This paper describes a new approach to automated well-to-well log correlation using artificial intelligence and principal component analysis. The approach to correlate wireline logging data is on the basis of a large set of subjective rules that are intended to represent human logical processes. The reliable correlation can be established from the first principal component logs derived from both the important informations around wellbore and the largest common part of variances of all available well log data. The data processed are mainly the qualitative informations such as the characteristics of the shapes extracted along log traces. The apparent geologic zones are identified by pattern recognition for the specific characteristics of the computed principal component log trace. The characteristics are collected as a set of objects by object oriented programming. The correlation of zones between wells is made by rule-based inference program.

Correlation with field log data shows that this approach can make well-to-well correlation more reliable and accurate. This method can be used for well-to-well log correlation with efficiency and accuracy.

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