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Lithofacies prediction from well log data using a multilayer perceptron (MLP) and Kohonen's self-organizing map (SOM) – a case study from the Algerian Sahara S.-A. Ouadfeul1 and L. Aliouane21Algerian Petroleum Institute, IAP, Boumerdes, Algeria
2LABOPHT, FHC, UMBB, Boumerdes, Algeria
Received: 18 Apr 2013 – Revised: 30 May 2013 – Accepted: 03 Jun 2013 – Published: 28 Jun 2013 Abstract. In this paper, a combination of supervised and unsupervised
leanings is used for lithofacies classification from well log data. The
main idea consists of enhancing the multilayer perceptron (MLP) learning by
the output of the self-organizing map (SOM) neural network. Application to
real data of two wells located the Algerian Sahara clearly shows that the
lithofacies model built by the neural combination is able to give better
results than a self-organizing map.
Citation: Ouadfeul, S.-A. and Aliouane, L.: Lithofacies prediction from well log data using a multilayer perceptron (MLP) and Kohonen's self-organizing map (SOM) – a case study from the Algerian Sahara, Pattern Recogn. Phys., 1, 59-62, doi:10.5194/prp-1-59-2013, 2013.
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