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. The considered models differ in the underlying hypotheses about the process structure. Once observation data become 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 to perform both Bayesian model selection and justifiability analysis after constructing surrogates for the competing biogeochemical models. Here, we will use the arbitrary polynomial chaos expansion. Considering that surrogate representations are only approximations of the analyzed original models, 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 model 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.

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. The considered models differ in the underlying hypotheses about the process structure. 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 to perform both Bayesian model selection and justifiability analysis after constructing surrogates for the competing biogeochemical models. Here, we will use the arbitrary polynomial chaos expansion. Considering that surrogate representations are only approximations of the analyzed original models, 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 anal-

<p class="western" align="justify"><span lang="en-US">The microbially induced calcite precipitation (MICP) process is a reactive transport, which consists of various important biogeochemical processes, namely precipitation, and dissolution of calcite, adhesion of the biomass on surfaces, detachment of the biomass from the biofilm as well as growth and decay of the biomass. Due to the accumulation of the biofilm and especially the calcite precipitation, the flow conditions in the subsurface can be modified and especially the porosity and permeability can be reduced, so that the existing leakages are sealed. This sealing property of MICP is of interest in different applications, such as sealing cracks in gas tanks or in a cap rock for CO</span><sub><span lang="en-US">2</span></sub><span lang="en-US"> underground storage</span></p> <p class="western" align="justify"><span lang="en-US">The process of biofilm growth in porous media using MICP can be described by many models with different complexity and assumptions. Typically, complex models require more measurement data to constrain their parameters. Therefore, there is a need to seek a balance between model complexity and efforts for acquiring field data. To do so, the modelers are interested in assessing the similarities among these models and their prediction accuracy by comparing them with field observation data. </span></p> <p class="western" align="justify"><span lang="en-US">In this study, we perform a Bayesian model legitimacy analysis to investigate the similarities among different MICP models and their prediction accuracy. Moreover, this analysis provides a model ranking based on computed model weights, achieved within the framework of Bayesian model selection (BMS). This framework requires many model evaluations, which makes the analysis intractable for computationally expensive MICP models. To overcome this issue, we use surrogate models that are constructed using arbitrary polynomial chaos expansion (aPCE). To account for the approximation error, we introduce a correction factor that compensates the inaccuracies due to replacing the original models by the surrogates. </span></p>