Biofilm growth in porous media: experiments, computational modeling at the porescale, and upscaling

TL;DR

This study integrates experiments, imaging, and computational modeling to understand biofilm growth in porous media, focusing on flow conditions, morphology, and upscaling properties like permeability and conductivity.

Contribution

It introduces a coupled biomass-nutrient growth model and demonstrates its effectiveness in reproducing biofilm morphologies and upscaling flow properties from porescale to corescale.

Findings

Computational model aligns well with experimental hydraulic data.

Biofilm morphology can be qualitatively reproduced by the model.

Upscaled conductivities match experimental measurements.

Abstract

Biofilm growth changes many physical properties of porous media such as porosity, permeability and mass transport parameters. The growth depends on various environmental conditions, and in particular, on flow rates. Modeling the evolution of such properties is difficult both at the porescale where the phase morphology can be distinguished, as well as during upscaling to the corescale effective properties. Experimental data on biofilm growth is also limited because its collection can interfere with the growth, while imaging itself presents challenges. In this paper we combine insight from imaging, experiments, and numerical simulations and visualization. The experimental dataset is based on glass beads domain inoculated by biomass which is subjected to various flow conditions promoting the growth of biomass and the appearance of a biofilm phase. The domain is imaged and the imaging data is used directly by a computational model for flow and transport. The results of the computational flow model are upscaled to produce conductivities which compare well with the experimentally obtained hydraulic properties of the medium. The flow model is also coupled to a newly developed biomass--nutrient growth model, and the model reproduces morphologies qualitatively similar to those observed in the experiment.