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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.