Porosity Prediction Using Bagging of Complementary Neural Networks
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Data mining applications in hydrocarbon exploration
Artificial Intelligence Review
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Reservoir characterization especially well log data analysis plays an important role in petroleum exploration. This is the process used to identify the potential for oil production at a given source. In recent years, support vector machines (SVMs) have gained much attention as a result of its strong theoretical background. SVM is based on statistical learning theory known as the Vapnik-Chervonenkis theory. The theory has a strong mathematical foundation for dependencies estimation and predictive learning from finite data sets. This paper presents investigation on the use of SVM in reservoir characterization. Initial results show that SVM can be an alternative intelligent technique for reservoir characterization.