Porosity is a key factor in reservoir characterizations and represents the capacity of the rock to store the fluids, while lithofacies, describe the physical properties of rocks including texture, mineralogy and grain size. Many empirical approaches, such as linear/non-linear regression or graphical techniques Were developed for predicting porosity. Recently, researches used another tool named Artificial Neural Networks (ANNs) to achieve better predictions. To demonstrate the usefulness of Artificial Intelligence technique in geoscience area, we describe and compare two types of machines, Multilayer Perception Neural Network (MLP) and Support Vector Machine (SVM), in prediction reservoir properties from well log data. This study explores the capability of both paradigms, as automatic systems for predicting reservoir porosity. As it was expected, these computational intelligence approaches overcome the weakness of the standard regression techniques. Generally, the results show that the performances of Support Vector Machine outperform that Multilayer Perceptron neural networks. In addition, Support Vector Machine (SVM) is more robust, easier and quicker to train.