  Geochemical processes in subsurface reservoirs affected by microbial activity
change the material properties of porous media. This is a complex
biogeochemical process in subsurface reservoirs that currently contains strong
conceptual uncertainty. This means, several modeling approaches describing the
biogeochemical process are plausible and modelers face the uncertainty of
choosing the most appropriate one. Once observation data becomes available, a
rigorous Bayesian model selection accompanied by a Bayesian model
justifiability analysis could be employed to choose the most appropriate model,
i.e. the one that describes the underlying physical processes best in the light
of the available data. However, biogeochemical modeling is computationally very
demanding because it conceptualizes different phases, biomass dynamics,
geochemistry, precipitation and dissolution in porous media. Therefore, the
Bayesian framework cannot be based directly on the full computational models as
this would require too many expensive model evaluations. To circumvent this
problem, we suggest performing both Bayesian model selection and justifiability
analysis after constructing surrogates for the competing biogeochemical models.
Here, we use the arbitrary polynomial chaos expansion. We account for the
approximation error in the Bayesian analysis by introducing novel correction
factors for the resulting model weights. Thereby, we extend the Bayesian
justifiability analysis and assess model similarities for computationally
expensive models. We demonstrate the method on a representative scenario for
microbially induced calcite precipitation in a porous medium. Our extension of
the justifiability analysis provides a suitable approach for the comparison of
computationally demanding models and gives an insight on the necessary amount
of data for a reliable model performance.
