Multiphase flow and mixing quantification using computational fluid dynamics and magnetic resonance imaging

被引:3
|
作者
Maru, Wessenu [1 ]
Holland, Daniel [2 ]
Lakshmanan, Susithra [1 ]
Sederman, Andy [3 ]
Thomas, Andrew [1 ]
机构
[1] Oil & Gas Measurement OGM Ltd, Ely, Cambs, England
[2] Univ Canterbury, Dept Chem & Proc Engn, Christchurch, New Zealand
[3] Univ Cambridge, Dept Chem Engn & Biotechnol, Cambridge, England
基金
“创新英国”项目; 英国工程与自然科学研究理事会;
关键词
Multiphase flow; Custody transfer; MRI; CFD; SIZE DISTRIBUTIONS; LIQUID-LIQUID; BUBBLY FLOW; WATER; VELOCITY; JETS; MRI; TOMOGRAPHY;
D O I
10.1016/j.flowmeasinst.2020.101816
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
This paper investigates the current challenges and practices of quality measurement in the oil & gas industry. It particularly focuses on automatic pipeline sampling of petroleum liquids according to ISO 3171. The problem is tackled using innovative diagnostic techniques, advanced design optimisation tools and a new mixing system that uses a Liquid Jet In Cross Flow (LJICF) configuration. First, a 2.5 '' diameter small multiphase flow loop (SMPFL) was developed and magnetic resonance (MR) was utilised to characterise the mechanistic behaviour of mixing and the mixing efficiency of various nozzles. Second, a computational fluid dynamics (CFD) model was developed and validated using MR measurements on the SMPFL. The CFD model was then used to optimise the nozzle design as well as the design of a 10 '' nominal diameter large multiphase flow loop (LMPFL). The LMPFL is a well instrumented facility and was used to conduct mixing experiments on low velocity, low density and low viscosity fluids flowing in a horizontal pipe, which constitute challenging conditions for a mixing device to create homogeneous mixture. To quantify the homogeneity of the mixture created by the new mixing system on the LMPFL, a multiport profile proving (MPP) technique was developed and used to conduct water injection testing in compliance with ISO 3171 and API 8.2 standards. The water volume fraction (WVF) determined by the MPP had low relative error when compared to the mean WVF measured by the water cut meters and samples analysed using Coulometric Karl-Fischer (KF). Additionally, in an earlier study [1], the MPP measurement was able to detect a density gradient across the diameter of the pipe, making it an appropriate method to judge the homogeneity of the mixture. Therefore, the new mixing system together with the MPP technology shows real promise as an effective sampling and proving system for the petrochemical industry.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Simulating multiphase flow in a two-stage pusher centrifuge using computational fluid dynamics
    Chong Pang
    Wei Tan
    Endian Sha
    Yuanqing Tao
    Liyan Liu
    Frontiers of Chemical Science and Engineering, 2012, 6 (3) : 329 - 338
  • [42] Multiphase flow and tromp curve simulation of dense medium cyclones using Computational Fluid Dynamics
    Farzanegan, A.
    Gholami, M.
    Rahimyan, M. H.
    JOURNAL OF MINING AND ENVIRONMENT, 2013, 4 (01): : 67 - 76
  • [43] Investigation of the effects of nasal surgery on nasal cavity flow using magnetic resonance velocimetry and computational fluid dynamics
    Han, Kyuho
    Lee, Sung-Gwang
    Kim, Kwanwoo
    Jeong, Baren
    Paek, Munyoung
    Lee, Whal
    Hwang, Wontae
    PHYSICS OF FLUIDS, 2023, 35 (11)
  • [44] Progress Towards Patient-Specific Computational Flow Modeling of the Left Heart via Combination of Magnetic Resonance Imaging with Computational Fluid Dynamics
    Nikoo R. Saber
    Nigel B. Wood
    A. D. Gosman
    Robert D. Merrifield
    Guang-Zhong Yang
    Clare L. Charrier
    Peter D. Gatehouse
    David N. Firmin
    Annals of Biomedical Engineering, 2003, 31 : 42 - 52
  • [45] Progress towards patient-specific computational flow modeling of the left heart via combination of magnetic resonance imaging with computational fluid dynamics
    Saber, NR
    Wood, NB
    Gosman, AD
    Merrifield, RD
    Yang, GZ
    Charrier, CL
    Gatehouse, PD
    Firmin, DN
    ANNALS OF BIOMEDICAL ENGINEERING, 2003, 31 (01) : 42 - 52
  • [46] Quantifying Mixing using Magnetic Resonance Imaging
    Tozzi, Emilio J.
    McCarthy, Kathryn L.
    Bacca, Lori A.
    Hartt, William H.
    McCarthy, Michael J.
    JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, 2012, (59): : 1 - 8
  • [47] QUANTIFICATION OF BLOOD-FLOW VELOCITY USING MAGNETIC-RESONANCE-IMAGING
    MATSUDA, T
    HASHIMOTO, S
    NONOGI, H
    SAKURAI, T
    TAMAKI, S
    KAWAI, C
    JAPANESE CIRCULATION JOURNAL-ENGLISH EDITION, 1986, 50 (06): : 518 - 518
  • [48] Phase-contrast magnetic resonance imaging and computational fluid dynamics assessment of thoracic aorta blood flow: a literature review
    Jarral, Omar A.
    Tan, Matthew K. H.
    Salmasi, Mohammad Yousuf
    Pirola, Selene
    Pepper, John R.
    O'Regan, Declan P.
    Xu, Xiao Y.
    Athanasiou, Thanos
    EUROPEAN JOURNAL OF CARDIO-THORACIC SURGERY, 2020, 57 (03) : 438 - 446
  • [49] Computational fluid dynamics analysis of upper airway reconstructed from magnetic resonance imaging data
    Mihaescu, M.
    Murugappan, S.
    Gutmark, E.
    Donnelly, I
    Kalra, M.
    SLEEP, 2007, 30 : A201 - A202
  • [50] Simulation of brisk and fast phase-contrast magnetic resonance imaging by computational fluid dynamics
    Hershey, BL
    Doyle, M
    Kortright, E
    Anayiotos, A
    2003 ADVANCES IN BIOENGINEERING, 2003, : 141 - 142