Transport in Porous Media · 2016 · 13 citations · 19 references
Natural SciencesComputational EngineeringPhysicsComputational PhysicsFluid DynamicsReservoir SimulationFluid-flow In Porous MediaMarkov Chain Monte CarloMonte Carlo MethodsMonte Carlo SimulationPorous Media FlowTransport In Porous MediaMetropolis AlgorithmMetropolis-hastings AlgorithmNumerical Simulation Of Flow And TransportPore Network AnalysisPore Network ModelsPorous Media Flow ModelingSteady-state BehaviorTwo-phase Flow Simulation
We present a Markov Chain Monte Carlo algorithm based on the Metropolis algorithm for simulation of the flow of two immiscible fluids in a porous medium under macroscopic steady-state conditions using a dynamical pore network model that tracks the motion of the fluid interfaces. The Monte Carlo algorithm is based on the configuration probability, where a configuration is defined by the positions of all fluid interfaces. We show that the configuration probability is proportional to the inverse of the flow rate. Using a two-dimensional network, advancing the interfaces using time integration, the computational time scales as the linear system size to the fourth power, whereas the Monte Carlo computational time scales as the linear size to the second power. We discuss the strengths and the weaknesses of the algorithm.
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Equation of State Calculations by Fast Computing Machines
N. Metropolis, Arianna W. Rosenbluth, M. N. Rosenbluth et al. · The Journal of Chemical Physics · 1953 · 37.1K citations
The Dynamics of Capillary Flow
Edward W. Washburn · Physical Review · 1921 · 6.9K citations
Engineering, Physics, Fluid Dynamics +13
Pore-scale imaging and modelling
Martin J. Blunt, Branko Bijeljic, Hu Dong et al. · Advances in Water Resources · 2012 · 1.9K citations · Full text