A framework for simulating the partially miscible multi-component hydrocarbon fluids in porous media via the pseudo-potential lattice Boltzmann model
Keywords:
Retrograde condensation, Pseudo-potential lattice Boltzmann models, Partially miscible, Partial miscibility, Multicomponent multiphase fluid, Lattice Boltzmann modelsAbstract
Retrograde condensation is a unique PVT behavior of partially multi-component hydrocarbon mixtures in porous media. However, some important physical properties, such as the component composition in each phase, the surface tension of the mixture, and the fluid wettability on specific rock surfaces at given temperatures, pressures, and molar compositions are difficult to evaluate dynamically in a laboratory. Previously, a multi-component multiphase (MCMP) model was proposed to simulate the behavior of fluids composed of multiple components, such as gas condensate fluids or volatile oil fluids, where the components are partially miscible with each other. In this study, we extend the previously developed MCMP lattice Boltzmann model (LBM) for partially miscible fluids by proposing a new framework to investigate the fluids’ phase behavior and flow dynamics under different phase conditions in porous media. The proposed framework integrates multiple lattice Boltzmann models to enable the convenient generation of desired wettability conditions on structural surfaces. Additionally, it incorporates a Voronoi tessellation process for the stochastic generation of more physically realistic porous media. This represents a notable improvement over previous models that relied on arbitrary geometries. The proposed framework can enhance the understanding of the behavior of these fluids under varying conditions and can provide valuable insights into the qualitative evaluation of the pore-scale multiphase flow mechanism. Overall, this work contributes to the development of a computational framework for studying partially miscible hydrocarbon mixtures, which has important implications for the oil and gas industry.
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Copyright (c) 2024 Zhicheng W. Wang, Cheng Peng, Luis F. Ayala, Seyyed A. Hosseini
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