Lithofacies Prediction from Well Logs Data using Different Neural Network Models

Leila Aliouane, Sid-Ali Ouadfeul, Noureddine Djarfour, Amar Boudella

Abstract

The main objective of this work is to predict lithofacies from well-logs data using different artificial neural network (ANN) models. The proposed technique is based on three classifiers types of ANN which are the self-organizing map (SOM), multilayer Perceptron (MLP) and radial basis function (RBF). The data set as an input of the neural network machines are the eight borehole measurements which are the total natural gamma ray; the three concentrations of the radioactive elements Thorium, Potassium and Uranium; the slowness of the P wave, the bulk density, the neutron porosity and the photoelectric absorption coefficient of two boreholes located in Algerian Sahara. Hence, the outputs of three neuronal kinds are the different lithological classes of clayey reservoir. These classes are obtained by supervised and unsupervised learning. The output results compared with basic stratigraphy show that the Kohonen map gives the best lithofacies classification where the thin beds intercalated in the reservoir, are identified. Consequently, the neural network technique is a powerful method which provides an automatic classification of the lithofacies reservoir.

References

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Paper Citation


in Harvard Style

Aliouane L., Ouadfeul S., Djarfour N. and Boudella A. (2013). Lithofacies Prediction from Well Logs Data using Different Neural Network Models . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: PRG, (ICPRAM 2013) ISBN 978-989-8565-41-9, pages 702-706. DOI: 10.5220/0004380707020706


in Bibtex Style

@conference{prg13,
author={Leila Aliouane and Sid-Ali Ouadfeul and Noureddine Djarfour and Amar Boudella},
title={Lithofacies Prediction from Well Logs Data using Different Neural Network Models},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: PRG, (ICPRAM 2013)},
year={2013},
pages={702-706},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004380707020706},
isbn={978-989-8565-41-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: PRG, (ICPRAM 2013)
TI - Lithofacies Prediction from Well Logs Data using Different Neural Network Models
SN - 978-989-8565-41-9
AU - Aliouane L.
AU - Ouadfeul S.
AU - Djarfour N.
AU - Boudella A.
PY - 2013
SP - 702
EP - 706
DO - 10.5220/0004380707020706