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 hasbecome a popular method for porosity estimation. Support vector machine (SVM) a new intelligentmethod with a great generalization potential of modeling non-linear relationships has been introducedfor 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 Iransouth oil fields from well log data, the SVR model is firstly constructed; then the performanceachieved is compared to that of an artificial neural network (ANN) model with a multilayerperceptron (MLP) architecture as a well-known method to account for the reliability of SVR or thepossible improvement made by SVR over ANN models. The results of this study show that byconsidering correlation coefficient and some statistical errors the performance of the SVR modelslightly improves the ANN porosity predictions.

Keywords:

Petrophysical Parameter , Reservoirs , Porosity , Well Log Data , Support Vector Machine