Abstract:The classification of seismic facies based on unsupervised neural network self-organizing analysis (SOMA) is a comprehensive attribute clustering method. The key to the application of this method is to optimize seismic attributes, determine the number of clustering types, and analyze the relationship between seismic facies and sedimentary facies. Under the guidance of seismic sedimentology theory, we use the SOMA (self-organizing analysis) technology for cluster analysis of attributes, carry out seismic- sedimentary facies analysis by combining basic geological data, and select four seismic attributes such as RMS amplitude, information entropy, chaotic Li and fractal correlation dimension for cluster analysis. Taking the Cretaceous Suhongtu Formation in the Aitgele sag as a case, and using the method, we found such sedimentary facies as fan delta, braided river delta, shallow shore lake and deep lake. Traditional seismic -sedimentary facies analysis can judge the type of seismic facies by artificially observing seismic reflection. In contrast, our technology can reduce the unreliability of sedimentary facies analysis in areas with less well data. It provides a new basis for sedimentary facies analysis for oil and gas exploration. Also it is a practical, objective and accurate technical means.
Saggaf M M,Toksoz M N,Marhoon M I. Seismic facies classification and identification by competritive neural networks[J]. Geophysics,2003,68(6):1984-1999.
[2]
王永刚,乐友喜,张军华. 地震属性分析技术[M].山东东营:中国石油大学出版社,2007,97-100.WANG Yonggang,YUE Youxi,ZHANG Junhua. Seismic Attribute Analysis Technique[M].China University of Petroleum Press,Dongying,Shandong,2007,97-100.
[3]
王开燕,徐清彦,张桂芳,等. 地震属性分析技术综述[J].地球物理学进展,2013,28(2):815-823.WANG Kaiyan,XU Qingyan,ZHANG Guifang,et al.Summary of seismic attribute analysis[J].Progress in Geophysics,2013,28(2):815-823.
[4]
吕公河,于常青,董宁. 叠后地震属性分析在油气田勘探开发中的应用[J]. 地球物理学进展,2006,21(1):161-166.LYU Gonghe,YU Changqing,DONG Ning. The application of post-stack seismic atttibute analysis in the oil-gas exploration and development[J]. Progress in Geophysics,2006,21(1):161-166.
[5]
朱剑兵,赵培坤. 国外地震相划分技术研究新进展[J]. 勘探地球物理进展,2009,32(3):167-171.ZHU Jianbing,ZHAO Peikun. Advances in seismic facies classification technology abroad[J]. Progress in Exploration Geophysics,2009,32(3):167-171.
[6]
张,郑晓东,李劲松,等.基于SOM和PSO的非监督地震相分析技术[J].地球物理学报,2015,58(9):3412-3423.ZHANG Xuan,ZHENG Xiaodong,LI Jinsong,et al. Unsupervised seismic facies analysis based on SOM and PSO[J].Chinese Journal of Geophysics,2015,58(9):3412-3423.
[7]
王新桐,卢双舫,肖佃师. 基于聚类分析的地震属性优化及储层预测——以敖包塔油田敖9工区为例[J]. 石油天然气学报,2013,35(3):61-66.WANG Xintong,LU Shuangfang,XIAO Dianshi. Optimization of seismic attributes and reservoir prediction based on cluster analysis:by taking Ao 9 working area in Aobaota Oilfield for example[J]. Journal of Oil and Gas Technology,2013,35(3):61-66.
[8]
林年添,付超,张栋,等,无监督与监督学习下的含油气储层预测[J]. 石油物探,2018,57(4):601-610.LIN Niantian,FU Chao,ZHANG Dong,et al.Supervised learning and unsupervised learning for hydrocarbon prediction using multiwave seismic data[J].Geophysical Prospecting for Petroleum,2018,57(4):601-610.
[9]
蔡涵鹏,胡浩炀,吴庆平,等.基于叠前地震纹理特征的半监督地震相分析[J].石油地球物理勘探,2020,55(3):504-509.CAI Hanpeng,HU Haoyang,WU Qingping,et al. Semi-supervised seismic facies analysis based on pre-stack seismic texture characteristics[J].Oil Geophy-sical Prospecting,2020,55(3):504-509.
[10]
de Matos M C,Osorio P L M,Johann P R S. Unsupervised seismic facis analysis using wavelet transform and selorganizing maps[J]. Geophysics,2007,72(1):9-21.
[11]
Roy A,Matos M,Marfurt K J. Automatic seisim facies classification with Kohonen Slf organizing maps:a tutorial[J]. Geohorizons Journal of Society of Petroleum Geophysicists,2010,6-14.
[12]
李芳,王守东,陈小宏,等.基于模糊逻辑的多属性融合油气预测方法[J].石油地球物理勘探,2014,49(1):197-204.LI Fang,WANG Shoudong,CHEN Xiaohong,et al. Multi attribute fusion oil and gas prediction method based on fuzzy logic[J].Oil Geophysical Prospecting,2014,49(1):197-204.
[13]
李春艳.自组织映射(SOM)型神经网络的实现[J].电脑知识与技术,2007,3(21):821-823.LI Chunyan. Implementation of SOM neural network[J].Computer Knowledge and Technology,2007,3(21):821-823.
[14]
张阳,邱隆伟,李际,等.基于模糊C均值地震属性聚类的沉积相分析[J].中国石油大学学报(自然科学版),2015,39(4):53-61.ZHANG Yang,QIU Longwei,LI Ji,et al. Sedimentary facies analysis based on fuzzy C-means seismic attribute clustering[J].Journal of China University of Petroleum(Natural Science Edition),2015,39(4):53-61.
[15]
胡英,陈辉,贺振华,等.基于地震纹理属性和模糊聚类划分地震相[J].石油地球物理勘探,2013,48(1):114-120.HU Ying,CHEN Hui,HE Zhenhua,et al. Seismic facies classification based on seismic texture attributes and fuzzy clustering[J].Oil Geophysical Prospecting,2013,48(1):114-120.
[16]
郭帅,陈莹,杨海长,等.少井区基于地震属性聚类的沉积相分析方法——以白云凹陷始新统文昌组为例[J].海洋地质前沿,2018,34(5):48-55.GUO Shuai,CHEN Ying,YANG Haichang,et al. Sedimentary facies analysis upon seismic attributes by k-means clustering algorihm in low-exploration area:a case study of wenchang formation in baiyun sag[J].Marine Geology Frontier,2018,34(5):48-55.
[17]
卢进才,魏建设,姜亭,等.银额盆地居延海坳陷钻井地层对比及对油气层时代的约束[J/OL].中国地质:1-25[2020-08-02]. LU Jincai,WEI Jianshe,JIANG Ting,et al. Drilling stratigraphic correlation of Juyanhai depression in Yin'e Basin and its constraints on hydrocarbon reservoir age[J/OL].Geology of China:1-25[2020-08-02].
[18]
白晓寅,贺永红,任来义,等.银根-额济纳旗盆地苏红图坳陷西区构造特征与演化[J].延安大学学报(自然科学版),2017,36(2):57-61.BAI Xiaoyin,HE Yonghong,REN Laiyi,et al. Structural characteristics and evolution of the western Suhongtu depression in the Yingen-Ejinaqi Basin[J].Journal of Yan'an University(Natural Science Edition),2017,36(2):57-61.
[19]
戚湧,胡俊,於东军.基于自组织映射与概率神经网络的增量式学习算法[J].南京理工大学学报,2013,37(1):1-6.QI Yong,HU Jun,YU Dongjun. Incremental learning algorithm based on self-organizing map and probabilistic neural network[J].Journal of Nanjing University of Technology,2013,37(1):1-6.
[20]
宋江雨. 银额盆地哈日凹陷沉积相及储层四性关系研究[D].西北大学,陕西西安,2018.SONG Jiangyu. Study on the Relationship Between Sedimentary Facies and Four Reservoirs in Hari Depression of Yin'e Basin[D].Northwest University,Xi'an,Shaanxi,2018.
[21]
李文厚,周立发.苏红图-银根盆地白垩纪沉积相与构造环境[J].地质科学,1997,55(3):387-396.LI Wenhou,ZHOU Lifa. Cretaceous sedimentary facies and tectonic environment in Suhongtu Yingen Basin[J].Geosciences,1997,55(3):387-396.