学术
利用Kohonen神经网络划分乌夏地区深层沉积相
张科[1] 王永刚[1] 乐友喜[1] 郭文建[2] 张吉辉[2] 高磊[1]
[1]中国石油大学地球资源与信息学院,山东东营257061 [2]中国石油新疆油田分公司勘探开发研究院,新疆克拉玛依834000
摘 要:
在对准噶尔盆地乌夏地区二叠系夏子街组的研究过程中,利用连片三维数据体的高信噪比和波组特征明显的优点,选择了可信度较高的地震反射内部结构和外部形态,辅助地震属性（瞬时振幅、瞬时频率和相关长度）,使用Kohonen神经网络方法对地震相进行了量化分析和命名,并且利用测井解释和岩心分析及古生物特征等分析成果,将地震相转换为沉积相,取得了良好的地质效果,并解决了深层井少情况下沉积相难于划分的问题。[著者文摘]
文章出处:
《新疆石油地质》-2007年28卷4期 -419-421页
栏目信息:
分 类 号:
文献标识码:
A
文章编号:
1001-3873（2007）04-0419-03
Application of Kohonen Neural Network to Deep Sedimentary Facies Division in Wu-Xia Area, Junggar Basin
ZHANG Ke, WANG Yong-gang, YUE You-xi, GUO Wen-jian, ZHANG Ji-hui, GAO Lei （1. Faculty of Geo-Resources and Information, China University of Petroleum, Dongying, Shandong 257061, China; 2.Research Institute of Exploration and Development, Xinjiang Oilfield Company, PetroChina. Karamay, Xinjiang 834000, China）
Abstract:
The 3D seismic data-processing assembly with the merit of high signal-to-noise ratio and the obvious wave characteristic is used to select high-reliability seismic reflection internal texture and external shape from Xiazijie formation of Permian in Wu-Xia area of Junggar basin. By means of such seismic attributes as instantaneous amplitude, instantaneous frequency and persistence length, the seismic facies is quantitatively analyzed and nominated using Kohonen neural network method. The results from well log interpretation, core analysis and palaeontologic evidence are applied to conversion of the seismic facies into sedimentary facies, thus well solving the problem unable to classify the sedimentary facies with few deep well in the past.[著者文摘]
Key words:
Junggar basin; seismic attribute; Kohonen neural network; deep stratum; sedimentary facies
基金资助:
致谢：在成文过程中得到新疆油田公司勘探开发研究院许多专家和同行的指导和帮助,特致谢意.
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