Dossier: Upscaling of Fluid Flow in Oil Reservoirs
Open Access
Numéro
Oil & Gas Science and Technology - Rev. IFP
Volume 59, Numéro 2, March-April 2004
Dossier: Upscaling of Fluid Flow in Oil Reservoirs
Page(s) 141 - 155
DOI https://doi.org/10.2516/ogst:2004011
Publié en ligne 1 décembre 2006
  • Adler, P. and Thovert, J.F. (1998) Real Porous Media: Local Geometry and Macroscopic Properties. Applied Mechanics Reviews, 51, 9. [CrossRef] [Google Scholar]
  • Alabert, F. (1989) Constraining Description of Randomly Heterogeneous Reservoirs to Pressure Test Data: a Monte-Carlo Study. Paper SPE 19600 in Proc. of the SPE Annual Technical Conference and Exhibition, San Antonio. [Google Scholar]
  • Benito, M. (2003) Non-stationnarité dans les modèles de type booléen: application à la simulation d’unités sédimentaires. Thèse de doctorat, École des mines de Paris. [Google Scholar]
  • Beucher, H., Galli, G., LeLoc’h, G. and Ravenne, C. (1993) Including a Regional Trend in Reservoir Modelling Using the Truncated Gaussian Method. In: Soares, A., ed., Geostatistics Troia'92, 1, 555-566, Kluwer Academic Pub. [CrossRef] [Google Scholar]
  • Blanc, G., Touati, M., Estebenet, T. and Hu, L.Y. (1998) Geostatistical Modelling on Flexible Grids. In: Proc. of the 6th European Conference on the Mathematics of Oil Recovery (ECMOR VI), Peebles, Scotland. [Google Scholar]
  • Caers, J. (2003) Efficient Gradual Deformation Using a Streamline-Based Proxy Method. Journal of Petroleum Science & Engineering, 39, 1-2. [Google Scholar]
  • Chilès, J.P. and Delfiner, P. (1999) Geostatistics: Modeling Spatial Uncertainty, Wiley, New York. [Google Scholar]
  • Deutsch, C.V. and Journel, A.G. (1992) GSLIB Geostatistical Software Library and User's Guide, Oxford University Press, New York. [Google Scholar]
  • Feraille, M., Roggero, F., Manceau, E., Hu, L.Y., Zabalza- Mezghani, I. and Reis, L.C. (2003) Application of Advanced History Matching Techniques to an Integrated Field Case Study. Paper SPE 84463 in Proc. of the SPE Annual Technical Conference and Exhibition, Denver. [Google Scholar]
  • Fournier, F. and Derain, J.F. (1995) A Statistical Methodology for Deriving Reservoir Properties from Seismic Data. Geophysics, 60, 5. [Google Scholar]
  • Freulon, X. and de Fouquet, C. (1993) Conditioning a Gaussian Model with Inequalities. In: Soares, A., ed., Geostatistics Troia'92, 1, 201-212, Kluwer Acad. Pub. [CrossRef] [Google Scholar]
  • Froidevaux, R. (1993) Probability Field Simulation. In: Soares, A., ed., Geostatistics Troia'92, 1, 73-83, Kluwer Academic Pub. [CrossRef] [Google Scholar]
  • Galli, A., Beucher, H., Le Loc’h, G., and Doligez, B. (1994) The Pros and Cons of the Truncated Gaussian Method. In: M. Armstrong and P.A. Down, eds., Geostatistical Simulations, Kluwer Academic Pub. [Google Scholar]
  • Geman, S. and Geman, D. (1984) Stochastic Relaxation, Gibbs Distribution and Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741. [Google Scholar]
  • Guardiano, F. and Srivastava, M. (1993) Multivariate Geostatistics: Beyond Bivariate Moments. In: Soares, A., ed., Geostatistics Troia'92, 1, 133-144, Kluwer Academic Pub. [CrossRef] [Google Scholar]
  • Haas, A. and Noetinger, B. (1997) Stochastic Reservoir Modelling Constrained by Well Test Permeabilities. In: E.Y. Baafi and N.A. Schofield, eds., Geostatistics Wollongong’96, Kluwer Academic Pub. [Google Scholar]
  • Hegstad, B.K., Omre, H., Tjelmeland, H. and Tyler, K. (1994) Stochastic Simulation and Conditioning by Annealing in Reservoir Description. In: Armstrong, M. and Dowd, P., eds., Geostatistical Simulations, 73-83, Kluwer Academic Pub. [Google Scholar]
  • Hu, L.Y., Joseph, P. and Dubrule, O. (1994) Random Genetic Simulation of the Internal Geometry of Deltaic Sand Bodies. SPE Formation Evaluation, December, 245-250. [Google Scholar]
  • Hu, L.Y., Blanc, G. and Nœtinger, B. (1998) Estimation of Lithofacies Proportions by Use of Well and Well-Test Data. SPE Reservoir Evaluation & Engineering, Feb. [Google Scholar]
  • Hu, L.Y. (2000) Gradual Deformation and Iterative Calibration of Gaussian-Related Stochastic Models. Math. Geology, 32, 1. [Google Scholar]
  • Hu, L.Y.,Blanc, G. and Noetinger, B. (2001) Gradual Deformation and Iterative Calibration of Sequential Stochastic Simulations. Math. Geology, 33, 4. [CrossRef] [Google Scholar]
  • Hu, L.Y. (2003) History Matching of Object-Based Stochastic Reservoir Models. Paper SPE 81503 in Proc. of the SPE 13th Middle East Oil Show & Conference, Bahrain. [Google Scholar]
  • Hu, L.Y. and Le Ravalec-Dupin (2003) Efficient Stochastic Optimization by Reconciling Random and Gradient Searches. In: Proc. of the Annual Conference of the International Association for Mathematical Geology (IAMG), Portsmouth, UK. [Google Scholar]
  • Jacod, J. and Joathon, O. (1971) Use of Random Genetic Models in the Study of Sedimentary Processes. Math. Geology, 3, 265-279. [CrossRef] [MathSciNet] [Google Scholar]
  • Journel, A.G. and Huijbregts, C.J. (1978) Mining Geostatistics. Academic Press, London. [Google Scholar]
  • Lantuéjoul, C. and Rivoirard, J. (1984) Une méthode de détermination d’anamorphose, Note du Centre de géostatistique, no. 916, École des mines de Paris, Fontainebleau, France. [Google Scholar]
  • Lantuéjoul, C. and Schmitt, M. (1991) Use of Two New Formulae to Estimate the Poisson Intensity of a Boolean Model. 13e Colloque GRETSI, 16-20 septembre, Juan-les-Pins. [Google Scholar]
  • Lantuéjoul, C. (1997) Iterative Algorithms for Conditional Simulations. In: Baafi et others, eds., Geostatistics Wollongong’96, 1, 27-40, Kluwer Academic Pub. [Google Scholar]
  • Le Loc'h, G. and Galli, A. (1997) Truncated Pluri-Gaussian Method: Theoretical and Practical Points of View. In: Baafi and others, eds., Geostatistics Wollongong'96, 1, 211-223, Kluwer Academic Pub. [Google Scholar]
  • LeRavalec, M.,Nœtinger, B. and Hu, L.Y. (2000) The FFT Moving Average (FFT-MA) Generator: An Efficient Numerical Method for Generating and Conditioning Gaussian Simulations. Math. Geology, 32, 6. [Google Scholar]
  • Le Ravalec-Dupin, M., Hu, L.Y. and Nœtinger, B. (2000) Sampling the Conditional Realization Space Using the Gradual Deformation Method. In: Kleingeld and Krige, eds., Geostatistics 2000 Cape Town, 1. [Google Scholar]
  • Le Ravalec, M., Hu, L.Y. and Nœtinger, B. (2001) Stochastic Reservoir Modeling Constrained to Dynamic Data: Local Calibration and Inference of the Structural Parameters. SPE Journal, March. [Google Scholar]
  • Le Ravalec-Dupin, M. and Fenwick, D. (2002) A Combined Geostatistical and Streamline-Based History Matching Procedure. Paper SPE 77378 in Proc. of the SPE Annual Technical Conference and Exhibition, San Antonio. [Google Scholar]
  • Lopez, S., Galli, A. and Cojan, I. (2001) Fluvial Meandering Channelized Reservoirs: A Stochastic & Process-Based Approach. In: Proc. of the of the Annual Conference of the International Association for Mathematical Geology, 6-12 September, Cancun, Mexico. [Google Scholar]
  • Marsily de, G.,Lavedan, G.,Boucher, M. and Fasanino, G. (1984) Interpretation of Interference Tests in a Well Field Using Geostatistical Techniques to Fit the Permeability Distribution in a Reservoir Model. In: Verly, G. and others, eds., Geostatistics for Natural Resources Characterization, Part 2, 831-849, D. Reidel Publ. Co. [Google Scholar]
  • Matheron, G. (1967) Éléments pour une théorie des milieux poreux, Masson, Paris. [Google Scholar]
  • Matheron, G. (1971) The Theory of Regionalized Variables and its Applications, Fascicule 5. Les Cahiers du Centre de morphologie mathématique, École des mines de Paris, Fontainebleau, France. [Google Scholar]
  • Matheron, G. (1973) The Intrinsic Random Functions and their Applications. Adv. Appl. Prob. 5, 439-468. [Google Scholar]
  • Matheron, G., Beucher, H., de Fouquet, C., Galli, A. and Ravenne, C. (1987) Conditional Simulation of the Geometry of Fluvio-Deltaic Reservoirs. Paper SPE 16753 in Proc. of the SPE Annual Technical Conference and Exhibition, Dallas. [Google Scholar]
  • Mezghani, M and Roggero, F. (2001) Combining Gradual Deformation and Upscaling Techniques for Direct Conditioning of Fine-Scale Reservoir Models to Dynamic Data. Paper SPE 71334 in Proc. of the SPE Annual Technical Conference and Exhibition, New Orleans. [Google Scholar]
  • Moulière, D., Beucher, H., Hu, L.Y., Fournier, F., Terdich, P., Melchiori, F. and Griffi, G., (1997) Integration of Seismic Derived Information for Reservoir Stochastic Modeling Using Truncated Gaussian Approach. In: E.Y. Baafi and N.A. Schofield, eds., Geostatistics Wollongong'96, 1, Kluwer Academic Pub. [Google Scholar]
  • Nœtinger, B (1993) A Pressure Moment Approach for Helping Pressure Transient Analysis in Complex Heterogeneous Reservoir. Paper SPE 26466 in Proc. of the SPE Annual Technical Conference and Exhibition, October, Dallas, USA. [Google Scholar]
  • Oliver, D.S.,Cunha, L.B. and Reynolds, A.C. (1997) Markov Chain Monte-Carlo Methods for Conditioning a Permeability Field to Pressure Data. Math. Geology, 29, 1, 61-91. [CrossRef] [Google Scholar]
  • Press, W.H, Teukolsky, S.A., Vettering, W.T. and Flannery, B.P. (1992) Numerical Recipes in Forthan. Cambridge University Press. [Google Scholar]
  • RamaRao, B.S.,LaVenue, A.M., de Marsily, G. and Marietta, M.G. (1995) Pilot Point Methodology for Automated Calibration of an Ensemble of Conditionally Simulated Transmissivity Fields, 1. Theory and Computational Experiments. Water Resour. Res., 31, 3, 475-493. [CrossRef] [Google Scholar]
  • Ravenne, C., Eschard, R., Galli, A., Mathieu, Y., Montadert, L. and Rudkiewicz, J.L. (1987) Heterogeneities and Geometry of Sedimentary Bodies in a Fluvio-Deltaic Reservoir. Paper SPE 16752 in Proc. of the SPE Annual Technical Conference and Exhibition, Dallas. [Google Scholar]
  • Reis, L.C. (2001) Intégration des données dynamiques dans un modèle géostatistique de réservoir. Thèse de doctorat, université Paris VI. [Google Scholar]
  • Roggero, F. and Hu, L.Y. (1998) Gradual Deformation of Continuous Geostatistical Models for History Matching. Paper SPE 49004 in Proc. of the SPE Annual Technical Conference and Exhibition, New Orleans. [Google Scholar]
  • Schaaf, T., Mezghani, M. and Chavent, G. (2002) Direct Conditioning of Fine-Scale Facies Models to Dynamic Data by Combining Gradual Deformation and Numerical Upscaling Techniques. In: Proc. of the 8th European Conference on Mathematics of Oil Recovery (ECMOR VIII), 3-6 Septembre, Freiberg, Germany. [Google Scholar]
  • Schaaf, T., Mezghani, M. and Chavent, G. (2003) In Search of an Optimal Parameterization: An Innovative Approach to Reservoir Data Integration. Paper SPE 84273 in Proc. of the SPE Annual Technical Conference and Exhibition, Denver. [Google Scholar]
  • Schmitt, M. (1991) Estimation of the Density in a Stationary Boolean Model. J. of Appl. Prob., 28. [Google Scholar]
  • Schmitt, M. and Beucher, H. (1997) On the Inference of the Boolean Model. In: E.Y. Baafi and N.A. Schofield, eds., Geostatistics Wollongong'96, 1, Kluwer Academic Pub. [Google Scholar]
  • Sen, M.K., Gupta, A.D., Stoffa, P.L. Lake, L.W. and Pope, G.A. (1992) Stochastic Reservoir Modeling Using Simulated Annealing and Genetic Algorithm. Paper SPE 24754 in Proc. of the SPE Annual Technical Conference and Exhibition, Washington, D.C. [Google Scholar]
  • Srivastava, R.M. (1994) The Interactive Visualization of Spatial Uncertainty. Paper SPE 27965 in Proc. of the University of Tulsa Centennial Petroleum Engineering Symposium, Tulsa. [Google Scholar]
  • Souza, O. (1997) Stratigraphie séquentielle et modélisation probabiliste des réservoirs d'un cône sous-marin profond. Thèse de doctorat, université Paris VI. [Google Scholar]
  • Stoyan, D.S., Kendall, W.S. and Mecke, J. (1995) Stochastic Geometry and its Applications, 2nd Edition, Wiley, Chichester. [Google Scholar]
  • Strebelle, S. and Journel, A. (2000) Sequential Simulation Drawing Structures from Training Images. In: Kleingeld and Krige, eds., Geostatistics 2000 Cape Town, 1. [Google Scholar]
  • Sun, N.Z. (1994) Inverse Problems in Groundwater Modeling, Kluwer Academic Pub., Dordrecht, The Netherlands. [Google Scholar]
  • Tarantola, A. (1987) Inverse Problem Theory - Methods for Data Fitting and Model Parameter Estimation, Elsevier, Amsterdam. [Google Scholar]

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