ogst
Accueil Tous les numéros Volume 69 / Numéro 4 (July-August 2014) Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles, 69 4 (2014) 603-617 Références
Dossier: Geosciences Numerical Methods
Open Access
Numéro
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 69, Numéro 4, July-August 2014
Dossier: Geosciences Numerical Methods
Page(s) 603 - 617
DOI https://doi.org/10.2516/ogst/2013166
Publié en ligne 21 janvier 2014
  • Rai K., Russell T.J., Larry W.L., Mojdeh D. (2009) Oil recovery predictions for surfactant polymer flooding, SPE Annual Technical Conference and Exhibition, SPE Paper 124001 [CrossRef] [Google Scholar]
  • Mattax C.C., Dalton R.L. (1990) Reservoir simulation, Vol. 13, SPE Monograph. [Google Scholar]
  • AlSofi A.M., Blunt M.J. (2011) Polymer flooding design and optimization under uncertainty, SPE Enhanced Oil Recovery Conference, Kuala Lumpur, Malaysia, SPE Paper 145110. [Google Scholar]
  • Gharbi R.B. (2001) Optimization of EOR processes using knowledge-based system: case studies, Petroleum Science and Technology 19, 7-8, 797–823. [CrossRef] [Google Scholar]
  • Horowitz B., Guimaraes L.J., Dantas V., Afonso S.M. (2010) A concurrent efficient global optimization algorithm applied to polymer injection strategies, Journal Petroleum Science and Engineering 71, 195–204. [CrossRef] [Google Scholar]
  • Zerpa L.E., Queipo N.V., Pintos S., del Zulia U., Tillero E., Alter D. (2007) An efficient response surface approach for the optimization of ASP flooding processes: ASP pilot project LL-03 reservoir, SPE Latin America and Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, April. [Google Scholar]
  • Feraille M., Marrel A. (2012) Prediction under uncertainty on a mature field, Oil Gas Science Technology 67, 2, 193–206. [Google Scholar]
  • Marrel A., Iooss B., Van Dorpe F., Volkova E. (2008) An efficient methodology for modeling complex computer codes with Gaussian processes, Computational Statistic and Data Analysis 52, 10, 4731–4744. [Google Scholar]
  • Scheidt C., Zabalza-Mezghani I., Feraille M., Collombier D. (2007) Toward a reliable quantification of uncertainty on production forecasts: adaptive experimental designs, Oil Gas Science Technology 62, 207–224. [Google Scholar]
  • Marrel A., Perot N. (2012) Development of a surrogate model and sensitivity analysis for an atmospheric dispersion computer code, PSAM 11 and ESREL 2012 Conference, Helsinki, Finland, June. [Google Scholar]
  • PumaFlowTM (2013) Reference manual, release v600, Beicip-Franlab. [Google Scholar]
  • PumaFlowTM (2013) Technical manual, release v600, Beicip-Franlab. [Google Scholar]
  • Pusch G., Müller Th. (1990) Modeling of multiphase flow with respect to low interfacial tension by pseudo-two-phase relative permeability functions, Revue de l’Institut Français du Pétrole 45, 1, 63–70. [Google Scholar]
  • Durrande N., Ginsbourger D., Roustand O., Carraro L. (2013) ANOVA kernels and rkhs of zero mean functions for model-based sensitivity analysis, Journal of Multivariate Analysis 115, 57–67. [CrossRef] [Google Scholar]
  • Sobol I.M. (1990) On sensitivity estimation for nonlinear mathematical models, Math. Comput. Modelling 2, 1, 112–118. [Google Scholar]
  • Saltelli A., Ratto M., Andres T., Campolongo F., Cariboni J., Gatelli D., Saisana M., Tarantola S. (2008) Global sensitivity analysis, Wiley. [Google Scholar]
  • Sacks J., Welch W.J., Mitchell T.J., Wynn H.P. (1989) Design and analysis of computer experiments, Statist. Sci. 4, 4, 409–423. [Google Scholar]
  • Fang K.T., Li R., Sudjianto A. (2006) Design and modeling for computer experiments, Chapman & Hall/CRC. [Google Scholar]
  • McKay M.D., Beckman R.J., Conover W.J. (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics 21, 2, 239–245. [CrossRef] [MathSciNet] [Google Scholar]
  • Ghanem R., Spanos P. (1991) Stochastic finite elements: a spectral approach, Springer-Verlag. [CrossRef] [Google Scholar]
  • Mara T. (2009) Extension of the RBD-FAST method to the computation of global sensitivity indices, Reliability Engineering and System Safety 94, 8, 1274–1281. [CrossRef] [Google Scholar]

Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.

Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.

Le chargement des statistiques peut être long.