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

Hajizadeh, Alireza; Farhadpour, Zahra

doi:10.1007/s11242-012-0028-7

Cited by 25 publications

(7 citation statements)

References 32 publications

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“…Another sampling method was proposed by Hajizadeh and Farhadpour 42 . In their method, a large sampling template is used to mark the central node as hard data and the other pixels are excluded as potential conditioning data.…”

Section: Methodsmentioning

confidence: 99%

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Reconstruction of 3D greyscale image for reservoir rock from a single image based on pattern dictionary

Teng

et al. 2021

Journal of Microscopy

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Most methods that model 3D porous media from 2D images are based on binary images. In this paper, we propose a method for reconstructing 3D greyscale isotropic porous media images from a single image. Our proposed method incorporates a fast‐sampling procedure to control the continuity and variability between adjoining reconstruction layers, a new similarity calculation method to obtain the most similar patterns from a pattern dictionary, and a central area simulation procedure to solve the block effect problem. The reconstruction results from application of our proposed method to a real reservoir 3D model obtained via computed tomography (CT) and a comparison with the original CT structure demonstrate that our proposed method can reproduce properties such as autocorrelation function, linear function, shape distribution, average shape factor, average pore radius size, average throat radius size, average pore volume, permeability and grey histogram. Further, the comparison results indicate that the statistical characteristics of the reconstructions match the training image and the CT model perfectly.

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Section: Methodsmentioning

confidence: 99%

“…As the layer‐by‐layer reconstruction method is used in this approach, controlling the continuity and variability between adjoining layers is critical 42 . An effective way to control the continuity and variability is to adopt an appropriate sampling method with the correct sampling rate.…”

Section: Key Proceduresmentioning

confidence: 99%

Reconstruction of 3D greyscale image for reservoir rock from a single image based on pattern dictionary

Teng

et al. 2021

Journal of Microscopy

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“…In traditional MPS method, a template extracts training 2D image patterns by scanning the image pixel‐by‐pixel to reproduce the 2D or 3D tomography images (Okabe & Blunt, 2005). Three‐step sampling (TSS) as a version of MPS is a powerful layer‐by‐layer reconstruction method which also tries to consider continuity and variability of adjacent layers in 3D porous media (Feng et al., 2018; Hajizadeh & Farhadpour, 2012). In TSS, the scanning of training image is conducted using three sequential steps:

5 \times 5

3 \times 3

and edge sampling.…”

Section: Image Reconstructionmentioning

confidence: 99%

Review of Data Science Trends and Issues in Porous Media Research With a Focus on Image‐Based Techniques

Rabbani

Fernando

Shams

et al. 2021

Water Resources Research

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“…The key issue in the layer-by-layer method is how to control continuity and variability between adjacent layers during the reconstruction procedure [30]. Continuity is the consistency and similarity of patterns in the corresponding position between adjacent layers, and variability is the difference and variety of the patterns in the corresponding position between adjacent layers.…”

Section: A Variability and Continuity Between Adjacent Layersmentioning

confidence: 99%

Reconstruction of three-dimensional porous media from a single two-dimensional image using three-step sampling

Gao

Teng

et al. 2015

Phys. Rev. E

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A random three-dimensional (3D) porous medium can be reconstructed from a two-dimensional (2D) image by reconstructing an image from the original 2D image, and then repeatedly using the result to reconstruct the next 2D image. The reconstructed images are then stacked together to generate the entire reconstructed 3D porous medium. To perform this successfully, a very important issue must be addressed, i.e., controlling the continuity and variability among adjacent layers. Continuity and variability, which are consistent with the statistics characteristic of the training image (TI), ensure that the reconstructed result matches the TI. By selecting the number and location of the sampling points in the sampling process, the continuity and variability can be controlled directly, and thus the characteristics of the reconstructed image can be controlled indirectly. In this paper, we propose and develop an original sampling method called three-step sampling. In our sampling method, sampling points are extracted successively from the center of 5×5 and 3×3 sampling templates and the edge area based on a two-point correlation function. The continuity and variability of adjacent layers were considered during the three steps of the sampling process. Our method was tested on a Berea sandstone sample, and the reconstructed result was compared with the original sample, using tests involving porosity distribution, the lineal path function, the autocorrelation function, the pore and throat size distributions, and two-phase flow relative permeabilities. The comparison indicates that many statistical characteristics of the reconstructed result match with the TI and the reference 3D medium perfectly.

show abstract

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

Cited by 25 publications

References 32 publications

Reconstruction of 3D greyscale image for reservoir rock from a single image based on pattern dictionary

Reconstruction of 3D greyscale image for reservoir rock from a single image based on pattern dictionary

Review of Data Science Trends and Issues in Porous Media Research With a Focus on Image‐Based Techniques

Reconstruction of three-dimensional porous media from a single two-dimensional image using three-step sampling

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