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基于测井数据的地质信息提取

Picking up Information from Well Log Data

【作者】 孙厚敏;

【导师】 余继峰;

【作者基本信息】 山东科技大学 , 矿产普查与勘探, 2006, 硕士

【摘要】 以层序地层学理论为指导,运用沉积学、小波分析、分形理论、主成分分析等方法充分挖掘测井数据序列中新的沉积学替代性指标并建立基于这些指标的地质信息模型,进而实现多分辨地层划分对比,储层识别、沉积相判识等。本文确定了基于测井数据的沉积学特征提取方法。采用主成分分析法,从多个具有复杂相关关系的测井参数中,提取最能反映地层特征的少数几个非相关的主成分,使其能有效地综合原来多各个测井参数所反映的地层信息,从而仅用少数几个主成分就能达到有效地划分地层的目的;测井信号不可避免的存在噪声,采用小波降噪,选择合适的小波函数进行测井信号分解,并对分解后测井信号的各层小波系数作用阈值,达到很好的降噪效果,大大提高了测井信号的信噪比;测井序列的小波变换可以直观方便地识别层序、准层序界面,大大提高了层序识别分辨率;测井曲线的分形维数在测井层序界面及海泛面处有异常反映,是高分辨层序分析及地质背景复杂性解释的有效参数;在Matlab编程环境下编写了可行有效的计算机应用程序来分别实现主成分分析及小波消噪,为充分利用测井数据解决深部勘探的地质问题开辟了新的途径。

【Abstract】 Guided by the theory of sequence stratigraphy, new sedimentary indexes are found in the well-logging data on the base of sedimentary theory, wavelet analysis, fractal theory and Principal Component Analysis, etc. And the geological model is set up based on the new indexes. Then the recognition of storing beds and sedimentary phases is measured and automatic. Also the previous well-logging data processing, the method of picking up the sedimentary characteristics and the measured assessment model of the storing beds are established in this thesis. The principal component analysis can pick up few irrelative principal components that can reflect stratum characteristics from relative well logging parameters. And the principal components can reflect the stratum characteristics synthetically and effectively. Using the principal components can reach the goal of measuring the stratum effectively. In the well-logging data noises exist inevitably. In this thesis wavelet denoising is adopted and suitable thresholds are chosen to deal with the coefficients after the well-logging data is decomposed by wavelet. After wavelet denoising, the noise intention is reduced greatly. The fractal dimensions of log curves change abnormally at SBs and fs, being a good parameter in high-resolution sequence stratigraphic analysis and explanation of the complexity of geological background. Computer programs have been written in Matlab workspace to run Principal Component Analysis and wavelet denoising, providing an effective tool in corresponding study and cutting a new way to make full use of the well-logging data to solve geological problems in mineral exploration, especially within deep areas.

  • 【分类号】P631.81
  • 【被引频次】4
  • 【下载频次】668
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