摘要
当地震波通过油气藏时,低频伴影是在油气储层的下方产生的低频的强反射能量团,低频伴影已作为一个油气标志广泛用于油气储层的预测.其中的特征频率是低频伴影研究中的一个重要参数,前人大多采用频率扫描的方法,人工比对找出低频伴影最明显的单频剖面.在频率的选择上依赖研究人员人工确定,存在准确率较低的问题.针对这个问题,本文根据多层快慢纵波转换模型,从双相介质中快慢纵波反射透射系数的渐近形式出发,推导了多层快慢纵波转换模型低频伴影特征频率公式,研究表明,影响特征频率的关键因数有气层渗透率、气层厚度和介质的模量等.该方法通过实际资料计算了低频伴影特征频率,并用广义S变换验证了实际地震资料中特征频率对应的低频伴影剖面,结果表明了低频伴影特征频率计算的正确性.
When the seismic wave passes through the oil and gas reservoir,the low frequency accompanying shadow is the low frequency strong reflection energy mass produced under the oil and gas reservoir,and the low frequency accompanying shadow has been widely used in the prediction of oil and gas reservoir as an oil and gas mark.The characteristic frequency is an important parameter in the research of low-frequency companion shadow.Most of the predecessors use the method of frequency scanning to find out the most obvious single-frequency profile of low-frequency companion shadow.The selection of frequency depends on the manual determination of researchers,which has the problem of low accuracy.In order to solve this problem,according to the multi-layer fast-slow P-wave conversion model and the asymptotic form of the reflection and transmission coefficient of fast-slow P-wave in two-phase medium,the low-frequency characteristic frequency formula of the multi-layer fast-slow P-wave conversion model is derived in this paper.the key factors affecting the characteristic frequency are gas reservoir permeability,gas reservoir thickness and medium modulus.In this method,the low-frequency shadow characteristic frequency is calculated from the actual data,and the low-frequency shadow section corresponding to the characteristic frequency in the actual seismic data is verified by generalized S-transform.the results show that the calculation of low-frequency shadow characteristic frequency is correct.
作者
贺东宣
李勇
牛聪
叶云飞
吴昊
HE DongXuan;LI Yong;NIU Cong;YE YunFei;WU Hao(Chengdu University of Technology,Chengdu 610059,China;CNOOC Research Institute,Beijing 100027,China)
出处
《地球物理学进展》
CSCD
北大核心
2024年第3期1241-1250,共10页
Progress in Geophysics
基金
国家科技重大专项“时频聚集流体识别方法研究”(2016ZX05026-001-04)资助。
关键词
储层预测
低频伴影
特征频率
双相介质
渐近方程
Reservoir prediction
Low frequency accompanying shadow
Characteristic frequency
Two-phase medium
Asymptotic equation