Sensor Data Fusion based Modelling of Drilling Fluid Return Flow through Open Channels
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In 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.
Has partsPaper 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/ecp17138385
Paper 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-z
Paper 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.