石油地球物理勘探2025，Vol.60Issue(3)：618-629,12.DOI:10.13810/j.cnki.issn.1000-7210.20240185
相关阈值约束类别确定的无监督叠前地震相分析
Unsupervised pre-stack seismic facies analysis based on category determination with correlation threshold constraints
摘要
Abstract
Ensuring strong correlation among samples of the same category in seismic facies images and deter-mining the number of seismic facies categories are the core of unsupervised pre-stack seismic facies analysis.This paper proposes an unsupervised pre-stack seismic facies analysis algorithm with correlation threshold con-straints for category determination.First,the pre-stack seismic trace set data is transformed into two-dimen-sional images,and the high-level nonlinear,discriminative,and invariant features of the images are extracted using unsupervised deep learning networks,which can highlight strongly concealed information.Subsequently,the threshold for the number of seismic facies categories is determined based on the cross-correlation values of deep features of pre-stack seismic images corresponding to different categories of seismic facies within the study area,ensuring that samples within the same class in the obtained pre-stack seismic facies images exhibit highly strong correlation,and the number of seismic facies categories is determined based on discriminant thresholds.Finally,the obtained pre-stack seismic facies images are calibrated using existing drilling information to provide a basis for geological experts to infer sedimentary environments and reservoir distributions.Theoretical model testing confirms that this method not only determines the number of pre-stack seismic facies depending on dis-criminant thresholds but also ensures strong correlation among samples within the same class in the seismic fa-cies images,demonstrating greater robustness compared to other methods.Application of actual data shows that this method improves the accuracy of predicting seismic facies of fracture-cavity reservoirs in the Permian Maokou Formation and provides a reliable scientific basis for well deployment and the discovery of undrilled fracture-cavity reservoirs.关键词
地震相分析/无监督深度学习/模式识别/缝洞储层识别/叠前地震数据Key words
seismic facies analysis/unsupervised deep learning/pattern recognition/fracture-cavity reservoir iden-tification/pre-stack seismic data分类
地质学引用本文复制引用
张旋,彭达,陈康,蔡涵鹏,杨军辉,许翔..相关阈值约束类别确定的无监督叠前地震相分析[J].石油地球物理勘探,2025,60(3):618-629,12.基金项目
本项研究受国家自然科学基金重点项目"知识图谱引导的碳酸盐岩缝洞型油气储集体智能识别与反演"(42130812)和面上项目"基于解耦深度特征分析的单一物理可解释地震属性提取方法及应用"(42474168)联合资助. （42130812）
