swMATH ID: 23340
Software Authors: Commer, M., Kowalsky, M.B., Doetsch, J., Newman, G.A., Finsterle, S.
Description: MPiTOUGH2: a parallel parameter estimation framework for hydrological and hydrogeophysical applications. We present a parallel joint hydrogeophysical parameter estimation framework specifically relevant for a class of inverse modeling applications where a large number of simulations of multi-phase, multi-component flow and transport through porous media impose exceedingly large computing demands. A modified Levenberg–Marquardt minimization algorithm provides for a robust and efficient calibration of complex models. The optimization framework is based on the parameter estimation and uncertainty analysis tool iTOUGH2, which we have parallelized using the Message Passing Interface in order to address the main computational burden of assessing parameter sensitivities. An underlying layer of hydrological and geophysical forward simulation operators use domain decomposition and parallel iterative Krylov solver techniques. The geophysical forward simulation operators originate from parallel algorithms for electrical and electromagnetic data types that have proven successful in solving large-scale imaging problems arising in geothermal as well as oil and gas exploration applications. We have pursued a consequent merge of the hydrological optimization framework with the geophysical component in order to maximize the efficiencies of the Message Passing Interface. The method offers new possibilities by combining hydrological data with geophysical measurements that involve, for example, time-harmonic electromagnetic fields. We first show improved model resolution capabilities on a synthetic joint inversion example where controlled-source electromagnetic observations are combined with hydrological data simulated from a conservative tracer injection experiment. Next, the method is applied to a 3-D joint inversion of field data from a CO2 injection experiment, where the required multi-phase, multi-component flow and transport simulations are highly computationally demanding. Overall improved data fits are achieved for both CO2 gas mole fractions and observed relative changes in electrical conductivity derived from geophysical measurements.
Homepage: https://www.sciencedirect.com/science/article/pii/S0098300413001751
Cited in: 2 Publications

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