Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/9259
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dc.contributor.authorErge, O.-
dc.contributor.authorOzbayoglu, M.-
dc.contributor.authorOzbayoglu, E.-
dc.date.accessioned2022-11-30T19:37:40Z-
dc.date.available2022-11-30T19:37:40Z-
dc.date.issued2022-
dc.identifier.isbn9780791885956-
dc.identifier.urihttps://doi.org/10.1115/OMAE2022-79623-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/9259-
dc.descriptionOcean, Offshore and Arctic Engineering Divisionen_US
dc.descriptionASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2022 -- 5 June 2022 through 10 June 2022 -- 183473en_US
dc.description.abstractIn this study, a deep learning model is proposed that can accurately predict the rate of penetration during geothermal or oil and gas well construction operations. Also, a genetic algorithm is applied and used together with the deep learning model to determine the optimum values for the drilling parameters: weight on bit (WOB) and drillstring rotation rate (RPM). It is vital to estimate the optimal set of values for drilling parameters to construct wellbores quickly and efficiently. Traditionally, drill-off tests are conducted by halting the normal drilling operation and manually changing the WOB and RPM values to search for the highest ROP output. This operation can be repetitive and can lead to an inaccurate estimation of parameters because only a few different parameters are tried. The proposed learning algorithm estimates the optimum WOB and RPM, based on the historical values and can keep learning as the drilling proceeds, which is essential for fully automated well construction. The proposed deep learning model is trained with actual drilling datasets that showed an accurate prediction of the rate of penetration and mechanical specific energy (MSE). This model is used together with the genetic algorithm and the optimum drilling parameters are determined that yield minimum MSE. The results showed a significant performance improvement compared to the historical values. The proposed model can be used as an advisory system to the driller or the output can be used within the control system to automate the drilling process. The proposed learning model showed the capability to further optimize the drilling-rate and mitigate any invisible lost time (ILT) and potential nonproductive time (NPT) by completing the well as soon as possible. Copyright © 2022 by ASME.en_US
dc.language.isoenen_US
dc.publisherAmerican Society of Mechanical Engineers (ASME)en_US
dc.relation.ispartofProceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAEen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectdrilling rate predictionen_US
dc.subjectgenetic algorithmen_US
dc.subjectDeep learningen_US
dc.subjectDrillsen_US
dc.subjectForecastingen_US
dc.subjectInfill drillingen_US
dc.subjectLearning algorithmsen_US
dc.subjectLearning systemsen_US
dc.subjectOil well drillingen_US
dc.subjectOil wellsen_US
dc.subjectParameter estimationen_US
dc.subjectRotation rateen_US
dc.subjectDeep learningen_US
dc.subjectDrilling parametersen_US
dc.subjectDrilling rate predictionen_US
dc.subjectDrilling ratesen_US
dc.subjectLearning modelsen_US
dc.subjectMechanical specific energiesen_US
dc.subjectRate of penetrationen_US
dc.subjectRate predictionsen_US
dc.subjectWeight on bitsen_US
dc.subjectWell constructionsen_US
dc.subjectGenetic algorithmsen_US
dc.titleA DEEP LEARNING MODEL FOR RATE OF PENETRATION PREDICTION AND DRILLING PERFORMANCE OPTIMIZATION USING GENETIC ALGORITHMen_US
dc.typeConference Objecten_US
dc.identifier.volume10en_US
dc.identifier.scopus2-s2.0-85140898956en_US
dc.institutionauthorÖzbayoglu, Ahmet Murat-
dc.identifier.doi10.1115/OMAE2022-79623-
dc.authorscopusid56017415500-
dc.authorscopusid57947593100-
dc.authorscopusid14629099300-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.ozel2022v3_Editen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering
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