Calibrating an ensemble of 1,000 realizations for estimating the uncertainty of aquifer properties in the vicinity of a long‐lived radioactive waste repository using a script-driven approach (on a Friday afternoon)

Groundwater models are simplified and simulated depictions of aquifer systems that represent flow and/or transport of groundwater. For decades, practitioners would construct a model with the help of a graphical user interface (GUI) for a simulator such as MODFLOW, a finite-difference groundwater flow modeling program written by the United States Geological Survey (USGS). Due to the nature of GUIs, most of the time (and therefore cost) went in to the creation of model input datasets, and the “calibration” of the model would be hastily rushed at the end of the project time window. Unfortunately, the calibration process will almost certainly reveal issues derived from early stages of the project, and in the GUI framework it can take significant time and effort to manually address issues with the upstream workflow elements.

More recently, script-driven modeling workflow tools have been made available for practitioners to use to mitigate the time and cost associated with undertaking complex groundwater modeling analyses. Tools like FloPy and pyEMU python packages for interfacing with MODFLOW and the parameter estimation software PEST and PEST++.  When used together, these tools allow for all workflow steps to be automated, including the creation of the model input datasets as well as deployment analyses like data assimilation and uncertainty analysis.  More importantly, a script-driven approach allows issues (which statistically will always occur) to be addressed cleanly and efficiently, with minimal effort and little to no loss of practitioner time.

In this presentation, we present the development and implementation of a decision-support workflow for the history matching of 1,000 geostatistical realizations of transmissivity, storage, anisotropy, and recharge using multiple simulations. This work is part of a larger risk-analysis for the Waste Isolation Pilot Plant (WIPP) project in southeastern New Mexico. The predictive focus is on estimating particle travel times of long-lived radionuclides. 

Using script-driven approaches and recent iterative ensemble smoother techniques, our team was able undertake an advanced data assimilation analysis in a few hours one afternoon using a single workstation.  Previously, a similar analysis for the WIPP project took months of practitioner time on a massively parallel super-computer.