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基于佐普里兹方程的AVO反演：采用改进的马尔可夫链蒙特卡罗方法

Zoeppritz-based AVO inversion using an improved Markov chain Monte Carlo method.

作者信息

Pan Xin-Peng, Zhang Guang-Zhi, Zhang Jia-Jia, Yin Xing-Yao

机构信息

School of Geosciences, China University of Petroleum (Huadong), Qingdao, 266580 Shandong China.

Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266071 Shandong China.

出版信息

Pet Sci. 2017;14(1):75-83. doi: 10.1007/s12182-016-0131-4. Epub 2016 Dec 20.

DOI:10.1007/s12182-016-0131-4
PMID:28239392
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5306083/
Abstract

The conventional Markov chain Monte Carlo (MCMC) method is limited to the selected shape and size of proposal distribution and is not easy to start when the initial proposal distribution is far away from the target distribution. To overcome these drawbacks of the conventional MCMC method, two useful improvements in MCMC method, adaptive Metropolis (AM) algorithm and delayed rejection (DR) algorithm, are attempted to be combined. The AM algorithm aims at adapting the proposal distribution by using the generated estimators, and the DR algorithm aims at enhancing the efficiency of the improved MCMC method. Based on the improved MCMC method, a Bayesian amplitude versus offset (AVO) inversion method on the basis of the exact Zoeppritz equation has been developed, with which the P- and S-wave velocities and the density can be obtained directly, and the uncertainty of AVO inversion results has been estimated as well. The study based on the logging data and the seismic data demonstrates the feasibility and robustness of the method and shows that all three parameters are well retrieved. So the exact Zoeppritz-based nonlinear inversion method by using the improved MCMC is not only suitable for reservoirs with strong-contrast interfaces and long-offset ranges but also it is more stable, accurate and anti-noise.

摘要

传统的马尔可夫链蒙特卡罗（MCMC）方法受限于提议分布的选定形状和大小，并且当初始提议分布远离目标分布时不易启动。为克服传统MCMC方法的这些缺点，尝试将MCMC方法中的两种有用改进方法——自适应梅特罗波利斯（AM）算法和延迟拒绝（DR）算法相结合。AM算法旨在通过使用生成的估计量来调整提议分布，而DR算法旨在提高改进后的MCMC方法的效率。基于改进后的MCMC方法，开发了一种基于精确佐普里兹方程的贝叶斯振幅与偏移距（AVO）反演方法，利用该方法可以直接获得纵波和横波速度以及密度，并且还估计了AVO反演结果的不确定性。基于测井数据和地震数据的研究证明了该方法的可行性和稳健性，并表明所有三个参数都得到了很好的反演。因此，使用改进后的MCMC的基于精确佐普里兹的非线性反演方法不仅适用于具有强对比界面和长偏移距范围的储层，而且更稳定、准确且抗噪。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd54/5306083/c10b6356485b/12182_2016_131_Fig11_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd54/5306083/c10b6356485b/12182_2016_131_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd54/5306083/6b7ce6b85322/12182_2016_131_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd54/5306083/ed720b29c45e/12182_2016_131_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd54/5306083/568b05c428d6/12182_2016_131_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd54/5306083/62743812ee76/12182_2016_131_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd54/5306083/c8c2999223a1/12182_2016_131_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd54/5306083/ed00a9aef2fc/12182_2016_131_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd54/5306083/cc4ec2f0a575/12182_2016_131_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd54/5306083/96af0380ed79/12182_2016_131_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd54/5306083/c10b6356485b/12182_2016_131_Fig11_HTML.jpg

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