核磁共振横向弛豫时间谱高斯混合聚类及应用

为使核磁共振测井横向弛豫时间(T2)谱的定量表征结果更为直观地反映储集层类型和孔隙结构,提出基于高斯混合模型(GMM)的T2谱无监督聚类和孔隙结构定量识别方法.首先对T2谱数据进行主成分降维,减弱数据间的相关性;其次采用高斯混合模型概率密度函数对降维数据进行拟合,结合期望值最大化算法和赤池信息准则变化率得到模型参数和最...

An unsupervised clustering method for nuclear magnetic resonance transverse relaxation spectrums based on the Gaussian mixture model and its application

To make the quantitative results of nuclear magnetic resonance(NMR)transversal relaxation(T2)spectrums reflect is proposed the type and pore structure of reservoir more directly,an unsupervised clustering method was developed to obtain the quantitative pore structure information from the NMR T2 spectrums based on the Gaussian mixture model(GMM).We conducted the principal component analysis on T2 spectrums in order to reduce the dimension data and the dependence of the original variables.The dimension-reduced data was fitted using the GMM probability density function,and the model parameters and optimal clustering numbers were obtained according to the expectation-maximization algorithm and the change of the Akaike information criterion.Finally,the T2 spectrum features and pore structure types of different clustering groups were analyzed and compared with T2 geometric mean and T2 arithmetic mean.The effectiveness of the algorithm has been verified by numerical simulation and field NMR logging data.The research shows that the clustering results based on GMM method have good correlations with the shape and distribution of the T2 spectrum,pore structure,and petroleum productivity,providing a new means for quantitative identification of pore structure,reservoir grading,and oil and gas productivity evaluation.