Expecting the Unexpected: The Influence of Elastic Parameter Variance on Bayesian Facies Inversion

Bayesian inversion is used for the prediction of lithology and fluids from AVO seismic data. We assume a multidimensional Gaussian rock physics prior model for the elastic parameters. In this study, we look at the role of the elastic parameters variance in the prior model and how it can impact facies predictions. When the facies classes contained in the prior model have different variance, this difference influences the inversion beyond just adding uncertainty to the seismic reflections. We examine the balance between the influence of this variance and the match with expected seismic data. Our results show that although the variance influence may lead to unexpected results in synthetic scenarios, it also helps to predict the facies configuration when the seismic data follows the prior distribution and forward model.