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小波分析在深水砂砾岩和泥页岩地层层序划分中的应用 被引量:19

Application of Wavelet Analysis in Sequence Division of Deepwater Glutenite and Shale Layer
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摘要 针对深水厚层砂砾岩和泥页岩沉积地层缺乏标志层,地层划分困难等问题,初步探索出一种以包含不同地质信息的自然伽马曲线和由声波时差曲线—电阻率测井曲线交会得到的ΔLogR曲线为对象,通过MATLAB软件对2类曲线分别进行db5小波变换,利用获得的能够充分显示深水砂砾岩和厚层泥页岩地层的内部旋回性结构的时频色谱图和小波系数重构曲线指导深水砂砾岩和泥页岩层序划分方法。以东营凹陷民丰地区永925井沙三中亚段—沙三下亚段近岸水下扇砂砾岩和湖相泥页岩沉积为例,利用db5小波分析进行了长期、中期和短期基准面旋回的划分。研究表明,自然伽马测井曲线小波分析对近岸水下扇砂砾岩体的划分与传统方法吻合较好;ΔLogR曲线小波分析能够有效刻画厚层湖相泥页岩的内部结构旋回,对其层序划分具有良好的指导效果,对小波分析在层序地层划分中的应用是一个重要发展和补充。 Sequence division of deepwater thick glutenite and shale layer is difficult for lacking of lithological marker horizons.Based on wavelet transform analysis of gamma logging data and ΔlogR data,a new method of sequence division for such sediment layers is obtained preliminarily through combination of interval transit time curve and resistivity curve.Time frequency chromatograms and wavelet coefficient reconstruction curves obtained through db5 wavelet transform,has the advantage to demonstrate the internal cyclicity structure of deepwater thick glutenite and shale layer clearly.Thus,division of such layers can be done with the guidance of the obtained chromatograms and curves.Taking as an example of nearshore subaqueous fan glutinite and lacustrine shale in middle-low section of Sha 3 Member in the Yong 925 well of Minfeng sub-sag,we use the db5 wavelet analysis of gamma logging data and ΔlogR data to divide the base level cycle into long term,middle term and short term.The research results show that the division of nearshore subaqueous fan glutinite by using the wavelet analysis of gamma logs is in good agreement with traditional method.Wavelet analysis of ΔlogR data,as a development and complement to wavelet analysis,effectively describe the internal structure of the thicker lacustrine shale.
出处 《天然气地球科学》 EI CAS CSCD 北大核心 2012年第2期251-258,共8页 Natural Gas Geoscience
基金 国家自然基金(编号:40972080 41102058) 国家科技重大专项(编号:2011ZX05009) 中国石油大学(华东)研究生自主创新科研计划项目(编号:12CX06003A)联合资助
关键词 深水沉积 小波分析 自然伽马测井曲线 ΔLogR曲线 db5小波 Deepwater sediment Wavelet analysis Gamma well logs ΔlogR data db5 wavelet.
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