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Geochemical processes in subsurface reservoirs affected by microbial activitychange the material properties of porous media. This is a complexbiogeochemical process in subsurface reservoirs that currently contains strongconceptual uncertainty. This means, several modeling approaches describing thebiogeochemical process are plausible and modelers face the uncertainty ofchoosing the most appropriate one. Once observation data becomes available, arigorous Bayesian model selection accompanied by a Bayesian modeljustifiability analysis could be employed to choose the most appropriate model,i.e. the one that describes the underlying physical processes best in the lightof the available data. However, biogeochemical modeling is computationally verydemanding because it conceptualizes different phases, biomass dynamics,geochemistry, precipitation and dissolution in porous media. Therefore, theBayesian framework cannot be based directly on the full computational models asthis would require too many expensive model evaluations. To circumvent thisproblem, we suggest performing both Bayesian model selection and justifiabilityanalysis after constructing surrogates for the competing biogeochemical models.Here, we use the arbitrary polynomial chaos expansion. We account for theapproximation error in the Bayesian analysis by introducing novel correctionfactors for the resulting model weights. Thereby, we extend the Bayesianjustifiability analysis and assess model similarities for computationallyexpensive models. We demonstrate the method on a representative scenario formicrobially induced calcite precipitation in a porous medium. Our extension ofthe justifiability analysis provides a suitable approach for the comparison ofcomputationally demanding models and gives an insight on the necessary amountof data for a reliable model performance.

Surrogate-based Bayesian Comparison of Computationally Expensive Models: Application to Microbially Induced Calcite Precipitation