A new stochastic method of reconstructing porous media

We present a new stochastic method of reconstructing porous medium from limited morphological information obtained from two-dimensional micro- images of real porous medium. The method is similar to simulated annealing method in the capability of reconstructing both isotropic and anisotropic structures of multi-phase but differs from the latter in that voxels for exchange are not selected completely randomly as their neighborhood will also be checked and this new method is much simpler to implement and program. We applied it to reconstruct real sandstone utilizing morphological information contained in porosity, two-point probability function and linear-path function. Good agreement of those references verifies our developed method’s powerful capability. The existing isolated regions of both pore phase and matrix phase do quite minor harm to their good connectivity. The lattice Boltzmann method (LBM) is used to compute the permeability of the reconstructed system and the results show its good isotropy and conductivity. However, due to the disadvantage of this method that the connectivity of the reconstructed system’s pore space will decrease when porosity becomes small, we suggest the porosity of the system to be reconstructed be no less than 0.2 to ensure its connectivity and conductivity.     

Percolation Effects of Grain Contacts in Partially Saturated Sandstones: Deviations from Archie’s Law

We study the resistivity index of Fontainebleau and Bentheimer sandstones at ambient conditions down to low water saturations both experimentally and numerically. Numerical simulations are in good agreement with experimental measurements of capillary drainage resistivity index by the porous plate method down to water saturations as low as S _w = 10 %. Fontainebleau sandstone exhibits a percolating network of grain contacts, while the higher porosity Bentheimer sandstone does not. We show that this difference in the topological connection of conductive films at low water saturations is responsible for the non-Archie behaviour of Fontainebleau sandstone. Furthermore, it is necessary to attribute a grain contact conductivity to the grain contacts in Fontainebleau sandstone to reconcile experiment and numerical simulation. Conductive films organised as pendular rings around grain contacts are not able to explain this result.     

Transport Properties of Stochastically Reconstructed Porous Media with Improved Pore Connectivity

Results from stochastic reconstruction of porous solids and from a direct comparison of calculated and experimental effective transport properties are presented. Eight porous solids of different microstructures were selected to evaluate the performance of two reconstruction methods based on simulated annealing. The common method was constrained by the two-point probability function and the lineal-path function for the void phase, whilst the constraints of our new method were further supplemented by the lineal-path function for the solid phase and by two adjustable parameters. The new method was capable of reproducing the void and solid phases as large clusters spanning the entire replicas. Non-percolating clusters formed minor volume fractions of both phases. Although the common method reproduced the microstructures quite well, their pore space connectivity was significantly poorer. Therefore, effective permeability, effective ordinary diffusivity, and effective Knudsen diffusivity calculated for the replicas obtained using the new method were always much greater than the same quantities related to the common reconstruction method. For most of the porous solids, values of the effective properties calculated on the basis of the new reconstruction method better matched their experimental counterparts than the corresponding values derived from the microstructures reproduced using the common reconstruction method.     

Effect of 2D Image Resolution on 3D Stochastic Reconstruction and Developing Petrophysical Trend

Multi-resolution digital rock physics (DRP) makes it possible to up-scale petrophysical properties from micron size to core sample size using two-dimensional (2D) thin section images. Resolution of 3D images and sample size are challenging problems in DRP where high-resolution images are acquired from small samples using inefficient and expensive micro-CT facilities. Three-dimensional stochastic reconstruction is an alternative approach to overcome these challenges. In this paper, we use multi-resolution images and investigate effect of 2D image resolution on 3D stochastic reconstruction and development of petrophysical trends for our two sandstone and carbonate original representative volume elements (RVEs). The proposed method includes three steps. In the first step, the spatial resolution of our original RVEs is decreased synthetically. In the second step, stochastic RVEs are realized for each resolution using two perpendicular images, correlation functions, and phase recovery algorithm. In the reconstruction method, a full set of two-point correlation functions (TPCFs) is extracted from two perpendicular 2D images. Then TPCF vectors are decomposed and averaged to realize 3D stochastic RVEs. In the third step, petrophysical properties like relative and absolute permeability as well as porosity and formation factor are computed. The output is used to develop trends for petrophysical properties in different resolutions. Experimental results illustrate that the proposed method can be used to predict petrophysical properties and reconstruct 3D RVEs for resolutions unavailable in the acquired 2D or 3D data.     

An Algorithm for 3D Pore Space Reconstruction from a 2D Image Using Sequential Simulation and Gradual Deformation with the Probability Perturbation Sampler

The construction of a faithful 3D pore space model of a porous medium that could reproduce the macroscopic behavior of that medium is of great interest in various fields including medicine, material science, hydrology and petroleum engineering. A computationally efficient algorithm is developed that uses the probability perturbation method and sequential multiple-point statistics simulations to generate 3D stochastic and equiprobable representations of random porous media when only a 2D thin section image is available. By employing the probability perturbation method as a gradual deformation technique, the pore patterns of a single 2D image are deformed to generate a series of 2D stochastically simulated images. The 3D pore structure is then generated by simply stacking the 2D-simulated images. The quality of the 3D reconstruction is critically dependent on the rate of deformation and a simple general procedure for choosing this parameter is presented. Various criteria such as porosity, two-point auto-correlation function, multiple-point connectivity function, local percolation probability, absolute permeability obtained by lattice-Boltzmann method (LBM), formation factor and two-phase relative permeability calculations are used to validate the results. The method is tested on two random porous solids; Berea Sandstone and synthetic Silica, for which directly measured 3D micro-CT images are available. The stochastically reconstructed 3D pore space preserves the low- and high-order spatial statistics, the macroscopic flow properties and the microstructure of the 3D micro-CT images.     

Predicting the Permeability of Very Low Porosity Sandstones

The permeability predictions of two geometric pore-scale models, one being predominantly granular and the other consolidated with tube-like pores, are compared with experimental results for Fontainebleau sandstones and the results interpreted. Percolation thresholds are determined from experimental data and applied in the modelling exercise by means of cut-off asymptotes on porosity. It is found that, although both granular and foamlike models yield plausible results, the granular model appears to be superior, at least for the sets of data considered. The Klinkenberg correction is analytically derived and incorporated into the models to relate gas and liquid permeabilities and an analytical expression for the Klinkenberg factor is proposed for each model. The permeability predictions are promising and yield an effective manner to correlate sandstone percolation data.     

Accurate Reconstruction of Porous Materials via Stochastic Fusion of Limited Bimodal Microstructural Data

Porous materials such as sandstones have important applications in petroleum engineering and geosciences. An accurate knowledge of the porous microstructure of such materials is crucial for the understanding of their physical properties and performance. Here, we present a procedure for accurate reconstruction of porous materials by stochastically fusing limited bimodal microstructural data including limited-angle X-ray tomographic radiographs and 2D optical micrographs. The key microstructural information contained in the micrographs is statistically extracted and represented using certain lower-order spatial correlation functions associated with the pore phase, and a probabilistic interpretation of the attenuated intensity in the tomographic radiographs is developed. A stochastic procedure based on simulated annealing that generalizes the widely used Yeong–Torquato framework is devised to efficiently incorporate and fuse the complementary bimodal imaging data for accurate microstructure reconstruction. The information content of the complementary microstructural data is systematically investigated using a 2D model system. Our procedure is subsequently applied to accurately reconstruct a variety of 3D sandstone microstructures with a wide range of porosities from limited X-ray tomographic radiographs and 2D optical micrographs. The accuracy of the reconstructions is quantitatively ascertained by directly comparing the original and reconstructed microstructures and their corresponding clustering statistics.