Investigating the effect of correlation-based feature selection on the performance of support vector machines in reservoir characterization

Permeability is an important property of hydrocarbon reservoir as crude oil lies underneath rock formations with lower permeability and its accurate estimation is paramount to successful oil and gas exploration. In this work, we investigate the effect of feature selection on the generalization performance and predictive capability of support vector machine (SVM) in predicting the permeability of carbonate reservoirs. The feature selection was based on estimating the correlation between the target attribute and each of the available predictors. SVM has been improved through the feature selection approach employed. The uniqueness of this approach is the fact that it employs fewer dataset in improving the performance of the SVM model. The effect of the approach has been investigated using real-industrial datasets obtained during petroleum exploration from five distinct oil wells located in a Middle Eastern oil and gas field. The results from this approach are very promising and suggest a way to improve on the performance of this algorithm and many other computational intelligence methods through systematic selection of the best features thereby reducing the number of features employed.