Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization

• Main Authors: Fatai Adesina, Anifowose, Jane, Labadin, Abdulazeez, Abdulraheem
• Format: Article
• Language: English
• Published: Elsevier B.V. 2015
• Subjects: L Education (General); T Technology (General)
• Online Access: http://ir.unimas.my/id/eprint/8464/1/Ensemble%20model%20of%20non-linear%20feature%20selection-based%20Extreme%20Learning%20Machine%20%28abstract%29.pdf; http://ir.unimas.my/id/eprint/8464/; http://www.researchgate.net/publication/272523954_Ensemble_model_of_non-linear_feature_selection-based_Extreme_Learning_Machine_for_improved_natural_gas_reservoir_characterization

The deluge of multi-dimensional data acquired from advanced data acquisition tools requires sophisticated algorithms to extract useful knowledge from such data. Traditionally, petroleum and natural gas engineers rely on “rules-of-thumb” in the selection of optimal features with much disregard to the hidden patterns in operational data. The traditional multivariate method of feature selection has become grossly inadequate as it is incapable of handling the non-linearity embedded in such natural phenomena. With the application of computational intelligence and its hybrid techniques in the petroleum industry, much improvement has been made. However, they are still incapable of handling more than one hypothesis at a time. Ensemble learning offers robust methodologies to handle the uncertainties in most complex industrial problems. This learning paradigm has not been well embraced in petroleum reservoir characterization despite the persistent quest for increased prediction accuracy. This paper proposes a novel ensemble model of Extreme Learning Machine (ELM) in the prediction of reservoir properties while utilizing the non-linear approximation capability of Functional Networks to select the optimal input features. Different instances of ELM were fed with features selected from different bootstrap samplings of the real-life field datasets. When benchmarked against existing techniques, our proposed ensemble model outperformed the multivariate regression-based feature selection, the conventional bagging and the Random Forest methods with higher correlation coefficient and lower prediction errors. This work confirms the huge potential in the capability of the new ensemble modeling paradigm to improve the prediction of reservoir properties.