Building informative priors and fast simulation methods for subsurface flow applications

Erik Nesvold
Uncertainty quantification of flow in the subsurface is traditionally hampered both (i) by a lack of use of prior knowledge in geostatistical simulation methods and (ii) repeated solution of costly reservoir flow equations. Computational sediment transport models and an abundance of inexpensive remote sensing data represent two potentially important sources of information about depositional patterns. By building up a database of prior knowledge and combining this with modern statistical and machine learning methods we aim to create fast and realistic simulation methods where there is less room for subjective decisions about uncertainty.