Abstract
A combination of fibre processes, a special version of marked point processes, and Gaussian random functions is applied to build a 3D model for fluvial reservoir. A fluvial reservoir consists of a set of channel-belts, each containing several river-beds.
The directions of the channel-belts are represented by fibres. Geological studies provide prior knowledge about the directions. Combining this information, according to Bayesian rules, with the well-observations, gives the posterior probability distributions of the directions. The joint distribution of the fibres includes spatial interaction.
The individual river-beds inside a channel-belt are modelled by correlated 1D Gaussian random functions. These Gaussian functions define the location, thickness and width of the river-beds. A fairly complex conditioning scheme is used for reproducing the conditioning pattern usually defined by geologists.
The simulation technique is based on the Ripley-Kelly algorithm for birth/death processes, and a standard filter procedure for the 1D Gaussian random functions. Examples are presented.
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References
R. Gundesø and O. Egeland. Sesimira — a new geological tool for 3-d modeling of heterogeneous reservoirs. In A. T. Buller, E. Berg, O. Hjelmeland, J. Kleppe, O. Torsæter, and J.O. Aasen, editors, North Sea Oil and Gas Reservoir — II, pages 363–371. Graham & Trotman, May 1989.
H. O. Augedal, K. O. Stanley, and H. Omre. Sisabosa, a program for stochastic modelling and evaluation of reservoir geology. In Proceedings from Conference on Reservoir Description and Simulation with Emphasis on EOR, Oslo, Oslo, Norway, September 1986. IFE.
J. O. Berger. Statistical Decision Theory and Bayesian Analysis. Springer series in statistics. Springer-Verlag, New York, 2. edition, 1985.
R. Clemetsen, A. R. Hurst, R. Knarud, and H. Omre. A computer program for evaluation of fluvial reservoirs. In A. T. Buller, E. Berg, O. Hjelmeland, J. Kleppe, O. Torsæter, and J. O. Aasen, editors, North Sea Oil and Gas Reservoir — II, pages 373–385. Graham & Trotman, May 1989.
O. Dubrule. A review of stochastic models for petroleum reservoirs. In Meeting on Quantification of Sediment Body Geometries and Their Internal Heterogeneities, London. British Society of Reservoir Geologists, March 1988.
H. H. Haldorsen, P. J. Brand, and C. J. MacDonald. Review of the stochastic nature of reservoirs. In S. Edwards and P. King, editors, Mathematics in Oil Production, pages 109–209. Oxford Science Publications, Oxford, 1988.
H. H. Haldorsen and E. Damsleth. Stochastic modeling. Journal of Petroleum Technology, pages 404–412, April 1990.
A. G. Journel and Ch. J. Huijbregts. Mining Geostatistics. Academic Press, London, 1978.
L. M. Meling, P. O. M0rkeseth, and T. Langeland. Production forecasting for gas fields with multiple reservoirs of limited extent. In 1988 SPE Annual Technical Conference and Exhibition, Houston, Texas, October, 1988. SPE 18287.
H. Omre. Stochastic models for reservoir characterization. In J. Kleppe and S. M. Skjæveland, editors, Recent Advances in Improved Oil Recovery Methods for North Sea Sandstone Reservoirs. Norwegian Petroleum Directorate, Stavanger, Norway, 1991.
H. Omre and K. B. Halvorsen. The Bayesian bridge between simple and universal kriging. Mathematical Geology, 21(7):767–786, 1989.
B. D. Ripley. Stochastic Simulation. John Wiley & Sons, New York, 1987.
K. O. Stanley, K. Jorde, N. Ræstad, and C. P. Stockbridge. Stochastic modelling of reservoir sand bodies for input to reservoir simulation, snorre field, northern north sea, norway. In A. T. Buller, E. Berg, O. Hjelmeland, J. Kleppe, O. Torsæter, and J. O. Aasen, editors, North Sea Oil and Gas Reservoir — II, pages 91–101. Graham & Trotman, May 1989.
D. Stoyan, W. S. Kendall, and J. Mecke. Stochastic Geometry and its Applications. John Wiley & Sons, New York, 1987.
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© 1993 Kluwer Academic Publishers
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Georgsen, F., Omre, H. (1993). Combining Fibre Processes and Gaussian Random Functions for Modelling Fluvial Reservoirs. In: Soares, A. (eds) Geostatistics Tróia ’92. Quantitative Geology and Geostatistics, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-1739-5_34
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DOI: https://doi.org/10.1007/978-94-011-1739-5_34
Publisher Name: Springer, Dordrecht
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