Modeling Permeability Prediction Using Extreme Learning Machines
| Autor: | Abdul Azeez Abdul Raheem, Sunday O. Olatunji, Ali Selamat |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Artificial neural network
Computer science business.industry Well logging Recurrent neural nets computer.software_genre Machine learning Support vector machine Permeability (earth sciences) Reservoir engineering Reservoir modeling Data mining Artificial intelligence business computer Extreme learning machine |
| Zdroj: | 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation. |
| DOI: | 10.1109/ams.2010.19 |
| Popis: | In this work, an extreme learning machine (ELM) has been used in predicting permeability from well logs data have been investigated and a prediction model has been developed. The prediction model has been constructed using industrial reservoir datasets that are collected from a Middle Eastern petroleum reservoir. Prediction accuracy of the model has been evaluated and compared with commonly used artificial neural network and support vector machines (SVM). We have applied an extreme learning machine (ELM) for single-hidden layer feed-forward neural networks (SLFNs). As the ELM has the advantage of fast learning speed and good generalization performance. The simulation results have shown a promising prospect for extreme learning machine in the field of reservoir engineering in particular and oil and gas exploration in general, as it outperforms ANN and SVM. |
| Databáze: | OpenAIRE |
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