Optimization under Uncertainty using DSI

Optimization under uncertainty is notoriously numerically intensive. However its numerical burden can be reduced if data space inversion (DSI) is used to construct a surrogate statistical model that can be used in place of the numerical model. This tutorial explores how new ideas from the petroleum industry can be explored using programs from the PEST and PEST++ suites.

The tutorial also demonstrates generation of two-dimensional stochastic hydraulic property fields that exhibit spatially-varying spatial correlation. This is an important component of many decision-support modelling processes, for it allows representation of rapid contaminant movement through hydraulically conductive alluvial channels.