the porosity prediction of one of iran south oil field carbonate reservoirs using support vector regression

porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. nowadays, using intelligent techniques has become a popular method for porosity estimation. support vector machine (svm) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced for both regression (support vector regression (svr)) and classification (support vector classification (svc)) problems. in the current study, to estimate the porosity of a carbonate reservoir in one of iran south oil fields from well log data, the svr model is firstly constructed; then the performance achieved is compared to that of an artificial neural network (ann) model with a multilayer perceptron (mlp) architecture as a well-known method to account for the reliability of svr or the possible improvement made by svr over ann models. the results of this study show that by considering correlation coefficient and some statistical errors the performance of the svr model slightly improves the ann porosity predictions.