3D pore-scale modeling of nanofluids-enhanced oil recovery

The numerical modeling of oil displacement by nanofluid based on three-dimensional micromodel of cores with different permeability was carried out by the volume of fluid(VOF)method with experimentally measured values of interfacial tension,contact angle and viscosity.Water-based suspensions of SiO_(2) nanoparticles with a concentration of 0–1%and different particle sizes were considered to study the effect of concentration and size of nanoparticles,displacement fluid flow rate,oil viscosity and core permeability on the efficiency of oil displacement by nanofluid.The oil recovery factor(ORF)increases with the increase of mass fraction of nanoparticles.An increase in nanoparticles’concentration to 0.5% allows an increase in ORF by about 19% compared to water flooding.The ORF increases with the decrease of nanoparticle size,and declines with the increase of displacing rate.It has been shown that the use of nanosuspensions for enhanced oil recovery is most effective for low-permeable reservoirs with highly viscous oil in injection modes with capillary number close to the immobilization threshold,and the magnitude of oil recovery enhancement decreases with the increase of displacement speed.The higher the oil viscosity,the lower the reservoir rock permeability,the higher the ORF improved by nanofluids will be.

Using an ILU/Deflation Preconditioner for Simulation of a PEMFuel Cell Cathode Catalyst Layer

Numerical aspects of a pore scale model are investigated for the simulation of catalyst layers of polymer electrolyte membrane fuel cells.Coupled heat,mass and charged species transport together with reaction kinetics are taken into account using parallelized finite volume simulations for a range of nanostructured,computationally reconstructed catalyst layer samples.The effectiveness of implementing deflation as a second stage preconditioner generally improves convergence and results in better convergence behavior than more sophisticated first stage pre-conditioners.This behavior is attributed to the fact that the two stage preconditioner updates the preconditioning matrix at every GMRES restart,reducing the stalling effects that are commonly observed in restarted GMRES when a single stage preconditioner is used.In addition,the effectiveness of the deflation preconditioner is independent of the number of processors,whereas the localized block ILU preconditioner deteriorates in quality as the number of processors is increased.The total number of GMRES search directions required for convergence varies considerably depending on the preconditioner,but also depends on the catalyst layer microstructure,with low porosity microstructures requiring a smaller number of iterations.The improved model and numerical solution strategy should allow simulations for larger computational domains and improve the reliability of the predicted transport parameters.The preconditioning strategies presented in the paper are scalable and should prove effective for massively parallel simulations of other problems involving nonlinear equations.