A Novel Multiagent Collaborative Learning Architecture for Automatic Recognition of Mudstone Rock Facies
<p dir="ltr">Recognizing mud rock lithofacies is essential for mapping the subsurface depositional environments and identifying oil and gas-bearing rock formations. Conventional well logs interpretation techniques are slow, costly and require high domain expertise. Machine learning (...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2024
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| _version_ | 1864513538131755008 |
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| author | Saurabh Tewari (22282594) |
| author2 | Arvind Prasad (19997799) Harsh Patel (9978387) Mueen Uddin (4903510) Taher Al-Shehari (21323711) Nasser A. Alsadhan (21324164) |
| author2_role | author author author author author |
| author_facet | Saurabh Tewari (22282594) Arvind Prasad (19997799) Harsh Patel (9978387) Mueen Uddin (4903510) Taher Al-Shehari (21323711) Nasser A. Alsadhan (21324164) |
| author_role | author |
| dc.creator.none.fl_str_mv | Saurabh Tewari (22282594) Arvind Prasad (19997799) Harsh Patel (9978387) Mueen Uddin (4903510) Taher Al-Shehari (21323711) Nasser A. Alsadhan (21324164) |
| dc.date.none.fl_str_mv | 2024-11-27T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3507569 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Novel_Multiagent_Collaborative_Learning_Architecture_for_Automatic_Recognition_of_Mudstone_Rock_Facies/30226858 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Earth sciences Geology Engineering Geomatic engineering Resources engineering and extractive metallurgy Information and computing sciences Artificial intelligence Machine learning Data imbalance Resampling techniques Machine learning Stacked generalization Multiagent collaborative learning |
| dc.title.none.fl_str_mv | A Novel Multiagent Collaborative Learning Architecture for Automatic Recognition of Mudstone Rock Facies |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Recognizing mud rock lithofacies is essential for mapping the subsurface depositional environments and identifying oil and gas-bearing rock formations. Conventional well logs interpretation techniques are slow, costly and require high domain expertise. Machine learning (ML) techniques have been implemented to automate the recognition of lithofacies from the bulk of well logs generated. However, the reservoir heterogeneity and uneven thickness of rock layers result in imbalanced data conditions that make the ML models biased. This study proposes a novel multiagent collaborative learning architecture (MCLA) to handle the imbalanced data problem during the identification of lithofacies. This research investigates four popular data resampling techniques, i.e. oversampling, SMOTE and ADASYN. Also, resampling techniques are combined with nine different ML classifiers, including Decision tree, ExtraTree, Random Forest, Logistic regression, Support vector machine, K-nearest Neighbour, Naïve Bayes and Ensemble methods. Stacking and voting ensembles combine the outcomes of diverse classifiers working as team members in MCLA. ADASYN, in combination with Stacking, has produced impressive results in terms of accuracy (99.41%) along with MCC (0.98) and G-mean (0.98). The proposed MCLA shows an enhancement of 2% in lithofacies accuracy and an approximately 4% increment in reliability compared with the top-performing Extra Tree classifier considered in this study.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3507569" target="_blank">https://dx.doi.org/10.1109/access.2024.3507569</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_9f31177575ee81774dedee7e0ce0d5ad |
| identifier_str_mv | 10.1109/access.2024.3507569 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30226858 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Novel Multiagent Collaborative Learning Architecture for Automatic Recognition of Mudstone Rock FaciesSaurabh Tewari (22282594)Arvind Prasad (19997799)Harsh Patel (9978387)Mueen Uddin (4903510)Taher Al-Shehari (21323711)Nasser A. Alsadhan (21324164)Earth sciencesGeologyEngineeringGeomatic engineeringResources engineering and extractive metallurgyInformation and computing sciencesArtificial intelligenceMachine learningData imbalanceResampling techniquesMachine learningStacked generalizationMultiagent collaborative learning<p dir="ltr">Recognizing mud rock lithofacies is essential for mapping the subsurface depositional environments and identifying oil and gas-bearing rock formations. Conventional well logs interpretation techniques are slow, costly and require high domain expertise. Machine learning (ML) techniques have been implemented to automate the recognition of lithofacies from the bulk of well logs generated. However, the reservoir heterogeneity and uneven thickness of rock layers result in imbalanced data conditions that make the ML models biased. This study proposes a novel multiagent collaborative learning architecture (MCLA) to handle the imbalanced data problem during the identification of lithofacies. This research investigates four popular data resampling techniques, i.e. oversampling, SMOTE and ADASYN. Also, resampling techniques are combined with nine different ML classifiers, including Decision tree, ExtraTree, Random Forest, Logistic regression, Support vector machine, K-nearest Neighbour, Naïve Bayes and Ensemble methods. Stacking and voting ensembles combine the outcomes of diverse classifiers working as team members in MCLA. ADASYN, in combination with Stacking, has produced impressive results in terms of accuracy (99.41%) along with MCC (0.98) and G-mean (0.98). The proposed MCLA shows an enhancement of 2% in lithofacies accuracy and an approximately 4% increment in reliability compared with the top-performing Extra Tree classifier considered in this study.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3507569" target="_blank">https://dx.doi.org/10.1109/access.2024.3507569</a></p>2024-11-27T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3507569https://figshare.com/articles/journal_contribution/A_Novel_Multiagent_Collaborative_Learning_Architecture_for_Automatic_Recognition_of_Mudstone_Rock_Facies/30226858CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/302268582024-11-27T06:00:00Z |
| spellingShingle | A Novel Multiagent Collaborative Learning Architecture for Automatic Recognition of Mudstone Rock Facies Saurabh Tewari (22282594) Earth sciences Geology Engineering Geomatic engineering Resources engineering and extractive metallurgy Information and computing sciences Artificial intelligence Machine learning Data imbalance Resampling techniques Machine learning Stacked generalization Multiagent collaborative learning |
| status_str | publishedVersion |
| title | A Novel Multiagent Collaborative Learning Architecture for Automatic Recognition of Mudstone Rock Facies |
| title_full | A Novel Multiagent Collaborative Learning Architecture for Automatic Recognition of Mudstone Rock Facies |
| title_fullStr | A Novel Multiagent Collaborative Learning Architecture for Automatic Recognition of Mudstone Rock Facies |
| title_full_unstemmed | A Novel Multiagent Collaborative Learning Architecture for Automatic Recognition of Mudstone Rock Facies |
| title_short | A Novel Multiagent Collaborative Learning Architecture for Automatic Recognition of Mudstone Rock Facies |
| title_sort | A Novel Multiagent Collaborative Learning Architecture for Automatic Recognition of Mudstone Rock Facies |
| topic | Earth sciences Geology Engineering Geomatic engineering Resources engineering and extractive metallurgy Information and computing sciences Artificial intelligence Machine learning Data imbalance Resampling techniques Machine learning Stacked generalization Multiagent collaborative learning |