Key parameter optimization and analysis of stochastic seismic inversion*

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Abstract Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density function (PDF) of subsurface models. The Markov chain Monte Carlo (MCMC) method is used to sample the posterior PDF and the subsurface model characteristics can be inferred by analyzing a set of the posterior PDF samples. In this paper, we first introduce the stochastic seismic inversion theory, discuss and analyze the four key parameters: seismic data signal-to-noise ratio (S/N), variogram, the posterior PDF sample number, and well density, and propose the optimum selection of these parameters. The analysis results show that seismic data S/N adjusts the compromise between the influence of the seismic data and geostatistics on the inversion results, the variogram controls the smoothness of the inversion results, the posterior PDF sample number determines the reliability of the statistical character istics derived from the samples, and well density infl uences the inversion uncertainty. Finally, the comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more reliable information of the subsurface character.

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	HUANG Zhe-Yuan
	GAN Li-Deng
	DAI Xiao-Feng
	LI Ling-Gao
	WANG Jun

Key words： stochastic seismic inversion signal-to-noise ratio variogram posterior probability distribution sample number well density

Received: 2011-02-19;

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This work was fi nancially supported by the National Major Science and Technology Project of China on Development of Big Oil-Gas Fields and Coalbed Methane (No. 2008ZX05010-002).

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HUANG Zhe-Yuan,GAN Li-Deng,DAI Xiao-Feng et al. Key parameter optimization and analysis of stochastic seismic inversion*[J]. APPLIED GEOPHYSICS, 2012, 9(1): 49-56.

[1]	Bortoli, L. J., Alabert, F., Haas, A., and Journel, A. G., 1993, Constraining stochastic images to seismic data:
[2]	Soares A., Geostatistics Trois, Volume 1, Dordrecht, Kluwer Academic Publ, 325 - 338.
[3]	Fugro-Jason, 2009, Jason Geoscience Workbench Software Manual: StatMod MC.
[4]	Gan, L. D., Dai, X. F., and Zhang, X., 2011, Research into poststack seismic inversions for thin reservoir
[5]	characterization: Deterministic and Stochastic 2011SPG/ SEG International Geophysical Conference, Shenzhen.
[6]	Grijalba-Cuenca, A., Torres-Verdin, C., and van der Made, P., 2000, Geostatistical inversion of 3D seismic data to
[7]	extrapolate wireline petrophysical variables laterally away from the well: SPE Paper 63283, SPE Annual
[8]	Technical Conference and Exhibition, Dallas, Texas.
[9]	Haas, A., and Dubrule, O., 1994, Geostatistical inversion - a sequential method of stochastic reservoir modeling
[10]	constrained by seismic data: First Break, 12(11), 561 - 569.
[11]	Hansen, T. M., Journel, A. G., Tarantola, A., and Mosegaard, K., 2006, Linear inverse Gaussian theory
[12]	and geostatistics: Geophysics, 71(6), R101 - R111.
[13]	Journel, A., 1989, Fundamentals of geostatistics in five lessons: Volume 8, Short Course in Geology, American
[14]	Geophysical Union, Washington D.C. Krige, D. G., 1951, A statistical approach to some mine
[15]	valuations and allied problems at the Witwatersrand: Masters’s Thesis, University of Witwatersrand, South
[16]	Africa.
[17]	Tarantola, A., 1987, Inverse problem theory: methods for data fitting and model parameter estimation: Elsevier
[18]	Science Publ. Co., Inc.

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