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Abstract: Fine-grained sedimentary rocks have become a research focus as important reservoirs and source rocks for tight and shale oil and gas. Laminae development determines the accumulation and production of tight and shale oil and gas in fine-grained rocks. However, due to the resolution limit of conventional logs, it is challenging to recognize the features of centimeter-scale laminae. To close this gap, complementary studies, including core observation, thin section, X-ray diffraction (XRD), conventional log analysis, and slabs of image logs, were conducted to unravel the centimeter-scale laminae. The laminae recognition models were built using well logs. The fine-grained rocks can be divided into laminated rocks (lamina thickness of <0.01?m), layered rocks (0.01–0.1?m), and massive rocks (no layer or layer spacing of >0.1?m) according to the laminae scale from core observations. According to the mineral superposition assemblages from thin-section observations, the laminated rocks can be further divided into binary, ternary, and multiple structures. The typical mineral components, slabs, and T2 spectrum distributions of various lamina types are unraveled. The core can identify the centimeter–millimeter-scale laminae, and the thin section can identify the millimeter–micrometer-scale laminae. Furthermore, they can detect mineral types and their superposition sequence. Conventional logs can identify the meter-scale layers, whereas image logs and related slabs can identify the laminae variations at millimeter–centimeter scales. Therefore, the slab of image logs combined with thin sections can identify laminae assemblage characteristics, including the thickness and vertical assemblage. The identification and classification of lamina structure of various scales on a single well can be predicted using conventional logs, image logs, and slabs combined with thin sections. The layered rocks have better reservoir quality and oil-bearing potential than the massive and laminated rocks. The laminated rocks’ binary lamina is better than the ternary and multiple layers due to the high content of felsic minerals. The abovementioned results build the prediction model for multiscale laminae structure using well logs, helping sweet spots prediction in the Permian Lucaogou Formation in the Jimusar Sag and fine-grained sedimentary rocks worldwide.