利用测井资料快速准确地确定沉积微相是油田勘探开发中急需研究解决的问题.沉积微相特征在测井曲线上有所反映,将常规测井资料及其解释成果中的地质资料同岩心资料相结合,分别通过主成分分析(PCA)和独立分量分析(ICA)提取反映沉积微相变化的特征,利用支持向量机(SVM)建立沉积微相的判别模型,根据该模型对未取心井段的沉积微相进行自动识别.该方法在小样本情况下,两者都比传统的不经过特征提取的SVM方法识别效率更高,且经过ICA特征提取后的识别率优于PCA方法.%Identifying sedimentary facies based on log data is one of the important and desiderated problems in oil field exploration and development. Microfacies characteristics are reflected from log data. Some features described the microfacies changes based on the combination of conventional logging data, the geological interpretation results and core data are extracted by the principal component analysis (PCA) and independent component analysis (ICA). Quantitative identification model of microfacies based on support vector machine can automaticly determine the sedimentary microfacies types of the non-cores information drills. Practical application shows that the proposed method is better than traditional SVM which without feature extraction in small sample cases, and the recognition rate after ICA feature extraction is better than that of PCA.
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