@inproceedings{c6d11ba3c8de4dbda6ad9a1e886fd51f,
title = "Ensemble learning model for petroleum reservoir characterization: A case of feed-forward back-propagation neural networks",
abstract = "Conventional machine learning methods are incapable of handling several hypotheses. This is the main strength of the ensemble learning paradigm. The petroleum industry is in great need of this new learning methodology due to the persistent quest for better prediction accuracies of reservoir properties for improved exploration and production activities. This paper proposes an ensemble model of Artificial Neural Networks (ANN) that incorporates various expert opinions on the optimal number of hidden neurons in the prediction of petroleum reservoir properties. The performance of the ensemble model was evaluated using standard decision rules and compared with those of ANN-Ensemble with the conventional Bootstrap Aggregation method and Random Forest. The results showed that the proposed method outperformed the others with the highest correlation coefficient and the least errors. The study also confirmed that ensemble models perform better than the average performance of individual base learners. This study demonstrated the great potential for the application of ensemble learning paradigm in petroleum reservoir characterization.",
keywords = "Artificial neural networks, Ensemble, Hidden neurons, Permeability, Porosity, Reservoir characterization",
author = "Fatai Anifowose and Jane Labadin and Abdulazeez Abdulraheem",
year = "2013",
doi = "10.1007/978-3-642-40319-4\_7",
language = "English",
isbn = "9783642403187",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "71--82",
booktitle = "Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops",
address = "Germany",
note = "2013 International Workshops on Data Mining Applications in Industry and Government, DMApps, Data Analytics for Targeted Healthcare, DANTH, Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models (QIMIE), Biologically Inspired Techniques for Data Mining, BDM, Constraint Discovery and Application, CDA, Cloud Service Discovery, CloudSD, and Behavior Informatics, BI in conjunction with 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 ; Conference date: 14-04-2013 Through 17-04-2013",
}