Common Uncertainty Research Explorer Uncertainty Estimation in Geological 3D Modelling

Abstract

Three-dimensional (3D) geological models describe geological information in a 3D space using structural data and topological rules as inputs. They are necessary in any project focused on / studying the properties of the subsurface as they express our understanding of geometries at depth. These models, however, are fraught with uncertainties originating from the inherent flaws of the modelling engines combined with input uncertainty. Because 3D geological models are often used for impactful decision making it is critical that all 3D geological models provide reliable estimates of uncertainty. This research focusses on the effect of various structural input data uncertainty propagation in 3D geological modelling. This aim is achieved using Monte Carlo simulation uncertainty estimation (MCUE), a stochastic method which samples from predefined probability distributions that are estimates of the uncertainty of the original input data set. MCUE is used to produce a series of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3D models. These models are then combined into a series of probabilistic models to propagate uncertainty from the input data to a probabilistic model. The present paper presents an innovative way to improve MCUE by using model clustering based on topological signatures and sensitivity analysis.