Abstract
Image logs are crucial in capturing high-quality geological information about subsurface formations. Among the various geological features that can be gleaned from Formation Micro Imager log, vugs are essential for reservoir evaluation. This paper introduces an automated Vug Detection Model, leveraging advanced computer vision techniques to streamline the vug identification process. Manual and semiautomated methods are limited by individual bias, labour-intensity and inflexibility in parameter finetuning. Our methodology also introduces statistical analysis on vug characteristics. Pre-processing steps, including logical file extraction and normalization, ensured standardized and usable data. The sixstep vug identification methodology encompasses top-k mode extraction, adaptive thresholding, contour identification, aggregation, advanced filtering, and optional filtering for low vuggy regions. The model's adaptability is evidenced by its ability to identify vugs missed by manual picking undertaken by experts. Results demonstrate the model's accuracy through validation against expert picks. Detailed metrics, such as count, mean, and standard deviation of vug areas within zones, were introduced, showcasing the model's capabilities compared to manual picking. The vug area distribution plot enhances understanding of vug types in the reservoir. This research focuses on the identification and characterization of vugs that in turn aids in the better understanding of reservoirs.
Abstract (translated)
图像日志在捕捉地下地层的高质量地质信息方面至关重要。在从岩性微成像测井中获取的各种地质特征中，孔穴（vugs）对于储层评价尤为关键。本文介绍了一种自动化的孔穴检测模型，该模型利用先进的计算机视觉技术来简化孔穴识别过程。手动和半自动方法受限于个体偏差、劳动强度以及参数微调的不灵活性。我们的研究方法还引入了对孔穴特征进行统计分析的方法。在预处理步骤中，包括逻辑文件提取和归一化等措施确保数据标准化且可使用。六步孔穴识别方法涵盖了top-k模式提取、自适应阈值设定、轮廓识别、聚类、高级过滤以及针对低孔隙区的可选过滤。该模型的适应性体现在其能够发现专家手动挑选过程中遗漏的孔穴，这一点通过与专家选择的结果进行对比验证了模型的准确性。详细度量指标包括各区域内的孔穴数量、平均面积和标准差等数据被引入，展示了模型相较于手工操作的能力。孔穴分布图有助于更深入地理解储层中的不同类型的孔穴。 这项研究专注于识别和表征孔穴，从而更好地了解储层特性。
URL
https://arxiv.org/abs/2507.02988
