Key parameter optimization and analysis of stochastic seismic inversion

作者：Huang Zhe Yuan; Gan Li Deng; Dai Xiao Feng; Li Ling Gao; Wang Jun

来源：Applied Geophysics, 2012, 9(1): 49-56.

DOI：10.1007/s11770-012-0313-9

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

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 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 characteristics derived from the samples, and well density influences 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.

• 出版日期2012-3
• 单位中国石油天然气股份有限公司勘探开发研究院