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Zoeppritz-based AVO inversion using an improved Markov chain Monte Carlo method.
Petroleum Science ( IF 5.6 ) Pub Date : 2016-12-20 , DOI: 10.1007/s12182-016-0131-4
Xin-Peng Pan 1 , Guang-Zhi Zhang 1, 2 , Jia-Jia Zhang 1, 2 , Xing-Yao Yin 1, 2
Affiliation  

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.

中文翻译:

使用改进的马尔可夫链蒙特卡罗方法,基于Zoeppritz的AVO反演。

常规的马尔可夫链蒙特卡洛(MCMC)方法仅限于提案分发的选定形状和大小,并且当初始提案分发距离目标分发很远时,不容易启动。为了克服常规MCMC方法的这些缺点,尝试将MCMC方法中的两个有用的改进,自适应大都会(AM)算法和延迟拒绝(DR)算法组合在一起。AM算法旨在通过使用生成的估计量来适应提案分配,而DR算法旨在提高改进的MCMC方法的效率。在改进的MCMC方法的基础上,开发了一种基于精确Zoeppritz方程的贝叶斯振幅对偏移(AVO)反演方法,通过该方法可以直接获得P波和S波的速度和密度,并估计了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鲁棒性,并表明对这三个参数都进行了很好的检索。因此,使用改进的MCMC进行精确的基于Zoeppritz的非线性反演方法,不仅适用于界面强烈,偏移距离长的储层,而且更加稳定,准确和抗噪。
更新日期:2016-12-20
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