Intelligent System For Estimating The Porosity In Sediments From The Analysis Of Signals GPR

Título: Intelligent System For Estimating The Porosity In Sediments From The Analysis Of Signals GPR

Autores: Araújo, Eduardo Henrique Silveira de; Lima Filho, Francisco Pinheiro; Dória Neto, Adrião Duarte

Resumo: This paper presents the elaboration of a methodological propose for the development of an intelligent system, able to automatically achieve the effective porosity, in sedimentary layers, from a database bank built with information from the Ground Penetrating Radar – GPR. The intelligent system was built to model the relation between the porosity (response variable) and the electromagnetic attribute from the GPR (explicative variables). Using it, the porosity was estimated using the artificial neural network (Multilayer Perceptron – MLP) and the multiple linear regression. The data from the response variable and from the explicative variables were acquired in laboratory and in GPR surveys outlined in controlled sites, on site and in laboratory. The proposed intelligent system has the capacity of estimating the porosity from any database bank available, which has the same variables used in this paper. The architecture of the neural network used can be modified according to the existing need, adapting to the data bank available. The use of the multiple linear regression model allowed the identification and quantification of the influence (level of effect) of each explicative variable in the estimation of the porosity. The proposed methodology an innovative approach the use of the GPR, not only for the imaging of the sedimentary geometry and faces, but mainly for the automatically achievement of the porosity – one of the most important parameters for the characterization of reservoir rocks (from petroleum or water).

Palavras-chave: Porosity; Artificial neural networks (ANN); GPR; Intelligent system

Páginas: 6

Código DOI: 10.21528/CBIC2013-160

Artigo em pdf: bricsccicbic2013_submission_160.pdf

Arquivo BibTex: bricsccicbic2013_submission_160.bib