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Comparison of commercial multiphase flow simulators

with experimental and field databases

R Belt1, B Djoric

2, S Kalali

2, E Duret

1, D Larrey

1

1Process Department, Total EP, France

2

Stavanger Research Centre, Total EP, Norway

ABSTRACT

Two commercial multiphase pipe flow simulators, OLGA 5.3 and LedaFlow, have

been tested on laboratory and field data. Regarding the laboratory data, the results are

comparable, however, two weak points have been identified: an overprediction of theliquid hold-up in stratified flow and a relative error up to 70% on the pressure-gradient in

intermittent flow. Two field cases, one oil-dominated and the other gas-dominated, have

been tested. The predictions show very good agreement with the available field

measurements of pressure drop and temperature. In the gas-condensate case, similar

turndown and liquid accumulation curves are obtained.

1 INTRODUCTION

Pipelines in the oil-and-gas industry often transport oil and gas at the same time. The

configuration in which the two phases flow can be quite complex (separated flow,

dispersed flow, intermittent flow) and depends on the flow rates, pipe diameter and

inclination. This makes the prediction of the pressure-drop and liquid hold-up in the pipe

using a 1D approach complicated. Nevertheless, accurate predictions are crucial for the

design of the pipeline in order to have the desired pressure drop over the pipeline and the

liquid amount recovered at the exit for further processing.

The multiphase flow simulator OLGA5.3 is well established commercial software and

most commonly used by the oil-and-gas companies for design purposes. LedaFlow is

another multiphase flow simulator developed by Sintef in Norway in partnership with

Total and ConocoPhillips as the code owners. A Customer Acceptance Test (CAT) was

conducted from 1stApril until 30thJune 2010 by both companies in Houston, Stavanger

and Paris. For this purpose, Total developed a methodology including statistical analysiswhere LedaFlow was extensively tested on a large number of experimental databases

and field data. The objective of this paper is to present a comparison of the results

obtained by both commercial software packages on selected experimental databases and

field data to illustrate advantages and shortcomings of both tools.

The experimental databases that have been used for tests in Total are obtained among

others from the Tiller loop, the Boussens loop, the Porsgrunn loop and the IFE

downward loop. Those tests sum up to too many results to present in one paper.

Therefore, in this paper, mostly results from the Boussens loop are presented. We note,

however, that the qualitative trends in the predictions of other databases are also found in

the Boussens database, the most important observation being that the results obtained

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with LedaFlow are very similar to those obtained with OLGA 5.3. The Boussens

database is interesting, since it was not made available to the software developers of

LedaFlowand OLGA5.3, hence testing the Boussens database corresponds to a good

a posteriori validation.

The field data correspond to measured data in one gas-dominated and one oil-dominated

field case. This will show how both prediction tools behave for real applications.

Note that LedaFlowis currently still in development, and models are updated regularly.

The comparison to the experimental data is made using the LedaFlow Point-Model

version 2.30, which is the latest available version for this paper. The comparison to the

field data was made by slightly older versions of the transient code, version 2.23 and

2.27, however, the updates in the models are small and no noticeable impact is expected

on the results for the field cases. Regarding OLGA, version 5.3 (for the Point-Model)

and version 5.3.2.4 (for the transient code) are used, since those versions correspond to

the latest OLGAversions that are validated within Total and used for design purposes.

2 BOUSSENS DATABASE

The Boussens database was measured in the flow loop of Elf, which consisted in two

inclinable pipes of different diameters. The pipe diameter was equal to 3 and 6

(internal diameter of 0.074 m and 0.146 m, respectively), and therefore the database may

show some upscaling effects. Measurements were done at pipe angles of 90, 75, 45,

15, 4, 1.1, 0, -0.6 and -2.9 from horizontal. Note that the measurements in the

small diameter pipe were done only at inclinations above 15. Two-phase flow

measurements were performed: natural gas was used for the gas phase, and gasoil,

condensate or water was used for the liquid phase. The pressure in the experiments was

between 5 and 50 bar. In total, the Boussens database consists of 1845 points for which

are reported: the measured pressure-gradient,

the measured liquid volume fraction and

the flow regime (bubbly, intermittent, stratified or annular).

It is noted that intermittent flow corresponds to slug flow in nearly horizontal pipes and

in nearly vertical pipes of small diameter. On the contrary, in nearly vertical pipes with a

diameter larger than roughly 4, it is known that slug flow with Taylor type of bubbles

does not occur (1). Instead, churn flow is observed. Consequently, in the Boussens

experiments, intermittent can also refer to churn flow. The churn flow regime is not

predicted by OLGA5.3 and LedaFlow, and it is possibly denominated by slug flow

by the two software packages.

This database is interesting, since many experiments are performed in the intermittent

flow regime, which is the most important regime from the operation point of view.

Furthermore, the flow conditions are as close as it can be obtained experimentally to

conditions in real pipelines. Since the database is not made available to LedaFlowand

OLGA5.3, it is ideally suited for an a posteriori validation.

Since the models in LedaFlow and OLGA 5.3 are different for each flow regime, it

does not make sense to compare the predictions to the experimental values for all the

flow regimes together. Therefore, the results will be discriminated per flow regime, and

only the most interesting results are highlighted. Next, the pipe inclination plays an

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important role. For example, for a large pipe inclination and a large liquid volume

fraction, the gravitational pressure-gradient is predominant with respect to the frictional

one. Therefore, in the analysis, the results will be presented for a given flow regime and

pipe inclination.

Note that some prediction results for the Tiller database are shown in the paper, when it

is required to show that the Boussens and Tiller databases have the same qualitativetrends. The large scale SINTEF Tiller loop consists of a 1 km long pipe of 8 and 12

diameter, operated with nitrogen and a light oil at pressures between 20 and 90 bars (2).

The longest part can be inclined by angles between -10 and 10, and the loop ends with

the vertical riser. Note that the measurement points used here have been collected during

the Leda R&D program experimental campaign that started in 2001.

2.1 Nearly vertical slug flow

In this section, the points of the Boussens database are considered for which intermittent

flow is experimentally reported in the pipes inclined by 90 and 75 (in total 234 data

points). Note that globally no differences were observed between 75 and 90. On the

other hand, a diameter effect may be expected, since slug flow occurs in the 3 pipe and

churn flow in the 6 pipe.

The total pressure-gradients predicted by LedaFlow and OLGA 5.3 are compared to

the experimental values in Figure 1. The relative error distribution is shown in Figure 2.

It can be observed that LedaFlow and OLGA 5.3 provide similar predictions. The

errors are more or less symmetrically distributed around a zero error. However, the

relative error distribution is quite large, and errors with an amplitude up to roughly 70%

can be observed (the error in percentage is defined as (xpred- xmeas)/xmeas100, with xpred

and xmeas the predicted and measured values, respectively). Figure 3 shows that the

largest errors occur for low liquid superficial velocities.

It is noted that such large errors are not specific to the Boussens database. Indeed, it hasbeen observed that large errors were also observed for the Tiller database, especially for

OLGA 5.3 (Figure 4). However, for the Tiller database, the large errors were mostly

positive, up to 80% for OLGA 5.3 and 50% for LedaFlow, for pressure-gradients

between roughly 2000 and 3000 Pa/m.

Figure 1: Predicted pressure-gradient as a function of the experimental one, by

LedaFlow (left) and OLGA 5.3 (right), for points in the Boussens database in

nearly vertical pipes and for which upward slug flow is reported. The lines

correspond to an error of 0%, 30% and -30%.

LedaFlow OLGA 5.3


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Figure 2: Distribution of the relative error in the prediction of the pressure-

gradient by LedaFlow

and OLGA

5.3, for points in the Boussens database in

nearly vertical pipes and for which upward slug flow is reported.

Figure 3: Relative error of the pressure-gradient predicted by LedaFlow(left) and

OLGA5.3 (right), as a function of the experimental liquid superficial velocity, for

points in the Boussens database in nearly vertical pipes and for which upward slug

flow is reported.

Figure 4: Relative error distribution for the pressure-gradient predicted by

LedaFlow

and OLGA5.3, for points of the Tiller database in vertical pipes. Since

the flow regime has not been reported, the error distribution for LedaFlow is

made for the points for which LedaFlowpredicts slug flow. The error distribution

for OLGA

5.3 is for points for which OLGA

5.3 predicts slug flow.

OLGA 5.3

LedaFlow OLGA 5.3

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In vertical pipes of small diameter, slug flow is the flow regime intermediary of bubbly

flow and churn/annular flow, and it is characterized by large Taylor bubbles with a well-

defined shape. For this type of flow, a drift flux model can provide an accurate

prediction of the velocity of the gas bubbles, and using mass balances, a good prediction

of the liquid volume fraction and total pressure-gradient (3). However, Omebere-Iyari et

al.(1) showed experimentally that Taylor bubbles do not occur in vertical pipes of large

diameter and the flow regime changes directly from a chaotic bubbly flow to a chaoticchurn flow. In churn flow, the drift flux model based on slug flow can fail, since the

model is based on Taylor bubbles. Therefore, to account for churn flow, LedaFlowuses

a modified drift flux model for pipes with a diameter larger than 4. The modification is

partly tuned on the Tiller database. This could be the explanation for the smaller error in

the LedaFlow predictions compared to the OLGA 5.3 predictions on the Tiller

database. We do not know whether in OLGA5.3 the modeling of slug flow has been

adapted to large diameter pipes.

For the Boussens database, large errors are found, while part of the database is measured

in a small diameter pipe (0.074 m). Therefore, churn flow cannot be the only explanation

for the large errors. Figure 3 shows that the error is the largest for small liquid superficialvelocities ULS. At those low ULS, the liquid volume fraction is smaller than 0.3

(Figure 5). In that case, it is likely that the flow regime is churn-annular like, with a

continuous gas core (4), (5), (6). Then, the flow configuration is annular, with a liquid

film flowing downward but with large waves characteristic of churn flow moving

upward. For this flow configuration, a drift flux model is likely to fail in any case, and a

modeling based on an annular flow configuration together with the results of Zabaras

et al. (6) would perhaps be more appropriate. Note that the predictions of the liquid

volume fraction in Figure 5 shows that the largest errors occur for low values, i.e. low

ULS, which is in accordance with the results on the pressure-gradient.

Figure 5: Predicted liquid volume fraction as a function of the experimental one, by


nearly vertical pipes and for which upward slug flow is reported. The lines


It is also noted that the flow regime prediction is not always accurate. This has been

reported before by Helgeland Sanns and Johnson (7). In this case, at low ULS, annular

flow is sometimes predicted for intermittent flow. However, the closures for upward

annular flow are not better adapted to churn/annular flow than the slug flow drift flux

model.

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2.2 Nearly horizontal slug flow

2.2.1. In pipes with a small positive inclination

The pressure-gradient predictions by LedaFlow and OLGA5.3 in slug flow at small

and positive inclination angles are compared to the experimental values in Figure 6. The

angles are between 0 and 4, hence horizontal slug flow is included (in total 407 data

points). The predictions at small negative angles are shown in next section. It can be seenthat the error for both LedaFlowand OLGA5.3 is smaller than 30%, except for a

small number of points. This error is significantly smaller than that observed for nearly

vertical slug flow. Comparing LedaFlow and OLGA 5.3, the error distribution in

Figure 7 shows that LedaFlow is more symmetric and centred on zero error than

OLGA5.3. With OLGA 5.3, a positive error between 10 and 15% is most likely to

occur in the Boussens dataset. Furthermore, OLGA5.3 has a larger amount of outliers

than LedaFlow, although that amount remains small.

Figure 6: Predicted pressure-gradient as a function of the experimental one, byLedaFlow (left) and OLGA 5.3 (right), for points in the Boussens database in

pipes with a small and positive inclination for which slug flow is reported. The lines



gradient by LedaFlow (left) and OLGA 5.3 (right), for points in the Boussens

database in pipes with a small and positive inclination and for which slug flow is

reported.

LedaFlow OLGA 5.3

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The predictions of the pressure-gradient for slug flow at small positive angles are much

better than the results shown in Section 2.1 for nearly vertical slug flow. This can look

remarkable, since the contribution of the frictional pressure-gradient to the total pressure-

gradient increases with decreasing angles, and its modeling is more complex than that of

the gravitational component. On the other hand, in nearly horizontal pipes for the pipe

diameter under consideration, there is no question about the flow regime as it was the

case for vertical pipes, since intermittent flow corresponds to slug flow. For slug flow, adrift flux model is accurate, and a good prediction of the gas bubble velocity can be

obtained. Furthermore, in slug flow, most of the wall friction occurs in the liquid slug,

because of the higher density and viscosity. With an accurate prediction of the liquid

slug velocity, it is likely that the wall friction in a liquid plug with bubbles can be

predicted accurately (the uncertainty remaining in the entrainment of bubbles in the

slug). Note that the pressure-gradient predictions in horizontal bubbly flow are also very

good, especially for LedaFlow(not shown in the paper). Hence, the better prediction in

nearly horizontal slug flow compared to nearly vertical slug flow is probably related to

the flow regime in nearly vertical pipes and to the fact that one drift flux model cannot

be used at the same time for slug flow, churn flow and churn/annular flow.

2.2.2. In pipes with a small negative inclination

The pressure-gradient predictions by LedaFlow and OLGA5.3 in slug flow at small

and negative inclination angles are compared to the experimental values in Figure 8 (in

total 114 data points). As for small positive inclination angles, the error for both

LedaFlowand OLGA5.3 is smaller than 30% for most of the points. However, it is

now OLGA5.3 which is more symmetric and centred on zero compared to LedaFlow

which has a positive error in the order of 10% (Figure 9). Furthermore, LedaFlowhas

more outliers than OLGA5.3.



pipes with a small and negative inclination for which slug flow is reported. The lines


OLGA 5.3LedaFlow


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gradient by LedaFlow (left) and OLGA 5.3 (right), for points in the Boussens

database in pipes with a small and negative inclination and for which slug flow is

reported.

2.3 Separated flow in horizontal pipesThe predictions in separated flow, either stratified or annular, are important, since it is

the flow regime that occur most in pipelines, the largest part of the pipelines being nearly

horizontal. In this section, only strictly horizontal flow is considered. This simplifies the

analysis, because it allows separating the effects of the frictional and gravitational

pressure-gradients. Indeed, in stratified flow, a small inclination can already have a large

effect on the total pressure-gradient due to the gravitational component. Note that in

horizontal pipes, OLGA 5.3 and LedaFlow do not distinguish between stratified and

annular flow, while in the Boussens database a distinction is made. Therefore, the points

of the database, for which stratified or annular flow are reported, are considered in this

section (in total 91 data points).

The predictions with OLGA 5.3 and LedaFlow of the pressure-gradient in separated

flow are shown in Figure 10 and 11. The points at large pressure-gradients in Figure 10

correspond to data points for which annular flow is reported. For those points, it can be

seen that the relative error is mostly between 30% for LedaFlow and OLGA5.3.


LedaFlow (left) and OLGA

5.3 (right), for points in the Boussens database in

strictly horizontal pipes for which separated flow is reported. The lines correspond

to an error of 0%, 30% and -30%.

OLGA 5.3LedaFlow

OLGA 5.3


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LedaFlow

(left) and OLGA


strictly horizontal pipes for which separated flow is reported (zoom of figure 9). The

lines correspond to an error of 0%, 30% and -30%.

However, it is more probable to have a positive error for both prediction tools, and to

have a larger error with OLGA5.3 than with LedaFlow. The points at small pressure-

gradients in Figure 10 correspond to data points in the stratified flow regime, and the

results are magnified in Figure 11. Except for very small pressure-gradients (roughly up

to 20 Pa/m), the relative error is again mostly between 30% for both prediction tools.

The predicted pressure-gradient by LedaFlow is systematically larger than that

predicted by OLGA 5.3. The relative error obtained with LedaFlow is often positive

for the Boussens database, while the error with OLGA5.3 is more broadly distributed

between roughly 30%.

The liquid volume fraction predictions in OLGA

5.3 and LedaFlow

are shown inFigure 12. It is remarkable to observe that the liquid volume fraction is mostly

overpredicted by both prediction tools, with relative errors that can be very high. The

predicted liquid volume fraction is systematically larger with OLGA 5.3 compared to

LedaFlow. For OLGA5.3, almost no negative relative errors are found. Note that the

overprediction is not specific to the Boussens database; it is also observed in the Tiller

database (Figure 13), although here some negative errors can be observed with

OLGA5.3. Therefore, the overprediction is unlikely to be caused by measurement

errors. The fact that OLGA 5.3 and LedaFlow have a similar error in the liquid

volume fraction is probably due to the fact that both software packages use the same

kind of modeling.

It is possible that the overprediction in the liquid volume fraction is introduced in

OLGA5.3 and LedaFlowin order to get the liquid accumulation and turndown curves

of field cases close to measurements. Indeed, in field cases, the grid size can be quite

large, in the order of 100 m. The pipeline can be horizontal on average over the length of

the grid cell, however, it will have small fluctuations in the inclination due to terrain

inclinations on scales smaller than the length of the grid cell. Gravitational effects

already have an impact at small inclinations, therefore, a pipeline is expected to

accumulate faster liquid compared to strict horizontal pipes as in laboratory experiments,

because of the small positive slope fluctuations on scales smaller than the grid cell

length. Note that from the users perspective doing a design, an overprediction of the

liquid volume is preferred compared to an underprediction.

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Figure 12: Predicted liquid volume fraction as a function of the experimental one,

by LedaFlow(left) and OLGA


strictly horizontal pipes for which separated flow is reported. The lines correspond

to an error of 0%, 30% and -30%.

Figure 13: Relative error distribution for the liquid volume fraction predicted by

LedaFlowand OLGA5.3, for points of the Tiller database in vertical pipes.

2.4 Separated flow in pipes with a small positive inclination

In this section, data points of the Boussens database are considered for which separated

flow (stratified and annular flow) is reported in pipes with an inclination of 4 and 1.1

(in total 112 data points). Although the angles are small, the gravitational pressure-

gradient can have an impact on the total pressure-gradient, for instance in stratified flow

at low velocities. Indeed, as an order of magnitude for oil with a density of 700 kg/m 3, a

liquid volume fraction of 0.1 in a pipe inclined by 4, the gravitational component is

equal to 49 Pa/m. Note also that these angles are common in real pipelines.

The total pressure-gradient predicted by LedaFlowand OLGA5.3 for separated flow

in pipes inclined by 1.1 and 4 are shown in Figure 14 and 15. As in strictly horizontal

flow, for high pressure-gradients, i.e. in the annular flow regime, most of the predictions

are between 30% for LedaFlow and OLGA 5.3 (Figure 14). However, the

predictions by OLGA5.3 show that a positive error is more probable for the Boussens

database, while the predictions by LedaFloware more centred on zero error. This result

was also obtained for strictly horizontal flow, which is simply explained by the fact that

the gravitational pressure-gradient has a small impact at high pressure-gradients.

OLGA 5.3LedaFlow

OLGA 5.3


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pipes inclined by 1.1 and 4 and for which separated flow is reported. The lines



LedaFlow

(left) and OLGA


pipes inclined by 1.1 and 4 and for which separated flow is reported (zoom of

figure 13). The lines correspond to an error of 0%, 30% and -30%.

For small pressure-gradients (Figure 15), it can be seen that LedaFlowperforms better

than OLGA 5.3. One could think that the better pressure-gradient prediction at small

values in LedaFlow is caused by a better prediction of the liquid volume fraction.

However, Figure 16 shows that the predictions of the liquid volume fraction by both

tools are quite similar. The predictions by LedaFloware systematically slightly smaller

compared to OLGA 5.3. Similarly to strict horizontal pipes, Figure 16 shows that the

liquid volume fractions tend to be overpredicted by both tools. OLGA5.3 does almost

not predict liquid volume fractions smaller than the experimental values, which is

remarkable. It can be noted that the better predictions of the total pressure-gradient with

LedaFlowcome from the fact that the errors of the frictional and gravitational pressure-

gradients cancel each other.

OLGA 5.3

OLGA 5.3

LedaFlow

LedaFlow


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Figure 16: Predicted liquid volume fraction as a function of the experimental one,

by LedaFlow(left) and OLGA


pipes inclined by 1.1 and 4 and for which separated flow is reported. The lines


3 FIELD DATA

In addition to the comparisons performed between LedaFlow predictions and

experimental data, comparisons were also performed against field data during the CAT

process. The comparison between LedaFlow predictions against field data has been

done for various types of fields ranging from low liquid loading gas condensate fields, to

oil wells and high liquid loading flow-lines. In this paper, the comparison results for only

one oil well case and one gas condensate transport line case are presented.

3.1 Field case 1Field case 1 is an oil well, the true vertical depth (TVD) is about 900 m and the

measured depth (MD) is 1700 m. The well profile is shown in Figure 17.

Fluid properties:

o Single phase oil out of the reservoir (oil and gas at surface)

o 2 phase

Well properties:

o MD=1700 m, TVD=900 m

o Diameter: 7

Inlet conditions (Reservoir data):

o Pressure = 226.5 barao Temperature = 52.5C

o Productivity Index = 1.035 10-5kg/s/Pa

o Uniform inflow distribution along bottom screen zone (length 140 m)

o Geothermal gradient: linear from 52.5C (reservoir depth) to 4C at wellhead

o Overall heat transfer coefficient (U-value) = 5 W/m2/K

LedaFlowand OLGA5.3 models were developed to simulate the flow in the well for

the conditions given above. Pressure and temperature predictions from the two codes at

the wellhead and at the gauge were compared to field measurements. The results are

summarized in Table 1. Figure 18 shows pressure and temperature profiles given by

LedaFlowv2.23 and OLGA5.3.

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The profiles for OLGA5.3 and LedaFloware identical. At the gauge and at the well

head, both LedaFlow and OLGA 5.3 give very close predictions to the measured

values (less than 2% error).

Table 1: Well measurements and simulation results

Measured LedaFlow

OLGA

5.3P at gauge (bara) 189.8 187.3 187.8

T at gauge (C) 54.0 53.5 53.5

P at wellhead (bara) 128.9 128.7 128.9

T at wellhead (C) 49.2 49.3 49.2

Figure 17: Well profile of field case 1.

Figure 18: Well pressure profile (left) and temperature profile (right) for

LedaFlowand OLGA5.3 for field case 1.

3.2 Field case 2

Field case 2 consists of a 150 km long, 22 pipe. The pipe is mostly flat with both

downward (inlet) and upward (outlet) riser. It connects two offshore platforms at a water

depth of about 120 m. This is a three phase system with possibility of MEG injection.

Fluid properties:

o Gas/Condensate/Water transport line

o Design rate: 7-14 MSm3/d


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Pipe properties:

o Diameter: 22

o Length: 150 km

Operating conditions:

o Outlet pressure: 100 bara

o Inlet fluid temperature: 38C

LedaFlow and OLGA 5.3 models were developed to simulate the operation of the

pipeline at different flow rates. Figure 19 shows results for pressure drop and liquid hold-

up predictions. Pressure drop values were also compared to field measurements.

Figure 19: Pressure drop vs. gas flow-rate (left) and total liquid content vs. gas

flow-rate (right) for field case 2.

It can be seen that the pressure drop predictions by both codes in the friction dominated

region (gas flow rate above 6 MSm3/d) are comparable within 10% uncertainty in

comparison with field data. In the gravity dominated region (gas flow rate belowapproximately 5 MSm3/d), both OLGA 5.3 and LedaFlow are significantly

overestimating the pressure drop. However, very few good quality field measurements

exist in this region due to the large instabilities. Furthermore, since it is not desired to

operate in this region, it is possible that the pipeline has not been operated over a period

long enough to obtain a steady-state situation and a stabilized liquid content. Thus, the

results cannot be objectively compared with the field data for gas flow rates below

approximately 5 MSm3/d. However, the performance of both codes in this region is very

similar.

Unfortunately, no field measurements of liquid holdup were available for comparison.

Comparison of LedaFlow

v2.27 and OLGA

5.3 predictions show very similar trends athigh flow rates. However, at low turndown rates, LedaFlow predicts slightly more

liquid accumulation than OLGA5.3. At the lowest simulated flow rate (2 MSm3/d), the

holdup predicted by LedaFlowis about 13% larger than that predicted by OLGA5.3.

4 CONCLUSIONS

Two commercial multiphase pipe flow prediction tools, OLGA 5.3 and LedaFlow,

have been extensively tested on laboratory and field data during the CAT process

conducted by Total. The analysis based on two selected sets of data (experimental and

field data) is presented in this paper with the objective to highlight performance


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differences between two codes. The conclusion from this analysis is that predictions

from both codes are comparable for the presented cases. Moreover, the same conclusion

was reached based on the larger test matrix not presented in this paper.

The experimental database in the current paper corresponds to the data obtained in the

Boussens loop. The database was not made available to the developers of OLGA 5.3

and LedaFlow

, and therefore it consists in a good a posteriori test. The analysisperformed for this selected dataset shows that OLGA 5.3 and LedaFlow are of the

same level. Apart from minor differences, both codes perform equally well when they

are expected to provide good results, and equally poor in complicated cases. For

instance, in vertical slug flow, the predictions are not good, but this can be explained by

the fact that part of the measurements were done in churn flow and churn/annular flow,

which is not modelled accurately in both tools. On the other hand, both tools show good

results in nearly horizontal slug flow. In nearly separated flow (stratified and annular

flow), the total pressure-gradient is well predicted by OLGA5.3 and LedaFlow, with

the slight advantage to LedaFlow. On the other hand, both tools do not predict the

liquid volume fractions correctly in separated flow.

Two field cases are used for the comparison between OLGA5.3 and LedaFlow. They

consist of an oil-dominated flow in a well, and of a gas-condensate flow between two

platforms. For both cases, LedaFlow and OLGA 5.3 predictions show very good

agreement with the available field measurements of pressure drop and temperature. In

the gas-condensate case, similar turndown and liquid accumulation curves are obtained,

which are crucial parameters for design purposes. Therefore, the selected field cases also

confirm that OLGA5.3 and LedaFlowpredictions are of the same level.

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(6) Zabaras, G., Dukler, A.E. and Moalem-Maron, D., 1986, Vertical upward

concurrent gas-liquid annular flow.AIChE J., 32(5), pp. 829-843.

(7) Helgeland Sanns, B. and Johnson, G.W., 2010, A comparison of OLGA 5.3 with

two- and three-phase high pressure pipe flow experiments: flow regime prediction.

Proc. 7th North American Conf. Multiphase Prod. Tech., Banff, Canada.


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