文献类型：期刊文献

中文题名：基于小波分析的储层流体性质识别

英文题名：Identification of the features of wavelet analysis based reservoir fluid

作者：张莹[1];潘保芝[2];何胜林[2];张培珍[1]

机构：[1]广东海洋大学海洋遥感与信息技术实验室,湛江524088;[2]吉林大学地球探测科学与技术学院,长春130026

年份：2012

卷号：27

期号：6

起止页码：2554

中文期刊名：地球物理学进展

外文期刊名：Progress in Geophysics

收录：CSTPCD、、北大核心2011、CSCD2011_2012、北大核心、CSCD

语种：中文

中文关键词：小波多分辨分析;小波包分析;流体识别;低阻储层

外文关键词：wavelet multiresolution analysis; wavelet packet analysis; fluid identification; low-resistivity reservoir

中文摘要：针对储层流体性质判别这一问题,从测井响应的物理意义出发,认为测井信号的总能量是由地层微观信息与宏观信息的能量共同构成,地层孔隙中所含流体的性质是微观信息,应与测井曲线的微小抖动相对应,即测井信号高频部分.由此,分别采用小波多分辨分析及小波包分析两种处理方式,选取db5小波基函数对深感应电阻率曲线进行不同频带和时段内的分解,提取不同尺度下油层、水层及干层段电阻率曲线高频部分能量,进而利用能量谱峰分析法划分流体类型.对比两种处理方式,小波包分析效果更佳,得到的能量谱主峰位置是区别不同流体类型的主要标志,该方法在电性差别不明显的低阻储层流体性质识别中也具有良好应用效果.

外文摘要：he present work is to determine the property of reservoir fluid from the physical meaning of logging response.The work is based on the precondition that the total energy of a logging signal is the energy sum of the microscopic and macro strata information,and the property of the fluid inside the strata pores,as microscopic information,should correspond to the small jitter of logging curves（i.e.,the high frequency segment of logging signal）.Thus,we adopt wavelet multiresolution analysis and wavelet packet analysis as approach.The deep induction resistivity curves were decomposed by db5 wavelet basis function through different frequency bands and time slots.The energy of high frequency segment of resistivity curves from different scales of oil,water and dry layers was picked out,and the fluid types were classified according to the energy spectrum analysis.The results showed that the main peak position obtained by wavelet packet analysis is a primary symbol to discriminate different types of fluid,which is better than wavelet multiresolution analysis.Good results can also be obtained via the proposed method during the fluid property discrimination of low resistivity reservoirs with unconspicuous electric differences.