Open Access
Numéro
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 74, 2019
Numéro d'article 42
Nombre de pages 13
DOI https://doi.org/10.2516/ogst/2019014
Publié en ligne 24 avril 2019
  • Aanonsen S.I., Naeval G. (2009) The ensemble Kalman filter in reservoir engineering-a review, SPE J. 14, 393–412. doi: 10.2118/117274-PA. [CrossRef] [Google Scholar]
  • Artus V., Noetinger B. (2004) Up-scaling two-phase flow in heterogeneous reservoirs: current trends, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 59, 185–195. [CrossRef] [Google Scholar]
  • Bratley P., Fox B.L., Niederreiter H. (1992) Implementation and tests of low discrepancy sequences, ACM. T. Model. Comput. Simul. 2, 195–213. doi: 10.1145/146382.146385. [CrossRef] [Google Scholar]
  • Chen Y., Oliver D.S., Zhang G.M. (2008) Efficient ensemble-based closed-loop production optimization, SPE J. 14, 20–23. doi: 10.2118/112873-PA. [Google Scholar]
  • Coats K.H., Dempsey J.R., Henderson J.H. (1971) The use of vertical equilibrium in two-dimensional simulation of three-dimensional reservoir performance, SPE J. 11, 63–71. [Google Scholar]
  • Coley D. (1999) An introduction to genetic algorithms for scientists and engineers, World Scientific Pub. Co., Inc., Singapore. [CrossRef] [Google Scholar]
  • Drew S., Homem-de-Mello T. (2006) Quasi-Monte Carlo strategies for stochastic optimization, in: Proceedings of the 2006 Winter Simulation Conference, 3–6 December, Monterey, CA, pp. 774–782. [Google Scholar]
  • Fayazi A., Bagherzadeh H., Shahrabadi A. (2016) Estimation of pseudo relative permeability curves for a heterogeneous reservoir with a new automatic history matching algorithm, J. Pet. Sci. Eng. 140, 154–163. doi: 10.1016/j.petrol.2016.01.013. [CrossRef] [Google Scholar]
  • Fouda M.A.G. (2016) Relative permeability upscaling for heterogeneous reservoir models, PhD Dissertation, Heriot-Watt University, Edinburgh, UK. [Google Scholar]
  • Glover F., Kochenberger G. (2003) Handbook of metaheuristics, Kluwer Academic Publishers, Dordrecht, The Netherlands. [CrossRef] [Google Scholar]
  • Hajizadeh Y., Demyanov V., Mohamed L., Christie M. (2010) Comparison of evolutionary and swarm intelligence methods for history matching and uncertainty quantification in petroleum reservoir models, in: Intelligent Computational Optimization in Engineering, Springer, Berlin/Heidelberg, Germany, pp. 209–240. [Google Scholar]
  • Haupt R.L., Haupt S.E. (2004) Practical genetic algorithms, 2nd edn., John Wiley & Sons, New York, NY. [Google Scholar]
  • Hearn C.L. (1971) Simulation of stratified water flooding by pseudo relative permeability curves, J. Pet. Technol. 23, 805–813. [CrossRef] [Google Scholar]
  • Hickernell F.J., Yuan Y. (1997) A simple multistart algorithm for global optimization, OR Trans. 1, 2. [Google Scholar]
  • Hou J., Wang D., Luo F., Zhang Y.H. (2012) A review on the numerical inversion methods of relative permeability curves, Procedia Eng. 29, 375–380. doi: 10.1016/j.proeng.2011.12.726. [CrossRef] [Google Scholar]
  • Jäckel P. (2002) Monte Carlo methods in finance, John Wiley and Sons, New York, NY. [Google Scholar]
  • Jacks H.H., Smith O.J.E., Mattax C.C. (1973) The modeling of a three-dimensional reservoir with a two-dimensional reservoir simulator – the use of dynamic pseudo functions, SPE J. 13, 175–185. [Google Scholar]
  • Johnson J.B., Nanney M.M., Killough J.E., Lin Y.T. (1982) The Kuparuk River field: a regression approach to pseudo-relative permeabilities, SPE 10531. doi: 10.2118/10531-MS. [Google Scholar]
  • Karaboga D., Akay B. (2009) A comparative study of Artificial Bee Colony algorithm, Appl. Math. Comput. 214, 1, 108–132. doi: 10.1016/j.amc.2009.03.090. [Google Scholar]
  • Kennedy J., Eberhart R. (1995) Particle swarm optimization, Proc. IEEE Int. Conf. Neural Netw. IV, 1942–1948. doi: 10.1109/ICNN.1995.488968. [CrossRef] [Google Scholar]
  • Kucherenko S. (2006) Application of Quasi Monte Carlo methods in global optimization, Glob. Optim. 5, 111–133. doi: 10.1007/0-387-30528-9_5. [CrossRef] [Google Scholar]
  • Kulkarni K.N., Datta-Gupta A. (2000) Estimating relative permeability from production data: a streamline approach, SPE J. 5, 4, 402–411. doi: 10.2118/66907-PA. [CrossRef] [Google Scholar]
  • Kyte J.R., Berry D.W. (1975) New pseudo functions to control numerical dispersion, SPE J. 15, 269–276. [Google Scholar]
  • Landa J., Kalia R.K., Nakano A., Nomura K., Vashishta P. (2005) History match and associated forecast uncertainty analysis-practical approaches using cluster computing, International Petroleum Technology Conference, 21–23 November, Doha, Qatar. IPTC-10751-MS. doi: 10.2523/IPTC-10751-MS. [Google Scholar]
  • Lee T., Seinfeld J.H. (1987) Estimation of absolute and relative permeabilities in petroleum reservoirs, Inverse Probl. 3, 4, 711–728. doi: 10.1088/0266-5611/3/4/015. [CrossRef] [Google Scholar]
  • Lei G. (2002) Adaptive random search in Quasi-Monte Carlo methods for global optimization, Comput. Math. Appl. 43, 747–754. doi: 10.1016/S0898-1221(01)00318-2. [CrossRef] [Google Scholar]
  • Niederreiter H. (1984) On the measure of denseness for sequences, in: Topics of classical number theory, Colloquia Math. Sot. Janos Bolyai. 34, North Holland, Amsterdam, 1163–1208. [Google Scholar]
  • Niederreiter H. (1987) Point sets and sequences with small discrepancy, Monatsch. Math. 104, 273–337. doi: 10.1007/bf01294651. [CrossRef] [Google Scholar]
  • Niederreiter H. (1994) Random number generation and Quasi-Monte Carlo methods, Society for Industrial and Applied Mathematics, Philadelphia, PA. [Google Scholar]
  • Simon D. (2013) Evolutionary optimization algorithms, John Wiley & Sons, Hoboken, New Jersey. [Google Scholar]
  • Sivanandam S., Deepa S. (2007) Introduction to genetic algorithms, Springer-Verlag, Berlin, Heidelberg, Germany. [Google Scholar]
  • Sobol I.M. (1976) Uniformly distributed sequences with an additional uniform property, U.S.S.R. Comput. Maths. Math. 16, 236–242. doi: 10.1016/0041-5553(76)90154-3. [CrossRef] [Google Scholar]
  • Tan T.B. (1995) Estimating two and three dimensional pseudo-relative permeabilities with non-linear regression, Reservoir Simulation Symposium, San Antonio, Texas. doi: 10.2118/29129-MS. [Google Scholar]
  • Wang K., Killough J.E., Sepehrnoori K. (2009) A new upscaling method of relative permeability curves for reservoir simulation, SPE Annual Technical Conference and Exhibition, 4–7 October, New Orleans, LA. doi: 10.2118/124819-MS. [Google Scholar]
  • Yu X., Gen M. (2010) Introduction to evolutionary algorithms, Springer-Verlag, Berlin, Heidelberg, Germany. [Google Scholar]
  • Zarifi A.H., Sahraei E., Parvizi H. (2012) Pseudo relative permeability compensation for numerical dispersion, Pet. Sci. Technol. 30, 15, 1529–1538. doi: 10.1080/10916466.2010.503217. [CrossRef] [Google Scholar]
  • Zhang F., Reynolds A.C. (2002) Optimization algorithms for automatic history matching of production data, Proceedings of 8th European Conference on the Mathematics of Oil Recovery, Freiberg, Germany. doi: 10.3997/2214-4609.201405958. [Google Scholar]

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