Quantifying similarities between computational expensive reactive transport models

Abstract

<p><span>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>2</span></sub><span> underground storage</span></p><p><span>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><span>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>