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dc.contributor.authorÖzbayoğlu, Murat-
dc.contributor.authorÖzbayoğlu, E.-
dc.contributor.authorÖzdilli, B.G.-
dc.contributor.authorErge, O.-
dc.descriptionOcean, Offshore and Arctic Engineering Divisionen_US
dc.description2021 40th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2021 -- 21 June 2021 through 30 June 2021 -- 172516en_US
dc.description.abstractDrilling practice has been evolving parallel to the developments in the oil and gas industry. Current supply and demand for oil and gas dictate search for hydrocarbons either at much deeper and hard-to-reach fields, or at unconventional fields, both requiring extended reach wells, long horizontal sections, and 3D complex trajectories. Cuttings transport is one of the most challenging problems while drilling such wells, especially at mid-range inclinations. For many years, numerous studies have been conducted to address modeling of cuttings transport, estimation of the concentration of cuttings as well as pressure losses inside the wellbores, considering various drilling variables having influence on the process. However, such attempts, either mechanistic or empirical, have many limitations due to various simplifications and assumptions made during the development stage. Fluid thixotropy, temperature variations in the wellbore, uncertainty in pipe eccentricity as well as chaotic motion of cuttings due to pipe rotation, imperfections in the wellbore walls, variations in the size and shape of the cuttings, presence of tool joints on the drillstring, etc. causes the modeling of the problem extremely difficult. Due to the complexity of the process, the estimations are usually not very accurate, or not reliable. In this study, data-driven models are used to address the estimation of cuttings concentration and frictional loss estimation in a well during drilling operations, instead of using mechanistic or empirical methods. The selected models include Artificial Neural Networks, Random Forest, and AdaBoost. The training of the models is determined using the experimental data regarding cuttings transport tests collected in the last 40 years at The University of Tulsa – Drilling Research Projects, which includes a wide range of wellbore and pipe sizes, inclinations, ROPs, pipe rotation speeds, flow rates, fluid and cuttings properties. The evaluation of the models is conducted using Root Mean Square Error, R-Squared Values, and P-Value. As the inputs of the data-driven models, independent drilling variables are directly used. Also, as a second approach, dimensionless groups are developed based on these independent drilling variables, and these dimensionless groups are used as the inputs of the models. Moreover, performance of the data-driven model results are compared with the results of a conventional mechanistic model. It is observed that in many cases, data-driven models perform significantly better than the mechanistic model, which provides a very promising direction to consider for real time drilling optimization and automation. It is also concluded that using the independent drilling variables directly as the model inputs provided more accurate results when compared with dimensional groups are used as the model inputs. Copyright © 2021 by ASME.en_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.subjectArtificial neural networksen_US
dc.subjectCuttings transporten_US
dc.subjectMachine learningen_US
dc.subjectRandom foresten_US
dc.subjectComplex networksen_US
dc.subjectDecision treesen_US
dc.subjectGas industryen_US
dc.subjectHorizontal wellsen_US
dc.subjectInfill drillingen_US
dc.subjectMachine learningen_US
dc.subjectMean square erroren_US
dc.subjectNeural networksen_US
dc.subjectOffshore oil well productionen_US
dc.subjectOil field equipmenten_US
dc.subjectOil wellsen_US
dc.subjectCuttings transporten_US
dc.subjectData-driven modelen_US
dc.subjectDimensionless groupsen_US
dc.subjectDrilling practicesen_US
dc.subjectFrictional pressure lossen_US
dc.subjectMechanistic modelsen_US
dc.subjectModel inputsen_US
dc.subjectRandom forestsen_US
dc.subjectAdaptive boostingen_US
dc.titleEstimation of cuttings concentration and frictional pressure losses during drilling using data-driven modelsen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
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item.openairetypeConference Object-
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
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