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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chikhi, S. |
| Copyright Year | 2005 |
| Description | Author affiliation: LIRE Lab., Mentouri Univ. (Chikhi, S.) |
| Abstract | We combine neural networks (NNs) and hidden Markov models (HMMs) techniques in order to obtain the lithology identification of wells situated in the Triasic province (Sahara). For the same aim, two systems based on adaptive resonance theory (ART), ART1 and fuzzy ART, are also developed. Our objective is to facilitate the work of the geological experts by permitting them to obtain quickly the structure and the nature of lands around the drilling. Lithology identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir characterisation. In this paper, we show that it is interesting to combine the respective capacities of the HMMs and NNs to produce a new effective hybrid models that draw their source in the two formalisms and can provide us a more reliable reservoir model. Comparisons are established to show that the results obtained by the NN-HMM hybrid system are close to those obtained by the fuzzy ART approach applied to the same borehole with the same well logs |
| Starting Page | 1884 |
| Ending Page | 1887 |
| File Size | 929921 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780395212 |
| DOI | 10.1109/ICTTA.2006.1684676 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-04-24 |
| Publisher Place | Syria |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Handwriting recognition Geology Subspace constraints Neural networks Hidden Markov models Multilayer perceptrons Reservoirs Fuzzy neural networks Computer networks Multi-layer neural network |
| Content Type | Text |
| Resource Type | Article |
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