石油地球物理勘探2025，Vol.60Issue(3)：545-554,10.DOI:10.13810/j.cnki.issn.1000-7210.20240133
知识图谱引导的缝洞体智能识别技术
Knowledge graph-guided intelligent identification of fracture-cave systems
摘要
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.关键词
溶洞/缝洞体/先验知识/知识图谱/深度学习/多任务学习Key words
karst cave/fracture-cave systems/geological prior knowledge/knowledge graph/deep learning/multi-task learning分类
地质学引用本文复制引用
杨存,伍新明,黄理力,许小勇,丁梁波,王冲..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):545-554,10.基金项目
本项研究受中国石油天然气股份有限公司重大科技专项"海外油气地质新理论资源评价新技术与超前选区研究"(2023ZZ07)资助. （2023ZZ07）
