河流相储层地震属性优选与融合方法综述

岳大力; 李伟; 杜玉山; 胡光义; 王文枫; 王武荣; 王政; 鲜本忠

doi:10.3799/dqkx.2022.221

Volume 47 Issue 11

Nov. 2022

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Article Navigation > Earth Science > 2022 > 47(11): 3929-3943

Yue Dali, Li Wei, Du Yushan, Hu Guangyi, Wang Wenfeng, Wang Wurong, Wang Zheng, Xian Benzhong, 2022. Review on Optimization and Fusion of Seismic Attributes for Fluvial Reservoir Characterization. Earth Science, 47(11): 3929-3943. doi: 10.3799/dqkx.2022.221

Citation:

Yue Dali, Li Wei, Du Yushan, Hu Guangyi, Wang Wenfeng, Wang Wurong, Wang Zheng, Xian Benzhong, 2022. Review on Optimization and Fusion of Seismic Attributes for Fluvial Reservoir Characterization. Earth Science, 47(11): 3929-3943. doi: 10.3799/dqkx.2022.221

Citation:

Yue Dali, Li Wei, Du Yushan, Hu Guangyi, Wang Wenfeng, Wang Wurong, Wang Zheng, Xian Benzhong, 2022. Review on Optimization and Fusion of Seismic Attributes for Fluvial Reservoir Characterization. Earth Science, 47(11): 3929-3943. doi: 10.3799/dqkx.2022.221

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Review on Optimization and Fusion of Seismic Attributes for Fluvial Reservoir Characterization

doi: 10.3799/dqkx.2022.221

Yue Dali^{1, 2
,
,
,},
Li Wei^{1, 2},
Du Yushan³,
Hu Guangyi⁴,
Wang Wenfeng⁵,
Wang Wurong^{1, 2},
Wang Zheng^{1, 2},
Xian Benzhong^{1, 2}

1.
State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China
2.
College of Geosciences, China University of Petroleum, Beijing 102249, China
3.
Shengli Oil Field Company Ltd., SINOPEC, Dongying 257015, China
4.
CNOOC Research Institute Co. Ltd., Beijing 100028, China
5.
Kunlun Digital Technology Co. Ltd., Beijing 100040, China

Received Date: 2022-03-21
Available Online: 2022-12-07
Publish Date: 2022-11-25

Abstract

Abstract

Seismic attribute analyses have been applied widely in hydrocarbon exploration and development of fluvial reservoirs, and obtained good results. The analysis procedure of seismic attributes mainly includes the extraction, the optimization and the fusion of attributes. In this paper it summarizes the main methods of attribute extraction, optimization and fusion, and evaluates their advantages, disadvantages and application conditions. Besides, the common misunderstanding genetically related to seismic resolution and interference of neighboring zone in attribute extraction is also analyzed. Generally, fusion methods of seismic attributes using linear models cannot significantly improve the results, and are suitable for areas with several or a few wells; fusion methods with intelligent models (mainly for supervised learning) are commonly suitable for the areas with dozens of wells, such as areas within oil development stage. Fusion methods based on unsupervised learning are suitable for areas with few and even no wells, which have an optimistic development prospect since they can make full use of the seismic information, and are not limited by wells. In addition, the new fusion methods of frequency-decomposed attributes, and of attributes from target and neighboring zones are also summarized in this paper.
- fluvial reservoir,
- seismic attribute,
- attribute optimization,
- attribute fusion,
- machine learning,
- sedimentology

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References(88)

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Review on Optimization and Fusion of Seismic Attributes for Fluvial Reservoir Characterization

doi: 10.3799/dqkx.2022.221

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通讯作者: 陈斌, bchen63@163.com

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