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1
Liu, Lei, Qiaoling Han, Yue Zhao, and Yandong Zhao. "A Novel Method Combining U-Net with LSTM for Three-Dimensional Soil Pore Segmentation Based on Computed Tomography Images." Applied Sciences 14, no. 8 (April 16, 2024): 3352. http://dx.doi.org/10.3390/app14083352.
Повний текст джерелаАнотація:
The non-destructive study of soil micromorphology via computed tomography (CT) imaging has yielded significant insights into the three-dimensional configuration of soil pores. Precise pore analysis is contingent on the accurate transformation of CT images into binary image representations. Notably, segmentation of 2D CT images frequently harbors inaccuracies. This paper introduces a novel three-dimensional pore segmentation method, BDULSTM, which integrates U-Net with convolutional long short-term memory (CLSTM) networks to harness sequence data from CT images and enhance the precision of pore segmentation. The BDULSTM method employs an encoder–decoder framework to holistically extract image features, utilizing skip connections to further refine the segmentation accuracy of soil structure. Specifically, the CLSTM component, critical for analyzing sequential information in soil CT images, is strategically positioned at the juncture of the encoder and decoder within the U-shaped network architecture. The validation of our method confirms its efficacy in advancing the accuracy of soil pore segmentation beyond that of previous deep learning techniques, such as U-Net and CLSTM independently. Indeed, BDULSTM exhibits superior segmentation capabilities across a diverse array of soil conditions. In summary, BDULSTM represents a state-of-the-art artificial intelligence technology for the 3D segmentation of soil pores and offers a promising tool for analyzing pore structure and soil quality.
2
Li, Mingjiang, Pan Zhang, and Tao Hai. "Pore extraction method of rock thin section based on Attention U-Net." Journal of Physics: Conference Series 2467, no. 1 (May 1, 2023): 012016. http://dx.doi.org/10.1088/1742-6596/2467/1/012016.
Повний текст джерелаАнотація:
Abstract This paper proposes a solution to the shortcomings of traditional segmentation methods. The labeling method uses the incomplete labeling method in weakly supervised labeling to simplify labeling and combines transfer learning to initialize the weight of the network in advance. According to the above ideas, an end-to-end deep learning model is trained. The fine rock particles have a greater segmentation impact, and in addition to that, when compared with the popular deep learning semantic segmentation approaches, they also have a significant improvement. The next phase is to continue improving the network by optimizing the parameters, with the number of network layers and the total number of parameters remaining unaltered. This requirement must be satisfied before moving on to the next stage. The capability of generalization enhances the impact of segmentation on particles as well as their accuracy. Experiments show that this method is significantly better than the traditional method for segmenting rock flakes with manual operation and has better results in the segmentation and extraction of fine particles compared with the mainstream convolutional neural network.
3
Berg, Steffen, Nishank Saxena, Majeed Shaik, and Chaitanya Pradhan. "Generation of ground truth images to validate micro-CT image-processing pipelines." Leading Edge 37, no. 6 (June 2018): 412–20. http://dx.doi.org/10.1190/tle37060412.1.
Повний текст джерелаАнотація:
Digital rock technology and pore-scale physics have become increasingly relevant topics in a wide range of porous media with important applications in subsurface engineering. This technology relies heavily on images of pore space and pore-level fluid distribution determined by X-ray microcomputed tomography (micro-CT). Digital images of pore space (or pore-scale fluid distribution) are typically obtained as gray-level images that first need to be processed and segmented to obtain the binary images that uniquely represent rock and pore (including fluid phases). This processing step is not trivial. Rock complexity, image quality, noise, and other artifacts prohibit the use of a standard processing workflow. Instead, an array of strategies of increasing sophistication has been developed. Typical processing pipelines consist of filtering, segmentation, and postprocessing steps. For each step, various choices and different options exist. This makes selection and validation of an optimum processing pipeline difficult. Using Darcy-scale quantities as a benchmark is not a good option because of rock heterogeneity and different scales of observation. Here, we present a conceptual workflow where noisy images are derived from a ground truth by systematically including typical image artifacts and noise. Artifacts and noise are not simply added to the images. Instead, tomographic forward projection and reconstruction steps are used to incorporate the artifacts in a physically correct way. A proof of concept of this workflow is demonstrated by comparing seven different image-segmentation pipelines ranging from absolute thresholding to a machine-learning approach (Trainable Weka Segmentation). The Trainable Weka Segmentation showed the best performance of the tested methods.
4
Yang, Eomzi, Dong Hun Kang, and Tae Sup Yun. "Reliable estimation of hydraulic permeability from 3D X-ray CT images of porous rock." E3S Web of Conferences 205 (2020): 08004. http://dx.doi.org/10.1051/e3sconf/202020508004.
Повний текст джерелаАнотація:
The hydraulic permeability is a key parameter for simulating the flow-related phenomenon so that its accurate estimation is crucial in both experimental and numerical simulation studies. 3D pore structure can be readily taken by X-ray computed tomography (CT) and it often serves as a flow domain for pore-scale simulation. However, one encounters the challenges in segmenting the authentic pore structure owing to the finite size of image resolution and segmentation methods. Therefore, the loss of structural information in pore space seems unavoidable to result in the unreliable estimation of permeability. In this study, we propose a novel framework to overcome these limitations by using a flexible ternary segmentation scheme. Given the pore size distribution curve and porosity, three phases of pore, solid, and gray regions are segmented by considering the partial volume effect which holds the composition information of unresolved objects. The resolved objects such as solid and pore phases are taken to equivalently solve Stokes equation while the fluid flow through unresolved objects is simultaneously solved by Stokes-Brinkmann equation. The proposed numerical scheme to obtain the permeability is applied to Indiana limestone and Navajo sandstone. The results show that the computed hydraulic permeability is similar to the experimentally obtained value without being affected by image resolution. This approach has advantages of achieving consistent permeability values, less influenced by segmentation methods.
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Idowu, N. A. A., C. Nardi, H. Long, T. Varslot, and P. E. E. Øren. "Effects of Segmentation and Skeletonization Algorithms on Pore Networks and Predicted Multiphase-Transport Properties of Reservoir-Rock Samples." SPE Reservoir Evaluation & Engineering 17, no. 04 (August 13, 2014): 473–83. http://dx.doi.org/10.2118/166030-pa.
Повний текст джерелаАнотація:
Summary Networks of large pores connected by narrower throats (pore networks) are essential inputs into network models that are routinely used to predict transport properties from digital rock images. Extracting pore networks from microcomputed-tomography (micro-CT) images of rocks involves a number of steps: filtering, segmentation, skeletonization, and others. Because of the amount of clay and its distribution, the segmentation of micro-CT images is not trivial, and different algorithms exist for achieving this. Similarly, several methods are available for skeletonizing the segmented images and for extracting the pore networks. The nonuniqueness of these processes raises questions about the predictive power of network models. In the present work, we evaluate the effects of these processes on the computed petrophysical and multiphase-flow properties of reservoir-rock samples. By use of micro-CT images of reservoir sandstones, we first apply three different segmentation algorithms and assess the effects of the different algorithms on estimated porosity, amount of clay, and clay distribution. Single-phase properties are computed directly on the segmented images and compared with experimental data. Next, we extract skeletons from the segmented images by use of three different algorithms. On the pore networks generated from the different skeletons, we simulate two-phase oil/ water and three-phase gas/oil/water displacements by use of a quasistatic pore-network model. Analysis of the segmentation results shows differences in the amount of clay, in the total porosity, and in the computed singlephase properties. Simulated results show that there are differences in the network-predicted single-phase properties as well. However, predicted multiphase-transport properties from the different networks are in good agreement. This indicates that the topology of the pore space is well preserved in the extracted skeleton. Comparison of the computed capillary pressure and relative permeability curves for all networks with available experimental data shows good agreements. By use of a segmentation that captures porosity and microporosity, we show that the extracted networks can be used to reliably predict multiphase-transport properties, irrespective of the algorithms used.
6
Lu, An Qun, Shou Zhi Zhang, and Qian Tian. "Matlab Image Processing Technique and Application in Pore Structure Characterization of Hardened Cement Pastes." Advanced Materials Research 785-786 (September 2013): 1374–79. http://dx.doi.org/10.4028/www.scientific.net/amr.785-786.1374.
Повний текст джерелаАнотація:
Based on Matlab image processing technique and backscattered electron image analysis method, a characterization method is set up to make quantitative analysis on pore structure of hardened cement pastes. Adopt Matlab to acquire images, and carry out gradation and binarization processing for them; use the combination method of local threshold segmentation and histogram segmentation to obtain pore structure characteristics. The results showed that evolution law of pore structure of fly ash cement pastes via Matlab image analysis method is similar to the conclusion obtained through BET and DVS. Selecting different angle of backscattered electron images in the same sample, its statistic results are more representative.
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Liu, Yifei, and Dong-Sheng Jeng. "Pore Structure of Grain-Size Fractal Granular Material." Materials 12, no. 13 (June 26, 2019): 2053. http://dx.doi.org/10.3390/ma12132053.
Повний текст джерелаАнотація:
Numerous studies have proven that natural particle-packed granular materials, such as soil and rock, are consistent with the grain-size fractal rule. The majority of existing studies have regarded these materials as ideal fractal structures, while few have viewed them as particle-packed materials to study the pore structure. In this study, theoretical analysis, the discrete element method, and digital image processing were used to explore the general rules of the pore structures of grain-size fractal granular materials. The relationship between the porosity and grain-size fractal dimension was determined based on bi-dispersed packing and the geometric packing theory. The pore structure of the grain-size fractal granular material was proven to differ from the ideal fractal structure, such as the Menger sponge. The empirical relationships among the box-counting dimension, lacunarity, succolarity, grain-size fractal dimension, and porosity were provided. A new segmentation method for the pore structure was proposed. Moreover, a general function of the pore size distribution was developed based on the segmentation results, which was verified by the soil-water characteristic curves from the experimental database.
8
Zel, Ivan, Murat Kenessarin, Sergey Kichanov, Kuanysh Nazarov, Maria Bǎlǎșoiu, and Denis Kozlenko. "Pore Segmentation Techniques for Low-Resolution Data: Application to the Neutron Tomography Data of Cement Materials." Journal of Imaging 8, no. 9 (September 7, 2022): 242. http://dx.doi.org/10.3390/jimaging8090242.
Повний текст джерелаАнотація:
The development of neutron imaging facilities provides a growing range of applications in different research fields. The significance of the obtained structural information, among others, depends on the reliability of phase segmentation. We focused on the problem of pore segmentation in low-resolution images and tomography data, taking into consideration possible image corruption in the neutron tomography experiment. Two pore segmentation techniques are proposed. They are the binarization of the enhanced contrast data using the global threshold, and the segmentation using the modified watershed technique—local threshold by watershed. The proposed techniques were compared with a conventional marker-based watershed on the test images simulating low-quality tomography data and on the neutron tomography data of the samples of magnesium potassium phosphate cement (MKP). The obtained results demonstrate the advantages of the proposed techniques over the conventional watershed-based approach.
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LIN, WEI, XIZHE LI, ZHENGMING YANG, LIJUN LIN, SHENGCHUN XIONG, ZHIYUAN WANG, XIANGYANG WANG, and QIANHUA XIAO. "A NEW IMPROVED THRESHOLD SEGMENTATION METHOD FOR SCANNING IMAGES OF RESERVOIR ROCKS CONSIDERING PORE FRACTAL CHARACTERISTICS." Fractals 26, no. 02 (April 2018): 1840003. http://dx.doi.org/10.1142/s0218348x18400030.
Повний текст джерелаАнотація:
Based on the basic principle of the porosity method in image segmentation, considering the relationship between the porosity of the rocks and the fractal characteristics of the pore structures, a new improved image segmentation method was proposed, which uses the calculated porosity of the core images as a constraint to obtain the best threshold. The results of comparative analysis show that the porosity method can best segment images theoretically, but the actual segmentation effect is deviated from the real situation. Due to the existence of heterogeneity and isolated pores of cores, the porosity method that takes the experimental porosity of the whole core as the criterion cannot achieve the desired segmentation effect. On the contrary, the new improved method overcomes the shortcomings of the porosity method, and makes a more reasonable binary segmentation for the core grayscale images, which segments images based on the actual porosity of each image by calculated. Moreover, the image segmentation method based on the calculated porosity rather than the measured porosity also greatly saves manpower and material resources, especially for tight rocks.
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Reimers, I. A., I. V. Safonov, and I. V. Yakimchuk. "Segmentation of 3D FIB-SEM data with pore-back effect." Journal of Physics: Conference Series 1368 (November 2019): 032015. http://dx.doi.org/10.1088/1742-6596/1368/3/032015.
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Wong, H. S., M. K. Head, and N. R. Buenfeld. "Pore segmentation of cement-based materials from backscattered electron images." Cement and Concrete Research 36, no. 6 (June 2006): 1083–90. http://dx.doi.org/10.1016/j.cemconres.2005.10.006.
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Zhang, Feng, Ghislain Bournival, Hamed Lamei Ramandi, and Seher Ata. "Digital Cake Analysis: A Novel Coal Filter Cake Examination Technique Using Microcomputed Tomography." Minerals 13, no. 12 (November 30, 2023): 1509. http://dx.doi.org/10.3390/min13121509.
Повний текст джерелаАнотація:
Filtration is crucial for separating solids and liquids in various industries. Understanding slurry properties and filter cake structures is essential for optimising filtration performance. Conventional methods focus on interpreting filtration data to improve the understanding of filtration mechanisms. However, examining fragile filter cakes is challenging, and current techniques often alter their structure. Conventional methods only provide an average representation of cake porosity, neglecting variations in porosity and pore distribution across the cake thickness. This study introduces the Digital cake analysis, a non-destructive technique for evaluating filter cake structure. Filtration experiments using a custom-built unit were conducted on coal slurries to obtain filter cake samples. X-ray-microcomputed tomography (µCT) imaging was utilized for cake analysis. Image enhancement techniques were employed to improve the quality of the µCT images. The enhanced images were segmented into three phases (resolved pore, subresolution pore, and solid phases) for quantitative analysis. This segmentation technique allocated partial pore volume to voxels in the subresolution phase based on their intermediate grey-scale intensity, enabling more accurate porosity calculations. Unlike conventional methods, this approach computed porosity values for resolved (100% void) and subresolution (partially void) pores. This image segmentation technique facilitated accurate computations of porosity, pore size distribution, and pore properties, significantly advancing the understanding of cake structures. Digital cake analysis produced porosity measurements similar to the experimental results.
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Ramandi, Hamed Lamei, Peyman Mostaghimi, Ryan T. Armstrong, Christoph H. Arns, Mohammad Saadatfar, Rob M. Sok, Val Pinczewski, and Mark A. Knackstedt. "Pore scale imaging and modelling of coal properties." APPEA Journal 55, no. 2 (2015): 468. http://dx.doi.org/10.1071/aj14103.
Повний текст джерелаАнотація:
A key parameter in determining the productivity and commercial success of coal seam gas wells is the permeability of individual seams. Laboratory testing of core plugs is commonly used as an indicator of potential seam permeability. The highly heterogeneous and stress-dependent nature of coal makes laboratory measurements difficult to perform and the results difficult to interpret. Consequently, permeability in coal is poorly understood. The permeability of coal at the core scale depends on the geometry, topology, connectivity, mineralisation and spatial distribution of the cleat system, and a better understanding of coal permeability, that and the factors that control this depends on having a better understanding and detailed characterisation of the system. The authors used high resolution micro-focus X-ray computed tomography and 2D-3D image registration techniques to overlay tomograms of the same core plug, with and without X-ray attenuating fluids present in the pore space, with 2D scanning electron microscope images to produce detailed 3D visualisations of the geometry and topology of the cleat systems in the coal plugs. Novel filtering algorithms were used to produce segmentations that preserve cleat aperture dimensions and together with large-scale fluid flow simulations, they performed directly on the images and were used to compute porosities and permeabilities. Image resolution and segmentation sensitivity studies are also described, which show that the core scale permeability is controlled by a small number of well-connected percolating cleats. The results of this study demonstrate the potential of simple image-based analysis techniques to provide rapid estimates of core plug permeabilities.
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Heylen, Rob, Aditi Thanki, Dries Verhees, Domenico Iuso, Jan De Beenhouwer, Jan Sijbers, Ann Witvrouw, Han Haitjema, and Abdellatif Bey-Temsamani. "3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts." Measurement Science and Technology 33, no. 4 (January 7, 2022): 045602. http://dx.doi.org/10.1088/1361-6501/ac459a.
Повний текст джерелаАнотація:
Abstract X-ray computed tomography (X-CT) plays an important role in non-destructive quality inspection and process evaluation in metal additive manufacturing, as several types of defects such as keyhole and lack of fusion pores can be observed in these 3D images as local changes in material density. Segmentation of these defects often relies on threshold methods applied to the reconstructed attenuation values of the 3D image voxels. However, the segmentation accuracy is affected by unavoidable X-CT reconstruction features such as partial volume effects, voxel noise and imaging artefacts. These effects create false positives, difficulties in threshold value selection and unclear or jagged defect edges. In this paper, we present a new X-CT defect segmentation method based on preprocessing the X-CT image with a 3D total variation denoising method. By comparing the changes in the histogram, threshold selection can be significantly better, and the resulting segmentation is of much higher quality. We derive the optimal algorithm parameter settings and demonstrate robustness for deviating settings. The technique is presented on simulated data sets, compared between low- and high-quality X-CT scans, and evaluated with optical microscopy after destructive tests.
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Patmonoaji, Anindityo, Kento Tsuji, and Tetsuya Suekane. "Pore-throat characterization of unconsolidated porous media using watershed-segmentation algorithm." Powder Technology 362 (February 2020): 635–44. http://dx.doi.org/10.1016/j.powtec.2019.12.026.
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Varghese, Anjli, Sahil Jain, Malathy Jawahar, and A. Amalin Prince. "Auto-pore segmentation of digital microscopic leather images for species identification." Engineering Applications of Artificial Intelligence 126 (November 2023): 107049. http://dx.doi.org/10.1016/j.engappai.2023.107049.
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ZHU, QINGYONG, WEIBIN YANG, and HUAIZHONG YU. "STUDY ON THE PERMEABILITY OF RED SANDSTONE VIA IMAGE ENHANCEMENT." Fractals 25, no. 06 (November 21, 2017): 1750055. http://dx.doi.org/10.1142/s0218348x17500554.
Повний текст джерелаАнотація:
Scanning electron microscopy (SEM) is of great importance for studying fractal permeability. In this work, we presented a new technique, by applying the high-order upwind compact difference schemes to solve the hyperbolic conservation laws, to enhance textural differences for accurate segmentation of the SEM images. From the enhanced SEM images, the channels and pores can be obtained by using the two-stage image segmentation. Combining with the box counting method, the key parameters for evaluation of the fractal permeability such as the tortuosity fractal dimension, the pore area fractal dimension and the maximum pore area can be derived from the segmented images. Application of the technique to the SEM images of a red sandstone from south China shows remarkable enhancement of edge details, allowing the more accurate segmentation of the SEM images. Rather than the original image algorithm, the fractal permeability derived from this new approach is closer to the experimental value, especially when the magnification falls in the range of 500–600. The results evidence that our enhanced images approach may provide stronger constraints on evaluations of permeability of sandstones.
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Atrash, Hasan, and Felicitasz Velledits. "Phase Segmentation Optimization of Micro X-Ray Computed Tomography Reservoir Rock Images Using Machine Learning Techniques." Geosciences and Engineering 10, no. 15 (2022): 63–79. http://dx.doi.org/10.33030/geosciences.2022.15.063.
Повний текст джерелаАнотація:
We studied the performance and accuracy of some basic segmentation techniques in the analysis of the pore space and matrix voxels obtained from a 3D volume of X-ray tomographic (XCT) grayscale rock images. The segmentation and classification accuracy of unsupervised (K-means, modified Fuzzy c-means, Minimum cross-entropy, and Type-2 fuzzy entropy) and supervised Naïve Bayes methods were tested using an XCT tomogram of a carbonate reservoir rock. K-fold- cross-validation techniques were applied in the evaluation of the accuracy of the unsupervised and supervised machine learning classifiers. The average porosity obtained was 31 ±6%, in good agreement with the ground truth image obtained by manual segmentation. In general, the accuracy of segmentation results can be strongly affected by the feature vector selection scheme, since it is difficult to isolate a particular machine learning algorithm for the complex phase segmentation problem. Therefore, our study provides a segmentation scheme that can help in selecting the appropriate machine learning techniques for phase segmentation.
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Hu, Qi Zhi, Jing Xia Wang, and Gao Liang Tao. "Quantitative Analysis of Soft Soil Microstructure in Unloading Levels." Applied Mechanics and Materials 401-403 (September 2013): 1529–33. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1529.
Повний текст джерелаАнотація:
Quantitative analysis of soft soil microstructure in unloading levels are made by using scanning electron microscope (SEM) images, IPP and PS of image technology ,which includes image segmentation, pore size measuring and counting, three dimensional simulation of soft soil microstructure, etc. The results indicate that, with the increase of unloading grade, pore number and area of big aperture are in a sharp increase, the corresponding porosity also in ascension, so the deformation of the soil is mainly due to the change of pore; compared with the apparent 3d images of soil under the transverse profile in unloading levels. The results also indicate that, with the increase of unloading grade, pore area of cross section are in a increase.
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Tsvetkov, A. V. "Object segmentation in images with complex background and morphology in the example of pore segmentation on microfiltration membranes." Pattern Recognition and Image Analysis 21, no. 3 (September 2011): 556–59. http://dx.doi.org/10.1134/s1054661811021082.
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Zhang, Hao, Hewen Liu, and Jinyong Bai. "Research on image recognition method of rock and soil porous media based on dithering algorithm." E3S Web of Conferences 283 (2021): 01025. http://dx.doi.org/10.1051/e3sconf/202128301025.
Повний текст джерелаАнотація:
Rock-soil mass is a kind of material with complex internal structure, and its macro-mechanical response and failure process are influenced by internal microscopic composition and structure. Based on the research results of digital image technology in quantitative aspects of internal structure of rock and soil, a method for segmentation of rock and soil pore images based on dithering algorithm and statistical method for multiple parameters of pores is proposed in this paper. The result of verification shows that the pore recognition method proposed in this paper is reliable, can obtain the pore distribution and related parameters quickly and effectively, which has certain academic value and research significance.
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Gou, Dazhao, Xizhong An, and Runyu Yang. "DEM investigation of the effect of particle breakage on compact properties." EPJ Web of Conferences 249 (2021): 07004. http://dx.doi.org/10.1051/epjconf/202124907004.
Повний текст джерелаАнотація:
Particle breakage during compaction affects compaction behavior and the quality of the formed compact. This work conducted a numerical study based on the discrete element method (DEM) to investigate the effect of particle breakage on compaction dynamics and compact properties, including particle size and density distributions, and pore properties. A force-based breakage criterion and Apollonian sphere packing algorithm were employed to characterize particle breakage behavior. The pore structures of the compacts were characterized by the watershed pore segmentation method. Calibrated with experimental data, the model was able to simulate the stress-strain relation comparable with experimental observation. During compaction, the particles were gradually broken from top to bottom with increasing pressure. Both density and pore size of the compacts had relatively uniform distribution at larger stress, while the pore size decreased sharply when the particles started to break, indicating that the smaller fragments in the compact system have a significant effect on the pore size distribution.
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Song, Wenlong, Junyu Li, Kexin Li, Jingxu Chen, and Jianping Huang. "An Automatic Method for Stomatal Pore Detection and Measurement in Microscope Images of Plant Leaf Based on a Convolutional Neural Network Model." Forests 11, no. 9 (September 1, 2020): 954. http://dx.doi.org/10.3390/f11090954.
Повний текст джерелаАнотація:
Stomata are microscopic pores on the plant epidermis that regulate the water content and CO2 levels in leaves. Thus, they play an important role in plant growth and development. Currently, most of the common methods for the measurement of pore anatomy parameters involve manual measurement or semi-automatic analysis technology, which makes it difficult to achieve high-throughput and automated processing. This paper presents a method for the automatic segmentation and parameter calculation of stomatal pores in microscope images of plant leaves based on deep convolutional neural networks. The proposed method uses a type of convolutional neural network model (Mask R-CNN (region-based convolutional neural network)) to obtain the contour coordinates of the pore regions in microscope images of leaves. The anatomy parameters of pores are then obtained by ellipse fitting technology, and the quantitative analysis of pore parameters is implemented. Stomatal microscope image datasets for black poplar leaves were obtained using a large depth-of-field microscope observation system, the VHX-2000, from Keyence Corporation. The images used in the training, validation, and test sets were taken randomly from the datasets (562, 188, and 188 images, respectively). After 10-fold cross validation, the 188 test images were found to contain an average of 2278 pores (pore widths smaller than 0.34 μm (1.65 pixels) were considered to be closed stomata), and an average of 2201 pores were detected by our network with a detection accuracy of 96.6%, and the intersection of union (IoU) of the pores was 0.82. The segmentation results of 2201 stomatal pores of black poplar leaves showed that the average measurement accuracies of the (a) pore length, (b) pore width, (c) area, (d) eccentricity, and (e) degree of stomatal opening, with a ratio of width-to-maximum length of a stomatal pore, were (a) 94.66%, (b) 93.54%, (c) 90.73%, (d) 99.09%, and (e) 92.95%, respectively. The proposed stomatal pore detection and measurement method based on the Mask R-CNN can automatically measure the anatomy parameters of pores in plants, thus helping researchers to obtain accurate stomatal pore information for leaves in an efficient and simple way.
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Yan, Ganggang, Wende Yan, Yingzhong Yuan, Xiujun Gong, Ziqi Tang, and Bai Xueyuan. "Research on the Method of Evaluating the Pore Throat Structure of Rock Microscopically Based on the 3D Pore Network Model of Digital Core." International Journal of Petroleum Technology 9 (December 5, 2022): 44–53. http://dx.doi.org/10.54653/2409-787x.2022.09.6.
Повний текст джерелаАнотація:
In order to solve the problems of time-consuming, poor repeatability and inability to directly reflect the pore structure of the core by traditional experimental methods to obtain the reservoir parameters, a method was proposed to study the pore structure of inner core using digital core and pore network model. Firstly, the core CT scan image is processed by filtering and denoising, threshold segmentation and pore-throat network skeleton extraction. Then, the digital core and pore network model are constructed by digital image technology and maximum sphere algorithm, and the core physical parameters are statistically analyzed. Finally, a digital core pore network model is used to simulate oil-water two-phase flow. The results show that the digital core pore network model can better reflect the real core pore space characteristics and accurately reflect the pore throat distribution and morphology characteristics. Through practical application, the 3D pore network model of a digital core can accurately reflect the core's microporosity and throat structure and fully understand the mechanism of fluid flow in porous media, which has high application value. In addition, the method can be repeated many times, which is time-consuming and controllable and makes up for the limitations of conventional physical experiments.
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Qin, Qiu Ju, Xin Jian Qiang, Ye Liu, and Jing Yang. "Rock Image Pore Identification Based on Fuzzy C-Means Clustering and Neural Networks." Applied Mechanics and Materials 571-572 (June 2014): 803–6. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.803.
Повний текст джерелаАнотація:
In order to realize the recognition automation of rock section pore images, a method combined Fuzzy C-Means clustering with BP neural network is proposed to recognize the pore of rock images. Firstly, Fuzzy C-Means clustering as segmentation algorithm are applied to the rock images and they are divided into two types, then using the BP neural network training and classification recognition. It is shown that the trained BP neural network can accurately identify the effective porosity in the casting image, and lay a good foundation for practical applications.
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Shen, Z. F., X. X. Li, X. J. Yi, Z. H. Wang, and H. M. Gao. "Preliminary study on the mechanical behavior of clay in one-dimensional compression with DEM simulations." IOP Conference Series: Earth and Environmental Science 1330, no. 1 (May 1, 2024): 012039. http://dx.doi.org/10.1088/1755-1315/1330/1/012039.
Повний текст джерелаАнотація:
Abstract The mechanical behavior and physical properties of clay are closely associated with its microstructure. Current research on the macroscopic mechanics and physical properties of clay is comprehensive and systematic. However, the microstructural variations underlying these characteristics have been predominantly examined under mercury intrusion porosimetry and scanning electron microscopy, while quantitative and systematic studies are notably limited. This research employs the discrete element method (DEM) simulation to investigate the microscopic responses of clay, simulating one-dimensional compression tests on two clay types: card-house and book-house structures. The analysis of pore size and morphology was conducted using virtual mercury intrusion porosimetry and the pore segmentation method. The clay microstructure was quantitatively characterized by parameters such as pore aspect ratio, orientation distribution, and cumulative pore volume curve. The findings demonstrate that DEM effectively replicates the fundamental mechanical behavior of clay, revealing and explaining the evolution pattern of clay pore structure during one-dimensional compression in terms of platelet alignment adjustment. This macroscopic compression is characterized by variations in pore size distribution, orientation, and aspect ratio.
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Tang, Xin, Ruiyu He, Biao Wang, Yuerong Zhou та Hong Yin. "Intelligent Identification and Quantitative Characterization of Pores in Shale SEM Images Based on Pore-Net Deep-Learning Network Model". Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 65, № 2 (1 квітня 2024): 233–45. http://dx.doi.org/10.30632/pjv65n2-2024a6.
Повний текст джерелаАнотація:
Among the various shale reservoir evaluation methods, the scanning electron microscope (SEM) image method is widely used. Its image can intuitively reflect the development stage of a shale reservoir and is often used for the qualitative characterization of shale pores. However, manual image processing is inefficient and cannot quantitatively characterize pores. The semantic segmentation method of deep learning greatly improves the efficiency of image analysis and can calculate the face rate of shale SEM images to achieve quantitative characterization. In this paper, the high-maturity shale of the Longmaxi Formation in the Changning area of Yibin City, Sichuan Province, and the low-maturity shale of Beibu Gulf Basin in China are studied. Based on the Pore-net network model, the intelligent identification and quantitative characterization of pores in shale SEM images are realized. The pore-net model is improved from the U-net deep-learning network model, which improves the ability of the network model to identify pores. The results show that the pore-net model performs better than the U-net model, FCN model, DeepLab V3 + model, and traditional binarization method. The problem of low accuracy of the traditional pore identification method is solved. The porosity of SEM images of high-maturity shale calculated by the pore-net network model is between 12 and 19% deviation from the experimental data. The calculated porosity of the SEM image of the low-maturity shale has a large deviation from the experimental data, which is between 14 and 47%. Compared with the porosity results calculated by other methods, the results calculated by pore-net are closer to the true value, which proves that the porosity calculated by the pore-net network model is reliable. The deep-learning semantic image segmentation method is suitable for pore recognition of shale SEM images. The fully convolutional neural network model is used to train the manually labeled shale SEM images, which can realize the intelligent recognition and quantitative characterization of the pores in the shale SEM images. It provides a certain reference value for the evaluation of shale oil and gas reservoirs and the study of other porous materials.
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Kazak, Andrey, Kirill Simonov, and Victor Kulikov. "Machine-Learning-Assisted Segmentation of Focused Ion Beam-Scanning Electron Microscopy Images with Artifacts for Improved Void-Space Characterization of Tight Reservoir Rocks." SPE Journal 26, no. 04 (March 8, 2021): 1739–58. http://dx.doi.org/10.2118/205347-pa.
Повний текст джерелаАнотація:
Summary The modern focused ion beam-scanning electron microscopy (FIB-SEM) allows imaging of nanoporous tight reservoir-rock samples in 3D at a resolution up to 3 nm/voxel. Correct porosity determination from FIB-SEM images requires fast and robust segmentation. However, the quality and efficient segmentation of FIB-SEM images is still a complicated and challenging task. Typically, a trained operator spends days or weeks in subjective and semimanual labeling of a single FIB-SEM data set. The presence of FIB-SEM artifacts, such as porebacks, requires developing a new methodology for efficient image segmentation. We have developed a method for simplification of multimodal segmentation of FIB-SEM data sets using machine-learning (ML)-based techniques. We study a collection of rock samples formed according to the petrophysical interpretation of well logs from a complex tight gas reservoir rock of the Berezov Formation (West Siberia, Russia). The core samples were passed through a multiscale imaging workflow for pore-space-structure upscaling from nanometer to log scale. FIB-SEM imaging resolved the finest scale using a dual-beam analytical system. Image segmentation used an architecture derived from a convolutional neural network (CNN) in the DeepUNet (Ronneberger et al. 2015) configuration. We implemented the solution in the Pytorch® (Facebook, Inc., Menlo Park, California, USA) framework in a Linux environment. Computation exploited a high-performance computing system. The acquired data included three 3D FIB-SEM data sets with a physical size of approximately 20 × 15 × 25 µm with a voxel size of 5 nm. A professional geologist manually segmented (labeled) a fraction of slices. We split the labeled slices into training, validation, and test data. We then augmented the training data to increase its size. The developed CNN delivered promising results. The model performed automatic segmentation with the following average quality indicators according to test data: accuracy of 86.66%, precision of 54.93%, recall of 83.76%, and F1 score of 55.10%. We achieved a significant boost in segmentation speed of 14.5 megapixel (MP)/min. Compared with 0.18 to 1.45 MP/min for manual labeling, this yielded an efficiency increase of at least 10 times. The presented research work improves the quality of quantitative petrophysical characterization of complex reservoir rocks using digital rock imaging. The development allows the multiphase segmentation of 3D FIB-SEM data complicated with artifacts. It delivers correct and precise pore-space segmentation, resulting in little turn-around-time saving and increased porosity-data quality. Although image segmentation using CNNs is mainstream in the modern ML world, it is an emerging novel approach for reservoir-characterization tasks.
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Choi, Chae-Soon, Yong-Ki Lee, and Jae-Joon Song. "Equivalent Pore Channel Model for Fluid Flow in Rock Based on Microscale X-ray CT Imaging." Materials 13, no. 11 (June 8, 2020): 2619. http://dx.doi.org/10.3390/ma13112619.
Повний текст джерелаАнотація:
Pore-scale modeling with a reconstructed rock microstructure has become a dominant technique for fluid flow characterization in rock thanks to technological improvements in X-ray computed tomography (CT) imaging. A new method for the construction of a pore channel model from micro-CT image analysis is suggested to improve computational efficiency by simplifying a highly complex pore structure. Ternary segmentation was applied through matching a pore volume experimentally measured by mercury intrusion porosimetry with a CT image voxel volume to distinguish regions denoted as “apparent” and “indistinct” pores. The developed pore channel model, with distinct domains of different pore phases, captures the pore shape dependence of flow in two dimensions and a tortuous flow path in three dimensions. All factors determining these geometric characteristics were identified by CT image analysis. Computation of an interaction flow regime with apparent and indistinct pore domains was conducted using both the Stokes and Brinkman equations. The coupling was successfully simulated and evaluated against the experimental results of permeability derived from Darcy’s law. Reasonable agreement was found between the permeability derived from the pore channel model and that estimated experimentally. However, the model is still incapable of accurate flow modeling in very low-permeability rock. Direct numerical simulation in a computational domain with a complex pore space was also performed to compare its accuracy and efficiency with the pore channel model. Both schemes achieved reasonable results, but the pore channel model was more computationally efficient.
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Van Eyndhoven, G., M. Kurttepeli, C. J. Van Oers, P. Cool, S. Bals, K. J. Batenburg, and J. Sijbers. "Pore REconstruction and Segmentation (PORES) method for improved porosity quantification of nanoporous materials." Ultramicroscopy 148 (January 2015): 10–19. http://dx.doi.org/10.1016/j.ultramic.2014.08.008.
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Ji, Yun Tao, Patrick Baud, Teng Fong Wong, and Li Qiang Liu. "Quantitative Characterization of 3D Pore Structure in Porous Limestone." Advanced Materials Research 671-674 (March 2013): 1830–34. http://dx.doi.org/10.4028/www.scientific.net/amr.671-674.1830.
Повний текст джерелаАнотація:
The pore structure in intact and inelastically deformed Indiana limestone have been studied using x-ray microtomography imaging. Guided by detailed microstructural observations and using Multi-level Otsu’s thresholding method, the 3D images acquired at voxel side length of 4 μm were segmented into three domains: solid grains, macropores and an intermediate zone dominated by microporosity. Local Porosity can be defined to infer the porosity of each voxel. The macropores were individually identified by morphological processing and their shape quantified by their sphericity and equivalent diameter. With this segmentation, we obtained statistics on macropores on intact and deformed Indiana limestone which shows that inelastic compaction was followed by a significant reduction in the number of macropores. And also our results revealed the great potentiality to produce a quantitative analysis on porous material with the aid of micro CT images.
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Li, Jiaming, Xiaoxun Zhang, Fang Ma, Shuxian Wang, and Yuanyou Huang. "Simultaneous Pore Detection and Morphological Features Extraction in Laser Powder Bed Fusion with Image Processing." Materials 17, no. 6 (March 17, 2024): 1373. http://dx.doi.org/10.3390/ma17061373.
Повний текст джерелаАнотація:
Internal pore defects are inevitable during laser powder bed fusion (LPBF), which have a significant impact on the mechanical properties of the parts. Therefore, detecting pores and obtaining their morphology will contribute to the quality of LPBF parts. Currently, supervised models are used for defect image detection, which requires a large amount of LPBF sample data, image labeling, and computing power equipment during the training process, resulting in high detection costs. This study extensively collected LPBF sample data and proposed a method for pore defect classification by obtaining its morphological features while detecting pore defects in optical microscopy (OM) images under various conditions. Compared with other advanced models, the proposed method achieves better detection accuracy on pore defect datasets with limited data. In addition, quickly detecting pore defects in a large number of labeling ground truth images will also contribute to the development of deep learning. In terms of image segmentation, the average accuracy scores of this method in the test images exceed 85%. The research results indicate that the algorithm proposed in this paper is suitable for quickly and accurately identifying pore defects from optical microscopy images.
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Hagenmuller, Pascal, Guillaume Chambon, Bernard Lesaffre, Frédéric Flin, and Mohamed Naaim. "Energy-based binary segmentation of snow microtomographic images." Journal of Glaciology 59, no. 217 (2013): 859–73. http://dx.doi.org/10.3189/2013jog13j035.
Повний текст джерелаАнотація:
AbstractX-ray microtomography has become an essential tool for investigating the mechanical and physical properties of snow, which are tied to its microstructure. To allow a quantitative characterization of the microstructure, the grayscale X-ray attenuation coefficient image has to be segmented into a binary ice/pore image. This step, called binary segmentation, is crucial and affects all subsequent analysis and modeling. Common segmentation methods are based on thresholding. In practice, these methods present some drawbacks and often require time-consuming manual post-processing. Here we present a binary segmentation algorithm based on the minimization of a segmentation energy. This energy is composed of a data fidelity term and a regularization term penalizing large interface area, which is of particular interest for snow where sintering naturally tends to reduce the surface energy. The accuracy of the method is demonstrated on a synthetic image. The method is then successfully applied on microtomographic images of snow and compared to the threshold-based segmentation. The main advantage of the presented approach is that it benefits from local spatial information. Moreover, the effective resolution of the segmented image is clearly defined and can be chosen a priori.
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Rabbani, Arash, Brittany Wojciechowski, and Bhisham Sharma. "Imaging based pore network modeling of acoustical materials." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A361. http://dx.doi.org/10.1121/10.0019165.
Повний текст джерелаАнотація:
The acoustical behavior of porous materials is dictated by their underlying pore network geometry. Given the complexity of accurately characterizing the various pore network features, current acoustical models instead rely on indirectly incorporating these features by accounting for them within acoustical transport properties, such as tortuosity, viscous and thermal characteristic lengths, and flow resistivity. In turn, these transport properties are currently identified using inverse characterization techniques or using multiphysics modeling techniques. Here, we propose the use of advanced image processing methods to characterize the pore network of acoustical materials and allow the direct calculation of their transport and acoustical properties. To establish the feasibility of this idea, we create 3D printable CAD models of porous materials with controlled pore geometries and use a Matlab-based watershed segmentation technique to calculate their effective pore and throat size distributions. These distributions are then used to calculate their transport properties and predict their sound absorption coefficients using the Johnson–Champoux–Allard model. For comparison, we calculate the transport properties using the hybrid multiphysics modeling technique and the inverse characterization method. The predictions from the three different methods are then compared with experimental measurements obtained by printing and testing the models using an impedance tube.
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Yosifov, Miroslav, Patrick Weinberger, Bernhard Plank, Bernhard Fröhler, Markus Hoeglinger, Johann Kastner, and Christoph Heinzl. "Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network." Acta Polytechnica CTU Proceedings 42 (October 12, 2023): 87–93. http://dx.doi.org/10.14311/app.2023.42.0087.
Повний текст джерелаАнотація:
This study demonstrates the utilization of deep learning techniques for binary semantic segmentation of pores in carbon fiber reinforced polymers (CFRP) using X-ray computed tomography (XCT) datasets. The proposed workflow is designed to generate efficient segmentation models with reasonable execution time, applicable even for users using consumer-grade GPU systems. First, U-Net, a convolutional neural network, is modified to handle the segmentation of XCT datasets. In the second step, suitable hyperparameters are determined through a parameter analysis (hyperparameter tuning), and the parameter set with the best result was used for the final training. In the final step, we report on our efforts of implementing the testing stage in open_iA, which allows users to segment datasets with the fully trained model within reasonable time. The model performs well on datasets with both high and low resolution, and even works reasonably for barely visible pores with different shapes and size. In our experiments, we could show that U-Net is suitable for pore segmentation. Despite being trained on a limited number of datasets, it exhibits a satisfactory level of prediction accuracy.
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Xu, J., X. Huang, Z. Zhang, and G. Jin. "Pore structure characteristics of coral reef limestone: a combined polarizing microscope and CT scanning study." IOP Conference Series: Earth and Environmental Science 1332, no. 1 (May 1, 2024): 012026. http://dx.doi.org/10.1088/1755-1315/1332/1/012026.
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Abstract Coral reef limestone is a special class of geological medium formed through long-term deposition following the death of reef-building coral groups. Because it retains the skeletal structure of marine organisms during its formation, its pore structure is hyper-developed and complex. Deciphering the pore structure of the coral reef limestone is important because it is closely related to its macroscopic physical and mechanical properties. This study conducted a comprehensive analysis of the pore structure features of two types of coral reef limestone collected from the construction site of a nuclear power station located in the South China Sea using a combination of polarizing microscopy and CT scanning technologies. The fractal dimension of the pore structure of the treated reef limestone image was calculated, and the pore structure characteristics were statistically analyzed by considering several parameters including porosity, pore size, pore equivalent radius, shape factor, etc. In addition, the directional feature of the pore structure was explored. The results show that the improved watershed segmentation algorithm can accurately segment the pore structure of reef limestone images; both coral reef limestone specimens are loose of high porosity; the fractal dimension of pore structure lay between 1.58∼1.75, indicative of a high self-similarity; the pore size of the two coral reef limestone specimens is quite different, and the distribution of equivalent pore radius conforms to the normal distribution law; the pore structure of the two samples had obvious directionality, which can be quantified using a directional tensor. This study sheds light on future investigations linking the microscopic structure and macroscopic properties of coral reef limestones.
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Choudhari, Khoobaram S., Pacheeripadikkal Jidesh, Parampalli Sudheendra, and Suresh D. Kulkarni. "Quantification and Morphology Studies of Nanoporous Alumina Membranes: A New Algorithm for Digital Image Processing." Microscopy and Microanalysis 19, no. 4 (May 24, 2013): 1061–72. http://dx.doi.org/10.1017/s1431927613001542.
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AbstractA new mathematical algorithm is reported for the accurate and efficient analysis of pore properties of nanoporous anodic alumina (NAA) membranes using scanning electron microscope (SEM) images. NAA membranes of the desired pore size were fabricated using a two-step anodic oxidation process. Surface morphology of the NAA membranes with different pore properties was studied using SEM images along with computerized image processing and analysis. The main objective was to analyze the SEM images of NAA membranes quantitatively, systematically, and quickly. The method uses a regularized shock filter for contrast enhancement, mathematical morphological operators, and a segmentation process for efficient determination of pore properties. The algorithm is executed using MATLAB, which generates a statistical report on the morphology of NAA membrane surfaces and performs accurate quantification of the parameters such as average pore-size distribution, porous area fraction, and average interpore distances. A good comparison between the pore property measurements was obtained using our algorithm and ImageJ software. This algorithm, with little manual intervention, is useful for optimizing the experimental process parameters during the fabrication of such nanostructures. Further, the algorithm is capable of analyzing SEM images of similar or asymmetrically porous nanostructures where sample and background have distinguishable contrast.
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Kwan Leung, Anthony, Jianbin Liu, and Zhenliang Jiang. "When nature meets technology: AI-informed discovery of soil-water-root physical interaction." E3S Web of Conferences 382 (2023): 21001. http://dx.doi.org/10.1051/e3sconf/202338221001.
Повний текст джерелаАнотація:
Nature-based solution using vegetation has been considered as a sustainable and environmentally friendly approach to improve slope performance through root reinforcement and variations of soil matric suction upon transpiration. During plant growth, roots explore soil pore space. How fundamentally the pore structure might evolve with time following root growth dynamics and how this dynamic soil-root interaction may modify the hydraulic properties of unsaturated soils remain unclear. This paper reports the use of advanced technologies including artificial intelligence (AI) to aid the discovery of soil-root-water physical interaction and the characterisation of the hydraulic properties of rooted soils. A newly developed miniature unsaturated triaxial apparatus that enables rooted soil samples to subject to simultaneous in-situ loading and X-ray imaging is introduced. An AI-informed image processing technique is illustrated, aiming to enhance the reliability of phase segmentation of X-ray computer tomography (CT) images of four-phase unsaturated rooted soils for quantifying 3-D pore structure and root phenotype. New discoveries of how roots interact with the pore space, including the dynamic changes in the distribution, orientation and connectivity of soil pore sizes, and how this pore-level information can be used to explain the hydraulic properties are discussed.
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Suo, Limin, Zhaowei Wang, Hailong Liu, Likai Cui, Xianda Sun, and Xudong Qin. "Innovative Deep Learning Approaches for High-Precision Segmentation and Characterization of Sandstone Pore Structures in Reservoirs." Applied Sciences 14, no. 16 (August 15, 2024): 7178. http://dx.doi.org/10.3390/app14167178.
Повний текст джерелаАнотація:
The detailed characterization of the pore structure in sandstone is pivotal for the assessment of reservoir properties and the efficiency of oil and gas exploration. Traditional fully supervised learning algorithms are limited in performance enhancement and require a substantial amount of accurately annotated data, which can be challenging to obtain. To address this, we introduce a semi-supervised framework with a U-Net backbone network. Our dataset was curated from 295 two-dimensional CT grayscale images, selected at intervals from nine 4 mm sandstone core samples. To augment the dataset, we employed StyleGAN2-ADA to generate a large number of images with a style akin to real sandstone images. This approach allowed us to generate pseudo-labels through semi-supervised learning, with only a small subset of the data being annotated. The accuracy of these pseudo-labels was validated using ensemble learning methods. The experimental results demonstrated a pixel accuracy of 0.9993, with a pore volume discrepancy of just 0.0035 compared to the actual annotated data. Furthermore, by reconstructing the three-dimensional pore structure of the sandstone, we have shown that the synthetic three-dimensional pores can effectively approximate the throat length distribution of the real sandstone pores and exhibit high precision in simulating throat shapes.
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Khimulia, V. V. "Study of structural characteristics of hydrocarbon reservoir pore space based on X-ray computed tomography images." Actual Problems of Oil and Gas, no. 43 (December 29, 2023): 44–57. http://dx.doi.org/10.29222/ipng.2078-5712.2023-43.art4.
Повний текст джерелаАнотація:
Digital studies of pore space and internal structure of hydrocarbon reservoir were conducted on the basis of multiscale X-ray computed tomography images and the analysis of heterogeneities, cavernosity, fracturing and bedding in the rock. Main results. 3D models of rock pore space were created on the basis of computed tomography images through segmentation. Porosity estimation based on the digital approach was performed. It was shown that the digitally obtained data are in good agreement with the results of laboratory measurements. Analysis and visualization of the structure of the main filtration channels in the rock were performed. Pore size distributions in the specimen were obtained, and 3D visualization of the largest pore size was shown. Conclusions. The pore space characteristics obtained on the basis of tomographic approach are valuable data for filling reservoir models and can be used in solving the problems of permeability reduction during different kinds of reservoir impacts. Application of the obtained results in combination with geomechanical tests of rocks is intended to expand existing approaches to complex analysis of core material of reservoirs, as well as to supplement and refine mathematical and operational models of the studied objects.
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Abrosimov, K. N., K. M. Gerke, I. N. Semenkov, and D. V. Korost. "Otsu’s Algorithm in the Segmentation of Pore Space in Soils Based on Tomographic Data." Eurasian Soil Science 54, no. 4 (April 2021): 560–71. http://dx.doi.org/10.1134/s1064229321040037.
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Tavanaei, Amirhossein, and Saeed Salehi. "PORE, THROAT, AND GRAIN DETECTION FOR ROCK SEM IMAGES USING DIGITALWATERSHED IMAGE SEGMENTATION ALGORITHM." Journal of Porous Media 18, no. 5 (2015): 507–18. http://dx.doi.org/10.1615/jpormedia.v18.i5.40.
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Ojeda-Magaña, B., J. Quintanilla Domínguez, R. Ruelas, J. J. Martín-Sotoca, and A. M. Tarquis. "Pore detection in 3-D CT soil samples through an improved sub-segmentation method." European Journal of Soil Science 70, no. 1 (October 24, 2018): 66–82. http://dx.doi.org/10.1111/ejss.12728.
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Martín-Sotoca, Juan J., A. Saa-Requejo, J. B. Grau, and A. M. Tarquis. "Local 3D segmentation of soil pore space based on fractal properties using singularity maps." Geoderma 311 (February 2018): 175–88. http://dx.doi.org/10.1016/j.geoderma.2016.11.029.
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Zhao, Xinli, Zhengming Yang, Xuewei Liu, Zhiyuan Wang, and Yutian Luo. "Analysis of pore throat characteristics of tight sandstone reservoirs." Open Geosciences 12, no. 1 (October 12, 2020): 977–89. http://dx.doi.org/10.1515/geo-2020-0121.
Повний текст джерелаАнотація:
AbstractThe characterization of pore throat structure in tight reservoirs is the basis for the effective development of tight oil. In order to effectively characterize the pore -throat structure of tight sandstone in E Basin, China, this study used high-pressure mercury intrusion (HPMI) testing technology and thin section (TS) technology to jointly explore the characteristics of tight oil pore throat structure. The results of the TS test show that there are many types of pores in the tight sandstone, mainly the primary intergranular pores, dissolved pores, and microfractures. Based on the pore throat parameters obtained by HPMI experiments, the pore throat radius of tight sandstone is between 0.0035 and 2.6158 µm. There are two peaks in the pore throat distribution curve, indicating that the tight sandstone contains at least two types of pores. This is consistent with the results of the TS experiments. In addition, based on the fractal theory and obtained capillary pressure curve by HPMI experiments, the fractal characteristics of tight sandstone pore throat are quantitatively characterized. The results show that the tight sandstones in E Basin have piecewise fractal (multifractal) features. The segmentation fractal feature occurs at a pore throat radius of approximately 0.06 µm. Therefore, according to the fractal characteristics, the tight sandstone pore throat of the study block is divided into macropores (pore throat radius > 0.06 µm) and micropores (pore throat radius < 0.06 µm). The fractal dimension DL of the macropores is larger than the fractal dimension DS of the micropores, indicating that the surface of the macropores is rough and the pores are irregular. This study cannot only provide certain support for characterizing the size of tight oil pore throat, but also plays an inspiring role in understanding the tight pore structure of tight sandstone.
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Jouini, M. S., S. Vega, and E. A. Mokhtar. "Multiscale characterization of pore spaces using multifractals analysis of scanning electronic microscopy images of carbonates." Nonlinear Processes in Geophysics 18, no. 6 (December 14, 2011): 941–53. http://dx.doi.org/10.5194/npg-18-941-2011.
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Abstract. Pore spaces heterogeneity in carbonates rocks has long been identified as an important factor impacting reservoir productivity. In this paper, we study the heterogeneity of carbonate rocks pore spaces based on the image analysis of scanning electron microscopy (SEM) data acquired at various magnifications. Sixty images of twelve carbonate samples from a reservoir in the Middle East were analyzed. First, pore spaces were extracted from SEM images using a segmentation technique based on watershed algorithm. Pores geometries revealed a multifractal behavior at various magnifications from 800x to 12 000x. In addition, the singularity spectrum provided quantitative values that describe the degree of heterogeneity in the carbonates samples. Moreover, for the majority of the analyzed samples, we found low variations (around 5%) in the multifractal dimensions for magnifications between 1700x and 12 000x. Finally, these results demonstrate that multifractal analysis could be an appropriate tool for characterizing quantitatively the heterogeneity of carbonate pore spaces geometries. However, our findings show that magnification has an impact on multifractal dimensions, revealing the limit of applicability of multifractal descriptions for these natural structures.
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Ponomarev, A. A., and M. D. Zavatsky. "METHODS OF APPLICATION OF COMPUTER MICROTOMOGRAPHY IN GEOLOGY." Oil and Gas Studies, no. 3 (June 30, 2015): 31–35. http://dx.doi.org/10.31660/0445-0108-2015-3-31-35.
Повний текст джерелаАнотація:
The world experience of tomography and micro-tomography methods application in petroleum geology was de-scribed in the article. The examples of determining the mineral composition of rock based on the visual analysis are described. The 3D model of the pore space with segmentation inside the pore fluids was constructed. The attempt is described to correlate the data of average equivalent-diameters with permeability, and a number of conclusions about the universality of application of the computer micro-tomography method in geology based on visual analysis of rocks tomograms are made. Some findings about isotropy / anisotropy of physico-mechanical and filtration properties are presented. The promising areas of application of the method in the petroleum geology sphere are highlighted.
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Jida, Safa, Hassan Ouallal, Brahim Aksasse, Mohammed Ouanan, Mohamed El Amraoui, and Mohamed Azrour. "Clay-Based Brick Porosity Estimation Using Image Processing Techniques." Journal of Intelligent Systems 29, no. 1 (February 23, 2019): 1226–34. http://dx.doi.org/10.1515/jisys-2018-0191.
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Abstract This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation.
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Hu, Zhazha, Rui Zhang, Kai Zhu, Dongyin Li, Yi Jin, Wenbing Guo, Xiao Liu, Xiaodong Zhang, and Qian Zhang. "Probing the Pore Structure of the Berea Sandstone by Using X-ray Micro-CT in Combination with ImageJ Software." Minerals 13, no. 3 (March 4, 2023): 360. http://dx.doi.org/10.3390/min13030360.
Повний текст джерелаАнотація:
During diagenesis, the transformation of unconsolidated sediments into a sandstone is usually accompanied by compaction, water expulsion, cementation and dissolution, which fundamentally control the extent, connectivity and complexity of the pore structure in sandstone. As the pore structure is intimately related to fluid flow in porous media, it is of great importance to characterize the pore structure of a hydrocarbon-bearing sandstone in a comprehensive way. Although conventional petrophysical methods such as mercury injection porosimetry, low-pressure nitrogen or carbon dioxide adsorption are widely used to characterize the pore structure of rocks, these evaluations are based on idealized pore geometry assumptions, and the results lack direct information on the pore geometry, connectivity and tortuosity of pore channels. In view of the problems, X-ray micro-CT was combined with ImageJ software (version 1.8.0) to quantitatively characterize the pore structure of Berea Sandstone. Based on its powerful image processing function, a series of treatments such as contrast enhancement, noise reduction and threshold segmentation, were first carried out on the micro-CT images of the sandstone via ImageJ. Pores with sizes down to 2.25 μm were accurately identified. Geometric parameters such as pore area, perimeter and circularity could thus be extracted from the segmented pores. According to our evaluations, pores identified in this study are mostly in the range of 30–180 μm and can be classified into irregular, high-circularity and slit-shaped pores. An irregular pore is the most abundant type, with an area fraction of 72.74%. The average porosity obtained in the image analysis was 19.10%, which is fairly close to the experimental result determined by a helium pycnometer on the same sample. According to the functional relationship between tortuosity and permeability, the tortuosity values of the pore network were estimated to be in the range of 4–6 to match the laboratory permeability data.
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Yoiti Ito Parada, Marcelo, Christian Matthias Schlepütz, René Michel Rossi, Dominique Derome, and Jan Carmeliet. "Two-stage wicking of yarns at the fiber scale investigated by synchrotron X-ray phase-contrast fast tomography." Textile Research Journal 89, no. 23-24 (May 2019): 4967–79. http://dx.doi.org/10.1177/0040517519843461.
Повний текст джерелаАнотація:
With synchrotron X-ray phase-contrast computed tomography, we document the wicking process at the fiber scale in cotton, polyethylene terephthalate and polypropylene yarns. A new segmentation procedure is developed, allowing a clear separation of the water and the fiber in the reconstructed images. From the water configurations, we obtain moisture content profiles over the height of the yarn and time-resolved three-dimensional visualization of the wicking process. The water filling over the height of the yarn is highly non-uniform, since the available pore space varies strongly along the yarn due to the twisting of the yarn. For the first time, a wicking in two stages is observed: an initial fast unsaturated wetting along the fiber direction followed by a main saturated flow characterized by large jumps in moisture content at discrete time steps. These jumps occur when large pore segments become filled suddenly from multiple entry points through small size throats connecting different pore segments.