波形分类技术在鄂北薄砂岩储层预测中的应用

在河流相砂体的沉积过程中,砂泥岩在空间上的位置、厚度方面可以形成多种岩性组合模式,因此其对应的地震反射波会有不同的波形。应用神经网络波形分类技术,可以对波形进行分析、归纳,从而实现在区域上预测砂体的沉积相分布状况。考虑到低分辨率的地震资料和薄砂层之间的矛盾,谨慎的保幅性提频处理是需要的,但须以更高的井-震相关性及不变的横向振幅关系为前提。针对鄂尔多斯盆地大牛地气田二叠系下石盒子组盒三段的沉积特征,采用神经网络波形分类技术对反映不同岩性组合模式的地震波形进行相关和归类,预测出了多种砂体沉积模式的平面展布趋势。通过与由实际钻井得到的砂体分布图进行对比,发现二者有较好的一致性,说明波形分类技术可以快速有效地预测砂体的分布规律并且对于薄砂体具有较好分辨能力。

Application of seismic waveform classification technique in thin sandstones reservoir prediction in northern Ordos Basin

During the deposition of fluvial sand body,various lithological assemblages can be formed due to different spatial distribution and thickness of sandstones and mudstones,and their corresponding seismic reflections have different waveform patterns.The technique of the neural network waveform classification can be used to analyze and summarize their seismic waveforms,and then predict regional distribution of sandstone sedimentary facies.Considering the low resolution of the seismic data and the thin thickness of sand layers,it is necessary to do a band spread processing with fidelity.However,this processing should be based on higher correlations between wells and seismic data and stable lateral amplitude trends.According to the sedimentary characteristics of the 3rd member of the Permian Xiashihezi Formation in Daniudi gas field in the Ordos Basin,we performed correlation and classification of seismic waveforms which reflect different lithological assemblages by using the neural network waveform classification technique,and predicted the distribution of several sandstone sedimentary models.Comparison with well data-based sandbody distribution map shows that they are consistent.The technique is quick and effective for sandbody prediction and has a better resolution for thin sand bodies.