Oil–Water Flow Pattern Transition Prediction in Horizontal Pipes

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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Investigation of the Flow Pattern Transition Behaviors of Viscous Oil–Water Flow in Horizontal Pipes

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.0c03561 ◽

2020 ◽

Vol 59 (47) ◽

pp. 20892-20902

Author(s):

Haili Hu ◽

Jiaqiang Jing ◽

Sara Vahaji ◽

Jiatong Tan ◽

Jiyuan Tu

Keyword(s):

Flow Pattern ◽

Water Flow ◽

Horizontal Pipes ◽

Viscous Oil ◽

Flow Pattern Transition ◽

Oil Water

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Experimental study of the factors affecting the flow pattern transition in horizontal oil–water flow

Experimental Thermal and Fluid Science ◽

10.1016/j.expthermflusci.2018.06.020 ◽

2018 ◽

Vol 98 ◽

pp. 534-545 ◽

Cited By ~ 11

Author(s):

Jiatong Tan ◽

Jiaqiang Jing ◽

Haili Hu ◽

Xiangyang You

Keyword(s):

Experimental Study ◽

Flow Pattern ◽

Water Flow ◽

Factors Affecting ◽

Flow Pattern Transition ◽

Oil Water

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OIL-WATER FLOW IN HORIZONTAL PIPES: A FLOW PATTERN AND PRESSURE DROP STUDY

Multiphase Science and Technology ◽

10.1615/multscientechn.v21.i1-2.30 ◽

2009 ◽

Vol 21 (1-2) ◽

pp. 25-35

Author(s):

Christophe Conan ◽

Sandrine Decarre ◽

Olivier Masbernat ◽

Alain Line

Keyword(s):

Pressure Drop ◽

Flow Pattern ◽

Water Flow ◽

Horizontal Pipes ◽

Oil Water

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GAS-LIQUID FLOW-PATTERN TRANSITION IN HORIZONTAL PIPES – ANALYSIS OF REPORTED MODELS

10.26678/abcm.encit2018.cit18-0562 ◽

2018 ◽

Author(s):

André Mendes Quintino ◽

Davi Lotfi Lavor Navarro da Rocha ◽

Oscar Mauricio Hernandez Rodriguez

Keyword(s):

Flow Pattern ◽

Liquid Flow ◽

Horizontal Pipes ◽

Flow Pattern Transition ◽

Gas Liquid Flow

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Surrogate Models for Predicting the Performance of a Liquid-Liquid Cylindrical Cyclone Separator

Process Industries ◽

10.1115/imece2006-14759 ◽

2006 ◽

Author(s):

Jorge E. Pacheco ◽

Miguel A. Reyes

Keyword(s):

Flow Pattern ◽

Mean Squared Error ◽

Surrogate Models ◽

Mechanistic Models ◽

Water Mixture ◽

Inlet Flow ◽

Prediction Tools ◽

Squared Error ◽

Cylindrical Cyclone ◽

Oil Water

Liquid-Liquid Cylindrical Cyclone (LLCC) separators are devices used in the petroleum industry to extract a portion of the water from the oil-water mixture obtained at the well. The oil-water mixture entering the separator is divided due to centrifugal and buoyancy forces in an upper (oil rich) exit and a bottom (water rich) exit. The advantages in size and cost compared with traditional vessel type static separators are significant. The use of LLCC separators has not been widespread due to the lack of proven performance prediction tools. Mechanistic models have been developed over the years as tools for predicting the behavior of these separators. These mechanistic models are highly dependent on the inlet flow pattern prediction. Thus, for each specific inlet flow pattern a sub-model has to be developed. The use of surrogate models will result in prediction tools that are accurate over a wider range of operational conditions. We propose in this study to use surrogate models based on a minimum-mean-squared-error method of spatial prediction known as Kriging. Kriging models have been used in different applications ranging from structural optimization, conceptual design, multidisciplinary design optimization to mechanical and biomedical engineering. These models have been developed for deterministic data. They are targeted for applications where the available information is limited due to the cost of the experiments or the time consumed in numerical simulations. We propose to use these models with a different framework so that they can manage information from replications. For the LLCC separator a two-stage surrogate model is built based on the Bayesian surrogate multistage approach, which allows for data to be incorporated as the model is improved. Cross validation mean squared error measurements are analyzed and the model obtained shows good predicting capabilities. These surrogate models are efficient and versatile predicting tools that do not require information about the physical phenomena that drives the separation process.

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