Uncertainty is present at every stage of the subsurface modelling workflow and understanding it is an ongoing challenge for the petroleum industry. Quantifying this uncertainty is a rapidly growing field of study as increasingly available high-performance computing enables the application of traditional statistical methods to this problem. However, the extension of these methods to spatial data remains a challenge for which there is no immediate solution. This paper describes the use of data analytics techniques to incorporate spatial uncertainty in reservoir surfaces into subsurface modelling. A metric usually applied in image analytics, the Modified Hausdorff Distance, is adapted for this purpose. The workflow involves sampling the domain of possible surface realisations, characterising them using this metric and determining the most efficient subset to represent the entire data set. The value of this process is that the selected subset captures spatial uncertainty in the surface rather than only gross rock volume. The proposed technique proved to be a simple process that was able to easily select these surfaces from a stochastically generated set and has been successfully applied to the top reservoir surfaces in two fields.