知识图谱引导的缝洞体智能识别技术

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中图分类号：P631 文献标识码：A DOI：10. 13810/j. cnki. issn. 1000⁃7210. 20240133

Abstract：Karst caves exhibit distinctive“string ⁃ of ⁃ beads”reflection configurations on seismic profiles，with their spatial distribution governed by intricate fracture networks and thus forming complex fracture ⁃ cave sys⁃ tems. Conventional methods，constrained by ambiguities in reservoir architecture and limited sample availability， face challenges in achieving accurate delineation. This study proposes a knowledge graph ⁃ guided intelligent identification technique based on coupled fracture ⁃cave modeling，which innovatively integrates geological prior knowledge with deep learning through encoding geological topological relationships into adjacency matrix con⁃ straints. The methodology establishes a multi ⁃ task learning framework by synergistically combining forward modeling ⁃ derived label data volumes with expert ⁃ annotated data volumes. The approach employs knowledge graphs to characterize connectivity relationships between fractures and karst caves and designs geologically inter⁃ pretable loss functions to dynamically adjust model optimization trajectories. Application in the Ordovician Lianglitage Formation of the Tarim Basin demonstrates substantial reduction in manual interpretation workload and significant enhancement in boundary delineation precision for fracture⁃cave systems. This methodology presents an innovative solution integrating knowledge⁃driven and data⁃driven approaches for prediction of strongly heterogeneous carbonate reservoirs.

Keywords：karst cave，fracture⁃cave systems，geological prior knowledge，knowledge graph，deep learning，multi task learning

杨存，伍新明，黄理力，等 . 知识图谱引导的缝洞体智能识别技术［J］. 石油地球物理勘探，2025，60（3）：545‑554.YANG Cun，WU Xinming，HUANG Lili，et al. Knowledge graph ⁃ guided intelligent identification of fracture ⁃cave systems［J］. Oil Geophysical Prospecting，2025，60（3）：545‑554.

0 引言

溶洞作为碳酸盐岩缝洞型油气藏中最重要的储集空间，在地震剖面上通常为多组强振幅反射的纵向叠合。（剩余13791字）