Electrofacies Modelling and Lithological Classification of Coals and Mud-bearing Fine-grained Siliciclastic Rocks Based on Neural Networks

Paula Schmitt, Mauricio R. Veronez, Francisco M. W. Tognoli, Viviane Todt, Ricardo C. Lopes, Carlos A. U. Silva

Abstract


The identification of lithofacies from well is usually an interpretative process based on geophysical logs since core and sidewall samples are not usually available. Despite being always sampled and described, cuttings are useful only as a reference for determining the rocks because a number of problems occur during the drilling and sampling activities. Well logs are in situ continuous records of different physical properties of the drilled rocks, which can be associated with different lithofacies by experienced log analysts. This task needs a relatively great amount of time and it is likely to be imperfect because the human analysis is subjective. Thus, any alternative method of classification with high accuracy and promptness is very welcome by the log analysts. This paper is based on Neural Networks (NNs) applied in well data from the Leão Coal Mine, southern Brazil, in order to classify organic mudrocks, coals and siliciclastic sandstones, the main rocks present in the Rio Bonito and Palermo formations, by using their well logs as database. The training and validation set of the NN contain data from eight cored and logged boreholes. The input included 409 values of depth and logs of gamma-ray, spontaneous potential, resistance and resistivity for each electrofacies. The neural network model was the feedforward multilayer perceptron (MLP) and the neural networks were trained with variations of the backpropagation algorithm: Levenberg-Marquardt and Resilient backpropagation. Although an accuracy of approximately 80% had been achieved in the general classification, discrepant accuracies in the classification of the different electrofacies are discussed in order to better understand the reasons that affected negatively the NN performance.

Full Text: PDF DOI: 10.5539/esr.v2n1p193

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Earth Science Research   ISSN 1927-0542 (Print)   ISSN 1927-0550 (Online)
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