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.

受微生物活动影响的地下储层中的地球化学过程会改变多孔介质的材料特性。这是地下储层中复杂的生物地球化学过程，目前包含很强的概念不确定性。这意味着，描述生物地球化学过程的几种建模方法是合理的，建模者面临选择最合适方法的不确定性。所考虑的模型在有关过程结构的基本假设方面有所不同。一旦获得观测数据，就可以采用严格的贝叶斯模型选择并伴随贝叶斯模型的合理性分析来选择最合适的模型，即根据可用数据最好地描述潜在物理过程的模型。然而，生物地球化学建模在计算上要求很高，因为它概念化了多孔介质中的不同相、生物质动力学、地球化学、沉淀和溶解。因此，贝叶斯框架不能​​直接基于完整的计算模型，因为这将需要太多昂贵的模型评估。为了规避这个问题，我们建议在为竞争生物地球化学模型构建代理后，同时进行贝叶斯模型选择和合理性分析。在这里，我们将使用任意多项式混沌展开。考虑到代理表示只是分析的原始模型的近似值，我们通过为结果模型权重引入新的校正因子来解释贝叶斯分析中的近似误差。从而，我们扩展了贝叶斯模型的合理性分析并评估了计算成本高的模型的模型相似性。我们在多孔介质中微生物诱导方解石沉淀的代表性场景中展示了该方法。我们对合理性分析的扩展为比较计算要求高的模型提供了一种合适的方法，并提供了有关可靠模型性能所需的数据量的见解。