利用多属性体分类技术预测扇三角洲砂体
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摘要
对单一地震属性采用神经网络技术进行分类,在低信噪比地区很难准确进行地震相分析和砂体预测。根据地震波形理论,基于Seisfacies的多属性体分类、PCA主成分分析、混合聚比法和自组织人工神经网络技术对层段内多种地震属性体按照相似性原则进行统计聚类分析,并在区域内进行地震相自动划分,得到地震相三维数据体。结合钻井资料,借助三维可视化技术在三维空间中预测储层砂体的空间分布,大大减少了地震相划分的多解性,提高了储层预测的精度和工作效率。对华北油田万庄地区扇三角洲砂体进行预测,在三维空间中精细刻画了沙三中段扇三角洲砂体的边界和空间展布,预测了有利储层的发育区,发现并落实了3个有利的岩性圈闭,部署钻探的T12X、T47等井均获工业油流,效果很好。
Conventional neural network technology for seismic waveform classification by using one single seismic attribute is very difficult to be used to predict seismic facies and sand body distribution in low signal and noisy ratio areas.Seisfacies multi-attribute volume classification technology is based on seismic wave theory,principal component analysis (PCA),hybrid classification method and self-organizing neural network technology,by using the principle of similarity to cluster analysis seismic attribute and also seismic facies are automatically analyzed.Thus a seismic facies classification volume is obtained.Integrated with well data,the seismic facies volume is analyzed in 3D visualization.It is better to predict reservoir sand body distribution in three-dimensional space and greatly reduce uncertainty caused by single attribute seismic facies analysis.This technology is used in Wanzhuang area,Huabei Oilfield.Fan delta sand body distribution is correctly described,three prospective lithologic reservoirs are predicted clearly.Based on these results,well T12X and T47 were drilled and encountered thicker oil pays,which proved the multi-attribute volume classification prediction results.
Conventional neural network technology for seismic waveform classification by using one single seismic attribute is very difficult to be used to predict seismic facies and sand body distribution in low signal and noisy ratio areas.Seisfacies multi-attribute volume classification technology is based on seismic wave theory,principal component analysis (PCA),hybrid classification method and self-organizing neural network technology,by using the principle of similarity to cluster analysis seismic attribute and also seismic facies are automatically analyzed.Thus a seismic facies classification volume is obtained.Integrated with well data,the seismic facies volume is analyzed in 3D visualization.It is better to predict reservoir sand body distribution in three-dimensional space and greatly reduce uncertainty caused by single attribute seismic facies analysis.This technology is used in Wanzhuang area,Huabei Oilfield.Fan delta sand body distribution is correctly described,three prospective lithologic reservoirs are predicted clearly.Based on these results,well T12X and T47 were drilled and encountered thicker oil pays,which proved the multi-attribute volume classification prediction results.
引文
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[2]张万选.陆相地震地层学[M].北京:石油工业出版社,1993.
[3]West B P,May S R,Eastwood J E,et al.Interactive seis-mic facies classification using textural attributes and neu-ral networks[J].The Leading Edge,2002,21(10):1042-1050.
[4]Barnes A R.Seismic attributes and their classification[J].The Leading Edge,2001,20(1):47-48.
[5]赵力民,郎晓玲,金凤鸣,等.波形分类技术在隐蔽油藏预测中的应用[J].石油勘探与开发,2001,28(6):53-55.
[6]赵力民,彭苏萍,郎晓玲,等.利用Stratimagic波形研究冀中探区大王庄地区岩性油藏[J].石油学报,2002,23(4):33-36.
[7]黄小平,杜洪凌,史晓川.地震相分析在石南21井区沉积相划分中的应用[J].新疆石油地质,2004,5(6):671-672.
[8]邓传伟.利用波形建立地震相[J].石油地球物理勘探,2004,39(5):539-542
[9]唐大海,谢继容.Stratimagic地震相分析技术在川中GSM油气勘探中的应用[J].四川地质学报,2004,24(3):185-189.
[10]邓传伟,李莉华,金银姬,等.波形分类技术在储层沉积微相预测中的应用[J].石油物探,2008,47(3):262-65.
[11]于红枫,王英民,李雪.Stratimagic波形地震相分析在层序地层岩性分析中的应用[J].煤田地质与勘探,2006,34(1):64-66.
[12]段玉顺,李芳.地震相的自动识别方法及应用[J].石油地球物理勘探,2004,39(2):158-162.
[13]Linari V,Azbel K,Poupon M.Seismic facies analysis based on3D multiattribute volume classification,La Palma Field,Maracaibo,Venezuela[J].The Leading Edge,2003,22(1):32-36.
[14]宋荣彩,张哨楠,李弢,等.廊固凹陷大兴断层对油气分布的控制研究[J].天然气工业,2006,26(8):30-33.
[15]范春花,王英民,陈振岩,等.陈家洼陷沙三下亚段层序地层和沉积相分析[J].西南石油大学学报:自然科学版,2009,31(2):31-35.
[16]郎晓玲,赵力民,康洪全.储层综合评价技术在复杂油藏中的应用[J].西南石油学院学报,1998,20(4):10-13.
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