Joint Bayesian Spatial Inversion of Lithology/fluid Classes, Petrophysical Properties and Elastic Attributes

Abstract

Summary We consider joint prediction of lithology/fluid classes, petrophysical properties and elastic attributes in a Bayesian spatial framework based on a set of geophysical observations. A probabilistic model accounting for both vertical and lateral spatial dependency is proposed based on a Markov random field prior model for the lithology/fluid classes. We discuss in specific the rock physics model for the elastic attributes, which is well-known to be multimodal and skewed due to the presence of different lithology/fluid classes and saturation effects of the subsurface. The posterior model is assessed by an efficient Markov chain Monte Carlo algorithm. The proposed workflow is demonstrated on a Norwegian Sea gas discovery, with realistic spatial continuity in the predictions.