Prediction of Pressure Gradients for Multiphase Flow in Pipes

Akintola, Sarah A. and Akpabio, Julius U. and Onuegbu, Mary-Ann (2021) Prediction of Pressure Gradients for Multiphase Flow in Pipes. In: New Ideas Concerning Science and Technology Vol. 12. B P International, pp. 109-122. ISBN 978-93-90888-12-2

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Abstract

Pressure traverse in multiphase flow differs from single phase flow due to the differential flow rates of the different phases. Correlations developed to predict multiphase flow pressure traverse are mostly for vertical wells but Beggs and Brill model is one of the few models that is used for inclined pipes. The work seeks to show the improvement in the modification of the model. This project is based on studies carried out on multiphase fluid flow in pipes of any inclination using the Beggs and Brill flow model as the focus. Two cases were considered, the liquid holdup correction and Gas Liquid Ratio (GLR) variations in which the Beggs and Brill and Beggs and Brill Traverse models were compared. Due to the empirical nature of the Beggs and Brill model, pressure gradient predictions are far from accurate when compared with measured data in the field. This project seeks to reduce the error margin between predicted pressure gradient values and measured data. It was observed that for the same reservoir, fluid, and pipe properties, the Beggs and Brill Traverse Model is a better prediction tool than the Beggs and Brill model. Prediction errors were seen to increase with increase in length for GLR above 400 scf/stb while they were more accurate for pipes between 12,000 and 17,000 ft and pressures between 3,000 and 4,500 psi. However, the Beggs and Brill Traverse Model, is limited by the choice of correlations used in the computation of fluid properties. It is necessary to predict pressure drop in vertical multiphase flow in order to effectively design tubing and optimum production strategies.

Item Type: Book Section
Subjects: GO for STM > Multidisciplinary
Depositing User: Unnamed user with email support@goforstm.com
Date Deposited: 02 Dec 2023 06:10
Last Modified: 02 Dec 2023 06:10
URI: http://archive.article4submit.com/id/eprint/1977

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