Calibrating Lattice Boltzmann flow simulations and estimating uncertainty in the permeability of complex porous media. (August 2016)

• Record Type: Journal Article
• Authors: Hosa, Aleksandra; Curtis, Andrew; Wood, Rachel
• Is Part Of: Advances in water resources. Volume 94(2016)
• Page Start: 60
• Page End: 74
• Publication Date: 2016-08
• Subjects: Uncertainty quantification -- Porous media -- Permeability -- BGK Lattice Boltzmann -- Fluid flow -- Bayesian; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2016.04.020
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: We use a Bayesian uncertainty quantification framework to calibrate the BGK Lattice Boltzmann model. We propagate the measured uncertainty to permeability predictions. The calibration uses porosity and permeability data for Fontainebleau sandstone. We showcase that previous calibrations based on Poiseuille flow lead to less accurate predictions. Abstract: A common way to simulate fluid flow in porous media is to use Lattice Boltzmann (LB) methods. Permeability predictions from such flow simulations are controlled by parameters whose settings must be calibrated in order to produce realistic modelling results. Herein we focus on the simplest and most commonly used implementation of the LB method: the single-relaxation-time BGK model. A key parameter in the BGK model is the relaxation time τ which controls flow velocity and has a substantial influence on the permeability calculation. Currently there is no rigorous scheme to calibrate its value for models of real media. We show that the standard method of calibration, by matching the flow profile of the analytic Hagen-Poiseuille pipe-flow model, results in a BGK-LB model that is unable to accurately predict permeability even in simple realistic porous media (herein, Fontainebleau sandstone). In order to reconcile the differences between predicted permeability and experimental data, we propose a method to calibrate τ using an enhanced Transitional Markov Chain Monte Carlo method, which is suitable for parallel computer architectures. We also propose a porosity-dependent τ calibration that provides an excellent fit to experimental data and which creates an empirical model that can be used to choose τ for new samples of known porosity. Our Bayesian framework thus provides robust predictions of permeability of realistic porous media, herein demonstrated on the BGK-LB model, and should therefore replace the standard pipe-flow based methods of calibration for more complex media. The calibration methodology can also be extended to more advanced LB methods.