The Prediction of Flow Patterns, Liquid Holdup and Pressure Losses Occurring During Continuous Two-Phase Flow In Horizontal Pipelines

Eaton, B.A.; Knowles, C. R.; Silberbrg, I.H.

doi:10.2118/1525-pa

Cited by 109 publications

(72 citation statements)

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“…Marcano compared five correlations; Lockhart and Martinelli (1949), Hughmark (1962), Dukler et al (1969), Eaton et al (1967), Guzhov et al (1967) and Beggs (1972) correlations using the data of Eaton (1966) and Beggs (1972). The total data points consisted of 238 natural gas-water (Eaton) and 58 air-water (Beggs).…”

Section: Marcano (1973) Horizontal Pipe Comparisonmentioning

confidence: 99%

“…After the initial screening process that primarily considered the completeness of the data set, the data of Eaton (1966), Beggs (1972), Spedding and Nguyen (1976), Mukherjee (1979), Minami and Brill (1987), Franca and Lahey (1992), Abdulmajeed (1996) and Sujumnong (1997) were selected with which the performance of the void fraction correlations are to be compared. The range of diameters, inclination angles, number of data points and fluids covered by the data is summarized in Table 2.…”

Section: Experimental Database and Fluid Propertiesmentioning

confidence: 99%

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Comparison of void fraction correlations for different flow patterns in horizontal and upward inclined pipes

Woldesemayat

Ghajar

2007

International Journal of Multiphase Flow

417

229

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Section: Marcano (1973) Horizontal Pipe Comparisonmentioning

confidence: 99%

Section: Experimental Database and Fluid Propertiesmentioning

confidence: 99%

Comparison of void fraction correlations for different flow patterns in horizontal and upward inclined pipes

Woldesemayat

Ghajar

2007

International Journal of Multiphase Flow

417

229

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“…Five pressure drop formulas are also included in the TGNET such as ''AGA,'' ''Colebrook,'' ''Pan (A),'' and ''Pan (B).'' [13][14][15][16][17][18][19] State equations …”

Section: Equations In Tgnet Softwarementioning

confidence: 99%

Analysis on hydraulic adaptability and pressure drop factors of low-pressure pipe network in coal-bed methane field

Yang

Yang²,

Jingyi³

et al. 2017

Advances in Mechanical Engineering

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Because the coal-bed methane field is influenced by factors such as low pressure, slow velocity, terrain, and condensed water, which lead to the fact that the design is different from gathering design of conventional gas field, how to make full use of wellhead pressure and maximize the wellhead pressure ability is an important link of surface engineering optimization. Therefore, we need to analyze the factors which influence the pressure drop of the gas-gathering pipe network to improve the efficiency of the coal-bed methane field surface gathering. We first contrasted the two hydraulic analysis software (PIPEPHASE and TGNET) for the analysis of coal seam gas field adaptability. By comparing the simulation results with the actual data, we found that the PIPEPHASE software is better to analyze the hydraulic situation of pipe network in the coal-bed methane field. Then, using PIPEPHASE for network simulation and contrasting the 9 state equations and 26 kinds of pressure drop formula calculation results, SRK state equation and BBM pressure drop formula are recommended. We analyze the inlet temperature, pressure, and other nine factors affecting gas extraction pipeline pressure drop according to the practical experience and put forward the suggestions as follows: (1) in order to reduce the pressure drop and increase the ability of pipeline, burying pipeline under the permafrost and improving the inlet pressure are suggested; (2) The higher the inlet pressure, the more obvious the pipeline efficiency influences the pipeline pressure drop, so the inlet pressure and pipeline efficiency should be reasonably controlled in order to reduce the pressure drop; (3) The higher the pipeline pressure, the greater the wall roughness influences the pipeline pressure drop, also the greater the pressure can damage, so we should try to use small wall roughness materials.

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“…In this thesis study, the Eaton, Knowles and Silberbrg (1967) and Reynolds number, then friction pressure drop in two-phase flow would be calculated through similarity analysis approach (Dukler, Wicks and Cleveland, 1964). Liquid holdup was obtained through a trial and error calculation procedure (Dukler et al, 1969).…”

Section: Dukler Et Al (1969) Correlationmentioning

confidence: 99%

A Combined Bottom-hole Pressure Calculation Procedure Using Multiphase Correlations and Artificial Neural Network Models

2014

All Days

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Artificial neural network (ANN) techniques have been adopted to predict bottom-hole pressures and have proved to have better, or at a minimum equivalent prediction performance than conventional prediction methods such as multiphase correlations and mechanistic modeling. With the applied design, the use of ANN techniques can be more fully investigated to aid in multiphase flow related issues. In this study, different artificial neural network models have been trained to solve two of the major problems of bottom-hole pressure calculationsflow regime recognition and pressure gradient prediction.A support vector machine model was trained for flow regime classification. In order to include inclination angle effects on flow regime transition, the model uses inclination angle and as well as gas and liquid velocity numbers as input variables. Four possible different flow patterns were considered for upward and horizontal multiphase flow, including bubble flow, slug flow, annular mist flow and stratified flow. Some 3-D plots of all the possible flow patterns at all inclination angles (from horizontal to upward vertical) within the studied condition range were generated based on model outputs.Previous back-propagation neural network models in the literature have been modified to fit into piece-wise calculation procedures of multiphase correlations to achieve higher prediction accuracy and broaden the prediction range. The model training requires wellsegment-scale data sets, which contain pressure gradients as the model output variable and the model input variables, including inclination angle, liquid superficial velocity, gas superficial velocity, gas-liquid surface tension, liquid density, specific gravity of free gas, liquid viscosity, gas viscosity, average pressure and average temperature. The training data was collected from literature and as well as some piece-wise calculation results of multiphase correlations. Different back-propagation neural network model structures have been tested to find a suitable neuron number on hidden-layer. Two pressure gradient prediction models iii were trained for slug flow and annular mist flow.Finally, a combined bottom-hole calculation procedure was designed based on multiphase correlations and trained artificial neural network models. The statistical test results using the collected data show that the combined procedure has the best prediction performance than the eleven multiphase correlations studied in this work with the lowest average absolute percent error of 3.1% and standard deviation of 0.034. Some independent field data was used to test the extendability of the combined procedure prediction range. Comparing to the multiphase correlations, the combined procedure gave fairly accurate predictions with an average absolute percent error of 23.0% and a standard deviation of 0.176. To facilitate field application, a multiphase flow bottom-hole pressure calculator with a user graphic interface was developed.

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The Prediction of Flow Patterns, Liquid Holdup and Pressure Losses Occurring During Continuous Two-Phase Flow In Horizontal Pipelines

Cited by 109 publications

References 0 publications

Comparison of void fraction correlations for different flow patterns in horizontal and upward inclined pipes

Comparison of void fraction correlations for different flow patterns in horizontal and upward inclined pipes

Analysis on hydraulic adaptability and pressure drop factors of low-pressure pipe network in coal-bed methane field

A Combined Bottom-hole Pressure Calculation Procedure Using Multiphase Correlations and Artificial Neural Network Models

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