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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...

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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
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spelling my.unimas.ir.84642022-09-29T06:16:08Z http://ir.unimas.my/id/eprint/8464/ Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization Fatai Adesina, Anifowose Jane, Labadin Abdulazeez, Abdulraheem L Education (General) T Technology (General) 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. Elsevier B.V. 2015 Article NonPeerReviewed text en http://ir.unimas.my/id/eprint/8464/1/Ensemble%20model%20of%20non-linear%20feature%20selection-based%20Extreme%20Learning%20Machine%20%28abstract%29.pdf Fatai Adesina, Anifowose and Jane, Labadin and Abdulazeez, Abdulraheem (2015) Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization. Journal of Natural Gas Science and Engineering, 2015. pp. 1-12. ISSN 1875-5100 http://www.researchgate.net/publication/272523954_Ensemble_model_of_non-linear_feature_selection-based_Extreme_Learning_Machine_for_improved_natural_gas_reservoir_characterization DOI: 10.1016/j.jngse.2015.02.012
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic L Education (General)
T Technology (General)
spellingShingle L Education (General)
T Technology (General)
Fatai Adesina, Anifowose
Jane, Labadin
Abdulazeez, Abdulraheem
Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization
description 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.
format Article
author Fatai Adesina, Anifowose
Jane, Labadin
Abdulazeez, Abdulraheem
author_facet Fatai Adesina, Anifowose
Jane, Labadin
Abdulazeez, Abdulraheem
author_sort Fatai Adesina, Anifowose
title Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization
title_short Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization
title_full Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization
title_fullStr Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization
title_full_unstemmed Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization
title_sort ensemble model of non-linear feature selection-based extreme learning machine for improved natural gas reservoir characterization
publisher Elsevier B.V.
publishDate 2015
url 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
_version_ 1745566042685440000
score 13.211869

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