摘要
基于自组织映射神经网络分析技术(SOMA)划分地震相是一种属性综合聚类方法,开展地震属性优选、确定聚类种数、分析地震相—沉积相关系是该方法应用过程中的关键。针对银额盆地白垩系苏红图组主力生油层系,充分挖掘叠后地震资料中反映的地震相类别信息,在地震沉积学理论指导下,应用SOMA进行属性聚类分析,并结合地质资料开展地震相—沉积相研究。优选均方根振幅、信息熵、混沌李、分形关联维等4种地震属性进行聚类分析。研究结果表明,艾特格勒凹陷苏红图组主要发育扇三角洲、辫状河三角洲、滨浅湖、深湖等沉积相。应用此技术降低了少井区地震相—沉积相分析结果的不可靠性,为油气新区勘探的沉积相分析提供了新的依据,是一种切实可行的技术。
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.
作者
王天云
韩小锋
许海红
孙小萍
李陶
侯艳
WANG Tianyun;HAN Xiaofeng;XU Haihong;SUN Xiaoping;LI Tao;HOU Yan(BGP Inc.,CNPC,Zhuozhou,Heibei 072751,China;Xi'an Geological Survey Center of China Geological Survey,Xi'an,Shaanxi 710054,China)
出处
《石油地球物理勘探》
EI
CSCD
北大核心
2021年第2期372-379,I0013,共9页
Oil Geophysical Prospecting
基金
中国地质调查局油气地质调查项目“银额盆地西部—北山盆地群油气基础地质调查”(DD20190092)资助。
