Extreme Learning Machine combined with a Differential Evolution algorithm for lithology identification

Camila Martins Saporetti, Grasiele Regina Duarte, Tales Lima Fonseca, Leonardo Goliatt da Fonseca, Egberto Pereira


Lithology identification, obtained through the analysis of several geophysical properties, has an important role in the process of characterization of oil reservoirs. The identification can be accomplished by direct and indirect methods, but these methods are not always feasible because of the cost or imprecision of the results generated. Consequently, there is a need to automate the procedure of reservoir characterization and, in this context, computational intelligence techniques appear as an alternative to lithology identification. However, to acquire proper performance, usually some parameters should be adjusted and this can become a hard task depending on the complexity of the underlying problem. This paper aims to apply an Extreme Learning Machine (ELM) adjusted with a Differential Evolution (DE) to classify data from the South Provence Basin, using a previously published paper as a baseline reference. The paper contributions include the use of an evolutionary algorithm as a tool for search on the hyperparameters of the ELM. In addition, an  activation function recently proposed in the literature is implemented and tested. The  computational approach developed here has the potential to assist in petrographic data classification and helps to improve the process of reservoir characterization and the production development planning.


Extreme Learning Machines; Differential Evolution; Lithology

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DOI: https://doi.org/10.22456/2175-2745.80702

Copyright (c) 2018 Camila Martins Saporetti, Grasiele Regina Duarte, Tales Lima Fonseca, Leonardo Goliatt da Fonseca, Egberto Pereira

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