基于地震波形聚类储集砂体边界识别与预测  

Boundary Identification and Prediction of Sand Body Based on Seismic Waveform

作　　者：李辉[1] 罗波[1] 何雄涛[1] 肖建玲[1] 

机构地区：[1]中国石油大港油田公司勘探开发研究院,天津300280

出　　处：《工程地球物理学报》2017年第5期573-577,共5页Chinese Journal of Engineering Geophysics

基　　金：中国石油股份公司重大科技专项(编号:2014E-06-07)

摘　　要：板桥斜坡BS37区块沙一下段储集砂体具有单层厚度薄、横向变化快的特点,传统的单属性地震储层预测精度低,识别难度大。通过典型井的测井响应与地震响应相结合,应用地震波形聚类技术能准确预测砂体分布。研究认为,BS37区块含油气砂体地震反射特征可划分为丘状反射型、平直反射型、下切反射型等类型。针对含油气砂岩反射特征精确标定井震关系,提取样本子波,应用约束条件下神经网络算法对研究区目的层地震波形进行相似性迭代聚类分析,有效地识别出主河道、河道侧翼等不同叠合模式砂体边界,为钻探部署提供了决策依据。The reservoir sand body of the BS37 block in Banqiao slope area has the characteristics of thin thickness of single layer and fast lateral change.The traditional single attribute seismic reservoir has low prediction accuracy and it is hard to be identified.Through the combination of well logging response and seismic response,the application of seismic waveform clustering technology can predict accurately the distribution of sand bodies.It is concluded that the seismic reflection characteristics of oil and gas sand bodies in BS37 block can be classified into mound-like type,flat reflection type and undercut type.Based on the reflection characteristics of oil and gas sandstone,it can demarcate accurately the well seismic relation and draw the sample wavelet.The seismic waveform of target stratum is analyzed by similarity iterative clustering under constraint condition of neural network algorithm,which can identify effectively the different sand body boundaries of the main channel and channel flank.The sandwich boundary of the overlapped model provides a basis for decision making.

关 键 词：波形聚类 分流河道 储集砂体 砂体边界 预测 

分 类 号：P631.4[天文地球—地质矿产勘探]