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dc.contributor.authorChhantyal, Khim
dc.date.accessioned2019-03-21T10:44:10Z
dc.date.available2019-03-21T10:44:10Z
dc.date.issued2018-09-20
dc.identifier.isbn978-82-7206-484-5
dc.identifier.issn2535-5252
dc.identifier.urihttp://hdl.handle.net/11250/2591042
dc.description.abstractIn drilling oil & gas wells, pressure control is essential for several reasons, but pri-marily for safety. The wellbore pressure should be maintained within the pressure window to avoid the kick and fluid loss while drilling. During drilling, wellbore pressure can be measured in real-time, but it is a challenge to determine the pressure window. One possible way to monitor wellbore pressure is the delta flow method, where the difference between inflow and return flow is utilized to indicate the kick or the fluid loss. For delta flow method, inflow measurement is comparatively easy as the inflowing fluid is a single phase fluid with known rheological parameters. The returning fluid is a multiphase fluid contaminated with rock cuttings, sand, for-mation fluids/gases, etc. and is a challenge to measure. The primary objective of this PhD work is to develop models or sensor systems to estimate the return flow through an open channel in drilling circulation loops. During the work, different flow measurement systems are analysed, modified, and developed. The performance of the measurement systems is evaluated based on the standard requirements needed for a suitable flowmeter. All the experimental works are performed using a flow loop available at University of South-Eastern Norway, Campus Porsgrunn. The flow loop consists of an open channel with Venturi constric-tion for flow measurement. For the study, drilling fluids with different rheological properties are used. The analysis performed using an already existing flow measurement systems for an open channel with uniform geometry shows that these measurement systems are limited by the fluid rheology and accuracy. Three different flow models (i.e., upstream-throat levels based, upstream level based and critical level based) for the fluid flow through an open channel with Venturi constrictions are analysed. All of the three models are accurate and meet the standard requirements in a favourable condition. Upstream-throat levels based flow model (with mean absolute percent-age error (MAPE) of 2.33%) and upstream level based flow model (with MAPE of 2.92%) need a proper tuning of a kinetic energy correction factor depending on the type of flow regime. The flow regime depends on the rheological parameters of a fluid and the rheological parameters of return flow changes in each circulation while drilling. Due to this reason, these two flow models are not reliable for return flow measurement without a proper tuning of the correction factor. The critical level based flow model (with MAPE of 5.81%) is comparatively less affected by the cor-rection factor. The limitation of this model is to locate a critical level position within the throat section along the Venturi constriction. In this study, instead of performing a direct critical level measurement, it is estimated based on the fuzzy logic regulator and fixed position upstream level measurement. The modifications in the critical level based flow model give improved estimates of the flow. One possible problem using the Venturi constriction can be an accumulation of solid particles within the conversing section of the constriction. In this case, return flow through an inclined open channel can be a simple solution, which accelerates the accumulated sediments. The flow study using an inclined open channel shows that the model is reliable up to the inclination angle of 0.4 [deg]. The results are valid for the geometry of the open channel used in the experiments. Due to the limitation of these flow models with the need for a proper selection of the correction factor, different machine learning based flow models are developed. Volumetric flow based machine learning models are highly accurate with MAPE up to 2.05 % and are applicable for fluids with different rheological parameters. These models are based on level measurements without cumbersome tuning of various pa-rameters and hence useful in open channel return flow measurements of any fluids.nb_NO
dc.language.isoengnb_NO
dc.publisherUniversity of South-Eastern Norwaynb_NO
dc.relation.ispartofseriesDoctoral dissertations at the University of South-Eastern Norway;10
dc.relation.haspartPaper A: Chhantyal, K., Viumdal, H. & Mylvaganam, S.: Online Drilling Fluid Flowmetering in Open Channels with Ultrasonic Level Sensors using Critical Depths. Linköping Electronic Conference Proceedings, The 58th International Conference of Scandinavian Simulation Society, SIMS 2017, pp. 385-390, 2017. https://doi.org/10.3384/ecp17138385nb_NO
dc.relation.haspartPaper B: Chhantyal, K., Viumdal, H. & Mylvaganam, S.: Soft Sensing of Non-Newtonian Fluid Flow in Open Venturi Channel Using an Array of Ultrasonic Level Sensors - AI Models and Their Validations. Online Drilling Fluid Flowmetering in Open Channels with Ultrasonic Level Sensors using Critical Depths. Sensors 17(11), (2017), 2458. https://doi.org/10.1007/s00193-018-0819-znb_NO
dc.relation.haspartPaper C: Chhantyal, K., Jondahl, M.H., Viumdal, H. & Mylvaganam, S.: Upstream Ultrasonic Level Based Soft Sensing of Volumetric Flow of Non-Newtonian Fluids in Open Venturi Channels. IEEE Sensors Journal 18(12), (2018), 5002-5013. https://doi.org/10.1109/JSEN.2018.2831445. Not available in USN Open.nb_NO
dc.rightsNavngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/deed.no*
dc.titleSensor Data Fusion based Modelling of Drilling Fluid Return Flow through Open Channelsnb_NO
dc.typeDoctoral thesisnb_NO
dc.description.versionpublishedVersionnb_NO
dc.rights.holder© 2018 Khim Chhantyal, except otherwise statednb_NO
dc.subject.nsiVDP::Technology: 500::Electrotechnical disciplines: 540nb_NO
dc.rights.license© The Author, except otherwise stated


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Navngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal