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Research Papers: Petroleum Engineering

Oil–Water Flow Pattern Transition Prediction in Horizontal Pipes

[+] Author and Article Information
Carlos F. Torres

Thermal Science Department,
University of Los Andes,
Campus La Hechicera, Edificio B,
Mérida 5101, Venezuela
e-mail: ctorres@ula.ve

Ram S. Mohan

Department of Mechanical Engineering,
The University of Tulsa,
800 South Tucker Drive,
Tulsa, OK 74104
e-mail: ram-mohan@utulsa.edu

Luis E. Gomez

MSI International Inc.,
7134 South Yale Avenue, Suite 710,
Tulsa, OK 74136
e-mail: luis-gomez@msieng-int.com

Ovadia Shoham

McDougall School of Petroleum Engineering,
The University of Tulsa,
800 South Tucker Drive,
Tulsa, OK 74104
e-mail: os@utulsa.edu

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received June 6, 2014; final manuscript received September 1, 2015; published online November 12, 2015. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 138(2), 022904 (Nov 12, 2015) (11 pages) Paper No: JERT-14-1178; doi: 10.1115/1.4031608 History: Received June 06, 2014; Revised September 01, 2015

Flow pattern transition prediction models are presented for oil–water flow in horizontal pipes. The transition between stratified and nonstratified flow is predicted using Kelvin–Helmholtz (KH) stability analysis for long waves. New, simplified, and more practical physical mechanisms/mechanistic models are proposed for the prediction of the transition boundaries to semidispersed and to fully dispersed flow. The proposed flow pattern classification significantly simplifies the flow pattern map for liquid–liquid flow and agrees well with the experimental data.

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References

Figures

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Fig. 1

Oil–water flow patterns after Trallero [1]

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Fig. 2

Schematic of two-fluid model

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Fig. 3

Comparison of model predictions with the experimental data of Trallero [1]

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Fig. 4

Comparison of model predictions with the experimental data of Angeli [4] with static mixer at the inlet of the pipe

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Fig. 5

Comparison of model predictions with the experimental data of Nädler and Mewes [16]

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Fig. 6

Comparison of model predictions with the experimental data of Soleimani [17]

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Fig. 7

Comparison of model predictions with the experimental data of Alkaya et al. [18]

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Fig. 8

Comparison of model predictions with the experimental data of Angeli and Hewitt [5] (acrylic pipe)

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Fig. 9

Comparison of model predictions with the experimental data of Fairuzov et al. [19]

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Fig. 10

Comparison of model predictions with the experimental data of Elseth [20]

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Fig. 11

Comparison of model predictions with the experimental data of Simmons and Azzopardi [21]

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Fig. 12

Comparison of model predictions with the experimental data of Oddie et al. [22]

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Fig. 13

Comparison of model predictions with the experimental data of Lovick and Angeli [23]

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Fig. 14

Comparison of model predictions with the experimental data of Vielma et al. [24]

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