OLPROC: Processing Observations Made Easy

OLPROC is a model dancing partner. Its role is to postprocess model outputs in order to match them with field measurements, as well as to assist in automatic creation of PEST input datasets involving complex, multi-component objective functions.

Starting from a set of field measurement data and model output files, this tutorial demonstrates how OLPROC can postprocess model results to maximize the ability of the history matching process to extract information from field data. These include time-interpolation of model results to field measurement times, calculation of time-differences between measurements and the calculation of secondary model outcomes.  The tutorial also demonstrates how to use OLPROC to construct a PEST input dataset that leverages these postprocessed results.  

The procedures described herein would normally be implemented as part of a PEST/PEST++ workflow, in which OLPROC is postprocessing outputs from a model that is undergoing history-matching under the control of PEST or PEST++. Although the workflow described herein is specific to MODFLOW 6, the concepts that it demonstrates can be easily adapted to other models and/or file structures.