Abstract
Selection of conditional simulations of acoustic variables is done by matching the simulated values with a series of actual seismic vertical sections. This is done by forward convolution of the simulated acoustic models. The proposed algorithm allows an iterative adjustment of the convolved simulated vertical section to the original seismic data: first impedances along a vertical trace are simulated, then this impedance trace is convolved into a synthetic trace at the seismic scale. An acceptance/rejection criterion based on a correlation function is applied to compare the synthetic with the actual trace. If accepted, the algorithm proceeds and builds the synthetic image by simulating an impedance trace at the next node, otherwise another simulation of the same trace is drawn. Particular attention is given to calibration of the synthetic seismograms at well locations and calibration of acoustic variables to porosity at the log scale. Once the adjustment of the vertical section is obtained, the stochastic impedance image -i.e. the adjusted section before convolution- is easily converted into a porosity image. This image can directly be used for interpretation or flow simulator processing. The method is developped and tested on a real field, a shaly/sand formation covered by an 3D seismic survey.
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References
F. Alabert, S. Thadani and A.G. Journel. An integrated geostatistical/pattern recognition technique for the characterization of reservoir variability. SEG Annual Meeting, New Orleans, 1987.
F. Alabert. Stochastic Imaging of Spatial Distributions Using Hard and Soft Information. Master’s thesis, Stanford University, Stanford, CA, 1987.
C. Deutsch, and A.G. Journel. GSLIB: Geostatistical Software Library and User’s guide, to be published by Oxford University Press, New York, 1992.
P. Doyen. Porosity from seismic data: A geostatistical approach. Geophysics, 53(10), pp 1263–1275, October 1988.
J. Gomez-Hernandez. A stochastic approach to the simulation of block conductivity fields conditioned upon data measured at the short scale. PhD thesis, Stanford University, Stanford, CA, 1991.
E. Isaaks and R. Srivastava. An introduction to applied geostatistics. Oxford University Press, New York, 1989.
A.G. Journel. Fundamentals of Geostatistics in Five Lessons. Short course in Geology, vol.8, AGU publ, Washington, D.C., 40p., 1989.
A.G. Journel and H. Zhu. Integrating soft seismic data: Markov-Bayes updating, an alternative to cokriging and traditional regression. In Report 3, Stanford Center for Reservoir Forecasting, Stanford, CA, 1990.
A. Marechal. Kriging seismic data in presence of faults. In G. Verly et al., editors, Geostatistics for natural resources characterization, pages 271–294, Reidel, Dordrecht, Holland, 1984.
W.J. Ostrander, Plane-wave reflection coefficients for gas sands at non-normal incidence. Geophysics, 49(10), pp 1637–1648, October 1984.
S.C. Key and S.C. Smithson. New approach to seismic-reflection event detection and velocity determination. Geophysics, 55(8), pp 1057–1069, August 1990.
H. Zhu. Modeling Mixture of Spatial Distributions with Integration of Soft Data. PhD thesis, Stanford University, Stanford, CA, 1991.
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© 1993 Kluwer Academic Publishers
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Bortoli, LJ., Alabert, F., Haas, A., Journel, A. (1993). Constraining Stochastic Images to Seismic Data. 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_27
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DOI: https://doi.org/10.1007/978-94-011-1739-5_27
Publisher Name: Springer, Dordrecht
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