Open Access
Issue
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 74, 2019
Article Number 42
Number of page(s) 13
DOI https://doi.org/10.2516/ogst/2019014
Published online 24 April 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. [CrossRef] [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]

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.