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

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• 作者：Xin-Peng Pan ; Guang-Zhi Zhang ; Jia-Jia Zhang ; Xing-Yao Yin
• 关键词：Adaptive Metropolis (AM) algorithm ; Delayed rejection (DR) algorithm ; Bayesian AVO inversion ; Exact Zoeppritz ; Nonlinear inversion
• 刊名：Petroleum Science
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：14
• 期：1
• 页码：75-83
• 全文大小：1607KB
• 刊物主题：Mineral Resources; Industrial Chemistry/Chemical Engineering; Industrial and Production Engineering; Energy Economics;
• 出版者：China University of Petroleum (Beijing)
• ISSN：1995-8226
• 卷排序：14

文摘

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.