Research on Intelligent Geological-Structural Modelling Guided by a Geological-Structure Knowledge Graph

Three-dimensional geological structural modelling provides the geometric framework for subsurface exploration and development. However, conventional workflows, driven primarily by seismic interpretation, often lack explicit constraints from expert knowledge and are difficult to update when interpretations evolve. This study proposes an intelligent modelling methodology guided by a geological structure knowledge graph. The method includes: (i) a Three-tier Knowledge Architecture (TKA) that formalises domain knowledge in entity, relationship and inference layers using RDF/OWL; (ii) a Knowledge-driven Intersection Line Generation Algorithm (KILGA) coupled with a hierarchical adaptive mesh refinement scheme based on a posteriori error estimation (HAMR-APEE) to integrate geological constraints and mitigate boundary aliasing; and (iii) a bidirectional linkage mechanism between the knowledge graph and 3D models to support incremental updates following knowledge revision. The approach is validated in three petroliferous basins in China (Ordos, Qaidam and Sichuan), representing micro-amplitude, thrust nappe and deep complex structural styles. Compared with a conventional Petrel-based workflow, the proposed method reduces modelling RMSE from 15–20 m to 5–8 m, improves geological reasonableness from ~85% to >95%, and shortens modelling cycles from months to weeks.