利用Kohonen神经网络划分乌夏地区深层沉积相

Application of Kohonen Neural Network to Deep Sedimentary Facies Division in Wu-Xia Area, Junggar Basin

在对准噶尔盆地乌夏地区二叠系夏子街组的研究过程中,利用连片三维数据体的高信噪比和波组特征明显的优点,选择了可信度较高的地震反射内部结构和外部形态,辅助地震属性(瞬时振幅、瞬时频率和相关长度),使用Kohonen神经网络方法对地震相进行了量化分析和命名,并且利用测井解释和岩心分析及古生物特征等分析成果,将地震相转换为沉积相,取得了良好的地质效果,并解决了深层井少情况下沉积相难于划分的问题。

The 3D seismic data-processing assembly with the merit of high signal-to-noise ratio and the obvious wave characteristic is used to select high-reliability seismic reflection internal texture and external shape from Xiazijie formation of Permian in Wu-Xia area of Junggar basin. By means of such seismic attributes as instantaneous amplitude, instantaneous frequency and persistence length, the seismic facies is quantitatively analyzed and nominated using Kohonen neural network method. The results from well log interpretation, core analysis and palaeontologic evidence are applied to conversion of the seismic facies into sedimentary facies, thus well solving the problem unable to classify the sedimentary facies with few deep well in the past.