Robust reservoir rock fracture recognition based on a new sparse feature learning and data training method

Taibi, Fatemeh; Akbarizadeh, Gholamreza; Farshidi, Ebrahim

doi:10.1007/s11045-019-00645-8

Cited by 21 publications

(6 citation statements)

References 45 publications

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“…In 2020, Fang et al [47] used the Zernike moment and Canny operator to carry on the edge sub-pixel points. In 2019, Taibi et al [48] combined Zernike, sparse coding, Steger, and Hong transformed to identify the cracks of the imaging accounts. In 2017, Huang et al [49] based on fuzzy means and K-means algorithms to assemble the initial images, then applying Zernike moments to detect the edge.…”

Section: Matrix-based Edge Detection Technologymentioning

confidence: 99%

A Review of the Edge Detection Technology

Hou¹,

Jia²,

Wang³

et al. 2021

STAIQC

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The edge detection-based has profoundly inspired recent works in image classification, object detection, segmentation, et al. With the growth of computer vision, the performance of edge detection has been notably improved. In this paper, we concentrate on presenting some edge detection technologies and grouping them into two major categories: classical edge detection technology and deep learning-based edge detection technology. For every team in the classification, the fundamental thoughts are first described, and then we probe beneath into the mainly related literature in recent years. Furthermore, the evaluation indicators are entirely elaborated. In addition, this conclusion is given by a comprehensive summary which provides suggestions on future perspectives.

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Section: Matrix-based Edge Detection Technologymentioning

confidence: 99%

A Review of the Edge Detection Technology

Hou¹,

Jia²,

Wang³

et al. 2021

STAIQC

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“…As an alternative approach, various supervised (Karimpouli et al 2020;Lu et al 2020;Lee et al 2021) and unsupervised (Taibi et al 2019;Zhang et al 2021) machine learning techniques were implemented for fracture identification and segmenting porous samples (Chauhan et al 2016). Among the unsupervised approaches, encoder-decoder networks in the form of CNNs received much attention during fracture identification in the DRP workflow (Varfolomeev et al 2019;Hong & Liu 2020;Karimpouli et al 2020;Kim et al 2020;Lu et al 2020;Lee et al 2021).…”

Section: Introductionmentioning

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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“…To address above issues, we have analysed the fractures' semantic information in logging images and utilized the pattern of fracture regions to determine fracture region. Different from the above methods, connectivity of fracture regions in logging images is considered a key standard where pixels corresponding to reservoir fractures connected two ends of an image [25]. On the other hand, heuristic algorithms have played an increasingly vital role in similar optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Fractures Based on Optimal Path Search in Well Logging Images

Zhang

et al. 2021

Journal of Sensors

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Reservoir fractures are essential locations to gather oil and gas. Recently, imaging logging technology has become a mainstream method for obtaining stratigraphic information. This paper proposed a combined optimal path search strategy to effectively identify and extract the fracture information in well logging images. Specifically, the threshold segmentation of logging images is used to obtain the binary image. In addition, the identification of connected fractures in the logging image is transformed into the optimal path search, and the identification and extraction of reservoir fractures are realized by constructing the optimal path between the two ends of fractures. Finally, an improved ant colony algorithm is applied to filter irrelevant information and extract fractures automatically by recording all the ants’ exploration trajectories in the ant colony. Compared with previous approaches, the proposed method can eliminate irrelevant background features and merely reserve pixels corresponding to fractures. Simultaneously, relative to the conventional strategy, the time consumption is reduced by more than 98%. The findings of this study can help for better extracting fractures automatically and reducing manual workload.

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Robust reservoir rock fracture recognition based on a new sparse feature learning and data training method

Cited by 21 publications

References 45 publications

A Review of the Edge Detection Technology

A Review of the Edge Detection Technology

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

Automatic Detection of Fractures Based on Optimal Path Search in Well Logging Images

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