부산대학교 도서관

Seismic frequency attributes are commonly used for describing geological structures and characterizing complex reservoirs. Although there are different kinds of machine learning (ML)-based methods proposed, how to select appropriate attributes and how to map them to reservoir thickness is still an open issue. In this study, we suggest an adaptive multifrequency attribute analysis (AMFAA)-based workflow to address these issues. We first utilize a generalized S-transform to extract multifrequency attributes, which can describe local time-frequency features of seismic data. Then, we propose a sensitive attribute analysis (SAA) method to reduce frequency attribute redundancy with the aid of hierarchical clustering and correlation analysis. Afterward, based on the selected sensitive attributes, we propose to adopt the potential of heat diffusion for affinity-based transition embedding (PHATE) for multifrequency attribute analysis, which can map seismic multifrequency attributes to reservoir thickness. To test the validity and effectiveness of our method, we first apply it to a synthetic wedge model and then post-stack field data in the Ordos Basin, Northwest China.