随着测井综合解释的成熟，在地质解释中综合应用各种测井曲线已成为一种趋势。本文以测井曲线为研究对象，对地层划分和测井相分析中的关键方法进行研究与实现。主要研究成果和创新点如下： 1.利用面向对象的解析方法，通过分析存储测井曲线的WIS文件，对测井曲线数据进行了正确的解析和存储。针对测井数据的影响因素，应用固定窗长相关对比法对曲线进行深度校正，应用Savitzky-Golay平滑滤波器滤除噪声，尽可能的消除非地层因素的影响，为测井曲线划分和测井相分析提供了正确的数据支持。 2.根据油田取芯数据的小样本特性，结合同区域地质特征相似性，研究基于支持向量机的测井曲线分层。针对传统多分类算法在计算上的不便及未考虑地层连续特性，改进了一对一多分类方法，提高了样本的学习和预测速度。由于每一段地层为一些连续点，应用滤波函数和阶梯函数识别地层分界面，进一步提升了地层划分的准确性。 3.结合同区域同层位内测井相的相似性，研究了基于测井曲线的支持向量机并用来进行测井相分析。能够反映测井相的测井曲线较多且这些数据之间存在相关性，因此应用主成分分析提取测井相主成分，降低了样本维数，有效地提升了计算效率。通过实验比较说明了编码多分类方法的有效性，应用基于主成分分析的支持向量机编码多分类方法对预测井进行了分析，得到了良好的分析结果。

With the maturity of logging comprehensive interpretation, it has become a tendency to make full use of different logging curves. This paper takes logging curve as the research object, discuss and realize some key methods in the stratigraphic division and log facies analysis. Primary research achievements and innovation points of this paper are described as follows: 1. Take advantage of the analytic method based on object-oriented, through analyzing WIS documents which contain logging curves, parse and store logging curves data accurately. In view of the influence factors of logging data, adopt fixed window length correlation contrast method for depth correction of curve, then use Savitzky-Golay smoothing filter to remove noises, which is aimed at eliminating noises caused by non-stratigraphic factors, which provide a scientific data support for stratigraphic division and log facies analysis. 2. According to the small sample properties of core oil fields, and the similarity of same regional geological characteristics, research log layering method based on support vector machine (SVM). On account of inconvenience in calculation and stratigraphic in continuity of traditional multi-classification algorithms, improve a one to one multi-classification method, so that enhance learning and prediction speed of the sample. As every stratum has some continuity points, we apply filter function and step function to recognize stratigraphic interface, further promoting the accuracy of stratigraphic division. 3. Combine with the similarity of log facies located at the same region and same layer, study support vector machine (SVM) algorithm based on logging curves, then use it for log facies analysis. Since logging curves reflecting log facies is more and there is correlation between curve data, we employ principal component to analyze and extract principal component of log facies, which not only reduce the sample dimension, but also improve computational efficiency. By comparing results of experiment, show the availability of coding multi-classification method. Meanwhile utilize support vector machine (SVM) coding multi-classification method based on principal component analysis to analyze prediction logging, which has obtained good analysis results.