Intelligent seismic identification technology of sedimentary facies guided by knowledge graph
YANG Cun1, MENG He1, YE Yueming1, CAO Xiaochu1, YONG Xueshan2
1. Research Institute of Geology, Research Institute of Exploration and Development, PetroChina, Hangzhou, Zhejiang 310023, China; 2. Northwest Branch, Research Institute of Exploration and Development, PetroChina, Lanzhou, Gansu 730020, China
Abstract：Relying on the prior knowledge of geological experts， the traditional identification methods for sedimentary facies use seismic and logging data to conduct a qualitative analysis of sedimentary environments with the aid of the storage and computation capacity of computers. As sedimentary facies identification based on seismic data requires a lot of manual interpretation， the accuracy and efficiency are not ideal. How to characterize the geological characteristics of sedimentary microfacies from seismic data and realize the three-dimensional spatial characterization of sedimentary microfacies remains to be studied. In recent years， the knowledge graph has attracted wide attention in the field of geoscience， and the traditional identification method for sedimentary facies can be improved by constructing the knowledge graph as a constraint. However， it is an urgent technical problem to further integrate the knowledge graph， deep learning， and seismic identification technology of sedimentary facies to form a fine identification technology of sedimentary microfacies constrained by the knowledge graph. By introducing geological prior knowledge into the knowledge graph， this paper constructs a high-level semantic cognition system for complex underground sedimentary patterns. The knowledge graph is used for computer representation of geological prior knowledge， which can serve as constraint conditions and quality control measures to guide the identification and modeling of sedimentary microfacies. It ultimately forms an intelligent identification and modeling technology for sedimentary microfacies guided by the knowledge graph. After digitizing geological prior knowledge， the presented method characterizes the spatial distribution of carbonate microbial mound-beach complexes and multi-stage foreset bodies in the Dengying Formation of the central Sichuan Basin. The predicted results are in line with the geological condition of the target area. The proposed method is suitable for deep lithologic trap identification and well demonstration， providing an effective basis for reservoir prediction and has good industrial application value.
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