Dossier: Quantitative Methods in Reservoir Characterization
Open Access
Issue
Oil & Gas Science and Technology - Rev. IFP
Volume 62, Number 2, March-April 2007
Dossier: Quantitative Methods in Reservoir Characterization
Page(s) 181 - 193
DOI https://doi.org/10.2516/ogst:2007016
Published online 14 June 2007
  • Aanonsen S.I., Aavatsmark I., Barkve T., Cominelli A., Gonard R. Gosselin O., Kolasinski M., and Reme H. (2003) E.ect of scale dependent data correlations in an integrated history matching loop combining production data and 4D seismic data (SPE 79665), in Proceedings of the 2003 SPE Reservoir Simulation Symposium. [Google Scholar]
  • Abreu C., Lucet N., Nivlet P., and Royer J.J. (2005) Improving 4D seismic data interpretation using geostatistical filtering. 9th International Congress of Brazilian Geophysical Society, Expanded abstracts. [Google Scholar]
  • Allard D. and Guillot G. (1999) Clustering geostatistical data. Geostatistics 2000 Cape Town, 1, 49-63. [Google Scholar]
  • Ambroise C. and Govaert G. (1995) Spatial clustering and the EM algorithm. http://www.isip.msstate.edu/projects/speech/support/help/bibliography/index.html. [Google Scholar]
  • Barker J.W.,Cuypers M., and Holden L. (2001) Quantifying uncertainty in production forecasts: Another look at the PUNQ-S3 problem. SPE J., 6, 433-441. [CrossRef] [Google Scholar]
  • Celeux G., and Govaert G. (1991) A classification EM algorithm for clustering and two stochastic versions. Rapports de Recherche. [Google Scholar]
  • Coleou T.L., Hoeber H., and Lecerf D. (2002) Multivariate geostatistical filtering of time-lapse seismic data for an improved 4D signature. 72nd Annual International Meeting, SEG, Expanded Abstracts, 1662-1665. [Google Scholar]
  • Dempster A.P.,Laird N.M., and Rubin D.B. (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B, 39, 1-38. [Google Scholar]
  • Diplaros A., Gevers T., and Vlassis N. (2004) Skin detection using the EM algorithm with spatial constraints. In Systems, Man and Cybernetics, 2004 IEEE International Conference, 4. [Google Scholar]
  • Dong Y., and Oliver D. (2003) Automatic history matching of production data and seismic impedance change data. In TUPREP Research Report 20. The University of Tulsa. [Google Scholar]
  • Evensen G. (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 143-162. [CrossRef] [Google Scholar]
  • Evensen G. (2003) The ensemble kalman lter: theoretical formulation and practical implementation. Ocean Dynam., 53, 343-367. [CrossRef] [Google Scholar]
  • Floris F.J.T.,Bush M.D.,Cuypers M.,Roggero F., and Syversveen A.-R. (2001) Methods for quantifying the uncertainty of production forecasts: A comparative study. Petrol. Geosci., 7, S87-S96. [CrossRef] [Google Scholar]
  • Gao G., and Reynolds A.C. (2006) An improved implementation of the LBFGS algorithm for automatic history matching. SPE J., 11, 5-17. [CrossRef] [Google Scholar]
  • Hartley H. (1958) Maximum likelihood from incomplete data. Biometrics, 14, 174-194. [CrossRef] [Google Scholar]
  • Hastie T., Tibshirani R., and Friedman J. (2001) The Elements of Statistical Learning, Springer-Verlag, New York. [Google Scholar]
  • Kitanidis P.K. (1995) Quasi-linear geostatistical theory for inversing. Water Resour. Res., 31, 2411-2419. [CrossRef] [Google Scholar]
  • Kung S.Y., Mak M.W., and Lin S.H. (2004) Biometric Authentication: A Machine Learning Approach. Prentice Hall. [Google Scholar]
  • Meng X.-L. (1993) Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika, 80, 267-278. [CrossRef] [MathSciNet] [Google Scholar]
  • Meng X.-L. (1994) On the rate of convergence of the ECM algorithm. Ann. Stat., 22, 326-339. [CrossRef] [Google Scholar]
  • Naevdal G., Mannseth T., and Vefring E.H. (2002) Near-well reservoir monitoring through ensemble Kalman filter (SPE-75235), in Proceeding of SPE/DOE Improved Oil Recovery Symposium. [Google Scholar]
  • Naevdal G., Johnsen L.M., Aanonsen S.I., and Vefring E.H. (2003) Reservoirmonitoring and continuous model updating using ensemble Kalman filter (SPE-84372), in 2003 SPE Annual Technical Conference and Exhibition. [Google Scholar]
  • Oliver D.S., He N., and Reynolds A.C. (1996) Conditioning permeability fields to pressure data, in European Conference for the Mathematics of Oil Recovery, V, 1-11. [Google Scholar]
  • Reynolds A.C., He N., and Oliver D.S. (1999) Reducing uncertainty in geostatistical description with well testing pressure data, in Reservoir Characterization – Recent Advances, (edited by Schatzinger R.A. and Jordan J.F.). American Association of Petroleum Geologists, 149-162. [Google Scholar]
  • Richardson S., and Green P.J. (1997) On Bayesian analysis of mixtrues with an unknown number of components. J. R. Stat. Soc., 59, 731-792. [CrossRef] [Google Scholar]
  • Zhao Y., Li G., and Reynolds A.C. (2006) Characterizing measurement error with the EM algorithm. TUPREP Report, 14-147. [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.