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

Zhang, Wei; Wu, Tong; Li, Zhipeng; Li, Yanjun; Qiu, Ao; Shi, Yibing

doi:10.1155/2021/5577084

Cited by 3 publications

(2 citation statements)

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“…While reservoir fractures are essential locations to gather oil and gas, imaging logging technology has become a mainstream method for obtaining stratigraphic information. In [33], W. Zhang et al have proposed an optimal path search strategy to effectively identify and extract the fracture information in well logging images. The logging image is first transformed into the optimal path search, and this is followed by the identification of reservoir fractures.…”

Section: Image and Video Processingmentioning

confidence: 99%

Sensors, Signal, and Artificial Intelligent Processing

2022

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Section: Image and Video Processingmentioning

confidence: 99%

Sensors, Signal, and Artificial Intelligent Processing

2022

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“…However, this method requires the pre-inpainting of gaps in the microresistivity images. For core and borehole images with abundant geologic features, some researchers employed machine learning methods to achieve relatively high detection precisions for fractures and unconformities [31]- [35].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Method for Core Orientation Based on Planar Geologic Features in Drill-Core Scans and Microresistivity Images

2022

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Drilling cores, the most direct and accurate first-hand data reflecting the subsurface geological information, are of unclear orientation for most boreholes. The image feature comparison method is one of the most widely used methods for core orientation. However, this method is mainly conducted by manual comparison, which is labor-intensive, time-consuming and inaccurate. In this paper, we propose an automatic core orientation method by comparing the planar geologic features (one of the most common features) in drill-core scans and microresistivity images. This method first automatically detects planar geologic features by combing the vertical gradient-based Otsu threshold segmentation, Hough transform and optimal sinusoid extraction and then determines correction difference of the core orientation by comparing the dip azimuths of the optimal sinusoids from the two types of images. This method is successfully applied to actual data. The automatic core orientation results are compared with the manual core orientation results. The results show that this method can eliminate human errors and has higher accuracy, especially for core scan images with non-distinct features. The proposed method has wide application potential in the fields of hydrocarbon exploration and development, scientific drilling research, and basic geological surveys.INDEX TERMS Bedding surfaces, borehole images, core orientation, core scan images, Hough transform.

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Improved algorithm for fracture-dissolution pore detection in resistivity imaging logging based on dung beetle optimization

Zhu,

Guo,

et al. 2024

Journal of Geophysics and Engineering

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Resistivity imaging logging has become a direct and precise method for visualizing the structural complexities of reservoir fractures and dissolution pores. The current use of Otsu's thresholding for segmentation results in poor segmentation quality and significant noise. Accurate segmentation of sub-images containing fracture and dissolution pore targets is essential for automated structure identification and subsequent parameter calculation. This study leverages the rapid convergence and robust global optimization capabilities of the Dung Beetle Optimizer to develop enhanced image segmentation approaches. Specifically, it introduces a refined K-Means algorithm for multi-category image segmentation and an Otsu algorithm for multi-threshold image segmentation, both optimized by the Dung Beetle Optimizer. Compared to conventional binary segmentation algorithms, this new algorithm effectively isolates noise and extracts multi-category information. Using the segmented sub-images, this paper integrates mathematical morphology techniques to compute parameters such as area, perimeter, tortuosity length, and pore shape factor for identified targets. Additionally, Principal Component Analysis is used to derive recognition factors for fractures and dissolution pores. Applications show that this factor can identify matrix, fracture, and dissolution pore targets in complex background images. By combining parameter information of the target area, the method effectively removes false information in resistivity imaging and segments sub-images of fractures and dissolution pores, calculating fracture area ratio, dissolution pore area ratio, and total area ratio.

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

Cited by 3 publications

References 20 publications

Sensors, Signal, and Artificial Intelligent Processing

Sensors, Signal, and Artificial Intelligent Processing

An Automatic Method for Core Orientation Based on Planar Geologic Features in Drill-Core Scans and Microresistivity Images

Improved algorithm for fracture-dissolution pore detection in resistivity imaging logging based on dung beetle optimization

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