Oil bearing layers and their sedimentary microfacies are generally analyzed by collecting oil well logs in the oilfield development. The well log time series is the correct reflection of geological characteristics of the oil bearing layers with quite a high probability. Unfortunately

feature extraction is of certain difficulty to the computer sedimentary microfacies recognition with well log time series. Oil bearing layers are the resultants of sedimentary layer sequence. The formation of oil bearing layers is very complicated and variable. Extracting the chaotic features is promising. But proving that well log time series are chaotic is prerequisite to extract their chaotic features. There are several methods developed for the chaos identification at present. In this paper

the well log time series is proved to be chaotic indeed by phase space reconstruction technology and G P algorithm

which is a practical algorithm for computing the correlation dimension of a time series. The experiment results indicate that the correlation dimension of an oil layer group is related to its structure. Another interesting phenomenon is that the correlation dimension of an oil layer group is generally larger than that of the whole well.