Open Access
Issue
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 69, Number 7, December 2014
Page(s) 1143 - 1154
DOI https://doi.org/10.2516/ogst/2012055
Published online 27 May 2013
  • Alavi M. (2004) Regional stratigraphy of the Zagross fold-thrust belt of Iran and its proforeland evolution, Am. J. Sci. 304, 1–20. [CrossRef] [Google Scholar]
  • Behrens R.A., Macleod M.K., Tran T.T., Alimi A.O. (1998) Incorporating seismic attribute maps in 3D reservoir models, SPE Reserv. Eval. 1, 122–126. [Google Scholar]
  • Bhatt A., Helle H.B. (2002) Committee neural networks for porosity and permeability prediction from well logs, Geophys. Prospect. 50, 645–660. [CrossRef] [Google Scholar]
  • Chiu S. (1994) Fuzzy model identification based on cluster estimation, J. Intelligent Fuzzy Syst. 2, 3, 267–278. [Google Scholar]
  • Daiguji M., Kudo O., Wada T. (1997) Application of wavelet analysis to fault detection in oil refinery, Comput. Chem. Eng. 21, S1117–S1122 Suppl.. [CrossRef] [Google Scholar]
  • Darabi H., Kavousi H., Moraveji A., Masihi M. (2010) 3D Fracture Modeling in Parsi Oil Feld Using Artificial Intelligence Tools, J. Petrol. Sci. Eng. 71, 67–76. [CrossRef] [Google Scholar]
  • Deutsch C.V., Journel A.G. (1992) GSLIB-Geostatistical Software Library and user’s guide, Oxford University Press, Oxford, 340 p. [Google Scholar]
  • Deutsch C.V. (2002) Geostatistical reservoir modeling, Oxford university press, New York. [Google Scholar]
  • Deutsch C.V. (2006) A sequential indicator simulation program for categorical variables with point and block data, Comput. Geosci. 32, 1669–1681. [CrossRef] [Google Scholar]
  • El Ouahed A.K., Tiab D., Mazouzi A. (2005) Application of artificial intelligence to characterize naturally fractured zones in Hassi Messaoud Oil Field, Algeria, J. Petrol. Sci. Eng. 49, 122–141. [CrossRef] [Google Scholar]
  • FitzGerald E.M., Bean C.J., Reilly R. (1999) Fracture-frequency prediction from borehole wireline logs using artificial neural networks, Geophys. Prospect. 47, 1031–1044. [CrossRef] [Google Scholar]
  • Geman S., Geman D. (1984) Stochastic Relaxation, Gibbs Distribution and the Bayesian Restoration of Images, IEEE Trans. Pattern Anal. Mach. Intell. 6, 6, 721–741. [CrossRef] [PubMed] [Google Scholar]
  • Gokceoglu C., Yesilnacar E., Sonmez H., Kayabasi A. (2004) A neuro-fuzzy model for modulus of joint rock masses, Comput. Geotechnics 31, 375–383. [CrossRef] [Google Scholar]
  • Gringarten E. (1998) Stochastic simulation of fractures in layered systems, Comput. Geosci. 26, 729–736. [CrossRef] [Google Scholar]
  • Gringarten E., Deutsch C.V. (1999) Methodology for Variogram Interpretation and Modeling for Improved Reservoir Characterization, Annual Technical Conference and Exhibition. Houston, Texas, 3-6 Oct., SPE 56654, 13 p. [Google Scholar]
  • Haller D., Porturas F. (1998) How to characterize fractures in reservoirs using borehole and core images: Case studies, Geol. Soc. London Spec. Publ. 136, 249–259. [CrossRef] [Google Scholar]
  • Hsu K., Brie A., Plumb R.A. (1987) A new method for fracture identification using array sonic tools, J. Pet. Technol. June, SPE Paper 14397, 677–683. [CrossRef] [Google Scholar]
  • Ja’fari A., Kadkhodaie-Ilkhchi A., Sharghi A., Ghanavati K. (2012) Fracture density prediction from petrophysical log data using adaptive neuro-fuzzy inference system, J. Geophys. Eng. 9, 105–114. [CrossRef] [Google Scholar]
  • Journel A.G. (1983) Nonparametric estimation of spatial distributions, Math. Geol. 15, 445–468. [CrossRef] [Google Scholar]
  • Journel A.G. (1993) Geostatistics: Roadblocks and Challenges, in Soares A. (ed.), Geostatistics Troia ’92, Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 213–224. [CrossRef] [Google Scholar]
  • Kadkhodaie-Ilkhchi A., Rezaee M.R., Rahimpour-Bonab H., Chehrazi A. (2009) Petrophysical data prediction from seismic attributes using committee fuzzy inference system, Comput. Geosci. 35, 2314–2330. [CrossRef] [Google Scholar]
  • Kadkhodaie-Ilkhchi A., Takahashi Monteiro S., Ramos F., Hatherly P. (2010) Rock Recognition from MWD Data: A Comparative Study of Boosting, Neural Networks and Fuzzy Logic, IEEE Trans. Geosci. Remote Sensing Lett. (GSRL) 7, 4, 680–684. [CrossRef] [Google Scholar]
  • Kelkar M. (2000) Application of Geostatistics for Reservoir Characterization Accomplishments and Challenges, J. Can. Pet. Technol. 39, 25–29. [Google Scholar]
  • Khoshbakht F., Memarian H., Mohammadnia M. (2009) Comparison of Asmari, Pabdeh and Gurpi formation’s fractures, derived from image log, J. Petrol. Sci. Eng. 67, 65–74. [CrossRef] [Google Scholar]
  • Labani M.M., Kadkhodaie-Ilkhchi A., Salahshoor K. (2010) Estimation of NMR log parameters from conventional well log data using a committee machine with intelligent systems: A case study from the Iranian part of the South Pars gas field, Persian Gulf Basin, J. Petrol. Sci. Eng. 72, 175–185. [CrossRef] [Google Scholar]
  • Liu Y., Journel A. (2007) A package for geostatistical integration of coarse and fine scale data, Comput. Geosci. 35, 527–547. [CrossRef] [Google Scholar]
  • Matheron G., Beucher H., de Fouquet C., Gralli A., Guerillot D., Ravenne C. (1987) Conditional simulation of the geometry of fluvio-deltaic reservoirs, Proc. SPE, Annual Technical Conference and Exhibition, Dallas, Texas, 27–30 Sept., SPE 16753, pp. 591–599. [Google Scholar]
  • Matlab User’s Guide (2007) Matlab CD-ROM, MathWorks, Inc. [Google Scholar]
  • Ouenes A. (1999) Practical application of fuzzy logic and neural networks to fractured reservoir characterization, Comput. Geosci. 26, 953–962. [CrossRef] [Google Scholar]
  • Petrel User’s Guide (2009) Petrophysical modeling, CD-ROM, Schlumberger Company. [Google Scholar]
  • Seifert D., Jensen J.L. (1999) using sequential indicator simulation as a tool in reservoir description: issues and uncertainties, Math. Geol. 31, 527–550. [CrossRef] [Google Scholar]
  • Serra O. (1989) Formation MicroScanner image interpretation, Schlumberger Education Services. [Google Scholar]
  • Song X., Zhu Y., Liu Q., Chen J., Ren D., Li Y., Wang B., Liao M. (1998) Identification and distribution of natural fractures, SPE International Oil and Gas Conference and Exhibition in China, Beijing, China, 2-6 Nov., SPE Paper 50877. [Google Scholar]
  • Tokhmchi B., Memarian H., Rezaee M.R. (2010) Estimation of the fracture density in fractured zones using petrophysical logs, J. Petrol. Sci. Eng. 72, 206–213. [CrossRef] [Google Scholar]
  • Western W.A., Bloschl G., Grayson R.B. (1998) How well do indicator variograms capture the spatial connectivity of soil moisture? Hydrol. Process. 12, 1851–1868. [CrossRef] [Google Scholar]
  • Yarus J.M., Chambers R.L. (2006) Practical Geostatistics – An Armchair Overview for Petroleum Reservoir Engineers, (Distinguished Author Series), J. Petrol. Technol. 58, 11, 78–86, SPE 103357. [CrossRef] [Google Scholar]

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