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|Title:||Pressure drop estimation in horizontal annuli for liquid-gas 2 phase flow: Comparison of mechanistic models and computational intelligence techniques||Authors:||Osgouei, Reza Ettehadi
Özbayoğlu, Ahmet Murat
Özbayoğlu, Evren M.
|Issue Date:||2-May-2015||Publisher:||Pergamon - Elsevier Science ltd.||Source:||Osgouei, R. E., Ozbayoglu, A. M., Ozbayoglu, E. M., Yuksel, E., & Eresen, A. (2015). Pressure drop estimation in horizontal annuli for liquid–gas 2 phase flow: Comparison of mechanistic models and computational intelligence techniques. Computers & Fluids, 112, 108-115.||Abstract:||Frictional pressure loss calculations and estimating the performance of cuttings transport during underbalanced drilling operations are more difficult due to the characteristics of multi-phase fluid flow inside the wellbore. In directional or horizontal wellbores, such calculations are becoming more complicated due to the inclined wellbore sections, since gravitational force components are required to be considered properly. Even though there are numerous studies performed on pressure drop estimation for multiphase flow in inclined pipes, not as many studies have been conducted for multiphase flow in annular geometries with eccentricity. In this study, the frictional pressure losses are examined thoroughly for liquid-gas multiphase flow in horizontal eccentric annulus. Pressure drop measurements for different liquid and gas flow rates are recorded. Using the experimental data, a mechanistic model based on the modification of Lockhart and Martinelli  is developed. Additionally, 4 different computational intelligence techniques (nearest neighbor, regression trees, multilayer perceptron and Support Vector Machines - SVM) are modeled and developed for pressure drop estimation. The results indicate that both mechanistic model and computational intelligence techniques estimated the frictional pressure losses successfully for the given flow conditions, when compared with the experimental results. It is also noted that the computational intelligence techniques performed slightly better than the mechanistic model. (C) 2014 Elsevier Ltd. All rights reserved.||URI:||https://www.sciencedirect.com/science/article/pii/S0045793014004253?via%3Dihub
|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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