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基于模糊C均值地震属性聚类的沉积相分析
张阳1,邱隆伟1,李际2,冯磊3,颜文远1,赵文睿4
(1.中国石油大学地球科学与技术学院,山东青岛 266580;2.中石油新疆油田分公司实验检测研究院,新疆克拉玛依 834000;3.河南理工大学资源环境学院,河南焦作 454000;4.冀东油田勘探开发建设项目部,河北唐山 063200)
摘要:
对于油气田深层和新区的勘探,常会由于未钻遇目的层或探井少而缺乏有效的岩心和测、录井等基础资料,给沉积相分析的准确性造成一定影响,为此提出基于模糊C均值地震属性聚类的沉积相分析方法。以孤北洼陷沙四上亚段为例,采用模糊C均值属性聚类得到地震相图,并结合地质基础资料确定沉积相类型,赋予每种地震相准确的沉积意义,进一步分析沉积体系展布规律。结果表明:孤北洼陷沙四上亚段主要发育三角洲、扇三角洲及湖相3种沉积相;利用本方法分析沉积相时应注意充分考虑地震属性优化与如何正确赋予地震相准确沉积意义这两个问题;本方法遵循属性聚类控面,岩心测井控点的思想,为深层和新区的沉积相分析提供可靠的依据,是一种切实可行、客观准确的新方法。
关键词:  模糊C均值聚类  地震属性  沉积相  孤北洼陷  沙四上亚段
DOI:10.3969/j.issn.1673-5005.2015.04.007
分类号::TE 121.3
基金项目:国家重大油气专项(2011ZX05009-002);中央高校基本科研业务费专项(15CX06010A)
Sedimentary facies analysis based on cluster of seismic attributes by fuzzy C-means algorithm
ZHANG Yang1, QIU Longwei1, LI Ji2, FENG Lei3, YAN Wenyuan1, ZHAO Wenrui4
(1.School of Geosciences in China University of Petroleum, Qingdao 266580, China;2.Experiment Testing Institute,PetroChina Xinjiang Oilfield Company, Karamay 834000,China;3.School of Resources and Environments, Henan Polytechnic University, Jiaozuo 454000, China;4.Exploration Development and Construction Department of Jidong Oilfield, Tangshan 063200, China)
Abstract:
Limited number of exploratory wells and unsuccessful drilling into targeted strata usually lead to insufficient cores and well logging data in deep strata and new exploratory areas, which leads to problems in accurate sedimentary analysis. The paper proposed an advanced sedimentary analysis method based on clustering seismic attributes using fuzzy C-means algorithm. Taking the upper 4th Member of Shahejie Formation in Gubei subsag for example, this method obtained the seismic facies by fuzzy C-means seismic attributes algorithm, determined the sedimentary types according to basic geological data, assigned the seismic facies accurate sedimentary meaning,and finally analyzed the distribution regularities of the sedimentary systems. The results show that the upper 4th Member of Shahejie Formation in Gubei subsag mainly developed delta, fan delta, and lakefacies.It is stressed that two questions need to be taken into full consideration applying the method:the optimization of seismic attributes and how to give accurate sedimentary meanings to different seismic facies clustered.The method follows the reasoning that cluster of attributes control the plane and cores and logging data control the points, and provides reliable arguments for the sedimentary analysis in the deep strata and new exploratory areas.
Key words:  fuzzy C-means clustering algorithm  seismic attributes  sedimentary facies  Gubei subsag  the upper 4th Member of Shahejie Formation
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