无监督神经网络地震属性聚类方法在沉积相研究中的应用

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—...

Study on sedimentary facies based on unsupervised neural network seismic attribute clustering

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.