西南石油大学学报(自然科学版) ›› 2012, Vol. 34 ›› Issue (3): 1-8.

• 地质勘探 •    下一篇

地震属性分析在河流相储层预测中的应用

陈恭洋1,2,陈 玲2,朱洁琼3,曹兰柱3,辛玮江2,3   

  1. 1. 长江大学计算机科学学院,湖北 荆州 434023;2. 长江大学地球科学学院,湖北 荆州 434023;3. 中国石油华北油田分公司,河北 任丘 062552
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2012-06-01 发布日期:2012-06-01

Application of Seismic Attributes Analysis Fluvial Reservoir to Prediction

Chen Gongyang , Chen Ling, Zhu Jieqiong, Cao Lanzhu, Xin Weijiang   

  1. 1. College of Computer Science,Yangtze University,Jingzhou,Hubei 434023,China2. College of Geoscience,Yangtze University,Jingzhou,Hubei 434023,China3. Huabei Oilfield Company,CNPC,Renqiu,Hebei 062552,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2012-06-01 Published:2012-06-01

摘要:
要:由河道砂岩体所形成的岩性油气藏的勘探越来越受到勘探家们的重视。但由于河流相储层形成的复杂性和
多样性,依靠钻井是很难对其分布规律进行有效控制和预测的。必须充分发挥地震资料信息的横向连续性优势,在井
资料的标定下,进行储层的横向预测与研究。通过地震属性分析,对渤海湾盆地冀中拗陷霸县凹陷文安斜坡古渐系沙
一上亚段—东营组河流相储层进行了预测。研究表明:(1)地震波的波峰和波谷强振幅分别反映了河道边、心滩砂体
的分布及河道水系的发育情况;(2)地震资料四阶导数振幅属性对振幅的微弱变化敏感,能较好地识别河道砂体分布,
尤其是对整体河床分布的刻画非常逼真;(3)河道沉积以低频反射为特征,尤以小于 10 Hz 的频率分布区与河道砂体
具有较好的对应关系;(4)基于神经网络的波形分类技术,可以极大地克服单一地震属性参数所带来的多解性或不确
定性,当用井资料标定后,其分类预测结果与地质认识具有很高的符合率。

关键词: 关键词:地震属性, 河道砂体, 储层预测, 文安斜坡, 霸县凹陷

Abstract: Abstract:The exploration of lithologic oil pool formed by the channel sand body is getting more and more attention by
explorationists. Because of the complexity and diversity of the fluvial reservoir,it is difficult to be controlled and predicated
effectively with well data. With the well data’s calibrating,the fluvial reservoir prediction must be made by sufficiently taking
advantage of the lateral continuity of seismic data. Wuth the analysis of seismic attribution,the paper presents the fluvial
reservoir predicting in the upper section of Es1and Ed formation of the Paleogene in Wen’an Slope,Baxian Depression,
Jizhong Sag,Bohaiwan Basin. The results are:(1)The high amplitude of peaks and troughs of seismic wave represent the
distribution of the fluvial sand body and the water system. (2)The amplitudes fourth derivative is sensitive to the weak change,
so it can identify the channel sand body easily,especially for carving the distribution of the channel bed. (3)The channel
deposit is characterized by low frequency seismic reflection,and the distribution of fluvial sand body was corresponded with
the area of frequency lower than 10 Hz. (4)Based on the waveform analysis technique with neural network,we can overcome
the ambiguity and uncertainty brought by single parameter in seismic attribution. Through calibrating with well data,the results
of classification highly correspond with the recognition of geology.

Key words: Key words:seismic attribution, fluvial sand body, reservoir predication, Wen’an Slope, Baxian Depression