dc.contributor.author | Amir, Mohammad | |
dc.contributor.author | Agonafir, Mesfin Belayneh | |
dc.date.accessioned | 2024-05-10T06:51:09Z | |
dc.date.available | 2024-05-10T06:51:09Z | |
dc.date.created | 2024-04-07T13:12:38Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Mohammad A, Belayneh M. (2024) Field Telemetry Drilling Dataset Modeling with Multivariable Regression, Group Method Data Handling, Artificial Neural Network, and the Proposed Group-Method-Data-Handling-Featured Artificial Neural Network. Applied Sciences, 14(6):2273. | en_US |
dc.identifier.issn | 2076-3417 | |
dc.identifier.uri | https://hdl.handle.net/11250/3129816 | |
dc.description.abstract | This paper presents data-driven modeling and a results analysis. Group method data handling (GMDH), multivariable regression (MVR), artificial neuron network (ANN), and new proposed GMDH-featured ANN machine learning algorithms were implemented to model a field telemetry equivalent mud circulating density (ECD) dataset based on surface and subsurface drilling parameters. Unlike the standard GMDH-ANN model, the proposed GMDH-featured ANN utilizes a fully connected network. Based on the considered eighteen experimental modeling designs, all the GMDH regression results showed higher R-squared and minimum mean-square error values than the multivariable regression results. In addition, out of the considered eight experimental designs, the GMDH-ANN model predicts about 37.5% of the experiments correctly, while both algorithms have shown similar results for the remaining experiments. However, further testing with diverse datasets is necessary for better evaluation. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.subject | AAN | en_US |
dc.title | Field Telemetry Drilling Dataset Modeling with Multivariable Regression, Group Method Data Handling, Artificial Neural Network, and the Proposed Group-Method-Data-Handling-Featured Artificial Neural Network | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © 2024 by The Author(s). | en_US |
dc.subject.nsi | VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 | en_US |
dc.source.volume | 14 | en_US |
dc.source.journal | Applied Sciences | en_US |
dc.source.issue | 6 | en_US |
dc.identifier.doi | 10.3390/app14062273 | |
dc.identifier.cristin | 2259629 | |
dc.source.articlenumber | 2273 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |