The predictive capability of a geo-simulator is strictly related to the quantification of uncertainties related to its inputs. This study addresses the problem of quantifying uncertainties for and from a geo-model system at basin length scales. The Logan prospect, deep water Gulf of Mexico, is used as an example to demonstrate our workflow. The effect of permeability, porosity and effective stress is shown in terms of uncertainties of pore pressure and porosity distribution. The basin modeling uncertainties, related to uncertainties of the input variable such as basal heat flow, thickness of the layers, among others affect directly the geo-simulator output. These uncertainties can be propagated into seismic velocity and anisotropic parameter derived from a rock model that has input from the geo-model. In this paper the review of stochastic geo-modeling is done while the application for rock physics modeling will be discussed in another paper.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.