Gaussian-weighted multiple seismic attribute analysis for delineating subtle reservoirs: Application to the Maokou Formation, Central Sichuan Basin

Abstract Accurately delineating the spatial distribution of subtle reservoirs in carbonate formations, particularly in overlying and underlying strong reflection layers, presents significant challenges in seismic interpretation. A Gaussian-weighted multiple seismic attribute analysis (GWMSAA) workflow was developed to delineate the spatial distribution characteristics of the subtle reservoirs. First, various seismic attributes were extracted from the post-stack seismic data volume. Subsequently, an adaptive Gaussian-weighted window was applied to extract surface attributes specific to the target reservoirs. Finally, the Isometric Mapping (ISOMAP) algorithm was used to extract low-dimensional concealed features, enabling precise delineation of the subtle reservoirs. This workflow was applied to synthetic and field seismic data of subtle carbonate reservoirs in the Maokou Formation of the central Sichuan Basin, SW China. The results indicated that the proposed GWMSAA workflow can effectively eliminate interference from overlying strong reflection layers and provide more accurate delineation of the subtle reservoirs compared to traditional seismic attribute methods. Furthermore, the GWMSAA workflow has the potential to be a powerful tool for improving exploration strategies in challenging reservoirs, with significant implications for petroleum exploration and reservoir management.

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Publication Details

Journal
Geophysics
Published
2025-10-27
DOI
https://doi.org/10.1190/geo-2025-0054
Primary Topic
Hydrocarbon exploration and reservoir analysis
Type
article
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article

Gaussian-weighted multiple seismic attribute analysis for delineating subtle reservoirs: Application to the Maokou Formation, Central Sichuan Basin

Naihao Liu, Zhiguo Wang, Guotao Duan, Jinghuai Gao et al.
Geophysics
Hydrocarbon exploration and reservoir analysis
article

Gaussian-weighted multiple seismic attribute analysis for delineating subtle reservoirs: Application to the Maokou Formation, Central Sichuan Basin

Naihao Liu, Zhiguo Wang, Guotao Duan, Jinghuai Gao, Yang Yang, Man Lu, Shengjun Li, Zezhou Zhang
article en

Abstract

Abstract Accurately delineating the spatial distribution of subtle reservoirs in carbonate formations, particularly in overlying and underlying strong reflection layers, presents significant challenges in seismic interpretation. A Gaussian-weighted multiple seismic attribute analysis (GWMSAA) workflow was developed to delineate the spatial distribution characteristics of the subtle reservoirs. First, various seismic attributes were extracted from the post-stack seismic data volume. Subsequently, an adaptive Gaussian-weighted window was applied to extract surface attributes specific to the target reservoirs. Finally, the Isometric Mapping (ISOMAP) algorithm was used to extract low-dimensional concealed features, enabling precise delineation of the subtle reservoirs. This workflow was applied to synthetic and field seismic data of subtle carbonate reservoirs in the Maokou Formation of the central Sichuan Basin, SW China. The results indicated that the proposed GWMSAA workflow can effectively eliminate interference from overlying strong reflection layers and provide more accurate delineation of the subtle reservoirs compared to traditional seismic attribute methods. Furthermore, the GWMSAA workflow has the potential to be a powerful tool for improving exploration strategies in challenging reservoirs, with significant implications for petroleum exploration and reservoir management.

GeophysicsVol. 91(1)
China University of Petroleum, Beijing (CN), Research Institute of Petroleum Exploration and Development (CN), Department of Physics, Mathematics and Informatics (BY), Imperial College London (GB), L-3 Communications (United States) (US), Xi'an Jiaotong University (CN), China National Petroleum Corporation (China) (CN)
Openalex Percentile: Top 64%
Hydrocarbon exploration and reservoir analysis
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