Numerical predictionn

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Numerical prediction of slugging problems in
pipes
Conference Paper · October 2014
DOI: 10.13140/2.1.3262.7205
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DOT-2014
Numerical prediction of slugging problems in pipes.
Okereke, N.U (Cranfield University) and Kara, F. (Cranfield University).
Copyright 2014, Deep Offshore Technology International
This paper was prepared for presentation at the Deep Offshore Technology International Conference held in Aberdeen, Scotland, 14-16 October 2014.
This paper was selected for presentation by the DOT Advisory Board following a review of information contained in an abstract submitted by the author(s). Contents of the paper may
not have been reviewed by the Deep Offshore Technology International Conference and are subject to correction by the author(s). The material does not necessarily reflect any
position of the Deep Offshore Technology International Conference, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written
consent of the Deep Offshore Technology International Conference is prohibited.
Abstract
Slugging which involves alternating flow of gas and liquid at different flow rates is a serious problem to the
efficient exploitation of oil and gas especially in typical deepwater fields. Slugging is common in complex pipe
networks with undulations that give rise to dips, where liquid slug bodies easily accumulate until they are
pushed off by the taylor bubbles (gas surge) when their velocity builds up. This cycle of slug build up and blow
off gives rise to flooding of separator inlets, topsides trips and fatigue of the pipe structures. Previous studies
show that 50% of production is lost as a result of slugging, hence the need to review slugging prediction
problems in pipes. Critical review will be done on existing numerical models, behind the key slug parameters
such as liquid holdup, pressure drop and slug frequency in order to validate the key models against simulation
results and gain clearer understanding of the mechanism of interaction between gas surge and liquid slug.
Currently, a sample deepwater oil field case study is being considered, which experienced hydrodynamic
slugging at 3000 bopd during the field early life. The possible causes of the hydrodynamic slugging are
currently being explored and sensitivity analysis will be carried out based on WHFP (Well head flowing
pressure), Q (flow rate) and some other critical parameters in order to clearly understand the interaction
between gas surge and liquid slugs during slugging. Key findings will include; • Clearer understanding of slug
parameters and the limitations of the correlations behind the slug parameters. • Generation of a slug envelope based on
[superficial velocity liquid (Usl) vs ID (slug identifier)], in order to ascertain the impact of increasing water-cut and low flow
rates on slugging in deepwater flow loops. • Clearer understanding of the interaction between Usl (superficial velocity
liquid) and liquid holdup ( during slugging flow regime.
Key words:
Numerical, prediction, slugging, superficial velocity liquid, superficial velocity gas, holdup, deepwater

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1.0 Introduction:
1.1 Background of Study:
The global energy demand has increased significantly in recent years, thereby pushing oil and gas exploration
and production into deepwater environments. This deepwater environment, with its associated high riser
heights and long flowlines, makes it challenging for flow of multiphase fluid from the reservoir to the topsides;
especially with the alternating flow of the gas and liquid phase, at different flow velocities leading to slugging.
Slugging is a phenomenon that has gained particular interest over the years in the oil and gas industry.
Slugging is known to occur as a result of oil and gas moving at alternating velocities [1] The formation nature
of slugs is quite complex, therefore making it difficult to theoretically predict the slug frequency, length and
pressure drop. Slugging can be generally characterized into hydrodynamic or severe slugging [2]
Hydrodynamic slugging is the most common type, occurring mainly in horizontal pipelines as a result of gas
flowing at a higher velocity than the liquid phase and is commonly experienced in undulating horizontal
pipelines. This category of slugging produces short slug lengths with high frequency [3]. When hydrodynamic
slugs accumulate, especially around the riser-base; they give rise to severe slugging which is even more
problematic.
A slug is known as a propagating breaking wave having both a leading edge, slug front and slug tail which
occurs in multiphase flow in pipeline due to various factors such as fluid properties, pipeline inclination,
geometry and hydrodynamics. A slug unit is referred to as the combination of liquid and gas bubble [4]
The formation of slug flow has become very popular in current deepwater developments and this study is
focussed on gaining a clear understanding of the interaction between the taylor gas bubbles and liquid slug
during the occurrence of slugging.
2.0 Critical review on existing numerical models behind key slug parameters:
In order for the behaviour of gas surge vs liquid slug interaction to be understood, there is a need to critically
review existing numerical models of key slug parameters such as liquid holdup, pressure drop and slug
frequency.
2.1 Liquid Holdup:
Liquid holdup is the liquid area fraction or volume fraction within a two-phase gas-liquid flow. For instance, if
the gas holdup or gas volume fraction = 0.25 occupies one quarter of the pipe section, then the liquid
volume fraction is given as = 1 – 0.25 = 0.75. Hence for slug flow, the liquid holdup is the liquid volume
fraction [4; 5]. Studies indicate that the slug liquid holdup is majorly affected by gas and liquid flow rates the
fluid properties and inclination angle of the pipe [6; 7]. All published models are based on the assumption that
liquid holdup in the slug two-phase flow ( is a unique function of mixture velocity regardless of gas and
liquid velocities.
Work by Hubbard (1965) and Dukler and Hubbard (1975) indicated some key slug flow behaviour relevant to
understanding liquid holdup;
 The liquid slug scoops up the liquid film ahead of it and re-deposits a new film in its wake and
 In the process of the scooping action, some gas entrainment occurs within the slug
Following critical literature review, liquid holdup empirical correlation based on work by Gregory et al, 1978
was identified as a fundamental correlation to study and match against current simulation in order to gain
some insight on the gas surge and liquid slug behaviour during slugging. One key behaviour identified is the
impact of superficial velocity liquid ( ) and gas ( ). From the results obtained by Gregory et al, 1978; the
impact of increasing ) on reducing the liquid holdup was evident. From previous work, Gregory et al, 1978,
correlation shows reasonable agreement with experiment based on light refined oil on 2.58cm and 5.12cm
diameter pipes [8; 9].
Current work, considered matching Gregory et al, 1978 correlation with current simulation based on flow loop
X1 (section 4.2) to gain understanding of the gas surge vs liquid slug behaviour. The correlation is given by;
and it is going to be matched against simulation. Matching Gregory et al, 1978 correlation
shows a good fit in the trend of the correlation results ( ) in blue and 3000 bopd simulation case (section
4.2) of flow loop X1 (section 3 in red as indicated in figure 1. However, the correlation over-predicts liquid

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holdup as compared to the simulation results. The variation is suggested to be as a result of some parameters
not captured in the correlation (pipe diameter, pipe inclination and fluid property). From the plot of impact of
Usg, liquid holdup (Hol.) was reduced with increasing Usg and liquid holdup (Hol.) was increased with
decrease in superficial gas velocity Usg.
Figure 1: Comparism of Gregory et al correlation vs Simulation
Figure 2: Impact of Usg on Hol.
0
2
4
6
8
10
12
14
16
0 1000 2000 3000 4000 5000
Superficialvelocity/Hol
Horizontal pipe length
Impact of Usg on Hol.
Usl
Usg
Hol
-1600
-1400
-1200
-1000
-800
-600
-400
-200
0
200
0 1000 2000 3000 4000 5000
Depth(m)/Liquidholdup[-]
Horizontal pipe length
Gregory correlation vs Simulation
ELS
Hls
Pipe profile

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Figure 3: Usg vs Hol at varied massflow (sensitivity)
From the impact of the massflow sensitivity, the impact of the decrease in Usg around the 1000m – 2000m
horizontal length was clearly indicated with the slight rise in Hol around the region.
Hence, from the results and comparisms, it is clear that the major factors influencing the behaviour of liquid
holdup are the superficial velocity liquid and gas.
2.2 Pressure drop:
Pressure drop is a reflection of the drop in pressure along the pipeline as the fluid flows from the reservoir to
the topsides. One of the core correlations that govern pressure drop is the Beggs and Brill correlation for
Horizontal and slightly horizontal pipeline. Hagerdoon correlation governs the vertical pipeline. The Beggs and
Brill correlation is represented in the core equations below;
(a) ) Friction =
(b) ) G =
(c) ) A = ) X F
(d) Total =
Where the key parameters are; - Friction factor, - Mixture density, - Mixture velocity,
- Superficial velocity gas, – gravity, d – diameter, - Pressure and - is Euler constant. It is important
to note that the frictional pressure gradient is dominated by friction, while the gravitational pressure gradient is
dominated by gravity and acceleration pressure gradient is dominated by acceleration effects. Effectively, the
pressure drop is given by a combination of all three key correlations as indicated in [10-12].
As part of this work, horizontal, slightly inclined and vertical simulation cases based on three phase tab. fluid
obtained from Olga basic manual [13] was compared with the corresponding correlations (Beggs and Brill
1973) and (Hagerdoon, 1965) to evaluate the accuracy of the correlations and understand pressure drop
behaviour in gas-liquid slug flow better.
The summary of the comparison is captured in appendix C - F. The comparison shows a close correlation
between the simulations and correlations based on Beggs and Brill, 1973 and Hagerdoon, 1965. However,
there was a relatively large variation between the vertical simulation result and the Hagerdoon vertical
correlation. Upon reviewing the results, this variation was attributed to the neglect of kinetic energy effect.
Also the fact that the empirical correlations were developed from experiments based on particular fluid type,
0
5
10
15
20
25
0 1000 2000 3000 4000 5000
UsgandHol
Hor. Pipe length
Usg vs Hol
Usg
Hol

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which is different from three phase.tab fluid file as well as the fact that the experiment was done based on
smaller pipe diameter.
2.3 Slug frequency:
Slug frequency is one of the fundamental parameters required in fatigue analysis. It is defined as the number
of slugs passing across a boundary in a pipeline over a specified period of time. Olga is based on Shea
correlation which is defined as as a basis for prediction of slug frequency, where is superficial
liquid velocity, is pipeline diameter and is slug length [4], [14].
As part of this work, simulation is run in Olga 7.2.2.0 for flow loop X1 based on 3000 bopd case 1, with source
1 defined as: gas mass flow – 1.542 kg/s, oil mass flow – 2.254 kg/s and water mass flow – 3.545 kg/s. While
source 2 is defined as: gas mass flow – 1.542 kg/s , oil mass flow – 2.542 kg/s and water mass flow – 4.542
kg/s. The corresponding values of , D (diameter) and L (Slug length) are
deduced from the simulation to match them against the Shea correlation ( ), Shell slug frequency
correlation (
√
) Gregory et al slug frequency correlation ( ( ) ( ) ) and
Heywood and Richardson slug frequency correlation fs = 0.0434[ in order to verify which
of the slug frequency correlation effectively predicts slug frequency. The results are shown in appendix H.
In case 2, the mass flows at the source 1 are then varied to; gas mass flow – 25.524 kg/s, oil mass flow –
3.254 kg/s, and water mass flow - 2.545 kg/s. While source 2 is defined as gas mass flow – 15.542 kg/s,
3.542 kg/s and water mass flow – 3.542 kg/s. The results are also shown in appendix G.
Finally, in case 3; source 1 is defined as gas mass flow – 4.524 kg/s, oil mass flow – 3.254 kg/s and water
mass flow – 35.545 kg/s and source 2; 45.542 kg/s, 3.542 kg/s and water mass flow – 3.542 kg/s. The results
are also shown in appendix G.
From the plot and comparisons in figure 4, it can be seen that the slug frequency correlations (Gregory et al
and Shell) in purple and green predicted the slug frequency better especially as water mass flow rate
increases, as a result of the integration of mixture velocity in the Gregory et al correlation and the emphasis
Fig.4: Slug frequency comparism with increasing fluid water dominance

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on Usg for the Shell correlation. The case 2 shows a variation of 38.91% (over prediction) from Gregory et al
as shown in appendix G. Heywood and Richardson correlation predicted relatively well initially, but after slight
increase in water mass flow rate in the case 2, there was a large variation in the prediction. However at case 3
the prediction improved. The variation with the Shea correlation is attributed to the dependence on slug length
and the fact that average slug length was used which may not effectively represent the slug length behaviour
on the flow loop.
From figure 4, the 3000 bopd simulation (section 4.2) slug frequency trend in blue suggests that as water
mass flow is increasing, the slug frequency is reducing. Hence, high gas mass flow rate has tendency to
increase slug frequency. Hence, one can deduce that increasing superficial velocity gas with decreasing
superficial velocity liquid will tend to increase slug frequency; while increasing superficial velocity liquid with
decreasing superficial velocity gas will tend to reduce slug frequency.
Literature review [4], suggests that Olga slug frequency prediction is built around shea correlation.
Considering the results obtained in figure 4 and appendix H, showing relatively large variation of - 67%
(under-prediction) of the Shea correlation, there will be need for active work in improving the correlation
behind slug frequency; as slug frequency prediction is very critical to pipeline fatigue issues especially in deep
water oil and gas exploitation.
2.4 Gas Surge/Liquid Slug Envelope – Water-Cut Impact:
As part of work to develop the gas surge/liquid slug envelope for the flow loop X1, trial simulation runs were
performed in Olga with various trial flow rates for source 1 and 2 as indicated below;
* Gas mass flow rate – 10 kg/s, Oil mass flow rate – 20 kg/s and Water mass flow rate – 30 kg/s (Trial 1);
* Gas mass flow rate – 15 kg/s, Oil mass flow rate – 30 kg/s and Water mass flow rate – 20 kg/s (Trial 2);
* Gas mass flow rate – 25kg/s, Oil mass flow rate – 10 kg/s and Water mass flow rate – 15 kg/s (Trial 3).
These trials did not yield relevant result as the flow was predominantly stratified. Hence, lower flow rates were
then tried as indicated below;
* Source 1 – Gas mass flow rate – 3.5kg/s, Oil mass flow rate – 2.53kg/s and Water mass flow rate –
4.55kg/s; Source 2 – Gas mass flow rate – 1.53kg/s, Oil mass flow rate – 3.53kg/s and Water mass flow rate
– 2.53kg/s (M1) and finally the last trial * Source 1 – Gas mass flow rate – 5kg/s, Oil mass flow rate – 4kg/s,
Water mass flow rate – 8kg/s; * Source 2 – Gas mass flow rate – 2.5kg/s, Oil mass flow rate – 5.45kg/s and
Water mass flow rate – 4.25kg/s (M2).
Relevant tables were generated based on the simulation runs and the tables are shown in appendix H. The
envelope was now plotted based on Usl (superficial velocity liquid) vs ID (slug identifier) in order to identify
flow velocities of slug dominance and the envelope plot is shown below in fig.5

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From the envelope, it is clear that slugging was predominant at the lower flow rates associated with M1
(* Source 1 – Gas mass flow rate – 3.5kg/s, Oil mass flow rate – 2.53kg/s and Water mass flow rate –
4.55kg/s; Source 2 – Gas mass flow rate – 1.53kg/s, Oil mass flow rate – 3.53kg/s and Water mass flow rate
– 2.53kg/s). We observe stratified flow for M2 - (* Source 1 – Gas mass flow rate – 5kg/s, Oil mass flow rate
– 4kg/s, Water mass flow rate – 8kg/s; * Source 2 – Gas mass flow rate – 2.5kg/s, Oil mass flow rate –
5.45kg/s and Water mass flow rate – 4.25kg/s).
The above result further reinforces the observation that slugging is a predominantly a low flow rate issue.
Hence, when the reservoir pressure begins to decline and water-cut begins to increase, the tendency for
slugging increases towards the left (low superficial liquid flow rate region ≤ 0.4m/s). We can observe that with
the trend of the 50% water-cut case with a trend equation of y= 4.583x – 0.6557, where y is equivalent to the
slug identifier and x (superficial velocity liquid).
While, the M2 case is predominantly a stratified case, with the slug identifier indicating 1 (stratified flow
regime).
3.0 Modelling Work on Flow loop X1 based on flow at 3000 bopd and 6722 bopd
3.1 Model description:
This study is focussed on a sample deepwater oil field. The field lies in water depths greater than 1000m. As
part of this study, a critical review is done on flow loops connecting the following relevant wells (X1, X10, X3
and X5). The wells are connected via a riser system, to the topsides. X1 and X2 are connected via WMX1.
X3, X4 and X5 are connected via WMX2. X10 and X11 are connected via WMX6. These loops are
considered because; X1 for instance with the field report obtained experienced hydrodynamic slugging when it
was operating at about 3000 bopd in the early life of the field. Although this was resolved via acidization of the
wells involved, this study is focussed on gaining a clearer understanding of the interaction between the taylor
gas bubbles and liquid slug during hydrodynamic slugging as well as exploring current mitigation strategies in
handling such scenario. Other loops (X10, X3 and X5) were considered because of their relatively low flow
rates, which has tendency for hydrodynamic slug formation.
Fig.5: Slug envelope at M1 and M2

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Below, in table 1 is the detailing of the flow loops geometry connecting the wells (X1, X10, X3 and X5) as well
as pressure and temperature readings at core points on the flow loop.
Table 1: Flow geometry, pressure and temperature readings at core points on the loops
Firstly, the field data was matched with simulation results, in order to build confidence in the
simulation results going forward.
Then, the study was focussed mainly on the current 6722 bopd case and the 3000 bopd case. Also,
sensitivity based on increasing water-cut is going to be considered; in order to evaluate the impact of
increasing water-cut on hydrodynamic slug formation.
Slug-tracking mode was activated in order to properly capture slug formation.
Fig. 6: Flow loop X1 Case @ 6722 bopd
Station
TVD (ft)
Pressure
(psia)
Temperature
(deg F) TVD (ft)
Pressure
(psia)
Temperature
(deg F) TVD (ft)
Pressure
(psia)
Temperature
(deg F)
Pressure
(psia)
Temperature
(deg F)
Separator 164 290 150* 164 290 140* 164 290 145* 290 140*
Manifold -4,800 1,300 168 -4,800 1,458 190 -4,800 1,702 189 1,150 163
Wellhead -4,750 1,678 180 -4,750 1,508 195 -4750 1,812 195 3,538 181
Sandface -12,850 3,444 213 -12,615 3,315 225 -12,770 4,350 220 5,250 215
-4,800
-4700
-13,450
X1 X10 X3 X5
TVD (ft)
164

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3.2 Fluid description:
The core fluid flowing through flow loop X1 is defined on PVTsim20, based on the fluid properties as given
below on table 3. The water-cut is defined as 3% as obtained from the field data. The GOR is verified as
385.91 Sm3/sm3 from the PT flash at pressure range of min. 1 bar and max. 300 bar and temperature range
of min. -20 degree celcuis and max. 120 degree celcius. This is to allow a suitable PT range for running
simulation. The API of the fluid is tuned to API 47 degree, to reflect the field scenario.
3.3 Boundary condition:
The flow loop X1 comingles two wells; X1 and X2. Well X1 flows (6722 bopd), (4 MMScf/d) and
(0 STB/d). The volumetric flow rates are converted to mass flow rates in order to derive input for the
model. Hence, for well X1, mass flow of 13.15 kg/s is used based on the calculations in Appendix A as input
and for well X2, 56.128 kg/s is used as mass flow based on Appendix B.
The fluids from the wells flow via a pipeline-riser system of diameter 8 inches (0.2032m) and pipe roughness
of 0.002m. The pipeline-riser system is connected via a jumper of 6 inches (0.1524m). The piping has two
walls, with wall 2 serving as insulation. The thickness of wall 1 is 0.009m to reflect the field scenario and the
thickness of the insulation is simplified to 0.011m.
The heat transfer is set at TAMBIENT (ambient temperature) 20 degree celcius and HAMBIENT (mean heat
transfer to outer wall surface 2.3 W/m
2
-K
It is important to note that beyond the manifold section, from pipe 1 at 1068m along the horizontal axis of the
flow loop, fluid from well X2 is comingled at 22157 bopd (oil), 23 MMScf/d (gas) and 6 STB/d (water). The
volumetric flow rates are converted to mass flow rates and represented as another source (Sour_2) on the
model. This extra flow impacted on the temperature and pressure profile at the output; hence it is
important to integrate Sour_2.
In order to build confidence, the temperature and pressure profile will be matched against field data obtained,
before further work on analysis of gas-liquid interaction based on some core parameters will be reviewed in
further modelling work.
The integration is defined with a time step of 10 seconds and endtime of 24hrs, to reflect the field scenario of
production in a day.
As part of the input for the simulation, the volumetric flow is converted to mass flow as in the Appendix A and
B respectively. The mass flow values generated are now used to run the simulation.

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3.3 Fluid Composition:
As part of the modelling, the fluid file is defined via pvt-sim based on the composition obtained from the field
data, as in the table 2 below.
Table 2: Fluid Composition
Component Composition Mol %
Composition
N2 0.130
CO2 0.810
C1 43.300
C2 7.490
C3 7.290
iC4 2.610
nC4 3.280
iC5 1.980
C6 2.720
C7 28.830
3.4 Preliminary results for field data vs simulation matching:
Fig. 7 : Temperature vs Geometry profile plot @ 6722 bopd for field data matching
In figure 7, the Olga simulation temperature profile in red is plotted against the geometry in black, to compare

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the simulation temperature behaviour with the field behaviour.
Fig. 8 : Field data vs simulation result matching (Temperature)
In figure 8, the field data is being matched against the simulation temperature profile.The plot in figure 8
shows some similarity in the trend of both field and simulation temperature profile. However the variation,
especially beyond the manifold is a result of the comingling effect of fluid from other wells connected to the
flow loop. One of the limitation in capturing this scenario is in defining the insulation; as the complex
field flow loop insulation was quite challenging to mimic, considering the several layers and the
materials involved which were not accessed in Olga.
Nevertheless, it is important to note that the total fluid volume along the loop is accounted for from the well
test data which indicated total fluid arrival. Also, the pressure reading at the topsides is based on the total fluid
at arrival.

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Fig. 9: Pressure vs Geometry plot @ 6722 bopd for field data matching
In figure 9, the pressure profile in red is plotted against pipe geometry in black in order to compare with the
field behaviour.
Fig 10: Field data vs simulation matching (Pressure)
The pressure plot in figure 10, also indicates some variation at the inlet as well as around the manifold area.
However, the pressure values of both the field data and simulation converges towards the topsides arrival
pressure with very small variation. It is also important to note the over-prediction of pressure by Olga,

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which is similar to trends in literature. The pressure matching towards the topsides can be attributed to the
fact that the mass flow at the source for the simulation were derived from the well test data obtained at
the topsides as given in the field data in table 1.
4.0 Results for analysis:
Fig 11: Flow regime ID profile plot vs geometry @ 6722 bopd
In figure 11, the ID profile plot for flow at 6722 bopd was now generated after running the simulation for 24
hours to reflect the field scenario and the performance showed stability in the flow across the loop which
confirms the field report that the field flow stabilized after acidization making approximately 7000 bopd. The
plot indicates predominantly stratified flow regime, even across the riser base. It is also important to note that
the low water-cut of 3% could also have influenced this behaviour, as it becomes easy for the gas flowing at
an average Usg of 10.6207 m/s to push off the liquid component of the multiphase flow, flowing at an average
Usl of 0.141305 m/s.

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Fig. 12: Hol vs geometry
In figure 12, the plot shows liquid surge around the manifold and riser base area, which is associated with the
extra fluid coming in from the manifold, as well as the change in configuration at that region. However, the
(superficial liquid velocity) Usl drops along the pipe profile as a result of gravitational effects as the fluid moves
along the riser, towards the topsides. The increasing (superficial gas velocity) Usg rates goes a long way to
support the stratified ID profile as it becomes easier to push off the liquid component of the multiphase fluid.
Fig 13: Usg and Usl vs geometry @ 6722 bopd
In figure 13, the increasing Usg (superficial gas velocity – red profile plot) of the fluid along the profile,
emphasizes the compressibility property of gas and hence it’s potential to constitute a gas surge challenge,
during hydrodynamic slugging regime. While we observe a drop in the Usl (superficial liquid velocity – blue
profile plot) along the flow loop profile (black plot) especially as we begin to move against gravity along the
riser. It is also important to note the sudden drop in Usg between horizontal pipe length of 1100m to about
1500m which could be attributed to pressure differential between the flow from source 1 (well X1) and source

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2 (well ).
Fig.14: AL (Gas –Void Fraction)
In figure 14 above, the plot shows that the gas void fraction in red has a tendency to increase or expand as
the fluid travels along the flow loop in black. From the plot, there is a noticeable sharp decline in the gas-void
fraction, which is attributed to the increase in the holdup around the manifold and riser-base region. The
change in configuration also influenced this behaviour. However, beyond the riser-base, the gas-void fraction
gradually increased along the riser section of the flow loop.
4.2 Work on @ 3000 bopd Case:
In the 3000 bopd case, the volumetric flow is converted to mass flow at both the X1 well head and then at the
manifold connecting X2 well along the flow loop X1. The corresponding mass flow rates (8.745 kg/s and 25.13
kg/s) are then used as input to run the simulation for end time of 24 hrs at a time step of 10 secs. Relevant
profile and trend plots (ID, PT, Usl, Usg, AL, ROG and ROL) are then generated, to understand the dynamics
of the fluid behaviour at 3000 bopd.

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Fig. 15 : Profile plot of ID vs Geometry
In figure 15 above, the profile plot shows a flow regime that initially starts flowing at stratified regime.
However, around the manifold and riser-base region, there is a transition from stratified through annular to
slugging regime and then bubble flow regime. The transition continues as the fluid flows up the riser from
bubble through slugging, annular and finally stratified regime again until the fluid gets to the topsides. This
transition supports the information from the field at about 3000 bopd. This suggests that at lower flow rates
and with the possibility of liquid accummulation around the riser-base area, it becomes difficult for the gas to
push off the fluid; hence the slugging tendency, especially towards the riser-base area.
Fig. 16 : Profile plot of Hol vs Geometry
In figure 16, the Hol vs geometry profile plot, shows a relatively moderate average holdup of about 0.25 [-]
around the riser-base. The holdup around the riser-base region impacted in the transition in the flow regime
around the riser-base.

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Fig. 17 : Profile plot of Gas and Liquid Density vs Geometry
In figure 17, the density of liquid in red continued to increase along the riser, which will impact on the holdup
(liquid accummulation), while the gas density in blue experienced a consistent decline which will make it
relatively easy for the gas to flow along the riser. Hence, a combination of the gas density and gas velocity
profile impacted on the transition in flow regime around the riser-base region.
Fig. 18 : Profile plot of Usg and Usl
In figure 18, the (superficial gas velocity in blue) Usg and (superficial liquid velocity in red) Usl plot showed a
drop between the 1000 m and 1500m horizontal length position. However, there was a gradual increase and
build up beyond the manifold position at about 2712 m horizontal length, which is attributed to the extra fluid
flowing into the loop from source 2. There was also a gradual increase in gas velocity (beyond the manifold
position – source 2) in blue, which played a significant role in the flow regime transition along the flow loop X1.

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4.3 Work on @ 6722 bopd water-cut sensitivity:
Fig. 19 : ID Profile plot vs Geometry @ 10% wc
In figure 19, the ID profile at 10% water-cut shows significant fluctuation indicated in the red profile plot, with
predominance of slugging regime around the manifold and riser-base region. This shows that increasing
water-cut plays a significant role in the flow regime behaviour of the fluid; leading to fluctuations that will give
rise to slugging regime around the riser-base region. Another critical factor is the pipe profile, with the
corresponding the bend at the riser-base.

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Fig. 20 : Usg and Usl Profile plot vs Geometry @ 10% wc
In figure 20, the (superficial gas velocity in blue) Usg and (superficial liquid velocity in red) Usl also shows an
increasing gas velocity behaviour along the pipe profile, while the liquid velocity is quite unstable. The gas
velocity ultimately dominates in pushing off the multiphase fluid to the topsides however with the increase in
liquid density, it becomes relatively difficult, for the accumulated dense liquid around the riser-base to be
pushed off; hence the fluctuation in transition in the flow regime especially around the riser-base region.
Fig. 21: Pressure Profile plot vs Geometry @ 6722 bopd 10% wc
In figure 21, the pressure profile in red, shows similar behaviour, as with 3% water cut, indicating the
possibility of water-cut, not having much effect on pressure behaviour.

21. DOT-2014
20
Fig. 22 : Hol Profile plot vs Geometry @ 6722 bopd 10% wc
In figure 22, the holdup profile in red, shows an increased accumulation from the wellhead region which
gradually drops as the fluid flows towards the topsides. However, the hold-up value of about 0.45 [-] around
the riser-base is still significant enough, blocking almost half of the pipe profile. This will definitely play a
significant role in the fluctuating flow regime experienced at around the riser-base.
Fig. 23: Parametric study pressure profile plot at the inlet @ 6722 bopd 10% wc
In figure 23, the parametric study was conducted based on increasing massflow, for five cases; C1 -12 kg/s,
C2 – 15kg/s, C3 – 20 kg/s, C4 – 25 kg/s and C5 – 50 kg/s. The result showed a significant variation in
pressure profile for C4 – 25 kg/s and C5 – 50kg/s. The result suggests that increased massflow will lead to an
increased pressure fluctuation within the pipe.

22. DOT-2014
21
Fig. 24: Parametric study ID profile plot @ 6722 bopd 10% wc
From the ID profile plot in figure 24, the most significant fluctuation was recorded for C5 – 50kg/s in green;
with fluctuations before the manifold and after the manifold as well as around the riser-base region.
Fig. 25: ROL and ROG Profile Plot @ 6722 bopd 10% wc
In figure 25 above, the plot shows the impact of increasing water-cut, on the density of the liquid which
experienced a continuous increase along the pipe profile as the impact of gravitational force increased along
the profile. The gas density experienced a decline along the flowloop, making it easy for the gas to move
across the loop.

23. DOT-2014
22
Fig. 26: ROL and ROG Trend Plot @ 6722 bopd 10% wc
The trend plot in figure 26 captures the initial fluctuations that persists from the O seconds to about 28,000
seconds of the simulation run, showing the impact of increasing water-cut in generating fluctuations in the
liquid and gas density which will have an impact on the pressure, hold-up and other relevant trends.
5.0 Conclusion:
Slugging is a major flow assurance issue, with tendency to reduce production by 50% as highlighted in
previous study in literature. It becomes even more critical in deepwater fields with tall risers and large
hydrostatic pressure to overcome in moving fluid to the topsides.
Critical review of liquid holdup indicates that increasing superficial velocity gas (Usg) gives rise to a drop in
liquid holdup. Review on pressure drop correlation Hagerdoon indicates need for modification on the
correlation to improve prediction of pressure drop on vertical loops. Slug frequency review indicates that
Gregory et al and Shell correlation actually performed better as matched against shea correlation. Hence, the
need for improvement of the Shea slug frequency correlation as literature suggests that Olga slug frequency
module is built around the Shea slug frequency correlation.
The slug envelope at M1 and M2 also reinforces the observation that slugging is a predominantly low flow rate
issue. Also, with increasing water-cut to 50% w/c the slugging tendency shifts towards the left with decreasing
Usl.
Considering the preliminary study carried out, it is clear that low flow rates with tendency for liquid holdup
accumulation around the riser-base area was a critical factor in enhancing slugging regime experience at
3000 bopd. The pipe profile/topography of sea bed also played a critical role as the undulations around the
riser-base area was a background for easy accumulation of liquid holdup.
Also, it was clear that at low flow rates, with a drop in superficial gas velocity as well, it becomes difficult to lift
liquid phase, thereby leading to increases liquid accumulation.

24. DOT-2014
23
At 6722 bopd, the increase in flow rate played out in the predominatly stratified flow regime experienced
through out the pipe profile. The gas velocity was also very high; thereby making lifting of liquid phase up the
riser easy.
Another major finding was that literature suggests that with increasing water-cut and the increasing liquid
density, that it becomes difficult for gas phase to lift liquid to close pipe cross-section and lead to slugging;
however, from my simulation, I discovered that with increasing water-cut, the tendency for slugging was rather
high, even with flow at 6722 bopd. This suggests that the pipe profile and the presence of undulations in pipe
profile is very significant in determing the occurrence of slugging as the dense liquid phase occupied the
undulation and over time weakened the gas velocity until there was a build up of gas velocity to blow off the
liquid accumulation, leading to a fluctuating growth and decay of slugs.
Finally, parametric study on massflow at 10% water-cut suggests that increasing mass flow will give rise to
pressure fluctuation within the pipe.

25. DOT-2014
24
Appendix A:
Volumetric flow conversion to mass flow:
Well X1:
= 6722 bopd
= 4 MMScf/d
= 0 STB/d
= 6722 = 0.012369 ; = = 0.3818m/s
=
= 24757.42
= 0.2865
= 8.8426 m/s
= 0.0414 [-]
= 0.0414 * 641 + (1 – 0.0414) * 18.2 = 43.9874 kg/
̇ ̇ = ( + ) = 13.15 kg/s

26. DOT-2014
25
Appendix B:
Well X2 (Volumetric flow conversion to mass flow) resulted in;
= 22,157 bopd
= 23 MMScf/d
= 6 STB/d
= 22,157 = 0.04077 ; = = 1.2583 m/s
=
= 142,348.79
= 1.64756
= 50.85 m/s
= 0.02415 [-]
= 0.02415 * 641 + (1 – 0.02415) * 18.2 = 33.24 kg/
̇ ̇ = ( + ) = 33.24 (0.04077 + 1.64756) kg/s = 56.12 kg/s
= (0.000008280) m/s
̇ =
̇ = 0.0081144 kg/s
̇ = 56.12 + 0.008114 = 56.128 kg/s

27. DOT-2014
26
Appendix C:
Comparison of correlation and simulation for horizontal <0 degree pipeline based on three phase.tab fluid file.
Pressure drop on horizontal:
(a) Frictional pressure gradient
(b) = ( )
(c) ( ) = (Where e = Euler Number = 2.71828)
(d) = 0.3906
(e) = 0.05603/(0.1719)
2
= 1.8961
(f) 0.3906
(g) ( )
⁄
= 0.0237 where
(h) = 31,699.88
(i) = 54.5 X 0.05603 + 2 (1 - 0.05603) = 4.9416 lb/ft
3
(j) = 54.5 X 0.1719 + 2 (1 – 0.1719) = 11.024
(k)
(l) Gravitational pressure gradient
g = 32.174 ft/s; gc = 32.174 ft/s
( ) = 0
= 1.6025
Acceleration pressure gradient ( ⁄ ) ( )
= 11.024 X 15.3268 X 14.46814 / 32.174 X 725.4337 X (1.6025)
= 0.1678
=
Comparing this with Olga’s = 725.8767 – 725.1893 = 0.68744 psia
% variation = 0.8990 – 0.68744 = = 23.53%, showed a relatively low variation in pressure
drop result, in line with literature [15].

28. DOT-2014
27
Appendix D:
Comparison of correlation and simulation for <40 degree pipeline configuration
Pressure drop on < 40
0
Pipe inclination:
(a) = 0.0549/ (0.193)
2
= 1.5780
(b) = 0.3744
(c) Frictional pressure gradient
(d) = ( )
(e) ( ) = (Where e = Euler Number = 2.71828)
(f) 0.3744
(g) ⁄
= 0.0183 where
(h) = 32,174.72
(i) = 4.8822 lb/ft
3
(j)
(k)
(l) Gravitational pressure gradient
g = 32.174 ft/s; gc = 32.174 ft/s
( ) = 6.0565 psf/ft
6
= 1.342
Acceleration pressure gradient ( ⁄ ) ( )
= 0.1648 psf/ft
= 7.5216 psf/ft = 0.0522 psi/ft (* 0.00694)
Comparing this with Olga’s = 729.2502 – 725.6693 = 3.5808 psia
% variation = 3.9241 – 3.4344 = 0.4897/3.9241 X 100 = 12.48 %. This scale of variation shows Olga
under-predicting pressure drop in line with [10; 15].

29. DOT-2014
28
Appendix E:
Comparison of correlation and simulation for <90 degree pipeline configuration
Validation for vertical pipe inclined at < 90
0
Based on [11];
Fluid: 3 phase.tab
Parameters:
Average pressure and superficial velocity values are used.
L = 20 m = 65.62 ft
d = 0.12 m = 0.3937 ft
Vsl = 0.81336 ft/s = 0.24791 m/s
Vsg = 13.87174 ft/s = 4.2281 m/s
Vm = Vsl + Vsg = 14.6851 ft/s = 4.4760 m/s
l = Vsl / Vm = 0.05539 [-]
= 25 cp
= 0.013456 cp
= 2 lb/ft3
= 54.5 lb/ft3
= 17.40271 dynes/cm
d = 0.12 m = 0.394 ft
= 4.90798 lb/ft
3
P = 727.44 psia (Average pressure on loop)
= 14.7 psia (Atmospheric pressure)
⁄ = 17.01
Determination of Duns and ROS dimensionless group.
( )

30. DOT-2014
29
( )
( )
= √ ⁄ = 0.1698
From Fig. 4.2 in [16],
= 0.008
X ( )
0.1
= 0.00002619 = 2.6 X 10 ^
-5
; = 0.22
Determining liquid holdup:
X ( )
0.1
= 0.00002619 = 2 .6 X 10 ^
-5
; = 0.22
= 0.22 from figure 4.1 (Shoham, O. 2006 and Hagerdoon, B. 1965)
= 1.38 X 10
-3
; = 1.0 [Based on fig. 4.3 Shoham, O. 2006].
; Hence = 0.22 X 1.0 = 0.22 (based on correction factor)
= 0.0705
= 599,362.7228
= 2.33 x 10
-4
√⁄ = 0.0174
= 1.78
Pressure gradient, neglecting K.E effects;
-( = 15.275 psf/ft ( /144) = 0.1061 psi/ft
- ( Olga = 4.70343 psia

31. DOT-2014
30
Implies 32.4 % variation (Under-prediction)
Appendix F:
Comparison of correlation and simulation <0 - <90 degree pipeline configuration:
A summary of the comparism of pressure drop from <0 degree to < 90 degree is shown in the figure below,
indicating the variation in between the simulation and correlation, although within similar range as the work
done by Belt et al, 2011 in comparing Olga and Ledaflow pressure drop performance. Also of concern is the
behaviour for vertical which showed a larger variation, attributed to neglect of kinetic energy effect. Also from
my review, there are deductions on the need to integrate pipe roughness, as this will impact significantly on
the pressure drop result especially for the vertical (<90 degree) pipeline case.
Figure F-1: Steady state pressure drop comparism for pipe inclined <00
- <900

32. DOT-2014
Appendix G:
Slug frequency correlation comparison
S/N Simulation fs Shea Shell Gregory et al fs (Hwood) Variation Shea % Var. Shell % Var Gregory % Var. (Hwood) %
1 4.906945068 1.604127 0.325502 0.799655449 3.310698505 -67.30905447 -93.366497 -83.70359892 -32.5304
2 2.011522103 0.660846 1.017022 2.794292002 9.539892311 -67.14696228 -49.440163 38.91430761 374.26
3 0.005694397 16.20572 1.384362 1.303188134 2.813500127 284490.7032 24210.9521 22785.45 49308.22

33. DOT-2014
32
Appendix H:
Slug Envelope Tables based on M1 and M2 (W/c 3%, 10%, 20%, 30%, 40% and 50%).
Max. DT 5s
W/c 3%
position Usl Usg ID Usl Usg ID
Inlet 23.706 0.547058 3.96064 1 0.762135 4.88405 1
47.411 0.546874 3.9661 1 0.761347 4.89731 1
1066.75 0.541453 4.14409 1 0.736303 5.34656 1
MF 2712.661 0.550401 3.46999 1 0.787415 4.87669 1
2738.556 0.540011 3.48575 3 0.713501 4.89294 1
RB 3023.412 0.532251 3.87319 3 0.686076 5.53998 1
3049.307 0.531562 3.91116 3 0.684018 5.59036 1
TP 4262.306 0.42638 6.67714 1 0.459919 13.5873 1
4309.61 0.423552 6.82754 1 0.454533 13.8798 1
Usl ID
M1 (Initial mass flow) M2 (Transition mass flow)
W/c10%
Usl Usg ID Usl Usg ID
23.706 0.543346 3.95276 1 0.77002 4.88405 1
47.411 0.543252 3.95816 1 0.76924 4.89731 1
1066.75 0.539973 4.1329 1 0.744555 5.34656 1
2712.661 0.553633 3.45721 1 0.791064 4.87669 1
2738.556 0.540507 3.47347 3 0.714738 4.89294 1
3023.412 0.53279 3.86081 3 0.68695 5.53998 1
3049.307 0.532108 3.89874 3 0.684872 5.59036 1
4262.306 0.427344 6.67625 1 0.459567 13.5873 1
4309.61 0.424441 6.8277 1 0.454193 13.8798 1

34. DOT-2014
33
W/c20% W/c 20%
Usl Usg ID Usl Usg ID
0.543346 3.95308 1 0.770007 4.8817 1
0.543251 3.95848 1 0.769227 4.89494 1
0.539967 4.13328 1 0.74453 5.34175 1
0.553627 3.45757 1 0.791043 4.86945 1
0.540506 3.47383 3 0.714714 4.88578 1
0.532785 0.532785 3 0.686914 5.53426 1
0.532102 0.532102 3 0.684836 5.58478 1
0.42731 9.13978 1 0.459488 13.6213 1
0.424406 9.27538 1 0.454113 13.9147 1
W/c 30% W/c 30%
Usl Usg ID Usl Usg ID
23.706 0.543345 3.95348 1 0.76999 4.88207 1
47.411 0.543248 3.95889 1 0.76921 4.89532 1
1066.75 0.539959 4.13376 1 0.744497 5.34227 1
2712.59 0.553621 3.45802 1 0.791016 5.53498 1
2733.542 0.540506 3.47428 3 0.714684 5.58551 1
3026.877 0.530732 3.97715 3 0.686869 5.53498 1
3047.83 0.530057 4.0166 3 0.684789 5.58551 1
4263.942 0.392531 9.14124 1 0.459388 13.6238 1
4291.376 0.39047 9.27686 1 0.454012 13.9173 1
W/c 40% W/c 40%
Usl Usg ID Usl Usg ID
0.543344 3.95402 1 0.769969 4.88255 1
0.543244 3.95942 1 0.769188 4.8958 1
0.539948 4.1344 1 0.744454 5.34296 1
0.553612 3.45861 1 0.79098 4.87076 1
0.540505 3.47489 3 0.714644 4.88711 1
0.530722 3.97793 3 5.53498 5.53593 1
0.530046 4.01739 3 5.58551 5.58648 1
0.392461 9.14318 1 0.459254 13.6271 1
0.390399 9.27883 1 0.453877 13.9208 1

35. DOT-2014
34
W/c 50% W/c 50%
Usl Usg ID Usl Usg ID
0.543342 3.95475 1 0.76994 4.8832 1
0.543238 3.96016 1 0.769158 4.89647 1
0.539933 4.13528 1 0.744393 5.3439 1
0.5536 3.45943 1 0.744393 4.8718 1
0.540503 3.47571 3 0.79093 4.88815 1
0.530707 3.979 3 0.686724 5.53726 1
0.53003 4.01849 3 0.684641 5.58782 1
0.392364 9.14587 1 0.459067 13.6318 1
0.390301 9.28156 1 0.453687 13.9256 1
Appendix I:
List of Abbreviations:
WHFP Well Head Flowing Pressure
WHFT Well Head Flowing Temperature
PVT Pressure Volume Temperature
NSLUG Number of Slug
MDC Marine Drilling Centre
GOR Gas Oil Ratio

36. DOT-2014
35
Appendix J:
List of Symbols:
Symbols Description Units
Gas hold-up [ - ]
Gas cross-sectional area [ m
2
]
Pipe cross-sectional area [ m
2
]
Liquid hold-up [ - ]
Liquid cross-sectional area [ m
2
]
Gas superficial velocity [ m/s ]
Gas volumetric flow rate [ m
3
/s ]
Liquid superficial velocity [ m/s ]
Liquid volumetric flow rate [ m
3
/s ]
Mixture velocity [ m/s ]
Liquid linear velocity [ m/s ]
Gas linear velocity [ m/s ]
Slip velocity [ m/s ]
Liquid density [ kg/m
3
]
Gas density [ kg/m
3
]
Slug frequency [Hz]
Mean liquid slug density [ kg/m
3
]
Mean gas bubble density [ kg/m
3
]
Liquid holdup in slug area [ - ]
Liquid holdup in bubble area [ - ]
Slip density [kg/m
3
]
Friction factor [-]
l No slip holdup [-]
Reynolds number [-]
Vsl Liquid superficial velocity [m/s]
Vsg Gas superficial velocity [m/s]
Vm Mixture velocity [m/s]
Froude number [-]
Gravity constant m/s
2

37. DOT-2014
36
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