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地震属性融合技术在海拉尔盆地乌东斜坡带南屯组储层预测中的应用

朱可丹 王雅春 衣启樊 王昕尧 李梦瑶 赵振铎 彭泷毅

物探化探计算技术2017,Vol.39Issue(1):109-115,7.
物探化探计算技术2017,Vol.39Issue(1):109-115,7.DOI:10.3969/j.issn.1001-1749.2017.01.16

地震属性融合技术在海拉尔盆地乌东斜坡带南屯组储层预测中的应用

Application of multi-seismic attribute information fusion in sandstone reservoir prediction in nantun formation in the eastern Wu slope in hailaer basin

朱可丹 1王雅春 2衣启樊 1王昕尧 2李梦瑶 1赵振铎 2彭泷毅1

作者信息

  • 1. 东北石油大学 地球科学学院,大庆 163318
  • 2. 东北石油大学 "非常规油气成藏与开发"省部共建国家重点实验室培育基地,大庆 163318
  • 折叠

摘要

Abstract

The characteristic of sandstone reservoir in Nantun formation in the eastern Wu slope in Hailaer basin is analyzed in this paper. It points out the exploration difficulties. Against the features and difficulties, the authors apply multiple linear regression in multi-seismic attribute information fusion to make an accurate thickness prediction of sandstone reservoir in N2I Bed. The general process of this method is as follows. Firstly, extract seismic attributes from 3D-seismic data, optimize sensitive attributes against sandstone reservoir among them. Then analyses the relationship between seismic attributes and thickness of sandstone reservoir. After that, establish the regression equation between seismic attributes and thickness of sandstone reservoir. Base on this regression equation, mapping the plane thickness distribution of sandstone reservoir in N2I bed. This method has good application results. With this result, the thickness feature of sandstone reservoir in N2I bed can be clearly recognized, which lay a solid foundation for the prediction of subtle traps in N2I bed in the eastern Wu slope in Hailaer basin.

关键词

乌东斜坡带/南屯组/地震属性融合/多元线性回归/储层砂岩预测

Key words

eastern Wu slope/Nantun formation/multi-seismic attribute information fusion/multiple linear regression/sandstone reservoir prediction

分类

天文与地球科学

引用本文复制引用

朱可丹,王雅春,衣启樊,王昕尧,李梦瑶,赵振铎,彭泷毅..地震属性融合技术在海拉尔盆地乌东斜坡带南屯组储层预测中的应用[J].物探化探计算技术,2017,39(1):109-115,7.

基金项目

国家自然科学基金(41440017) (41440017)

黑龙江省自然科学基金(D201406) (D201406)

物探化探计算技术

OACSCDCSTPCD

1001-1749

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