Rapid standardization of rock color determination is a crucial aspect of geological research. Current methods primarily rely on expert visual description, Munsell color chart comparison, and spectral analysis, which fall short of meeting the demands for rapid and accurate rock color identification and digital standardization during extensive indoor and outdoor core observations. This paper proposes a computer vision-based intelligent rock color recognition method. Based on the Codes for names and colors of rocks in the petroleum geology (SY/T 5751—2012), a standard database containing 112 rock colors is constructed. Three algorithms—color thresholding, edge detection, and GrabCut—are designed to accurately segment target rock regions. Digital analysis, including color feature extraction, is performed on coordinate-based and gridded images to calculate the color vector of the rock image. Addressing the challenge of accurately identifying similar color systems, a weighted multi-feature fusion color matching algorithm is designed, incorporating evaluation indicators such as cosine similarity, Euclidean distance, RGB component differences, and brightness factor, significantly improving the system's ability to identify similar hues. A visualized intelligent rock color recognition system is developed using Python programming language and the PyQT6 framework, achieving digital quantitative identification of rock colors and improving recognition accuracy and efficiency. The results show that the system's identification results for 35 rock samples are highly consistent with the Munsell color chart interpretation results, with the consistency of hue (H), value (V), and chroma (C) reaching 91.43%, 85.71%, and 71.43%, respectively. The system also shows complete consistency in the identification of independent test samples, verifying the correctness of the core algorithm logic, and the average identification time can reach the millisecond level.

The fine-grained sedimentary rocks have numerous bedding-parallel fractures that are essential for the migration, enrichment, and efficient development of oil and gas. However, because of their variety and the complexity of the factors that affect them, their spatial prediction by the industrial community becomes challenging. Based on sample cores, thin sections, and well-logging and seismic data, this study employed a multi-scale data matching approach to quantitatively predict the development of beddingparallel fractures and investigate their spatial distribution. Bedding-parallel fractures in the Lucaogou Formation in Jimusar Sag frequently occur along preexisting bedding planes and lithological interfaces. Unfilled bedding-parallel fractures inside or near source-rocks exhibit enhanced oil-bearing capacity. They were identified on micro-resistivity scanning images by the presence of regularly continuous black or nearly black sinusoidal curves. Overall, the developmental degree of bedding-parallel fractures was positively related to the brittle mineral and total organic carbon contents and negatively related to single reservoir interval thickness. The maintained porosity of the reservoir matrix contributed to a thorough response to factors affecting the development of bedding-parallel fractures. Here, an effective and objective method was proposed for predicting the development and distribution of bedding-parallel fractures in the fine-grained sedimentary rocks. The method was based on the matched reservoir interval density, reservoir interval density, and matched sweet spot density of bedding-parallel fractures. The prediction method integrated the significant advantages of high vertical resolution from logging curves and strong lateral continuity from seismic data. The average relative prediction error was 8% in the upper sweet spot in the Lucaogou Formation, indicating that the evaluation parameters for beddingparallel fractures in fine-grained sedimentary rocks were reasonable and reliable and that the proposed prediction method has a stronger adaptability than the previously reported methods. The workflow based on multi-scale matching and stepwise progression can be applied in similar fine-grained sedimentary rocks, providing reliable technological support for the exploration and development of hydrocarbons.