In geological surface modelling, uncertainty analysis is used to provide information about the reliability of the three-dimensional (3D) geological model. The uncertainty analysis of interpolated surfaces can be calculated by the estimation error, which is the difference between the estimated values in the interpolated surface and the reference dataset. Geostatistical tools are used to assess the uncertainty related to how closely the interpolated surface honors the geological dataset.
Most geological surfaces, within the 3D models developed at the Alberta Geological Survey are modelled using Petrel’s convergent interpolation algorithm because it typically produces a more realistic representation in areas of complex geology compared to algorithms in other software. Unfortunately, it is difficult to assess the uncertainty for these surfaces using the current surface modelling methodologies in Petrel.
To solve this problem, a unique workflow for assessing prediction uncertainty was developed using a combination of Python, Matlab code, and Petrel software. Two separate workflow methodologies have been developed to assess both the global and local uncertainty of our geological surfaces.