Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7237
Full metadata record
DC FieldValueLanguage
dc.contributor.authorÖzbayoğlu, Evren-
dc.contributor.authorÖzbayoğlu, Murat-
dc.contributor.authorÖzdilli, Barış Güney-
dc.contributor.authorErge, Öney-
dc.date.accessioned2021-09-11T15:56:04Z-
dc.date.available2021-09-11T15:56:04Z-
dc.date.issued2021en_US
dc.identifier.issn1996-1073-
dc.identifier.urihttps://doi.org/10.3390/en14051484-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/7237-
dc.description.abstractEffectively transporting drilled cuttings to the surface is a vital part of the well construction process. Usually, mechanistic models are used to estimate the cuttings concentration during drilling. Based on the results from these model, operational parameters are adjusted to mitigate any nonproductive time events such as pack-off or lost circulation. However, these models do not capture the underlying complex physics completely and frequently require updating the input parameters, which is usually performed manually. To address this, in this study, a data-driven modeling approach is taken and evaluated together with widely used mechanistic models. Artificial neural networks are selected after several trials. The experimental data collected at The University of Tulsa-Drilling Research Projects (in the last 40 years) are used to train and validate the model, which includes a wide range of wellbore and pipe sizes, inclinations, rate-of-penetration values, pipe rotation speeds, flow rates, and fluid and cuttings properties. It is observed that, in many cases, the data-driven model significantly outperforms the mechanistic models, which provides a very promising direction for real-time drilling optimization and automation. After the neural network is proven to work effectively, an optimization attempt to estimate flow rate and pipe rotation speed is introduced using a genetic algorithm. The decision is made considering minimizing the required total energy for this process. This approach may be used as a design tool to identify the required flow rate and pipe rotation speed to acquire effective hole cleaning while consuming minimal energy.en_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofEnergiesen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectcuttings transporten_US
dc.subjectartificial neural networksen_US
dc.subjectoptimizationen_US
dc.subjecthole cleaningen_US
dc.subjectmachine learningen_US
dc.subjectdata drivenen_US
dc.titleOptimization of Flow Rate and Pipe Rotation Speed Considering Effective Cuttings Transport Using Data-Driven Modelsen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume14en_US
dc.identifier.issue5en_US
dc.authorid0000-0001-7998-5735-
dc.authorid0000-0002-2687-9135-
dc.identifier.wosWOS:000628111300001en_US
dc.identifier.scopus2-s2.0-85106282220en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.identifier.doi10.3390/en14051484-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record



CORE Recommender

WEB OF SCIENCETM
Citations

5
checked on Mar 9, 2024

Page view(s)

18
checked on Mar 25, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.