模糊聚类在三维地震参数处理中的应用

Fuzzy Clustering for 3D Seismic Parameters Processing

三维地震参数是一个庞大的三维数据体,能从不同侧面反映储集层的特征。通过对三维地震参数的分析,可以评估油藏表征的参数(沉积相、构造、生长历史、流体饱和度等)。针对传统的聚类方法不能很好地综合考虑各个参数之间的相似程度,提出了用模糊C-均值聚类(fuzzy c-means clustering,简称FCM)对三维地震参数进行处理。从三维地震参数和油藏表征参数之间的关系出发,采用模糊C-均值聚类方法对三维地震参数进行处理,依此评估油藏表征的参数。测试结果表明,模糊C-均值聚类方法能够对三维地震参数进行较为准确的分类,并为储集层的研究提供了很好的依据。

3D seismic parameter is a very large data sets, it can reflect the features of petroleum reservoir from different profiles. By analyzing the 3D seismic parameter, we can assess the parameters of the reservoir characterization, such as deposition, structure and growth history, fluid saturation and so on. The traditional clustering methods can＇t capture the degree of similarity between reservoir parameters well, so we introduced in this paper the application of Fuzzy C-means （FCM） clustering for the processing of 3D seismic parameters. It begins with the analyzing the relationship between 3D seismic parameter and reservoir characterization parameters, and then we process the 3D seismic parameters with fuzzy C-means clustering and assess the parameters of reservoir characterization. The testing result shows that fuzzy C-means clustering can classify the 3D parameters more accurately and it can provide a good evidence for the research of petroleum reservoir.