| 随机地震反演关键参数优选和效果分析(英文) |
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| 引用本文： | 黄哲远,甘利灯,戴晓峰,李凌高,王军.随机地震反演关键参数优选和效果分析(英文)[J].应用地球物理,2012,9(1):49-56. |
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| 作者姓名： | 黄哲远 甘利灯 戴晓峰 李凌高 王军 |
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| 作者单位： | 中国石油勘探开发研究院石油物探技术研究所 |
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| 基金项目： | 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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| 摘 要： | 随机地震反演技术是将地质统计理论和地震反演相结合的反演方法,它将地震资料、测井资料和地质统计学信息融合为地下模型的后验概率分布,利用马尔科夫链蒙特卡洛(MCMC)方法对该后验概率分布采样,通过综合分析多个采样结果来研究后验概率分布的性质,进而认识地下情况。本文首先介绍了随机地震反演的原理,然后对影响随机地震反演效果的四个关键参数,即地震资料信噪比、变差函数、后验概率分布的样本个数和井网密度进行分析并给出其优化原则。资料分析表明地震资料信噪比控制地震资料和地质统计规律对反演结果的约束程度,变差函数影响反演结果的平滑程度,后验概率分布的样本个数决定样本统计特征的可靠性,而参与反演的井网密度则影响反演的不确定性。最后通过对比试验工区随机地震反演和基于模型的确定性地震反演结果,指出随机地震反演可以给出更符合地下实际情况的模型。
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| 关 键 词： | 随机地震反演 地震资料信噪比 变差函数 后验概率分布样本个数 井网密度 |
Key parameter optimization and analysis of stochastic seismic inversion |
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| Zhe-Yuan Huang,Li-Deng Gan,Xiao-Feng Dai,Ling-Gao Li,Jun Wang.Key parameter optimization and analysis of stochastic seismic inversion[J].Applied Geophysics,2012,9(1):49-56. |
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| Authors: | Zhe-Yuan Huang Li-Deng Gan Xiao-Feng Dai Ling-Gao Li Jun Wang |
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| Institution: | 1. Research Institute of Petroleum Exploration and Development, PetroChina, Beijing, 100083, China
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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 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. |
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| Keywords: | stochastic seismic inversion signal-to-noise ratio variogram posterior probability distribution sample number well density |
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