基于 K 维树搜索提示SAM模型的砂体追踪

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中图分类号：P631 文献标识码：A DOI：10.13810/j.cnki.issn.1000-7210.20240382

SAM prompted by KD-tree for sand body tracking

Wei Yanwen1，²， Zhu Zhenyu1，²，Ding Jicai1，2 （1.CNOOC Research InstituteCo.，Ltd.，Beijing 1OO028，China;2.National Engineering Research Centerof Offshore Oil and Gas Exploration，Beijing 10oo28，China）

Abstract： Sand body are a kind of common reservoir unit，and their accurate identification and tracking are the key to discovering oil and gas fields and supporting the increase in oil and gas reserves and production. Existing methods such as attribute analysis and deep learning stillface challenges such as low boundary identification accuracy，complex parameter selection，and poor noise resistance. To this end， this paper proposes a prompt prediction method based on the Segment Anything Model （SAM），a visual image segmentation foundation model. This method requires no modeltraining， and bysimply employing the boundary promptpoints oftargetsand bodies， precise identification and tracking results of the boundaries：of target sand bodies can be obtained.To address the prediction error of the prompt encoder in SAM when applied to seismic profiles，this paper proposes a KD-tree search method.By calculating the shortest distance from the prompt points to the potential sand body segmentation blocks，the optimal sand body prediction results are determined. After conducting verification with actual target area data and comparison with the customized training of a U-Net modelon thetarget area data，it is demonstrated that the sand body tracking method based on SAM depicts more lateral changes of sand body boundaries and the boundaries are more consistent with seismic amplitude variations.

Keywords： sand body identification，thin sand body， deep learning，foundation model， KD-treealgorithm

Wei Yanwen， Zhu Zhenyu，Ding Jicai.SAM prompted by KD-treefor sand body tracking[J].Oil Geophysi-cal Prospecting，2026，61（2）： 273-282.

0 引言

砂体作为一种常见的碎屑岩储集单元，广泛存在于陆相沉积盆地中[1-2]。（剩余16998字）