Three-Dimensional Separation and Characterization of Fractures in X-Ray Computed Tomographic Images of Rocks

Cappuccio, Francesco P.; Toy, Virginia; Mills, Steven; Adam, Ludmila

doi:10.3389/feart.2020.529263

Cited by 6 publications

(3 citation statements)

References 42 publications

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“…The local fracture apertures were measured by a modified version of the Fracture Trace Point (FTP) algorithm (Cappuccio et al 2020) on the segmentation output of all algorithms. The algorithm operates on sets of 2D grayscale images by collecting the values across CT profiles (orthogonal to the x-and y-axis).…”

Section: Fracture Aperture and Roughnessmentioning

confidence: 99%

Benchmarking conventional and machine learning segmentation techniques for digital rock physics analysis of fractured rocks

et al. 2022

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Image segmentation remains the most critical step in Digital Rock Physics (DRP) workflows, affecting the analysis of physical rock properties. Conventional segmentation techniques struggle with numerous image artifacts and user bias, which lead to considerable uncertainty. This study evaluates the advantages of using the random forest (RF) algorithm for the segmentation of fractured rocks. The segmentation quality is discussed and compared with two conventional image processing methods (thresholding-based and watershed algorithm) and an encoder–decoder network in the form of convolutional neural networks (CNNs). The segmented images of the RF method were used as the ground truth for CNN training. The images of two fractured rock samples are acquired by X-ray computed tomography scanning (XCT). The skeletonized 3D images are calculated, providing information about the mean mechanical aperture and roughness. The porosity, permeability, flow fields, and preferred flow paths of segmented images are analyzed by the DRP approach. Moreover, the breakthrough curves obtained from tracer injection experiments are used as ground truth to evaluate the segmentation quality of each method. The results show that the conventional methods overestimate the fracture aperture. Both machine learning approaches show promising segmentation results and handle all artifacts and complexities without any prior CT-image filtering. However, the RF implementation has superior inherent advantages over CNN. This method is resource-saving (e.g., quickly trained), does not need an extensive training dataset, and can provide the segmentation uncertainty as a measure for evaluating the segmentation quality. The considerable variation in computed rock properties highlights the importance of choosing an appropriate segmentation method.

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Section: Fracture Aperture and Roughnessmentioning

confidence: 99%

Benchmarking conventional and machine learning segmentation techniques for digital rock physics analysis of fractured rocks

et al. 2022

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“…Interpreting fractured reservoir properties can be achieved using two approaches, which are direct and indirect methods [5]. Direct methods include evaluating and interpreting core samples (including analysing the outcrop samples and computed tomography scan of core samples) and image logs (electrical and acoustic) [6][7][8][9][10][11]. According to [12], these two approaches are probably the most essential to evaluate fractures since they have a higher resolution and a small margin of error, especially for depth conversion, when compared to the seismic method, but some studies also discussed the limitations of both methods [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

Hasan,

Tóth

2024

Minerals

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Many methods have been developed to detect and predict the fracture properties of fractured rocks. The standard data sources for fracture evaluations are image logs and core samples. However, many wells do not have these data, especially for old wells. Furthermore, operating both methods can be costly, and, sometimes, the data gathered are of bad quality. Therefore, previous research attempted to evaluate fractures indirectly using the widely available conventional well-logs. Sedimentary rocks are widespread and have been studied in the literature. However, fractured reservoirs, like igneous and metamorphic rock bodies, may also be vital since they provide fluid migration pathways and can store some hydrocarbons. Hence, two fractured metamorphic rock bodies are studied in this study to evaluate any difference in fracture responses on well-log properties. Also, a quick and reliable prediction method is studied to predict fracture density (FD) in the case of the unavailability of image logs and core samples. Gene expression programming (GEP) was chosen for this study to predict FD, and ten conventional well-log data were used as input variables. The model produced by GEP was good, with R2 values at least above 0.84 for all studied wells, and the model was then applied to wells without image logs. Both selected metamorphic rocks showed similar results in which the significant parameters to predict FD were the spectral gamma ray, resistivity, and porosity logs. This study also proposed a validation method to ensure that the FD value predictions were consistent using discriminant function analysis. In conclusion, the GEP method is reliable and could be used for FD predictions for basement metamorphic rocks.

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“…However, for the detection of water seepage and the location of damage of ancient buildings, GPR has advantages of portability, precision, and low cost (Adachi et al, 2020;Dao et al, 2020;Lieblappen et al, 2020;Khan et al, 2021). GPR can detect the internal structure of ancient buildings, identify possible cracks and cavities in the walls, and determine water seepage areas of ancient buildings (Cappuccio et al, 2020;Ziegler et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Detection of cracks in cemented loess of ancient buildings using remote sensing

Liu

Bao

et al. 2022

Front. Mater.

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The bases of ancient Chinese buildings are prone to deformation, cracks, and other hidden problems due to their age and other reasons. Rainfall and water seepage produce great harm to the cracks of soil plinths. Based on a ground-penetrating radar method, detecting cracks and water erosion defects is important for protecting ancient buildings. This study examines the reflection characteristics of ground-penetrating radar (GPR) waves at different lithological interfaces. Physical experiments and their numerical and physical laws were used to study the reflection characteristics of GPR incidents from brick to air, unsaturated soil, water, and metal interfaces. The model was applied to detect defects of the Xi’an Bell Tower and the main research results are as follows. The echo amplitude of GPR was positively correlated with the relative permittivity of the bedding layer, and its basic law conforms to the positive logarithmic curve. The hyperbolic opening of water erosion imaging of the base-compacted soil is larger, the attenuation effect increases and the signal reflection is obvious. These results provide the theoretical basis and technical support for the actual detection of water erosion deterioration of loess in similar projects, and this provides theoretical and technical references for rapid identification and management of defects in ancient buildings.

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Three-Dimensional Separation and Characterization of Fractures in X-Ray Computed Tomographic Images of Rocks

Cited by 6 publications

References 42 publications

Benchmarking conventional and machine learning segmentation techniques for digital rock physics analysis of fractured rocks

Benchmarking conventional and machine learning segmentation techniques for digital rock physics analysis of fractured rocks

Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

Detection of cracks in cemented loess of ancient buildings using remote sensing

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