von Wolff, L., Weinhardt, F., Class, H., Hommel, J., & Rohde, C. (2021). Investigation of Crystal Growth in Enzymatically Induced Calcite Precipitation by Micro-Fluidic Experimental Methods and Comparison with Mathematical Modeling.
Transport in Porous Media,
137(2), Article 2.
https://doi.org/10.1007/s11242-021-01560-y Abstract
Enzymatically induced calcite precipitation (EICP) is an engineering technology that allows for targeted reduction of porosity in a porous medium by precipitation of calcium carbonates. This might be employed for reducing permeability in order to seal flow paths or for soil stabilization. This study investigates the growth of calcium-carbonate crystals in a micro-fluidic EICP setup and relies on experimental results of precipitation observed over time and under flow-through conditions in a setup of four pore bodies connected by pore throats. A phase-field approach to model the growth of crystal aggregates is presented, and the corresponding simulation results are compared to the available experimental observations. We discuss the model's capability to reproduce the direction and volume of crystal growth. The mechanisms that dominate crystal growth are complex depending on the local flow field as well as on concentrations of solutes. We have good agreement between experimental data and model results. In particular, we observe that crystal aggregates prefer to grow in upstream flow direction and toward the center of the flow channels, where the volume growth rate is also higher due to better supply.BibTeX
Scheurer, S., Schäfer Rodrigues Silva, A., Mohammadi, F., Hommel, J., Oladyshkin, S., Flemisch, B., & Nowak, W. (2021). Surrogate-based Bayesian comparison of computationally expensive models: application to microbially induced calcite precipitation.
Computational Geosciences.
https://doi.org/10.1007/s10596-021-10076-9 Abstract
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.BibTeX
Hommel, J., Akyel, A., Frieling, Z., Phillips, A. J., Gerlach, R., Cunningham, A. B., & Class, H. (2020). A Numerical Model for Enzymatically Induced Calcium Carbonate Precipitation.
Applied Sciences,
10(13), 4538.
https://doi.org/10.3390/app10134538 BibTeX
Cunningham, A. B., Class, H., Ebigbo, A., Gerlach, R., Phillips, A., & Hommel, J. (2019). Field-scale modeling of microbially induced calcite precipitation.
Computational Geosciences,
tbd.
https://doi.org/10.1007/s10596-018-9797-6 BibTeX
Hommel, J., Coltman, E., & Class, H. (2018). Porosity-Permeability Relations for Evolving Pore Space: A Review with a Focus on (Bio-)geochemically Altered Porous Media.
Transport in Porous Media,
2(124), Article 124.
https://doi.org/10.1007/s11242-018-1086-2 BibTeX
