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TOUGH: Model Use, Calibration, and Validation

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Transactions of the ASABE. 55(4): 1275-1290. (doi: 10.13031/2013.42240) @2012
Authors:   S. Finsterle, M. B. Kowalsky, K. Pruess
Keywords:   iTOUGH2, Nonisothermal multiphase flow simulation, Parameter estimation, Sensitivity analysis, TOUGH2, Uncertainty quantification

Simulation models for subsurface flow and transport require the estimation of many disparate parameters to capture properties and processes that cannot be explicitly represented in the model. The main challenges for model calibration reside in the formulation of the forward model, the identification of influential parameters, the selection of appropriate calibration data with sufficient information content about these parameters, and the performance of a robust and efficient minimization algorithm capable of handling complex topologies of the resulting objective function. The large variety of subsurface systems and related engineering problems that are addressed by numerical simulation prevents us from compiling definite lists of critical input parameters and desirable observation data to be used for model calibration. Instead, we describe the approach that we consider necessary to arrive at a defensible model for predictive simulations. This process includes sensitivity analyses and synthetic inversions prior to data collection, and detailed residual, error, and uncertainty analyses after automatic model calibration. We illustrate the approach by discussing applications of the iTOUGH2 simulation-optimization code, which supports the development of TOUGH2 models for nonisothermal, multiphase, multicomponent flow and transport processes in fractured porous geological media. We conclude that the calibration of models representing complex subsurface systems remains challenging and requires research that is targeted at the needs of scientists and engineers who address simulation problems of practical relevance.

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