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Pore network models (PNM) of Berea and Fontainebleau sandstones were constructed using nonlinear programming (NLP) and optimization methods. The constructed PNMs are considered as a digital representation of the rock samples which were based on matching the macroscopic properties of the porous media and used to conduct fluid transport simulations including single and two-phase flow. The PNMs consisted of cubic networks of randomly distributed pores and throats sizes and with various connectivity levels. The networks were optimized such that the upper and lower bounds of the pore sizes are determined using the capillary tube bundle model and the Nelder–Mead method instead of guessing them, which reduces the optimization computational time significantly. An open-source PNM framework was employed to conduct transport and percolation simulations such as invasion percolation and Darcian flow. The PNM model was subsequently used to compute the macroscopic properties; porosity, absolute permeability, specific surface area, breakthrough capillary pressure, and primary drainage curve. The pore networks were optimized to allow for the simulation results of the macroscopic properties to be in excellent agreement with the experimental measurements. This study demonstrates that non-linear programming and optimization methods provide a promising method for pore network modeling when computed tomography imaging may not be readily available. [ABSTRACT FROM AUTHOR]

We show that accurate numerical micropermeametry measurements can be performed on three-dimensional (3D) digitized images of sedimentary rock. The sample size can be very small, making it possible to predict properties from core material not suited for laboratory testing (e.g., drill cuttings, sidewall core and damaged core plugs). Simulation of fluid permeability on microtomographic images of Fontainebleau sandstone on sample sizes of less than 1 mm3 are in good agreement with experimental measurements over a wide range of porosities. [Copyright &y& Elsevier]