Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach

The accurate classification of reservoir recovery factor is dampened by irregularities such as noisy and high-dimensional features associated with the reservoir measurements or characterization. These irregularities, especially a larger number of features, make it difficult to perform accurate class...

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Bibliographic Details
Published in:	Journal of marine science and engineering Vol. 9; no. 8; p. 888
Main Authors:	Al-Tashi, Qasem, Akhir, Emelia Akashah Patah, Abdulkadir, Said Jadid, Mirjalili, Seyedali, Shami, Tareq M., Alhusssian, Hitham, Alqushaibi, Alawi, Alwadain, Ayed, Balogun, Abdullateef O., Al-Zidi, Nasser
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01-08-2021
Subjects:	Accuracy Algorithms Artificial intelligence Artificial neural networks Classification Datasets Decision making Error reduction Feature selection Genetic algorithms Hydrocarbons Irregularities Machine learning MOGWO multi-objective optimization Multiple objective analysis Neural networks oil and gas Oil recovery Oil reservoirs Optimism Pareto optimization Particle swarm optimization Physics Production planning Recovery recovery factor reservoir Reservoirs Sorting algorithms
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Summary:	The accurate classification of reservoir recovery factor is dampened by irregularities such as noisy and high-dimensional features associated with the reservoir measurements or characterization. These irregularities, especially a larger number of features, make it difficult to perform accurate classification of reservoir recovery factor, as the generated reservoir features are usually heterogeneous. Consequently, it is imperative to select relevant reservoir features while preserving or amplifying reservoir recovery accuracy. This phenomenon can be treated as a multi-objective optimization problem, since there are two conflicting objectives: minimizing the number of measurements and preserving high recovery classification accuracy. In this study, wrapper-based multi-objective feature selection approaches are proposed to estimate the set of Pareto optimal solutions that represents the optimum trade-off between these two objectives. Specifically, three multi-objective optimization algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Particle Swarm Optimization (MOPSO)—are investigated in selecting relevant features from the reservoir dataset. To the best of our knowledge, this is the first time multi-objective optimization has been used for reservoir recovery factor classification. The Artificial Neural Network (ANN) classification algorithm is used to evaluate the selected reservoir features. Findings from the experimental results show that the proposed MOGWO-ANN outperforms the other two approaches (MOPSO and NSGA-II) in terms of producing non-dominated solutions with a small subset of features and reduced classification error rate.
ISSN:	2077-1312 2077-1312
DOI:	10.3390/jmse9080888