F. G. Alabert and V. Et-modot, Stochastic Models of Reservoir Heterogeneity : Impact on Connectivity and Average Permeabilities, Proceedings of the 67 th SPE Annual Technical Conference and Exhibition, 1992.

J. Amiotte, J. Voillemont, and J. Et-mallet, Hyper dsi : N-dimensional space interpolation, Proceedings of the 22nd Gocad Meeting, 2002.

I. A. Antonov and V. M. Et-saleev, An economic method of computing LP??-sequences, USSR Computational Mathematics and Mathematical Physics, vol.19, issue.1, pp.252-256, 1979.
DOI : 10.1016/0041-5553(79)90085-5

G. Archer, A. Saltelli, and I. Et-sobol-', Sensitivity measures,anova-like Techniques and the use of bootstrap, Journal of Statistical Computation and Simulation, vol.2, issue.2, pp.99-120, 1997.
DOI : 10.1142/S0129183195000204

R. Archer, G. Zakeri, and T. Et-vaudrey, Splines as an Optimization Tool in Petroleum Engineering, SPE Annual Technical Conference and Exhibition, 2005.
DOI : 10.2118/95601-MS

H. Ates, Use of Streamline Simulations for Integrated Reservoir Modeling, 2005.

H. Ates, A. Bahar, S. El-abd, M. Charfeddine, M. Kelhar et al., Ranking and Upscaling of Geostatistical Reservoir Models Using Streamline Simulation : A Field Case Study, Proceedings of the SPE 13th Middle East Oil Show and Conference, 2003.

P. Audigane, Caractérisation microsismique des masifs rocheux fracturés. Modélisation thermo-hydraulique. Application au concept géothermique de soultz, 2000.

K. Aziz and A. Et-settari, Petroleum Reservoir Simulation, Applied Science Publishers, 1979.

P. Ballin, K. Aziz, A. Journel, and L. Et-zuccolo, Quantifying the Impact of Geological Uncertainty on Reservoir Performing Forecasts, SPE Symposium on Reservoir Simulation, 1993.
DOI : 10.2118/25238-MS

R. Barret, M. Berry, T. Chan, J. Demmel, J. Donato et al., Templates for the Solution of Linear Systems : Building Blocks for Iterative Methods, SIAM, 1994.
DOI : 10.1137/1.9781611971538

B. Batycky and R. P. , A Three-Dimensional Two-Phase Field Scale Streamline Simulator, 1997.

R. P. Batycky, M. J. Blunt, and M. R. Et-thiele, A 3D Field-Scale Streamline-Based Reservoir Simulator, Proceedings of the SPE Annual Technical Conference and Exhibition, pp.6-9, 1996.
DOI : 10.2118/36726-PA

R. P. Batycky, M. R. Thiele, and M. J. Et-blunt, A Streamline-Based Reservoir Simulation of the House Moutain Waterflood, Proceedings of the SCRF Meeting, p.8, 1997.

J. Bear, Dynamics of Fluids in Porous Media, Soil Science, vol.120, issue.2, 1972.
DOI : 10.1097/00010694-197508000-00022

R. Berenblyum, A. Shapiro, and E. Et-stenby, Reservoir streamline simulation accounting for effects of capillirity and wettability, 9th European Conference on the Mathematics of Oil Recovery, 2004.

M. Blunt, K. Liu, and M. Et-thiele, A generalized streamline method to predict reservoir flow, Petroleum Geoscience, vol.2, issue.3, pp.256-269, 1996.
DOI : 10.1144/petgeo.2.3.259

G. Box and N. Et-draper, Empirical Model-Building and Response Surfaces, J. Wiley & Sons, 1987.

I. Brandsaeter, H. T. Wist, A. Naess, O. Lia, O. J. Arntzen et al., Ranking of stochastic realizations of complex tidal reservoirs using streamline simulation criteria, Petroleum Geoscience, vol.7, issue.S, pp.53-63, 2001.
DOI : 10.1144/petgeo.7.S.S53

D. Bursztyn and D. Steinberg, Comparison of designs for computer experiments, Journal of Statistical Planning and Inference, vol.136, issue.3, 2004.
DOI : 10.1016/j.jspi.2004.08.007

M. D. Bush and J. N. Carter, Applications of a modified genetic algorithm to parameter estimation in the petroleum industry : Intelligent engineering systems through artificial neural networks, p.397, 1996.

J. Carter, Using Bayesian Statistics to Capture the Effects of Modelling Errors in Inverse Problems, Mathematical Geology, vol.36, issue.2, pp.187-216, 2004.
DOI : 10.1023/B:MATG.0000020470.51595.6d

J. Carter, P. Ballester, Z. Tavassoli, and P. Et-king, Our calibrated model has poor predictive value: An example from the petroleum industry, Reliability Engineering & System Safety, vol.91, issue.10-11, pp.1373-1381, 2006.
DOI : 10.1016/j.ress.2005.11.033

A. Castellini, J. L. Landa, and J. Et-kikani, Practical Methods For Uncertainty Assesment of Flow Predictions For Reservoir with Significant History, Proceedings of the 9 th European Conference on the Mathematics of Oil Recovery, 2004.

G. Caumon, S. Strebelle, J. K. Caers, and A. G. Et-journel, Assesment of global uncertainty for early appraisal of hydrocarbon fields, Proceedings of the SPE Annual Technical Conference and Exhibition, 2004.

T. Charles, J. M. Guemene, B. C. Vincent, and O. Et-dubrule, Experience with the Quantification of Subsurface Uncertainties, SPE Asia Pacific Oil and Gas Conference and Exhibition, 2001.
DOI : 10.2118/68703-MS

W. H. Chiang and W. Et-kinzelbach, 3D Groundwater Modeling with PMWIN, 2001.
DOI : 10.1007/978-3-662-05549-6

N. Choudhuri, Bayesian bootstrap credible sets for multidimensional mean functional, The Annals of Statistics, vol.26, issue.6, pp.2104-2127, 1998.
DOI : 10.1214/aos/1024691463

M. Christie and M. Blunt, Tenth SPE Comparative Solution Project: A Comparison of Upscaling Techniques, SPE Reservoir Evaluation & Engineering, vol.4, issue.04, pp.308-317, 2001.
DOI : 10.2118/72469-PA

C. Chu, Prediction of Steamflood Performance in Heavy Oil Reservoirs Using Correlations Developed by Factorial Design Method, SPE California Regional Meeting, 1990.
DOI : 10.2118/20020-MS

R. Cognot, La méthode D.S.I. : optimisation, implémentation et applications, 1996.

R. M. Cooke and J. M. Et-noortwijk, Local probabilistic sensitivity measures for comparing FORM and Monte Carlo calculations illustrated with dike ring reliability calculations, Computer Physics Communications, vol.117, issue.1-2, pp.86-98, 1999.
DOI : 10.1016/S0010-4655(98)00166-0

B. Corre, P. Thore, V. De-feraudy, and G. Vincent, Integrated Uncertainty Assessment For Project Evaluation and Risk Analysis, SPE European Petroleum Conference, 2000.
DOI : 10.2118/65205-MS

R. I. Cukier, C. M. Fortuin, K. E. Schuler, A. G. Petschek, and J. H. Et-schaibly, Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory, The Journal of Chemical Physics, vol.12, issue.8, pp.3873-3878, 1973.
DOI : 10.1137/1012102

L. P. Dake, Fundamentals of Reservoir Engineering. Developements in petroleum science, 2001.

E. Damsleth, A. Hage, and R. Et-volden, Maximum Information at Minimum Cost : A North Sea Field Development Study with an Experimental Design, Journal of Petroleum Technology, pp.1350-1356, 1992.

A. Datta-gupta and M. J. King, A Semianalytic Approach to Tracer Flow Modeling in Heterogeneous Permeable Media, Advances in Water Ressources, pp.9-24, 1995.

J. Dejean and G. Et-blanc, Managing Uncertainties on Production Predictions Using Integrated Statistical Methods, SPE Annual Technical Conference and Exhibition, 1999.
DOI : 10.2118/56696-MS

C. Deutsch, Constrained Smoothing of Histograms and Scatterplots With Simulated Annealing, Technometrics, vol.11, issue.3, pp.266-274, 1996.
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C. V. Deutsch and A. G. Et-journel, GSLIB Geostatistical Software Library and User's Guide, 1992.

O. Dubrule, Geostatistics in Petroleum Geology. The American Association of Petroleum Geologist, 1998.

B. Essenfeld and M. , The Use of Cubic Splines : A Method of Interpolation with Immediate Applications in Petroleum Engineering, 1969.

R. L. Eubank, Spline Smoothing and Nonparametric Regression, 1988.

K. Fang, D. Lin, P. Winker, and Y. Et-zhang, Uniform Design: Theory and Application, Technometrics, vol.34, issue.3, pp.237-248, 2000.
DOI : 10.1007/978-1-4612-1690-2_31

M. Feraille and F. Et-roggero, Uncertainty Quantification for Mature Field Combining the Bayesian Inversion Formalism and Experimental Design Approach, Proceedings of the 9 th European Conference on the Mathematics of Oil Recovery, 2004.

E. Fetel, Constraining response surface with secondary information for reservoir flow performance assessment, Proceedings of the 25 th Gocad Meeting, 2005.

F. Friedmann, A. Chawathé, and D. K. Et-larue, Assessing Uncertainty in Channelized Reservoirs Using Experimental Designs, Proceedings of the 2001 SPE Annual Technical Conference and Exhibition, 2001.

A. Galli and E. Et-murillo, Dual Kriging -Its Propeties and it Uses in Direct Contouring, Geostatistics for Natural Ressources Characterization -Part 2, NATO-ASI -Advanced Geostatistics, pp.621-634, 1984.

J. R. Gilman, H. Meng, M. J. Uland, P. J. Dzurman, and S. Et-cosic, Statistical Ranking of Stochastic Geomodels Using Streamline Simulation: A Field Application, SPE Annual Technical Conference and Exhibition, 2002.
DOI : 10.2118/77374-MS

P. Goovaerts, Geostatistics for Natural Resources Evalutation Applied Geostatistics Series, 1997.

B. Guyaguler, Optimization of Well Placement and Assessment of Uncertainty, 2002.

B. Guyaguler and R. N. Et-horne, Uncertainty Assesment of Well Placement Optimization, Proceedings of the SPE Annual Technical Conference and Exhibition, 2001.

J. Helton, F. Davis, and J. Et-johnson, A comparison of uncertainty and sensitivity analysis results obtained with random and Latin hypercube sampling, Reliability Engineering & System Safety, vol.89, issue.3, pp.305-330, 2005.
DOI : 10.1016/j.ress.2004.09.006

K. B. Hird and O. Et-dubrule, Quantification of Reservoir Connectivity for Reservoir Description Applications, Proceedings of the SPE Annual Technical Conference and Exhibition, 1995.
DOI : 10.2118/30571-PA

B. Hoffman and J. Et-caers, Geostatistical History Matching Using a Regional Probability Perturbation Method, SPE Annual Technical Conference and Exhibition, pp.5-8, 2003.
DOI : 10.2118/84409-MS

T. Homma and A. Et-saltelli, Importance measures in global sensitivity analysis of nonlinear models, Reliability Engineering & System Safety, vol.52, issue.1, pp.1-17, 1996.
DOI : 10.1016/0951-8320(96)00002-6

L. Hu, Gradual deformation and iterative calibration of gaussian-related stochastic models, Mathematical Geology, vol.32, issue.1, pp.87-108, 2000.
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C. Huijbregts and G. Et-matheron, Universal kriging -An optimal approach to trend surface analysis. Decision Making in the Mineral Industry, pp.159-169, 1971.

E. A. Idrobo, M. K. Choudhary, and A. Et-datta-gupta, Swept Volume Calculations and Ranking of Geostatistical Reservoir Models Using Streamline Simulation, SPE/AAPG Western Regional Meeting, 2000.
DOI : 10.2118/62557-MS

R. Iman and W. Et-conover, Small sample sensitivity analysis techniques for computer models with an application to risk assessment, communications in Statistics Theory and Methods A9, pp.1749-1874, 1980.

R. Iman and S. Et-hora, An Investigation of Uncertainty and Sensitivity Analysis Techniques for Computer Models, Risk Analysis, vol.83, issue.2, pp.71-90, 1990.
DOI : 10.1063/1.445400

E. H. Isaaks and R. M. Srivastava, A Introduction to Applied Geostatistics, 1989.

T. Ishigami and T. Et-homma, An importance quantification technique in uncertainty analysis for computer models, [1990] Proceedings. First International Symposium on Uncertainty Modeling and Analysis, pp.3-5, 1990.
DOI : 10.1109/ISUMA.1990.151285

N. Johnson and S. Et-kotz, Continuous Univariate Distributions, 1970.

M. Jones, On correcting for variance inflation in kernel density estimation, Computational Statistics & Data Analysis, vol.11, issue.1, pp.3-15, 1991.
DOI : 10.1016/0167-9473(91)90049-8

P. Kedzierski, A. L. Solleuz, J. Mallet, and J. Et-royer, Sedimentological and stratigraphic modeling combining membership functions and sequence stratigraphy principles, Proceedings of the 25 th Gocad Meeting, 2005.

P. R. King, S. V. Buldyrev, N. V. Dokholyan, S. Havlin, Y. Lee et al., Predicting oil recovery using percolation theory, Petroleum Geoscience, vol.7, issue.S, pp.105-107, 2001.
DOI : 10.1144/petgeo.7.S.S105

L. Labat, Simulation stochastiques defacì es pas la méthode des membership functions, 2004.

J. L. Landa and B. Et-guyaguler, A Methodology for History Matching and the Assessment of Uncertainties Associated with Flow Prediction, SPE Annual Technical Conference and Exhibition, 2003.
DOI : 10.2118/84465-MS

B. Lee, T. Kravaris, C. Et-seinfeld, and J. , History Matching by Spline Approximation and Regularization in Single-Phase Areal Reservoirs, SPE Reservoir Engineering, vol.1, issue.05, pp.521-534, 1986.
DOI : 10.2118/13931-PA

M. Liefvendahl and R. Et-stocki, A study on algorithms for optimization of Latin hypercubes, Journal of Statistical Planning and Inference, vol.136, issue.9, 2004.
DOI : 10.1016/j.jspi.2005.01.007

Y. Ma and J. Et-royer, Local geostatistical filtering -application to remote sensing, Geomathematics and geostatistics analysis applied to space and time dependent data -Part 1, 1988.

L. Mace and E. Et-fetel, Fracture Density Prediction Using Response Surface in Naturally Fractured Reservoirs, Proceedings of the 26 th Gocad Meeting, 2006.

J. L. Mallet, Discrete Smooth Interpolation in Geometric Modeling, Computer Aided Design, vol.24, issue.4, pp.177-191, 1992.

J. L. Mallet, Discrete modeling for natural objects, Mathematical Geology, vol.41, issue.1, pp.199-219, 1997.
DOI : 10.1007/978-94-011-2556-7_11

J. L. Mallet, Geomodeling, 2002.

J. L. Mallet and A. Et-sthuka, Modeling multivariate density and application, Proceedings of the 20st Gocad Meeting, 2000.

E. Manceau, M. Mezghani, I. Zabalza-mezghani, and F. Et-roggero, Combination of Experimental Design and Joint Modeling Methods for Quantifying the Risk Associated With Deterministic and Stochastic Uncertainties - An Integrated Test Study, SPE Annual Technical Conference and Exhibition, 2001.
DOI : 10.2118/71620-MS

A. Maréchal, Kriging Seismic Data in Presence of Faults, Geostatistics for Natural Resources Characterization, pp.271-294, 1984.
DOI : 10.1007/978-94-009-3699-7_17

G. Massonnat, Sampling Space of Uncertainty Through Stochastic Modelling of Geological Facies, SPE Annual Technical Conference and Exhibition, 1997.
DOI : 10.2118/38746-MS

G. Massonnat, Can We Sample the Complete Geological Uncertainty Space in Reservoir-Modeling Uncertainty Estimates?, SPE Journal, vol.5, issue.01, pp.46-59, 2000.
DOI : 10.2118/59801-PA

G. Matheron, Les Variables Régionalisées et leur Estimation, une Application de la Téorie des Fonctions Aléatoires aux Sciences de la Nature. Masson et Cie, 1965.

G. Matheron, La théroie des variables régionalisées et ses applications Fasicule 5, Les cahiers du centre de morphologie mathématique, 1970.

M. Mckay, Evaluating prediction uncertainty, Los Alamos National Laboratories Report NUREG, vol.6311, 1995.
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M. Mckay, R. Beckman, and W. Et-conover, A comparison of three methods for selecting values of input varibles in the analysis of output from a computer code, Technometrics, vol.21, pp.239-245, 1979.

N. Metropolis and S. Et-ulman, The Monte Carlo Method, Journal of the American Statistical Association, vol.44, issue.247, pp.335-341, 1949.
DOI : 10.1080/01621459.1949.10483310

B. Minasny and A. Et-mcbratney, A conditioned Latin hypercube method for sampling in the presence of ancillary information, Computers & Geosciences, vol.32, issue.9, pp.1378-1388, 2006.
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M. Morris and T. Mitchell, Exploratory designs for computational experiments, Journal of Statistical Planning and Inference, vol.43, issue.3, pp.381-402, 1995.
DOI : 10.1016/0378-3758(94)00035-T

K. Mosegaard and A. Et-tarantola, Monte Carlo sampling of solutions to inverse problems, Journal of Geophysical Research: Solid Earth, vol.10, issue.6, pp.431-12447, 1995.
DOI : 10.1029/RG010i001p00251

K. Mosegaard and A. Et-tarantola, Probabilistic approach to inverse problems. International Handbook of Earthquake & egineering Seismology, Part A, pp.237-265, 2002.

P. Muron, A. Tertois, J. Mallet, and J. Et-hovadik, An Efficient and Extensible Interpolation Framework Based on the Matrix Formulation of the Discrete Smooth Interpolation, Proceedings of the 25 th Gocad Meeting, 2005.

D. Myers, Cokriging -new developments, Geostatistics for natural ressources characterization, pp.295-305, 1984.

J. Nelder and R. Et-wedderburn, Generalized Linear Models, Journal of the Royal Statistical Society. Series A (General), vol.135, issue.3, pp.370-384, 1983.
DOI : 10.2307/2344614

Y. Pan and R. N. Et-horne, Improved Methods for Multivariate Optimization of Field Development Scheduling and Well Placement Design, SPE Annual Technical Conference and Exhibition, 1998.
DOI : 10.2118/49055-MS

E. Parzen, On Estimation of a Probability Density Function and Mode, The Annals of Mathematical Statistics, vol.33, issue.3, pp.1065-1076, 1962.
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D. W. Peaceman, Interpretation of Well-Block Pressures in Numerical Reservoir Simulation With Nonsquare Grid Blocks and Anisotropic Permeability, Society of Petroleum Engineers Journal, vol.23, issue.03, 1983.
DOI : 10.2118/10528-PA

S. Peddibhotla, H. Cubillos, A. Datta-gupta, and C. H. Et-wu, Rapid Simulation of Multiphase Flow Through Fine-Scale Geostatistical Realizations Using a New, 3-D, Streamline Model: A Field Example, Petroleum Computer Conference, 1996.
DOI : 10.2118/36008-MS

D. W. Pollock, Semianalytical Computation of Path Lines for Finite-Difference Models, Ground Water, vol.25, issue.3, pp.743-750, 1988.
DOI : 10.1111/j.1745-6584.1988.tb00425.x

W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Et-flannery, Numerical recipes in C++ : the art of scientific computing, 2002.

B. Prévost, M. Edwards, M. Et-blunt, and M. , Streamline Tracking on Curvilinear Structured and Unstructured Grids, Proceedings of the SPE Reservoir Simulation Symposium, pp.11-14, 2001.

H. Rabitz, F. Aì-i, J. Shorter, and K. Et-shim, Efficient input???output model representations, Computer Physics Communications, vol.117, issue.1-2, pp.11-20, 1999.
DOI : 10.1016/S0010-4655(98)00152-0

J. K. Ravalico, H. R. Maier, G. C. Dandy, J. P. Norton, and B. F. Et-croke, A Comparison of Sensitivity Analysis Techniques for Complex Models for Environmental Management, Proceedings of the International Congress on Modelling and Simulation, pp.12-15, 2005.

J. Royer and P. Et-vierra, Dual Formalism of Kriging, Geostatistics for Natural Ressources Characterization -Part 2, NATO-ASI -Advanced Geostatistics, pp.691-702, 1984.
DOI : 10.1007/978-94-009-3701-7_8

N. Saad, V. Maroongroge, and C. T. Et-kalkomey, Ranking Geostatistical Models Using Tracer Production Data, European 3-D Reservoir Modelling Conference, 1996.
DOI : 10.2118/35494-MS

A. Saltelli and R. Et-bolado, An alternative way to compute Fourier amplitude sensitivity test (FAST), Computational Statistics & Data Analysis, vol.26, issue.4, pp.445-460, 1998.
DOI : 10.1016/S0167-9473(97)00043-1

A. Saltelli and I. M. Sobol-', About the use of rank transformation in sensitivity analysis of model output, Reliability Engineering & System Safety, vol.50, issue.3, pp.225-239, 1995.
DOI : 10.1016/0951-8320(95)00099-2

A. Saltelli, S. Tarantola, and F. Et-campolongo, Sensitivity Analysis as an Ingredient of Modeling, Statistical Science, vol.15, issue.4, pp.377-395, 2000.

A. Saltelli, S. Tarantola, and K. P. Et-chan, A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output, Technometrics, vol.60, issue.1, pp.4139-56, 1999.
DOI : 10.2307/2371267

K. Sato, Comparison of the boundary element methods for streamline tracking, Journal of Petroleum Science and Engineering, vol.30, issue.1, pp.29-42, 2001.
DOI : 10.1016/S0920-4105(01)00099-7

C. Scheidt and I. Et-zabalza-mezghani, Assessing Uncertainty and Optimizing Production Schemes -Experimental Designs for Nonlinear Prodcution Responses Modelling An Application to Early Water Breakthrough, Proceedings of the 9th European Conference on the Mathematics of Oil Recovery, 2004.

D. Scott, Multivariate Density Estimation : Theory, Practice and Visualization, 1992.
DOI : 10.1002/9781118575574

B. W. Silverman, Density Estimation for Statistics and Data Analysis. Chapman and Hall, 1986.

T. Skalicky, Laspack reference manual. Dresden Univ. of Fechnology -Institue for Fluid Mechanics, 1996.

I. M. Sobol-', On the distribution of points in a cube and the approximate evaluation of integrals, USSR Computational Mathematics and Mathematical Physics, vol.7, issue.4, pp.86-112, 1967.
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I. M. Sobol-', Sensitivity Estimates for Nonlinear Mathematical Models [Translated as Sensitivity Analysis for Nonlinear Mathematical Models, Mathematicheskoe Modelirovanie Math. Modeling comput. Experiment, vol.2, issue.1, pp.112-118, 1990.

T. T. Soong, Probabilistic Modeling and Analysis in Science and Engineering, 1981.

. Streamsim, 3DSL, User Manual -Version 2.20. Streamsim Technologies, 2004.

H. Sturges, The Choice of a Class Interval, Journal of the American Statistical Association, vol.21, issue.153, pp.65-66, 1926.
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A. Tarantola, Inverse Problem Theory and Methods for Model Parameter Estimation, SIAM, vol.342, 2005.
DOI : 10.1137/1.9780898717921

H. Tchelepi and F. Et-orr, Interaction of Viscous Fingering, Permeability Heterogeneity, and Gravity Segregation in Three Dimensions, SPE Reservoir Engineering, vol.9, issue.04, pp.266-271, 1994.
DOI : 10.2118/25235-PA

G. Terrell and D. Scott, Variable Kernel Density Estimation, The Annals of Statistics, vol.20, issue.3, pp.1236-1265, 1992.
DOI : 10.1214/aos/1176348768

M. Thiele, R. Batycky, M. Blunt, and F. O. Et-jr, Simulating Flow in Heterogeneous Systems Using Streamtubes and Streamlines, Proceedings of the SPE/DOE Improved Oil Recovery Symposium, 1994.
DOI : 10.2118/27834-PA

J. Voillemont, Caractérisation par micro-sismicité induite des milieux poreux fracturés. Modélisations par la méthode des lignes de courant d'un site géothermique HDR (Soultz-sous- Forêts, France), 2003.

J. Voillemont and J. Et-royer, Building 3d streamlines in gocad, Proceedings of the 21st Gocad Meeting, 2001.

J. Voillemont and J. Et-royer, Streamline-based reservoir characterization : Latest advances, Proceedings of the 23 rd Gocad Meeting, 2003.

Y. Wang and A. R. Et-kovseck, Integrating production history into reservoir models using streamline-based time-of-flight ranking, Petroleum Geoscience, vol.9, issue.2, pp.163-174, 2003.
DOI : 10.1144/1354-079302-498

B. Yeten, A. Castellini, B. Guyaguler, and W. Et-chen, A Comparison Study on Experimental Design and Response Surface Methodologies, SPE Reservoir Simulation Symposium, 2005.
DOI : 10.2118/93347-MS

I. Zabalza, G. Blanc, D. Collombier, and M. Et-mezghani, Use of experimental design in resolving inverse problems : Application to history matching, Proceedings of the 7th European Conference on the Mathematics of Oil Recovery, 2000.

I. Zabalza, J. Dejean, and D. Et-collombier, Prediction and Density Estimation of a Horizontal Well Productivity Index Using Generalized Linear Models, ECMOR VI, 6th European Conference on the Mathematics of Oil Recovery, 1998.
DOI : 10.3997/2214-4609.201406664

I. Zabalza-mezghani, Analyse statistique et plannification d'expérience en ingéniérie de réservoir, 2000.

B. Zabalza-mezghani, I. Manceau, E. Feraille, M. Et-jourdan, and A. , Uncertainty management: From geological scenarios to production scheme optimization, Journal of Petroleum Science and Engineering, vol.44, issue.1-2, pp.11-25, 2004.
DOI : 10.1016/j.petrol.2004.02.002

I. Zabalza-mezghani, E. Manceau, and F. Et-roggero, A New Approach For Quantifying the Impact of Geostatistical Uncertainty on Production Forecasts : The Joint Modelling Method, Proceedings of the IAMG, 2001.

A. Annexe and A. Simulation-d-'´-ecoulement-sur-lignes-de-courant-dans-le-géomodeleur-gocad-sommaire, 1 Principe d'une simulation d'´ ecoulement biphasique sur lignes de courant, p.138

T. Est-une-matrice-contenant-les-transmissibilités, que ce soit entre cellules voisines ou entre les cellules traversées par un puits et celui-ci. Dans le cadre de la formulation IMPES, cette matrice est carré, symétrique, définie positive et a pour taille le nombre de cellules de la grille plus le nombre de puits ouverts et contrôlés en débit

. Dans-ce-manuscrit, 9)) ontétéontété obtenusàobtenus`obtenusà l'aide d'un gradient conjugué avec un préconditionnement de type SSOR. Tous deux sont inclus dans la librairie dédiée au calcul matriciel LASPACK-1.12.2 [Skalicky, 1996]. Pour plus de détails sur ces méthodes, le lecteur est invitéinvitéà consulter l'abondante littérature sur le sujet, 1994.

L. Objets and N. , NdCube sont deux structures de données développées et implantées durant ces travaux de thèse pour permettre la représentation et l'interpolation d'une fonction dans un espace de dimension quelconque. Dans ce cadre, ces structures représentent, respectivement, un ensemble de points et une grillerégulì ere structurée plongés dans un espace de dimension quelconque

?. Enparalì-ele, la gestion globale des propriétés s'effectue dans un NdDataPack qui dérive du PropertyDB Cette classe contient la liste des propriétés (classes Property) associées associéesà l'objet courant et g` ere de façon effective la taille du DataRec. En particulier, elle est chargée de le re

. Dans-le-détail and . La-structure-d-'un, NdPointSet est relativement simple et s'intègre facilement dans le géomodeleur Gocad Les deux points importants sont, d'une part, le stockage dans une même sous-structure compacte de toutes les informations (que ce soit la géométrie ou les propriétés) nécessaire pour définir un point et, d'autre part, l'organisation de l'ensemble des points sous forme d'une liste doublement cha??néecha??née o` u chaque point est indépendant