基于支持向量机和主成分分析的辫状河储层夹层识别

Identification of interlayers in braided river reservoir based on support vector machine and principal component analysis

以大庆长垣北部的喇嘛甸油田PI2辫状河砂体为例,应用支持向量机(SVM)算法,结合主成分分析(PCA)数据降维,通过4种测井数据开展辫状河储层夹层的自动识别。以4类测井曲线12种特征参数作为输入变量,以夹层类型作为输出变量,建立支持向量机模型,利用高斯径向基核函数及网格搜索确定最优参数(核函数半径g和惩罚因子C)。结果表明:基于未降维的测井特征参数的识别准确率为86.17%,经PCA降维的测井特征参数的识别准确率为92.55%,提高了6.38%;钙质夹层识别精度最高,由于岩性相近测井响应差异不明显以及测井参数的局限性,泥质和物性夹层之间出现误判;但基于主成分分析的SVM算法对于夹层的识别具有更高的可靠性,可以满足地质解释的需要。

Taking the PI2 braided river sand body of the Lamadian Oilfield in the northern Daqing Placanticline as an example,the support vector machine(SVM)algorithm,combined with principal component analysis(PCA)data dimension reduction was applied to realize automatic identification of braided river interlayer based on four types of logging data.The SVM model was established with twelve characteristic parameters using these four logging curves as input and interlayer types as output,and the optimal parameters(kernel function radius g and penalty factor C)were determined using Gaussian radial basis kernel function and grid search.The results show that the identification accuracy rate of logging feature parameters without dimension reduction was 86.17%,and the accuracy rate of logging feature parameters with PCA dimension reduction was 92.55%,with an increase of 6.38%.The identification accuracy of calcareous interlayers is the highest,and misjudgments occur between muddy and physical interlayers due to insignificant differences in logging response with similar lithology and limitation of logging parameters.However,the SVM algorithm based on PCA has a better reliability for the identification of interlayers and can meet the needs of geological interpretation.