Log-based direct identification of complex lithology is still challenging in complex reservoir evaluation due to the ambiguity and uncertainty of results.The principal component variables constructed based on logging curves and having an accumulative variance contribution ratio more than 85% were selected to represent the inputs of multidimensional variables.This method can simplify the data structure and solve the problem of carbonate lithology identification through replacing the original multidimensional variables with a small amount of comprehensive variables while keeping the original information as much as possible.Five comprehensive variables including F1-F5 are constructed by using the measured GR

CNL

DEN

AC and RT of the Cambrian carbonate reservoirs in YH field.Among these variables

the F1 and F2 principle variables account for 87.73% of the total contribution ratio

so they can replace the multidimensional variables efficiently.The case study shows that the lithology of carbonates can be effectively identified through analysis on crossplot of of F1 and F2.This method has been successfully applied to identify the lithology of the Cambrian carbonates in YH field.