2020/11/25 by Stefania Scheurer, Aline Schäfer Rodrigues Silva, Scheurer, Stefania +11 · 1 citation
Engineering · #Applications (stat.AP) #Drilling and Well Engineering #FOS: Computer and information sciences #Hydraulic Fracturing and Reservoir Analysis #Reservoir Engineering and Simulation Methods
paper · pdf · doi:10.48550/arxiv.2011.12756
openalex publication_date 2020/11/25 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Geochemical processes in subsurface reservoirs affected by microbial activity\nchange the material properties of porous media. This is a complex\nbiogeochemical process in subsurface reservoirs that currently contains strong\nconceptual uncertainty. This means, several modeling approaches describing the\nbiogeochemical process are plausible and modelers face the uncertainty of\nchoosing the most appropriate one. Once observation data becomes available, a\nrigorous Bayesian model selection accompanied by a Bayesian model\njustifiability analysis could be employed to choose the most appropriate model,\ni.e. the one that describes the underlying physical processes best in the light\nof the available data. However, biogeochemical modeling is computationally very\ndemanding because it conceptualizes different phases, biomass dynamics,\ngeochemistry, precipitation and dissolution in porous media. Therefore, the\nBayesian framework cannot be based directly on the full computational models as\nthis would require too many expensive model evaluations. To circumvent this\nproblem, we suggest performing both Bayesian model selection and justifiability\nanalysis after constructing surrogates for the competing biogeochemical models.\nHere, we use the arbitrary polynomial chaos expansion. We account for the\napproximation error in the Bayesian analysis by introducing novel correction\nfactors for the resulting model weights. Thereby, we extend the Bayesian\njustifiability analysis and assess model similarities for computationally\nexpensive models. We demonstrate the method on a representative scenario for\nmicrobially induced calcite precipitation in a porous medium. Our extension of\nthe justifiability analysis provides a suitable approach for the comparison of\ncomputationally demanding models and gives an insight on the necessary amount\nof data for a reliable model performance.\n