This tutorial introduces data space inversion (DSI). DSI can be used to explore the uncertainties of predictions made by complex models with complicated hydraulic property fields. The model run burden is extremely low, and unrelated to the complexity of the complex model’s construction or parameterisation.
There is considerable overlap between this tutorial and the “Four Ways to Explore Model Predictive Uncertainty” tutorial. This tutorial demonstrates methodologies that are covered in the previous tutorial. However it also demonstrates the DSI2 and DSIMOD utilities. These can be used to construct and run a surrogate model parameterised through principle component analysis of covariances between past and future system behaviour. Robust exploration of history-match-constrained uncertainties of future system behaviour can be accomplished very quickly using:
- PEST’s predictive analyser
- Linear analysis