Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《应用地球物理(英文版)》 2012-01
Add to Favorite Get Latest Update

Key parameter optimization and analysis of stochastic seismic inversion

Huang Zhe-Yuan1, Gan Li-Deng1, Dai Xiao-Feng1, Li Ling-Gao1, and Wang Jun11. Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China.  
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 characteristics derived from the samples, and well density in? 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.
【Fund】: supported by the National Major Science and Technology Project of China on Development of Big Oil-Gas Fields and Coalbed Methane (No. 2008ZX05010-002)
【CateGory Index】: P631.4
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved