Adaptive Multifrequency Attribute Analysis and Its Application on Reservoir Characterization

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 o...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-10
Main Authors: Zhang, Zezhou, Liu, Naihao, Liu, Rongchang, Lu, Man, Wei, Tao, Gao, Jinghuai
Format: Article
Language:English
Subjects:
Analysis
Cluster analysis
Clustering
Correlation analysis
Embedding
Geological structures
Machine learning
Redundancy
Reservoirs
Seismic data
Thickness
Workflow
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Summary: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.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3373393