Open Access
Numéro
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 74, 2019
Numéro d'article 15
Nombre de pages 16
DOI https://doi.org/10.2516/ogst/2018096
Publié en ligne 27 février 2019
  • Abbaszadeh M., Fujii H., Fujimoto F. (1996) Permeability prediction by hydraulic flow units-theory and applications, SPE Form. Eval. 11, 263–271. [CrossRef] [Google Scholar]
  • Al-ajmi F.A., Aramco S., Holditch S.A. (2000) Permeability estimation using hydraulic flow units in a central Arabia reservoir, SPE Annual Technical Conference and Exhibition held in Dallas, Texas, 1–4 October. [Google Scholar]
  • Almeida F.R., Davolio A.D., Schiozer D.J. (2014) A new approach to perform a probabilistic and multi-objective history, SPE Annual Technical Conference and Exhibition held in Amsterdam, The Netherlands, 27–29 October. [Google Scholar]
  • Aminian K., Ameri S., Oyerokun A., Thomas B. (2003) Prediction of flow units and permeability using artificial neural networks, SPE Western Regional/AAPG Pacific Section Joint Meeting held in Long Beach, California, USA., 19–24 May. [Google Scholar]
  • Avansi G.D., Schiozer D.J. (2015) A new approach to history matching using reservoir characterization and reservoir simulation integrated studies, Offshore Technology Conference held in Houston, Texas, 4–7 May. [Google Scholar]
  • Bertolini A.C., Maschio C., Schiozer D.J. (2015) A methodology to evaluate and reduce reservoir uncertainties, J. Pet. Sci. Eng. 128, 1–14. DOI: 10.1016/j.petrol.2015.02.003. [Google Scholar]
  • Bourbiaux B. (2010) Fractured reservoir simulation: A challenging and rewarding issue, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 65, 2, 227–238. DOI: 10.2516/ogst/2009063. [CrossRef] [Google Scholar]
  • Bourbiaux B., Cacas M.C., Sarda S., Sabathier J.C. (1998) A rapid and efficient methodology to convert fractured reservoir images into a dual-porosity model, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 53, 6, 785–799. DOI: 10.2516/ogst:1998069. [Google Scholar]
  • Corbett P.W.M., Jensen J.L. (1992) Estimating the mean permeability: how many measurements do you need? First Break 10, 3, 89–94. DOI: 10.3997/1365-2397.1992006. [Google Scholar]
  • Correia M.G., Hohendorff J., Gaspar A.T.F.S., Schiozer D.J. (2015) UNISIM-II-D: Benchmark case proposal based on a carbonate reservoir, SPE Latin American and Caribbean Petroleum Engineering Conference held in Quito, Ecuador, 18–20 November. [Google Scholar]
  • Correia M.G., Maschio C., Schiozer D.J. (2014) Upscaling approach for meso-scale heterogeneities in naturally fractured carbonate reservoirs, J. Pet. Sci. Eng. 15, 90–101. DOI: 10.1016/j.petrol.2014.01.008. [Google Scholar]
  • Delorme M., Oliveira Mota R., Khvoenkova N., Fourno A., Nœtinger B. (2014) A Methodology to characterize fractured reservoirs constrained by statistical geological analysis and production: A real field case study, Geol. Soc. London Spec. Publ. 374, 1, 273–288. DOI: 10.1037/h0042162. [CrossRef] [Google Scholar]
  • Enayati-Bidgoli A.H., Rahimpour-Bonab H. (2016) A geological based reservoir zonation scheme in a sequence stratigraphic framework: A case study from the Permo-Triassic gas reservoirs, Offshore Iran, Mar. Pet. Geol. 73, 36–58. DOI: 10.1016/j.marpetgeo.2016.02.016. [Google Scholar]
  • Enayati-Bidgoli A., Rahimpour-Bonab H., Mehrabi H. (2014) Flow unit characterization in the Permian-Triassic carbonate reservoir succession at South Pars Garfield, Offshore Iran, J. Pet. Geol. 37, 205–230. DOI: 10.1111/jpg.12580. [CrossRef] [Google Scholar]
  • Hatampour A., Schaffie M., Jafari S. (2015) Hydraulic flow units, depositional facies and pore type of Kangan and Dalan formations, South Pars Gas Field, Iran, J. Nat. Gas Sci. Eng. 23, 171–183. DOI: 10.1016/j.jngse.2015.01.036. [Google Scholar]
  • Hearn C.L., Tye R.S., Ranganathan V. (1984) Geological factors influencing reservoir performance of the Hartzog Draw field, Wyoming, J. Pet. Technol. 36, 1335–1344. DOI: 10.2118/12016-PA. [CrossRef] [Google Scholar]
  • Jerry J.L., Lake L.W., Corbett P.W.M., Goggin D.J. (1997) Statistics for Petroleum Engineers and Geoscientists, Prentice Hall, New Jersey. [Google Scholar]
  • Lemonnier P., Bourbiaux B. (2010a) Simulation of naturally fractured reservoirs. State of the art – Part 1 – Physical mechanisms and simulator formulation, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 65, 2, 239–262. DOI: 10.2516/ogst/2009066. [CrossRef] [Google Scholar]
  • Lemonnier P., Bourbiaux B. (2010b) Simulation of naturally fractured reservoirs. State of the art – Part 2 – Matrix-fracture transfers and typical features of numerical studies, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 65, 2, 263–286. DOI: 10.2516/ogst/2009067. [CrossRef] [Google Scholar]
  • Lopez B., Aguilera R. (2015) Flow units in shale condensate reservoirs process speed and flow units, SPE Unconventional Resources Technology Conference held in San Antonio, Texas, USA, 20–22 July. [Google Scholar]
  • Mahjour S.K., Al-Askari M.K.G., Masihi M. (2016) Identification of flow units using methods of Testerman Statistical Zonation, Flow Zone Index, and Cluster Analysis in Tabnaak Gas Field, J. Pet. Explor. Prod. Tech. 6, 577–592. DOI: 10.1007/s13202-015-0224-4. [CrossRef] [Google Scholar]
  • Mesquita F.B., Davolio A., Schiozer D.J. (2015) A systematic approach to uncertainty reduction with a probabilistic and multi-objective history matching, EUROPEC 2015 held in Madrid, Spain, 1–4 June. [Google Scholar]
  • Noetinger B., Roubinet D., Russian A., Le Borgne T., Delay F., Dentz M., Gouze P. (2016) Random walk methods for modeling hydrodynamic transport in porous and fractured media from pore to reservoir scale, Trans. Porous Media. 115, 2, 345–385. DOI: 10.1007/s11242-016-0693-z. [CrossRef] [Google Scholar]
  • Oda M. (1985) Permeability tensor for discontinuous rock mass, Geotechnique 35, 4, 483–495. DOI: 10.1680/geot.1985.35.4.483. [CrossRef] [Google Scholar]
  • Pandit S., Gupta S. (2011) A comparative study on distance measuring approaches for clustering, Int. J. Res. Comp. Sci. 2, 1, 29–31. DOI: 10.7815/ijorcs.21.2011.011. [CrossRef] [Google Scholar]
  • Schiozer D.J., Avansi G.D., Santos S. (2017) Risk quantification combining geostatistical realizations and discretized latin hypercube, J. Braz. Soc. Mech. Sci. Eng. 39, 2, 575–587. DOI: 10.1007/s40430-016-0576-9. [CrossRef] [Google Scholar]
  • Shan L., Cao L., Guo B. (2018) Identification of flow units using the joint of WT and LSSVM based on FZI in a heterogeneous carbonate reservoir, J. Pet. Sci. Eng. 161, 219–230. DOI: 10.1016/j.petrol.2017.11.015. [Google Scholar]
  • Soto R.B., Garcia J.C., Torres F., Perez G.S. (2001) Permeability prediction using hydraulic flow units and hybrid soft computing systems, SPE Annual Technical Conference and Exhibition, New Orleans, 30 September–3 October. DOI: 10.2118/71455-MS [Google Scholar]
  • Stinco L., Elphick R., Moore W. (2001) Electrofacies and production prediction index determination in El Tordillo Field, San Jorge Basin, Argentina, 42nd Society of Professional Well Log Analyst Annual Symposium, Houston. [Google Scholar]
  • Svirsky D., Ryazanov A., Pankov M. (2004) Hydraulic flow units resolve reservoir description challenges in a Siberian Oil Field, SPE Asia Pacific Conference on Integrated Modelling for Asset Management held in Kuala Lumpur, Malaysia, 29–30 March. DOI: 10.2523/87056-MS. [Google Scholar]
  • Trupti M.K., Makwana P.R. (2013) Review on determining the number of cluster in K-means clustering, Int. J. Adv. Res. Comp. Sci. Manag. Stud. 1, 6, 90–95. [Google Scholar]
  • Verscheure M., Fourno A., Chilès J.-P. (2012) Joint inversion of fracture model properties for CO2 storage monitoring or oil recovery history matching, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 67, 2, 221–235. DOI: 10.2516/ogst/2011176. [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.