In this work, two data mining techniques are studied and applied to petroleum datasets. so as to extract useful information such as: correlations and interdependencies among the attributes. This helps to better understand important characteristics that can increase oil production. The two data mining techniques, Artificial Neural Networks and the Decision Tree algorithm, are combined to form the Neural Decision Tree (NDT) model. The NDT model enhances classification accuracy of the decision tree algorithm. The Neural Decision Tree model generates statistical results of classification and graphical representation of data in the form of a tree data structure, which supports data analysis in more detail, and useful knowledge can be extracted from it. The NDT model is useful for discovering interdependencies and for explaining correlations among the attributes of the data. The results generated from application of the Neural Decision Tree model can provide initial estimates of some parameters in the statistical modeling so that correlations among the parameters can be derived. The knowledge on correlations can support petroleum experts in making important decisions such as cost estimation and required resources for oil production.