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Abstract

Pore network modeling (PNM) has been widely used to study the multiphase flow and transport in porous media. Although a number of recent papers discussed the PNM validation on core scale parameters such as permeability, relative permeability, capillary pressure etc; quantitative predictive potential of PNM on pore by pore basis is rarely been studied. In this article, A PNM validation workflow against micro model experiment on pore scale is firstly discussed. A glass etched micro model is used to quantify the accuracy of a dynamic PNM solver on pore and core level. Two phase drainage micro fluidic experiments at different flow conditions are performed on micro models. PNM simulations are performed on the same pattern and flow conditions as used in micro model experiments. The two phase distribution extracted from experiment images are registered onto results of PNM simulations for direct pore to pore comparison. An image processing tool is developed to extract pore to pore oil/water distribution from micro model images for further repeatability check and pore by pore comparisons to PNM simulations. Pore scale matching level is found around 75% for all three test cases, which indicates the oil/water displacement in 75% pores can be predicted by PNM. Compared to the matching level of repeated experiments around 84%, the agreement between PNM and experiments on pore by pore is considered as reasonable. The matching level of core scale parameters such as Swc and oil phase permeability varies from case to case; the relative error to micro model experiment measurements varies from 15% to 60%. Possible reasons leading to discrepancies on core scale parameters are discussed. Imperfection of micro model fabrication and lack of consideration of experimental uncertainty in validation can be two principal factors. In general PNM simulations produced positive results against micro model experiments and PNM is a promising tool for complex flow study in porous media.

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/content/papers/10.3997/2214-4609.201601739
2016-08-29
2020-04-02
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201601739
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