Prediction of Oil and Gas Reservoir Properties using Support Vector Machines

Artificial Intelligence techniques have been used in petroleum engineering to predict various reservoir properties such as<br>porosity, permeability, water saturation, lithofacie and wellbore stability. The most extensively used of these techniques is<br>Artificial Neural Networks (ANN). More recent techniques such as Support Vector Machines (SVM) have featured in the<br>literature with better performance indices. However, SVM has not been widely embraced in petroleum engineering as a<br>possibly better alternative to ANN. ANN has been reported to have a lot of limitations such as its lack of global optima. On the<br>other hand, SVM has been introduced as a generalization of the Tikhonov Regularization procedure that ensures its global<br>optima and offers ease of training.