Automatic classification of carbonate rocks permeability from super(1)H NMR relaxation data

• Published in: Expert systems with applications Vol. 42; no. 9; pp. 4299 - 4309
• Main Authors: da Silva, Pablo Nascimento da, Goncalves, Eduardo Correa, Rios, Edmilson Helton, Muhammad, Asif, Moss, Adam, Pritchard, Tim, Glassborow, Brent, Plastino, Alexandre, de Azeredo, Rodrigo Bagueira
• Format: Journal Article
• Language: English
• Published: 01.06.2015
• Subjects: Carbonates; Classification; Magnetic permeability; Mathematical models; Nuclear magnetic resonance; Permeability; Reservoirs; Rocks
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2015.01.034

The accurate permeability mapping, even with the aid of modern borehole geophysics methods, is still a big challenge on the reservoir management framework. One concern within the petrophysics community is that rock permeability value predicted by well logging should not be considered as absolute, mainly for carbonates, but a relative index for identifying more permeable zones. Therefore, in this paper a permeability classification methodology, based exclusively on super(1)H NMR (Nuclear Magnetic Resonance) relaxation data, was evaluated for the first time as an alternative to the prediction of permeability as a continuous variable. To pursue this, a side-by-side comparison of different data mining techniques for the permeability classification task was performed using a petrophysical dataset with 78 rock samples from six different carbonate reservoirs. The effectiveness of six classification algorithms (k-NN, Naive Bayes, C4.5, SMO, Random Forest and Multilayer Perceptron) was evaluated to predict the rock permeability class according to the following ranges: low (<1 mD), fair (1-10 mD), good (10-100 mD) and excellent (>100 mD). Discretization and feature selection strategies were also employed as preprocessing steps in order to improve the classification accuracy. For the studied dataset, the results demonstrated that the Random Forest and SMO strategies delivered the best classification performance among the selected classifiers. The computational experiments also evidenced that our approach led to more accurate predictions when compared with two methods widely adopted by the petroleum industry (Kenyon and Timur-Coates models).