1887

Abstract

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.

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/content/papers/10.3997/2214-4609-pdb.280.iptc14514_noPW
2012-02-07
2021-10-16
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.280.iptc14514_noPW
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