Seismic amplitude variation with offset (AVO) inversion is an important approach for quantitative prediction of rock elasticity, lithology and fluid properties. With Biot–Gassmann’s poroelasticity, an improved statistical AVO inversion approach is proposed. To distinguish the influence of rock porosity and pore fluid modulus on AVO reflection coefficients, the AVO equation of reflection coefficients parameterized by porosity, rock-matrix moduli, density and fluid modulus is initially derived from Gassmann equation and critical porosity model. From the analysis of the influences of model parameters on the proposed AVO equation, rock porosity has the greatest influences, followed by rock-matrix moduli and density, and fluid modulus has the least influences among these model parameters. Furthermore, a statistical AVO stepwise inversion method is implemented to the simultaneous estimation of rock porosity, rock-matrix modulus, density and fluid modulus. Besides, the Laplace probability model and differential evolution, Markov chain Monte Carlo algorithm is utilized for the stochastic simulation within Bayesian framework. Models and field data examples demonstrate that the simultaneous optimizations of multiple Markov chains can achieve the efficient simulation of the posterior probability density distribution of model parameters, which is helpful for the uncertainty analysis of the inversion and sets a theoretical fundament for reservoir characterization and fluid discrimination.

中文翻译：

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结合孔隙弹性的地震AVO统计反演

具有偏移的地震振幅变化（AVO）反演是定量预测岩石弹性，岩性和流体性质的重要方法。利用Biot–Gassmann的孔隙弹性，提出了一种改进的统计AVO反演方法。为了区分岩石孔隙度和孔隙流体模量对AVO反射系数的影响，首先从Gassmann方程和临界孔隙度模型推导了由孔隙度，岩体模量，密度和流体模量参数化的反射系数的AVO方程。从模型参数对所提出的AVO方程的影响分析来看，岩石孔隙度的影响最大，其次是岩体的模量和密度，而流体模量对模型参数的影响最小。此外，运用统计AVO逐步反演方法来同时估算岩石的孔隙度，岩体模量，密度和流体模量。此外，利用拉普拉斯概率模型和微分演化，马尔可夫链蒙特卡罗算法对贝叶斯框架内的随机仿真进行了研究。模型和现场数据实例表明，多个马尔可夫链的同时优化可以有效地模拟模型参数的后验概率密度分布，这有助于反演的不确定性分析，为储层表征和流体判别奠定了理论基础。 。利用拉普拉斯概率模型和微分演化，将马尔可夫链蒙特卡罗算法用于贝叶斯框架内的随机模拟。模型和现场数据实例表明，多个马尔可夫链的同时优化可以有效地模拟模型参数的后验概率密度分布，这有助于反演的不确定性分析，为储层表征和流体判别奠定了理论基础。 。利用拉普拉斯概率模型和微分演化，将马尔可夫链蒙特卡罗算法用于贝叶斯框架内的随机模拟。模型和现场数据实例表明，多个马尔可夫链的同时优化可以有效地模拟模型参数的后验概率密度分布，这有助于反演的不确定性分析，为储层表征和流体判别奠定了理论基础。 。