摘要
薄层、薄互层厚度的预测是储层横向预测的一个重要环节。常规计算薄层厚度的方法是在时间域或频率域上通过提取单参数来实现的。本文则利用小坡变换，在时一频域进行最大滴分析，并提取多种特征参数；然后利用对薄层厚度敏感的地震特征参数之间的非线性关系，使用神经网络算法，建立了一套计算薄层及薄互层厚度的方法。理论模型的正反演结果表明：该算法对薄层厚度及薄互层累积厚度的预测均有较好的效果，且具有一定的抗噪声能力。我们还利用本文所述方法对TZ地区DL－92－04测线部分剖面的石炭系I油组薄互层砂岩的累积厚度进行了预测，结果令人满意。
The thickness prediction of thin layer or thin interbedded strata is an impor-tant part of lateral reservoir prediction. Conventional methods for thin-layer thick-ness estimation are achived by deriving single parameter from time-domain or fre-quency-domain seismic data. This method consists of following essential steps:. Do maximum entropy analysis in time-frequency domain by making wavelettransform.. Extract multiple characteristic parameters.. Compute thickness of thin layer or thin-interbedded strata by using nonlin-ear relations among seismic characteristic parameters which are sensitive to thin-layer thickness,and by taking neural network algorithm.The forward and inversion results of theoretic model show that this method,being of some noise resistant ability,brings good effects in predicting both thin-lay-er thickness and accumulative thickness of thin-interbedded strata- This method wasused to predict the accumulative thickness of thin-interbedded sand strata in Car- boniferous reservoir I of partial DL92－O4 seismic section In IZ area．The predic－ tion result is satisfactory．
出处
《石油地球物理勘探》
EI
CSCD
北大核心
1998年第2期204-213,共10页
Oil Geophysical Prospecting
基金
国家自然科学基金
关键词
小波变换
最大熵
地震特征
薄层
计算
地震勘探
wavelet transform,maximum entropy,seismic characteristic parameter,thin layer,thin-interbedded strata,neural network
