Locating and quantifying geological uncertainty in three-dimensional models: Analysis of the Gippsland Basin, southeastern Australia

Geological three-dimensional (3D) models are constructed to reliably represent a given geological target. The reliability of a model is heavily dependent on the input data and is sensitive to uncertainty. This study examines the uncertainty introduced by geological orientation data by producing a suite of implicit 3d models generated from orientation measurements subjected to uncertainty simulations. The resulting uncertainty associated with different regions of the geological model can be located, quantified and visualised, providing a useful method to assess model reliability. The method is tested on a natural geological setting in the Gippsland Basin, southeastern Australia, where modelled geological surfaces are assessed for uncertainty. The concept of stratigraphic variability is introduced and analysis of the input data is performed using two uncertainty visualisation methods. Uncertainty visualisation through stratigraphic variability is designed to convey the complex concept of 3D model uncertainty to the geoscientist in an effective manner.Uncertainty analysis determined that additional seismic information provides an effective means of constraining modelled geology and reducing uncertainty in regions proximal to the seismic sections. Improvements to the reliability of high uncertainty regions achieved using information gathered from uncertainty visualisations are quantified in a comparative case study. Uncertainty in specific model locations is identified and attributed to possible disagreements between seismic and isopach data. Further improvements to and additional sources of data for the model are proposed based on this information. Finally, a method of introducing stratigraphic variability values as geological constraints for geophysical inversion is presented.

► We examine the effects of data input error on three-dimensional geological model reliability.
► We demonstrate how model uncertainty can be identified, quantified and visualised.
► We apply a visualisation technique to a natural geological case study in southeastern Australia.
► We show how the appropriate additional data can improve model reliability.
► We suggest how further improvements can be made to the Gippsland Basin model.