Machine Learning
Artificial Neural Networks: An Introduction to Ann Theory and Practice
Artificial Neural Networks: An Introduction to Ann Theory and Practice
Machine Learning
An ensemble of support vector machines for predicting virulent proteins
Expert Systems with Applications: An International Journal
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
IEEE Transactions on Knowledge and Data Engineering
Financial distress prediction using support vector machines: Ensemble vs. individual
Applied Soft Computing
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Relationships between Diversity of Classification Ensembles and Single-Class Performance Measures
IEEE Transactions on Knowledge and Data Engineering
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The acquisition of huge sensor data has led to the advent of the smart field phenomenon in the petroleum industry. A lot of data is acquired during drilling and production processes through logging tools equipped with sub-surface/down-hole sensors. Reservoir modeling has advanced from the use of empirical equations through statistical regression tools to the present embrace of Artificial Intelligence (AI) and its hybrid techniques. Due to the high dimensionality and heterogeneity of the sensor data, the capability of conventional AI techniques has become limited as they could not handle more than one hypothesis at a time. Ensemble learning method has the capability to combine several hypotheses to evolve a single ensemble solution to a problem. Despite its popular use, especially in petroleum engineering, Artificial Neural Networks (ANN) has posed a number of challenges. One of such is the difficulty in determining the most suitable learning algorithm for optimal model performance. To save the cost, effort and time involved in the use of trial-and-error and evolutionary methods, this paper presents an ensemble model of ANN that combines the diverse performances of seven "weak" learning algorithms to evolve an ensemble solution in the prediction of porosity and permeability of petroleum reservoirs. When compared to the individual ANN, ANN-bagging and RandomForest, the proposed model performed best. This further confirms the great opportunities for ensemble modeling in petroleum reservoir characterization and other petroleum engineering problems.