah Y.D., Somuah S.K., Saeed M.R. (1993) A new and reliable model for predicting oilfield scale formation, in: SPE International Symposium on Oilfield Chemistry, Society of Petroleum Engineers, New Orleans, Louisiana. [Google Scholar]
  • Bertero L., Chierici G.L., Gottardi G., Mesini E., Mormino G. (1988) Chemical equilibrium models: Their use in simulating the injection of incompatible waters, SPE-14126-PA, SPE Reservoir Engineering, 3, 1, 288–294. [CrossRef] [Google Scholar]
  • Thomas L.G., Albertsen M., Perdeger A., Knoke H.H.K., Horstmann B.W., Schenk D. (1995) Chemical characterization of fluids and their modelling with respect to their damage potential in injection on production processes using an expert system, in: SPE International Symposium on Oilfield Chemistry, Society of Petroleum Engineers, San Antonio, Texas. [Google Scholar]
  • Jamialahmadi M., Muller-Steinhagen H. (2008) Mechanisms of scale deposition and scale removal in porous media, Int. J. Oil Gas Coal Technol. 1, 81–108. [CrossRef] [Google Scholar]
  • Creton B., Lévêque I., Oukhemanou F. (2019) Equivalent alkane carbon number of crude oils: A predictive model based on machine learning, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 74, 30. [CrossRef] [Google Scholar]
  • Rostami A., Shokrollahi A., Ghazanfari M.H. (2018) New method for predicting n-tetradecane/bitumen mixture density: Correlation development, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 73, 35. [CrossRef] [Google Scholar]
  • Sales L.d.P.A, Pitombeira-Neto A.R., de Athayde Prata B. (2018) A genetic algorithm integrated with Monte Carlo simulation for the field layout design problem, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 73, 24. [CrossRef] [Google Scholar]
  • Ferreira C. (2006) Designing neural networks using gene expression programming. In Applied soft computing technologies: The challenge of complexity, Springer, Berlin, Heidelberg, pp. 517–535. [CrossRef] [Google Scholar]
  • Gharagheizi F., Ilani-Kashkouli P., Farahani N., Mohammadi A.H. (2012) Gene expression programming strategy for estimation of flash point temperature of non-electrolyte organic compounds, Fluid Phase Equilib. 329, 71–77. [Google Scholar]
  • Gharagheizi F., Eslamimanesh A., Sattari M., Mohammadi A.H., Richon D. (2013) Development of corresponding states model for estimation of the surface tension of chemical compounds, AIChE J. 59, 613–621. [Google Scholar]
  • Merdhah A. (2007) The study of scale formation in oil reservoir during water injection at high-barium and high-salinity formation water, in: Chemical and Natural Resources Engineering, Universiti Teknologi, Malaysia. [Google Scholar]
  • Merdhah A.B., Yassin M., Azam A. (2008) Study of scale formation due to incompatible water, Jurnal Teknologi 49, 9–26. [Google Scholar]
  • Merdhah A., Yassin A. (2009) Scale formation due to water injection in Berea sandstone cores, J. Appl. Sci. 9, 3298–3307. [CrossRef] [Google Scholar]
  • Merdhah A.B., Yassin A.A.M., Muherei M.A. (2010) Laboratory and prediction of barium sulfate scaling at high-barium formation water, J. Pet. Sci. Eng. 70, 79–88. [Google Scholar]
  • Rostami A., Kamari A., Joonaki E., Ghanaatian S. (2018) Accurate estimation of minimum miscibility pressure during nitrogen injection into hydrocarbon reservoirs, in 80th EAGE Conference and Exhibition 2018, Copenhagen, Denmark. [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. [Google Scholar]
  • Moghadasi R., Rostami A., Hemmati-Sarapardeh A., Motie M. (2019) Application of Nanosilica for inhibition of fines migration during low salinity water injection: Experimental study, mechanistic understanding, and model development, Fuel 242, 846–862. [CrossRef] [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]
  • 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. [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. [Google Scholar]
  • Rostami A., Kalantari-Meybodi M., Karimi M., Tatar A., Mohammadi A.H. (2018) Efficient estimation of hydrolyzed polyacrylamide (HPAM) solution viscosity for enhanced oil recovery process by polymer flooding, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 73, 22. [CrossRef] [Google Scholar]
  • Rostami A., Arabloo M., Ebadi H. (2017) Genetic programming (GP) approach for prediction of supercritical CO2 thermal conductivity, Chem. Eng. Res. Des. 122, 164–175. [Google Scholar]
  • Rostami A., Hemmati-Sarapardeh A., Karkevandi-Talkhooncheh A., Husein M.M., Shamshirband S., Rabczuk T. (2019) Modeling heat capacity of ionic liquids using group method of data handling: A hybrid and structure-based approach, Int. J. Heat Mass Trans. 129, 7–17. [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]
  • 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., Kamari A., Panacharoensawad E., Hashemi A. (2018) New empirical correlations for determination of Minimum Miscibility Pressure (MMP) during N2-contaminated lean gas flooding, J. Taiwan Ins. Chem. Eng. 91, 369–382. [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‐Pacific J. Chem. Eng. 13, e2152. [Google Scholar]
  • Rostami A., Hemmati-Sarapardeh A., Shamshirband S. (2018) Rigorous prognostication of natural gas viscosity: Smart modeling and comparative study, Fuel 222, 766–778. [CrossRef] [Google Scholar]
  • Rostami A., Baghban A., Mohammadi A.H., Hemmati-Sarapardeh A., Habibzadeh S. (2019) Rigorous prognostication of permeability of heterogeneous carbonate oil reservoirs: Smart modeling and correlation development, Fuel 236, 110–123. [CrossRef] [Google Scholar]
  • Rostami A., Shokrollahi A., Esmaeili-Jaghdan Z., Ghazanfari M.H. (2019) Rigorous silica solubility estimation in superheated steam: Smart modeling and comparative study, Environ. Prog. Sustain. Energy, doi: 10.1002/ep.13089, in press. [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-Pacific J. Chem. Eng. 12, 884–893. [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. [Google Scholar]
  • Rostami A., Ebadi H., Mohammadi A.H., Baghban A. (2018) Viscosity estimation of Athabasca bitumen in solvent injection process using genetic programming strategy, Energy Sources Part A Recovery Utilization Env. Eff. 40, 922–928. [CrossRef] [Google Scholar]
  • Ferreira C. (2001) Gene expression programming: A new adaptive algorithm for solving problems, Compl. Syst. 13, 87–129. [Google Scholar]
  • Koza J.R. (1992) Genetic programming: On the programming of computers by means of natural selection, MIT Press, Cambridge, Massachusetts, USA. [Google Scholar]
  • Teodorescu L., Sherwood D. (2008) High energy physics event selection with gene expression programming, Comput. Phys. Commun. 178, 409–419. [Google Scholar]
  • Ferreira C. (2006) Gene expression programming: Mathematical modeling by an artificial intelligence, 2nd edn., Springer, Berlin, Heidelberg. [Google Scholar]
  • Shokrollahi A., Safari H., Esmaeili-Jaghdan Z., Ghazanfari M.H., Mohammadi A.H. (2015) Rigorous modeling of permeability impairment due to inorganic scale deposition in porous media, J. Pet. Sci. Eng. 130, 26–36. [Google Scholar]
  • Moghadasi J., Müller-Steinhagen H., Jamialahmadi M., Sharif A. (2004) Model study on the kinetics of oil field formation damage due to salt precipitation from injection, J. Pet. Sci. Eng. 43, 201–217. [Google Scholar]
  • Yassin M.R., Arabloo M., Shokrollahi A., Mohammadi A.H. (2014) Prediction of surfactant retention in porous media: A robust modeling approach, J. Dispers. Sci. Technol. 35, 1407–1418. [Google Scholar]
  • BinMerdhah A.B., Yassin A.A.M., Muherei M.A. (2010) Laboratory and prediction of barium sulfate scaling at high-barium formation water, J. Pet. Sci. Eng. 70, 79–88. [Google Scholar]
  • Kamari A., Arabloo M., Shokrollahi A., Gharagheizi F., Mohammadi A.H. (2015) Rapid method to estimate the minimum miscibility pressure (MMP) in live reservoir oil systems during CO2 flooding, Fuel 153, 310–319. [CrossRef] [Google Scholar]
  • Ferreira C. (2002) Gene expression programming in problem solving. In Soft computing and industry, Springer, London, pp. 635–653. [CrossRef] [Google Scholar]
  • Goodall C.R. (1993) Computation using the QR decomposition, in: Handbook of Statistics, Elsevier, Amsterdam, North Holland, 467–508. [CrossRef] [Google Scholar]
  • Eslamimanesh A., Gharagheizi F., Mohammadi A.H., Richon D. (2013) Assessment test of sulfur content of gases, Fuel Process. Technol. 110, 133–140. [CrossRef] [Google Scholar]
  • Gramatica P. (2007) Principles of QSAR models validation: Internal and external, QSAR Comb. Sci. 26, 694–701. [Google Scholar]
  • Fayazi A., Arabloo M., Shokrollahi A., Zargari M.H., Ghazanfari M.H. (2014) State-of-the-art least square support vector machine application for accurate determination of natural gas viscosity, Ind. Eng. Chem. Res. 53, 945–958. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.