# 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

- Aliouane. L, Ouadfeul.S and Boudella. A, 2011, Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network. Arab J Geosci, DOI 10.1007/s12517-011-0459-4.
- Aliouane L., Ouadfeul S., Djarfour N., Boudella A., 2012. Petrophysical parameters estimation from well-logs data using Multilayer Perceptron and Radial Basis Function neural networks. T. Huang et al (Eds): ICONIP 2012, PartV, LNCS 7667, pp. 730-736, Springer-Verlag.
- Aminian. K, Ameri.S, 2005, Application of artificial neural networks for reservoir characterization with limited data, Journal of Petroleum Science and Engineering 49 (2005) 212- 222.
- Aminzadeh. F, Barhen . J, Glover. C. W and Toomarian. N. B, 1999, Estimation of reservoir parameter using a hybrid neural network, Journal of Petroleum Science and Engineering 24 _1999. 49-56
- Ellis D., V., Singer J. M., 2008. Well logging for earth scientists. 2nd edition, Spriger.
- Kohonen T., 1982. Organization and associative memory. Springer Series in information sciences, Vol. 8, Springer-Verlag.
- Kohonen T., 2000. Self-organizing map. Third edition, Springer.
- Lim S., L., 2005: Reservoir properties determination using fuzzy logic and neural networks from welldata in offshore Korea, Journal of Petroleum Science and Engineering 49 (2005) 182- 192.
- Ellis D. V., Singer J. M., 2008. Well logging for earth scientists. 2nd edition, Spriger.
- Ouadfeul S., Aliouane L., 2012. Lithofacies classification using Multilayer perecptron and the Self Organizing neural network. T. Huang et al. (Eds.): ICONIP 2012, Part V, LNCS 7667, pp. 737-744. Springer-Verlag.
- Powell, M. J. D., 1985. Radial Basis Functions for Multivariable Interpolation: A Review in IMA Conference on Algorithms for the Approximation of Functions and Data. RMCS, Shirvenham, UK, pp. 143-167.
- Sonatrach and Shlumberger, 2007. Well Evaluation Conference, Algeria.

#### 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