Show simple item record

dc.contributor.authorAl-Tashi, Qasem
dc.contributor.authorAkhir, Emelia Akashah Patah
dc.contributor.authorAbdulkadir, Said Jadid
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorShami, Tareq M
dc.contributor.authorAlhusssian, Hitham
dc.contributor.authorAlqushaibi, Alawi
dc.contributor.authorAlwadain, Ayed
dc.contributor.authorBalogun, Abdullateef O
dc.contributor.authorAl-Zidi, Nasser
dc.date.accessioned2021-09-09T01:15:09Z
dc.date.available2021-09-09T01:15:09Z
dc.date.issued2021
dc.identifier.issn2077-1312
dc.identifier.doi10.3390/jmse9080888
dc.identifier.urihttp://hdl.handle.net/10072/407827
dc.description.abstractThe 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.
dc.description.peerreviewedYes
dc.languageen
dc.publisherMDPI AG
dc.relation.ispartofpagefrom888
dc.relation.ispartofissue8
dc.relation.ispartofjournalJournal of Marine Science and Engineering
dc.relation.ispartofvolume9
dc.subject.fieldofresearchMarine engineering
dc.subject.fieldofresearchMaritime engineering
dc.subject.fieldofresearchOceanography
dc.subject.fieldofresearchFisheries sciences
dc.subject.fieldofresearchcode401501
dc.subject.fieldofresearchcode4015
dc.subject.fieldofresearchcode3708
dc.subject.fieldofresearchcode3005
dc.titleClassification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationAl-Tashi, Q; Akhir, EAP; Abdulkadir, SJ; Mirjalili, S; Shami, TM; Alhusssian, H; Alqushaibi, A; Alwadain, A; Balogun, AO; Al-Zidi, N, Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach, Journal of Marine Science and Engineering, 9 (8), pp. 888
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-09-09T00:37:01Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorMirjalili, Seyedali


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record