Premium
A revised model for microbially induced calcite precipitation: Improvements and new insights based on recent experiments
Author(s) -
Hommel Johannes,
Lauchnor Ellen,
Phillips Adrienne,
Gerlach Robin,
Cunningham Alfred B.,
Helmig Rainer,
Ebigbo Anozie,
Class Holger
Publication year - 2015
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2014wr016503
Subject(s) - calcite , calibration , permeability (electromagnetism) , precipitation , inverse , biological system , soil science , kinetics , sensitivity (control systems) , environmental science , chemistry , mineralogy , mathematics , engineering , physics , statistics , meteorology , biochemistry , geometry , membrane , biology , quantum mechanics , electronic engineering
The model for microbially induced calcite precipitation (MICP) published by Ebigbo et al. (2012) has been improved based on new insights obtained from experiments and model calibration. The challenge in constructing a predictive model for permeability reduction in the underground with MICP is the quantification of the complex interaction between flow, transport, biofilm growth, and reaction kinetics. New data from Lauchnor et al. (2015) on whole‐cell ureolysis kinetics from batch experiments were incorporated into the model, which has allowed for a more precise quantification of the relevant parameters as well as a simplification of the reaction kinetics in the equations of the model. Further, the model has been calibrated objectively by inverse modeling using quasi‐1D column experiments and a radial flow experiment. From the postprocessing of the inverse modeling, a comprehensive sensitivity analysis has been performed with focus on the model input parameters that were fitted in the course of the model calibration. It reveals that calcite precipitation and concentrations ofNH 4 +andCa 2 +are particularly sensitive to parameters associated with the ureolysis rate and the attachment behavior of biomass. Based on the determined sensitivities and the ranges of values for the estimated parameters in the inversion, it is possible to identify focal areas where further research can have a high impact toward improving the understanding and engineering of MICP.