Edmilson Helton Rios
Petrobras
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Publication
Featured researches published by Edmilson Helton Rios.
Expert Systems With Applications | 2015
Pablo Nascimento da Silva; Eduardo Corrêa Gonçalves; Edmilson Helton Rios; Asif Muhammad; Adam Moss; Tim Pritchard; Brent Glassborow; Alexandre Plastino; Rodrigo Bagueira de Vasconcellos Azeredo
This work investigates the effectiveness of data mining analysis on NMR data.The goal is to accurately predict the permeability class of carbonate rocks.Our approach outperforms the traditional NMR models Timur-Coates and Kenyon.Traditional models ignore the singular relationship between T2 bins and pore throat.Data mining models capture the influence of each T2 bin over the permeability class. 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 1H 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 ( 100mD). 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).
Journal of Applied Geophysics | 2011
Edmilson Helton Rios; Paulo Frederico de Oliveira Ramos; Vinicius de França Machado; Giovanni Chaves Stael; Rodrigo Bagueira de Vasconcellos Azeredo
Petrophysics | 2014
Willian Trevizan; Paulo Netto; Bernardo Coutinho; Vinicius de França Machado; Edmilson Helton Rios; Songhua Chen; Wei Shao; Pedro Romero
Geophysical Journal International | 2016
Edmilson Helton Rios; Irineu Figueiredo; Adam Moss; Tim Pritchard; Brent Glassborow; Ana Beatriz Guedes Domingues; Rodrigo Bagueira de Vasconcellos Azeredo
OTC Brasil | 2015
Willian Trevizan; Bernardo Coutinho; Paulo Netto; Edmilson Helton Rios; P. Ramos; J. Salazar; M. Bressan
Archive | 2015
Edmilson Helton Rios; Adam Moss; Tim Pritchard; Ana Beatriz; Guedes Domingues; Rodrigo Bagueira
Seg Technical Program Expanded Abstracts | 2014
Edmilson Helton Rios; Irineu Figueiredo; André Compan; Bernardo Santos; Willian Trevizan
SPWLA 55th Annual Logging Symposium | 2014
Bernardo Coutinho; Paulo Netto; Willian Trevizan; Edmilson Helton Rios; Vinicius de França Machado; Wei Shao; Songhua Chen
SPWLA 55th Annual Logging Symposium | 2014
Edmilson Helton Rios; Irineu Figueiredo; Asif Muhammad; Rodrigo Bagueira de Vasconcellos Azeredo; Adam Moss; Tim Pritchard; Brent Glassborow
SPWLA 55th Annual Logging Symposium | 2014
Giovanna Carneiro; Andre Souza; Austin Boyd; L. Schwartz; Yi-Qiao Song; Rodrigo Bagueira de Vasconcellos Azeredo; Willian Trevizan; Bernardo Santos; Edmilson Helton Rios; Vinicius de França Machado