Discrete Phase Modeling of Oil Droplets in the Gas ...
Transcript of Discrete Phase Modeling of Oil Droplets in the Gas ...
1 Copyright © 2014 by ASME
DISCRETE PHASE MODELING OF OIL DROPLETS IN THE GAS COMPARTMENT OF A PRODUCTION SEPARATOR
ABSTRACT Computational Fluid Dynamics (CFD) is a powerful
engineering tool that has different applications in the Petroleum
Industry. In recent years, CFD has been used to analyze the
complex 3D multiphase flow inside production separators. Due
to changing reservoir conditions oil companies replace old
internals with upgraded ones. In this study, a numerical
simulation of the turbulent multiphase flow using the Discrete
Phase Model (DPM) is used to assess the effects of the oil
droplet size distribution on the oil carry-over in a production
separator. Liquid droplet size distributions, meant to represent
fine and coarse populations of oil droplets, were generated at
the inlet of the separator within the range of sizes recommended
in the literature for design purposes. The DPM model accounts
for the key phenomena of droplets coalescence and breakup.
Although the real case includes three phases, the present DPM
simulations do not account for the water phase due to its
negligible volume fraction and its prevailing gravitational
settling compared to the carry-over effect. The new internals
included; an inlet device known as Schoepentoeter,
agglomerator, parallel-plates coalescer, and cyclonic mist
extractor. Unlike many of the CFD studies reported in the
literature, usually representing the internals by numerical
models for simplicity, the internals of the separator were
replicated with the maximum of geometrical details in this
study. The present work was compared with field tests and
previous numerical simulations using the Population Balance
Model PBM. The PBM simulations considered the whole
separator volume and the presence of three phases (gas, oil,
water). The mean residence time obtained from the simulations
agreed reasonably with some of the results published in the
literature using semi-empirical formulas and experiments. The
new internals were seen to promote droplet coalescence with
minimal breakup. The new inlet device (Schoepentoeter), in
particular, was found to contribute considerably to the
coalescence of droplets and, hence, to separation.
INTRODUCTION
The Separation of produced oil mixture into its gas, water
and oil components is an important upstream operation in
surface facilities of the petroleum industry. This conversion
from a mixture of substances into two or more distinct products
is usually based on gravity settling using several types of
separators. These separators are considered one of the main
tools in the upstream petroleum industry that have a significant
economic impact on the quality of the produced oil.
In the oil and gas industry, the produced wellhead fluids
consist of a complex mixture of different compounds of
hydrogen and carbon that have various densities, viscosities,
surface tensions, vapor pressures and other physical properties.
Y. F. Qaroot Mechanical Engineering Department,
Petroleum Institute Abu Dhabi, United Arab Emirates
N. Kharoua Mechanical Engineering Department,
Petroleum Institute Abu Dhabi, United Arab Emirates
L. Khezzar Mechanical Engineering Department,
Petroleum Institute Abu Dhabi, United Arab Emirates
Proceedings of the ASME 2014 International Mechanical Engineering Congress and Exposition IMECE2014
November 14-20, 2014, Montreal, Quebec, Canada
IMECE2014-37999
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As the well stream exits from a pressurized oil reservoir, it
undergoes massive pressure reduction. Therefore, most of
gases evolve from the liquid mixture entraining some liquid
droplets whilst some gas bubbles are entrained by the liquid
phase as well. This leads to enormous changes in the properties
of the well stream mixtures [1]. The mechanical separation of
liquid-gas phases is considered one of the main operations in
the production, processing and treatment of oil and gas.
With the increase in water cut and gas to oil ratio, several
operating problems are experienced with first stage production
separators. These problems include the carry-over of liquids
with the gas; out of specifications crude oil and produced water.
To address these problems, debottlenecking operations are
conducted where old internals are replaced by new high
efficiency ones [2].
A literature survey on CFD studies related to gravity
separators was conducted by [3] summarizing the main findings
of the most important contributions where more details can be
found.
As most of the investigations were conducted for industrial
purposes, only the overall steps of CFD modeling have
been provided while the details of developed CFD models
were, usually, omitted.
Many of the previous CFD studies, on horizontal
separators, used only two phases in transient time mode
which showed acceptable results when compared to
experimental observations. On the other hand, three-phase
(oil, gas and water) simulations are scant due to their
complexity in addition to the considerable time and
computational resources required.
Most of the CFD studies on three-phase separators
considered only one half of the symmetric domain in order
to reduce the computational time. However, this
assumption might not be realistic for plug flow.
Several multiphase flow models have been used such as:
interface capturing techniques like the Volume of Fluid
(VOF) [4] and those based on two-fluid approaches with
inter-penetrating media such as the Eulerian-Eulerian
model [5-7], the Eulerian-Lagrangian Discrete Phase
Model (DPM) [8], the Drift Flux Model (Advance Mixture
Model) [9] and a combined DPM-VOF model [10-11].
Although the two-equation standard k-ε turbulence model
remains the only model used because of its simplicity,
modest resource requirements and robustness, the
turbulence model was not always clearly described in
several articles.
The combined DPM-VOF model showed the most realistic
simulation when compared to the experimental
observations [10-11].
The DPM can be used effectively for modeling variable
droplet diameters.
The PBM showed a clear improvement on the separation
efficiency when compared to the results of mono-dispersed
size distributions [7].
Using indirect factors for evaluating the oil-water
separation efficiency such as: volumetric utilization [12-13]
and the standard deviation of time-averaged velocity was
not always an accurate measurement of the separation
efficiency.
The computational grids used were of modest sizes,
ranging from 100,000 to 300,000 mesh cells, for
conventional sizes of horizontal separators (10-25m long
and 1-5m diameter), except for the studies of [6] who used
105 million cells for 45.5 m long separator with 4.26m
diameter and [2,14] who used 8 million mesh cells for 14 m
long separator with 3.4m diameter.
The quality of the computational grid system used was not
always validated and grid independence tests were not
performed, presumably because of the limited resources
available and the time required for conducting such tests.
The main aim of this study is to assess the performance of a
three-phase horizontal separator, in terms of oil carry-over,
using the Discrete Phase Model DPM compared to the
Population Balance Model PBM, both, implemented in the
ANSYS FLUENT 14.0 code. The k-ε turbulence model, known
to be the appropriate approach in terms of compromising
numerical accuracy and computational cost, is combined with
the DPM model to simulate the liquid carry-over phenomenon
in the gas compartment of the separator. The liquid
compartments are not considered due to the limitation of the
DPM model. In fact, DPM, as implemented in ANSYS
FLUENT, should be combined with an Eulerian model which
provides the background phases for the DPM droplets. All the
Eulerian models in ANSYS FLUENT assume a primary phase
and one, or more, secondary phases while DPM droplets cannot
interact with the secondary phases of the Eulerian models.
Thus, no Eulerian model is used which limits the study to the
gas-liquid separation in the upper part (gas compartment) of the
separator.
The present work intends to study the effect of coalescence
and breakup of oil droplets on the separation efficiency and to
analyze the multiphase flow behavior inside the separator, the
effectiveness of the internals, residence time and the effect of
drop size distributions on the separator performance.
SIMULATION METHODOLOGY The geometry of the separator is, first, presented followed
by a brief description of the Discrete Phase Model used, details
about the boundary conditions imposed and the simulation
approach adopted. Details about the previous studies, using the
PBM model, can be found in [15].
Geometry and Computational Mesh Figure 1 illustrates the positions of the new internals inside
the 3.4m diameter separator having a length equal to 14m. The
Schoepentoeter is an inlet device which dampens the inlet
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velocity considerably in a smooth way between curved sheets
acting as diffusers. Two perforated plates (baffles) are added to
stabilize the oil-water mixture by forcing the flow towards
quiescent conditions so that to enhance the settling separation
mechanism. The coalescer consists of inclined parallel plates
fixed in the lower part of the separator and occupying more than
half of its cross-section. This device is omitted since the study
is limited to the upper gas compartment. At the same location
in the upper part, an agglomerator, formed by corrugated
parallel plates, is used for mist extraction. At the gas outlet, a
battery of cyclones, called Spiraflow, is used as mist extrator.
However, contrary to the previous study [15], the small
cyclones were replicated by a fan numerical model to test this
option of reduced geometrical complexity.
A mutli-block technique was used to mesh the
computational domain. Since the geometry of the model
contains many complicated internals, hybrid grids of hexahedral
and tetrahedral mesh cells were used. Fine-hexahedral meshes
were created in regions of interest such as: inlet,
Schoepentoeter, baffles, Agglomerator and Spiraflow while a
tetrahedral mesh was used in locations with complex
geometrical details. The number of grid cells is around 2.6
million, the minimum cell size is around 2 mm and the
maximum cell size is around 0.3 m. The quality of the
produced mesh was examined using the skewness factor and it
indicated that only a small fraction of cells (< 0.1%) were of
relatively poor quality.
Mathematical model The gas-oil flow, in the three-phase separator, was assumed
to be unsteady and turbulent. Hence, it was solved using the
Lagrangian-Eulerian DPM and turbulence k-ε models. Details
on the well-known set of equations solved can be found in [16].
The DPM is an Eulerian–Lagrangian model that solves the
force balance equation for the discrete phase by tracking their
trajectories through the calculated flow domain. Then, their
effects are injected into the continuous fluid phase through
appropriate source terms. The model includes particle-particle
interactions through breakup, coalescence, and collision sub-
models. The drag between phases was estimated using
spherical drag law. The Saffman lift force was considered as
well.
FIG. 1. GEOMETRY OF THE GAS COMPARTMENT
The turbulent dispersion was included through the Discrete
Random Walk model. The DPM has some restrictions [16]
which are the validity for only low volume fraction of the
dispersed phase typically less than 10% and the limitation of the
coalescence/collision model to injections of the same material
only. Since the lower part of the separator, where an
accumulation of a layer of oil occurs, is omitted, the assumption
of low oil volume fraction remained valid in the gas
compartment.
Since the Weber number We of the injected droplets is
relatively low (< 100 in this study), the Taylor Analogy Breakup
TAB and O’Rourke coalescence model were used [16-17]. The
TAB model is based on the analogy between an oscillating and
distorting droplet and a spring mass-damper system. It assumes
that droplet breakup will occur when the distortion of the
droplet (displacement of the droplet equator from its
equilibrium position) grows to a critical ratio of its radius ≈
0.5r. When a steady state solution is reached, the breakup
condition requires a Weber number We >12.
On the other side, O’Rourke coalescence model is based on
a stochastic estimate of collisions between droplets parcels that
only locate within the same continuous phase cell, this collision
results in coalescence only when droplets collide head-on. The
probability of coalescence within the cell is found from the
offset of the collector (larger) droplet center and the trajectory
of the smaller droplet. The collision is assumed to result in
droplets coalescence when a collision parameter, which is a
function of the relative radii of the collector and the smaller
droplet, is less than a critical collision value that is a function of
the Weber number and the relative radii.
Boundary conditions
According to the inlet flow regime, a turbulence intensity
of 2 % was prescribed. The secondary phase (oil) was injected
as poly-dispersed droplets in the continuous gaseous phase with
the same velocity of the gas equal to 7 m/s. It is worth to
mention that the real separator receives a mixture of 2% water,
6% oil and 92% gas by volume. This justifies the omission of
the water phase in addition to the fact that no water carry-over
was observed during field tests and previous simulation studies
[2, 7]. More details about the physical properties of the phases
can be found in [2, 7]. At the walls, no slip condition, with a
standard wall function [16], was imposed. The droplets are
assumed to be reflected at the walls. At the bottom wall,
however, a constant shear stress was specified with the
following values: τxz = 2.44e-05 Pa, τyz= 2.67e-06 Pa and τxy= 0
Pa. These values were approximated from the simulation work
done by [2,14] where information was extracted from the gas-
liquid interface. The particles escape the computational domain
when they reach the bottom surface. A symmetry boundary
condition was applied on the median plane of the separator in
order to reduce the number of grid cells considering, thus, only
half of the separator geometry. A fan boundary condition was
used inside each tube within the Spiraflow mist extractor, which
consists of 26 tubes. This boundary condition was implemented
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on the gas-phase flow of both models. The main aim of the fan
boundary condition is to generate a swirling flow, i.e. tangential
and radial velocities, inside the tube and create pressure drop
across the fan. The values of the pressure drop, tangential
velocity and radial velocity were estimated based on previous
work done by [2,14], and these values were found to be equal to
1545 Pa, 2.5m/s and 0.5m/s for the pressure drop, tangential
velocity and the radial velocity, respectively.
A pressure boundary condition was adopted at the outlets
of the separator. The pressure at the gas outlet was equal to
17.2 barg to replicate the real working conditions.
In accordance with common practice, the perforated baffles
were modeled as porous media with a porosity of 0.21 and 0.36
according to the baffles-open-area fraction. Resistance
coefficients through perforated plates are usually used in the
source terms of the momentum equations to replicate the baffle
resistance effect. The effect of the source terms is transmitted
to the droplets when the continuous-phase velocity field is
implemented in the equations of the multiphase model.
Size Distribution. The size distribution used is represented
by seven individual bins. Naturally, the accuracy of the
simulations might be improved with a higher number of bins,
but with a penalty on the computational cost that has to be kept
reasonable for any practical application.
The size distribution to be used at the inlet can be assumed
to be normally distributed using the Rosin-Rammler function
[16].
n
)d/d(
deY
(1)
Yd is the mass or volume fraction of the droplets which
diameter is greater than d.
The Rosin-Rammler approach requires two parameters
which are the mean diameter and the spread parameter n.
The maximum stable diameter, at the inlet of the separator,
obtained using different correlations from the literature, related
to droplets in turbulent fluid streams, was in the range 2400-
6000 μm which is not compatible with the values mentioned in
the literature for the design of separators. Three oil droplet size
distributions for the PBM model were generated to be imposed
at the inlet of the separator (Fig. 2). It is worth to mention that
the distribution profiles represents bin volume fraction. The
total area below the curves corresponds to the volume fraction
of the phase taken as unity to adjust the peaks to a comparable
scale for all the distributions. These are meant to represent
arbitrary coarse and fine distributions compared with the cut off
size of 0.1-0.14 mm at the settling compartment since no
indications, about the real distributions, are available. For the
DPM model, four distributions were generated (Fig. 3).
For the spread parameter n (see Equ. 1), Laleh [10] used
an average value of 2.6 extracted from the experiments of [18-
19].
0
0.05
0.1
0.15
0.2
0.25
0.3
0 50 100 150 200 250 300 350
Volu
me
frac
tion
Droplet diameter (μm)
50 microns80 microns140 microns
FIG. 2. DROPLET SIZE DISTRIBUTIONS AT THE INLET OF THE
SEPARATOR FOR DIFFERENT MEAN OIL DIAMETERS FOR
THE PBM SIMULATIONS
0.00
0.10
0.20
0.30
0.40
0 40 80 120 160
Volu
me
frac
tion
Droplet diameter (μm)
10 microns
30 microns
50 microns
80 microns
FIG. 3. DROPLET SIZE DISTRIBUTIONS AT THE INLET OF THE
SEPARATOR FOR DIFFERENT MEAN OIL DIAMETERS FOR
THE DPM SIMULATIONS
Relying on the limited information from the literature, a spread
parameter of 2.6 was used in the present study.
Simulation strategy The PBM simulations, necessitated 720 CPU hours of
continuous run to simulate 20 minutes for the transient period
and additional 10 minutes for the calculation of the mean field
properties of real time on 48 parallel processors of a High
Performance Cluster. The DPM simulations were less
demanding and were run in a powerful workstation. The
computational CPU time was in the range 72-120 hours for the
DPM simulations. The number of droplet parcels tracked
within the computational domain was in the range 3000-90000
depending on the size of particles considered. RESULTS AND DISCUSSION
The gravity separator, considered in the present study, was
simulated using different multiphase models and under different
working conditions previously [2,7,14-15]. The focus in this
work will be on the performance of the DPM and PBM models
for this type of multiphase flows in industry. Results describing
the separation efficiency, the residence time, and the droplet
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behavior are presented in this section and compared with
findings from the literature. For all the results presented in this
section, the distributions are represented by their mean
diameters.
Separation Efficiency Figure 4 illustrates the separation efficiency changing with
the representative mean diameter of the distributions
considered. The separation efficiency was estimated by relating
the mass flow rate of oil in the gas outlet to that of oil at the
inlet. Although some distributions were not used for both
models, they are included in Fig. 4 just for indication of the
performance of each model. The overall separation efficiency
is in a good agreement with the existing results from the
literature, e.g., [1], recommending cutoff diameters of 100-140
μm for the oil phase when similar separator configurations are
used.
The DPM simulation results showed that when the effect of
coalescence and breakup was omitted, the overall separation
efficiency increased as the mean droplet size became coarser
(Fig. 4). However, introducing the effects of coalescence and
breakup led to perfect separation efficiencies regardless of the
size distribution used. This is because the coalescence
phenomenon was found to be prevalent and influences the
separator performance significantly by generating coarser
droplets right at the Schoepentoeter.
In general, the DPM showed acceptable separation
efficiencies that were in good agreement with the PBM for
relatively large mean droplet diameters (> 80 microns), and also
lay within the range given by the field performance test (< 0.1
USG/MMSCF) bearing in mind that test results are field results
of which the precision is not known. However, the DPM
coalescence model was found to most probably overestimate the
coalescence rate in the Schoepentoeter region, which resulted in
higher separation efficiencies compared to PBM and the field
performance test, when finer size distributions were used.
0
20
40
60
80
100
120
0 50 100 150
Sep
arat
ion
eff
icie
ncy
(%
)
Mean droplet diameter (μm)
DPM without coal. & breakup
DPM with coal. & breakup
PBM
FIG. 4. SEPARATION EFFICIENCY
The discrepancy of the results (DPM against PBM and
field tests) can be attributed to:
The omission of water phase in the DPM and hence the
omission of liquid-liquid interactions, the assumption that
the oil-gas interface can be represented by a fixed surface,
which in fact does not replicate the free surface behavior
appropriately
The difference in the method used to solve the secondary
phases (DPM is Lagrangian and PBM is Eulerian): DPM
solves the force balance equation for each droplet to obtain
positions and velocities while PBM solves an equation for
the probability density function of the secondary phase in
each computational cell.
The assumption made by the coalescence model of the
PBM which assumes that the largest droplets cannot
undergo coalescence, i.e. new bins with larger sizes than
those chosen as maximum sizes (see Fig. 2) cannot be
created. This last limitation does not exist in the DPM
coalescence model (droplets coalescence without
constraints).
To further analyze the separation efficiency, the available
results from field tests, PBM [7] and DPM (with/without
coalescence and breakup) are plotted, in terms of field units, as
a function of the representative mean droplet diameter in Fig. 5.
The DPM model seems to really over-predict the coalescence
rate since it eliminates any entrainment for the realistic case
with coalescence even for very fine droplets smaller than the
sizes speculated in the literature of separator design guidelines.
Residence Time In addition to the separation efficiency, validated with the
field test results, the mean residence time MRT was compared
with the existing correlation from the literature [1, 20-21].
Danckwerts [22] stated that the MRT can be estimated simply
from:
phase
phase
Q
VMRT
(2)
where V is the volume occupied by the phase and Q is the
volumetric flow rate of the same phase at the inlet of the
separator. Thus, the MRT is calculated by averaging the
volume occupied by the phase inside the separator within a
period of quasi-steady flow regime.
The MRT for the gas-liquid separation (Fig. 6), obtained
using PBM, exhibits a decreasing trend with increasing mean
diameters of the inlet size distributions which is caused by the
rapid settling of the larger droplets. The simulation values are
three times larger than those recommended by [20] and about
40% lower than Arnold and Stewart method.
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0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0 20 40 60 80 100 120 140
Oil
in g
as (
USG
/MM
SCF)
Mean droplet diameter (μm)
DPM Without Coal. & breakup
DPM with coal. & breakup
Field tests
PBM
FIG. 5. AMOUNT OF OIL IN GAS
Machado et al. [21] used the tracer technique to measure
the MRT for gas-liquid separation in a battery of three
separators operating in serial mode. Although the dimensions
of the separators were not mentioned, their values obtained by
experiments and simulations are reported in Fig. 6 and are
comparable to the recommended values in [20]. The two
dashed lines represent two different separators.
The MRT obtained from the DPM calculations ranges from
35 to 40 sec, which is in an acceptable agreement with results
from [20] and the typical MRT found by [1] for gas-liquid
separation (30 sec to 3min). It can be seen that the DPM seems
to be insensitive to the size distributions used in this study.
Indeed, similar trends were observed for the separation
efficiency in Figs. 4 and 5. It is probably due to an
overestimation of the coalescence phenomenon as mentioned
previously. Using smaller diameters would be an interesting
test for this insensitivity to droplet sizes.
0
100
200
300
400
500
600
0 50 100 150
Mea
n re
side
nce
time
(s)
Mean diameter (μm)
[1][20][21]PBMDPM: single injectionDPM: without coal. and breakup
FIG. 6. MEAN RESIDENCE TIME VS. MEAN DROPLET
DIAMETER
However, a discrepancy was found against the MRT
calculated using PBM. This was attributed to the different type
of model used for solving the secondary phases, the omission of
the water phase in the DPM and the elimination of the
Coalescer in the present geometrical model.
It is worth to mention that the large scale of the geometry,
its complexity and the scarce source of information, for this
industrial application, do not allow a deep and accurate
comparison such in fundamental research.
Phase Behavior
However, a discrepancy was found against the MRT
calculated using PBM. This was attributed to the different type
of model used for solving the secondary phases, the omission of
the water phase in the DPM and the elimination of the
Coalescer in the present geometrical model.
It is worth to mention that the large scale of the geometry,
its complexity and the scarce source of information, for this
industrial application, do not allow a deep and accurate
comparison such in fundamental research.
Tracking the amount of oil in gas at different locations. The white vertical lines, in Fig. 7, represent the
planes created to quantify the amount of entrained oil at
different positions inside the separator and the plotted values
correspond to the mass flow rate (kg/s) through these planes.
Wherever the entrainment is marginal it was omitted.
It can be seen, from Fig.7, that the entrained oil amount
decreases throughout the separator volume when injecting
coarser distributions. Downstream of the Schoepentoeter, the
entrained oil-in-gas amount, for the fine distribution (50 μm), is
almost equal to that at the inlet while it decreases by 20% each
time the inlet size distribution was made coarser.
FIG. 7. CONTOURS OF THE OIL VOLUME FRACTION IN THE
SYMMETRY PLANE (PBM MODEL).
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In the settling compartment, the oil is entrained with the
gas through both the coalescer and the agglomerator for the fine
distribution whereas no entrainment occurs at the level of the
agglomerator for the two other distributions.
Although the coalescer is designed for oil/water separation,
it contributes to the oil/gas separation through its unsubmerged
part and is seen to have a considerable effect on the fine
distribution, by reducing the amount of entrained oil-in-gas by
70 %, contrary to the coarser ones for which no effects are
noticed. It can be concluded that the coalescer is inefficient for
the medium and coarse distributions. In addition, the
agglomerator seems to have a weak contribution by reducing
the amount of entrained oil-in-gas by only 3% for the fine
distribution which explains the high amount seen at the gas
outlet.
In Figs. 8 and 9, the amount of entrained oil at 6 different
locations across the axial-direction of the separator is illustrated
for three cases (10, 50 and 80 microns) using the DPM model
with and without breakup and coalescence.
When the effect of breakup and coalescence is present,
more than 90% of the inlet flow rate was separated by the
Schoepentoeter independently of the droplet diameter.
This is because the Schoepentoeter is designed in such a
way to enhance coalescence of droplets under the effect of
centrifugal acceleration imparted by the curved blade cascades
[14]. Thus, most of the droplet collisions and coalescence
occur within the Schoepentoeter region, and hence are
separated in the mixing compartment.
FIG. 8. OIL MASS FLOW RATES (Kg/s) DISTRIBUTION (DPM
WITH COALESCENCE AND BREAKUP)
FIG. 9. OIL MASS FLOW RATES (Kg/s) DISTRIBUTION (DPM
WITHOUT COALESCENCE AND BREAKUP)
There is a qualitative agreement of the DPM results,
without coalescence and breakup, with the predictions of the
PBM model although important quantitative differences are
generated by the two approaches. In the absence of any evident
experimental results describing the coalescence phenomenon in
different parts of the separator, it could be concluded that CFD
showed the importance of the coalescence phenomenon
although rigorous validations are necessary to quantify its
amplitude and locations. It could be, also, assumed that either
DPM overestimates the coalescence or PBM underestimate it.
Tracking of local size distribution. The size distributions
were tracked in the same planes shown in Figs. 7-9.
Table 1 illustrates that the Schoepentoeter plays an
essential role in coalescing the oil droplets into larger ones (20
times larger than the inlet mean sizes). This is because the
design of the Schoepentoeter enhances coalescing of droplets,
as it consists of several lateral flow passages with guiding vanes
act like diffusers that change the flow direction of the inlet
mixture to gain centrifugal forces, which in turn eject the
droplets towards the outer wall and thereby enhance liquid
droplets collisions, coalescing and separation. Therefore, most
all of the droplets (> 99%) are separated in the Schoeptoeter
and the gravity settling compartment, thus evaluating the other
internals might not be realistic when the coalescence model is
present.
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TABLE 1. MEAN DROPLET DIAMETER UPSTREAM AND
DOWNSTREAM THE INTERNALS (WITH COALESCENCE AND
BREAKUP)
Location Mean diameter (μm)
Inlet 10 30 50 80
DPM downstream Schoepentoeter 271 421 738 2021
PBM downstream Schoepentoeter - - 80 150
DPM upstream Agglomerator 43 34 38 51
PBM upstream Agglomerator - - 80 150
DPM downstream Agglomerator
18 32 43 48
PBM downstream Agglomerator - - 80 150
DPM gas outlet 22 15 35 33
PBM gas outlet - - 80 -
The PBM model in turn, shows an increase of the mean
droplet size within the Schoepentoeter being limited as
explained previously. Then, the same size distribution persists
within the whole internal volume of the separator which reflects
a negligible breakup effect. At this stage, reliable experimental
results are required to judge the correct behavior of the droplet
in terms of size distribution through the separator internals.
The local size distributions of the DPM case, with 50
microns with coalescence and breakup, were also investigated
across the internals, and then compared against the results
obtained using PBM. Overall, it was found that the
Schoepentoeter was the most important element affecting the
coalescence of the fine droplets considerably.
The DPM simulation results (Fig. 10) show that the Rosin-
Rammler distribution at the inlet of the separator yields a
distribution with very large sizes downstream the
Schoepentoeter.
This proves the aforementioned fact about the significant
role of the Schoepentoeter in coalescing the small droplets into
larger ones. Similar trend was observed downstream the
Schoepentoeter by the PBM but with no noticeable
perturbations of smaller droplets; however, the diameter of the
mono-dispersed distribution did not exceed the specified
maximum size (92 micron), which is an assumption made only
by the coalescence model of the PBM.
0
0.2
0.4
0.6
0.8
1
1.2
0 200 400 600 800 1000
Bin
vol
ume
frac
tion
Diameter (μm)
PBM: Inlet
PBM: Downstream Schoep
DPM: Inlet
DPM: Downstream Schoep
FIG. 10. OIL FRACTION DISTRIBUTION OF THE CASE OF 50
MICRONS MEAN DIAMETER AT THE INLET AND
DOWNSTREAM SCHOEPENTOETER
Viteri et al. [23] mentioned that the Schoepentoeter
separates 60-70 % of the incoming liquid but nothing has been
mentioned about the assessment approach and how the
separation efficiency was estimated. Mosca et al. [24]
explained briefly that the Schoepentoeter efficiency was
estimated based on the information upstream (inlet) and
downstream (column diameter of vertical separator) which is
similar to what was done in the present study by creating a
plane crossing the gas phase immediately downstream of the
Schoepentoeter.
To sum up, different distributions were generated by the
two models (PBM and DPM), when the coalescence was
present especially in the settling compartment. This was due to
the excessive amount of coalescences in the DPM simulation
compared to the PBM. Nearly similar behavior was observed
when the coalescence model was neglected, with some
deviations due to the differences in the method used to account
for the secondary phases (DPM is Lagrangian and PBM is
Eulerian). Finally, the breakup phenomenon was less important
especially at small droplet sizes. To prove the absence of
breakup phenomenon from the DPM simulations, seven random
droplets were chosen from the regions characterized by a high
turbulence level (Inlet and Schoepentoeter regions) and thus
higher probability of breakup. The corresponding Weber
number was calculated for each droplet (case of mean diameter
equals 50 μm) as illustrated in Table 2.
From Table 2, it can be seen that none of the droplets had
met the breakup condition (We > 12), which explains fairly the
absence of any noticeable breakup effect under the working
conditions considered. One of the reasons that led to this low
rate of breakup is probably the use of the Porous Media Model
to account for the effect of the perforated baffles where breakup
is more likely to occur as mentioned by [25].
9 Copyright © 2014 by ASME
TABLE 2 RELATIVE VELOCITY AND WE NUMBER FOR
SEVEN SELECTED DROPLETS
Droplet Vdroplet (m/s) Vgas (m/s) d (m) We
1 7.78 7.53 4.5e-3 0.1
2 1.36 1.16 4.5e-3 0.01
3 7.65 7.61 1.8e-2 0.01
4 0.76 0.79 4e-4 0.000015
5 0.79 7.53 2.4e-4 0.1
6 0.41 0.26 2.4e-4 0.002
7 4.80 0.6 8.3e-5 2
CONCLUSIONS Series of numerical simulations to study the turbulent
multiphase flow using the Discrete Phase Model (DPM) and the
Population Balance Model (PBM) were conducted. The PBM
results were extracted from previous publications of the same
authors for comparison with the DPM findings. The PBM
model has the limitation of a maximum droplet size fixed a
priori which is not the case of the DPM model. Although the
PBM simulations considered three phases and the whole
separator including all the internals, only those relevant to gas-
oil separation were considered in the present work due to
limitations related to the combination of Eulerian primary phase
with DPM secondary ones. The objective was to study the
effect of the oil droplet size distribution, at the inlet of the
separator, on the liquid carry-over in the gas compartment. The
droplet size distributions were generated based on
recommendations from the literature for usual design size
ranges. The internals were represented by realistic geometrical
models with the maximum of details to minimize the
computational errors due to the approximation of the internals
by numerical models. However, only the gas compartment was
included in the DPM simulations.
The present parametric study could confirm some known
features from the literature and field tests, such as the residence
time, the cut off droplet size, and the separation efficiency
which allows acquiring more confidence that CFD is a good
tool for the study of such large-scale industrial processes. The
mean residence time obtained from the simulations agreed
reasonably with some of the results published in the literature
using semi-empirical formulas and experimental results. The
new internals are well designed for the improvement of droplet
coalescence with negligible shearing effects leading to a
minimal breakup rate confirmed by the small Weber number for
the range of droplet size considered. The new inlet device
(Schoepentoeter), in particular, was found to play a key role in
coalescing droplets and enhancing separation.
ACKNOWLEDGMENTS The authors acknowledge the technical support from Dr.
Hisham Saadawi and the financial support from Abu Dhabi
Company for Onshore Oil Operation (ADCO). They are, also,
thankful to the Petroleum Institute of Abu Dhabi for providing
High Performance Computing facilities.
NOMENCLATURE d Droplet diameter
d Mean droplet diameter
n Spread parameter for the Rosin-Rammler size
distribution
Q Volumetric flow rate
We Weber number
x, y, z Coordinates
Yd Mass or volume fraction of the droplets which
diameter is greater than d
Greek Symbols τxz, τyz, τxy Shear stress components
Abbreviations
API American Petroleum Institute
DPM Discrete Phase Model
MMSCF Million standard cubic feet
MRT Mean residence time
USG United States Gallons
VOF Volume of Fluid
REFERENCES [1] Arnold, K. and Stewart, M., 2008, Surface Production
Operations, Volume 1: Design of Oil-Handling Systems and
Facilities, Gulf Publishing Company, Houston, Texas, USA.
[2] Kharoua, N., Khezzar, L. and Saadawi, H., 2012,
“Application of CFD to Debottleneck Production Separators in
a Major Oil Field in the Middle East,” Proc. SPE Annual
Technical Conference and Exhibition, San Antonio, Texas,
USA.
[3] Qaroot, Y. F., 2013, “Simulation of Three-Phase Separator
Performance,” Ph.D. thesis, Mech. Eng. Dept., Petroleum
Institute, Abu Dhabi, UAE.
[4] Frankiewicz, T., and Lee, C. M., 2002, “Using
Computational Fluid Dynamics (CFD) Simulation to Model
Fluid Motion in Process Vessels on Fixed and Floating
Platforms,” Proc. IBC 9th Annual Production Separation
Systems Conference, London, UK. [5] Abdulkadir, M. and Perez, V. H., “The Effect of Mixture Velocity and Droplet Diameter on Oil-Water Separator using Computational Fluid Dynamics (CFD),” World Academy of Science Engineering and Technology, 61, pp. 35-43. [6] Vilagines, R. D. and Akhras, A. R., 2010, “Three-Phase Flows Simulation for Improving Design of Gravity Separation Vessels,” Proc. SPE Annual Technical Conference and Exhibition, Florence, Italy.
10 Copyright © 2014 by ASME
[7] Kharoua, N., Khezzar, L. and Saadawi, H., 2014, “Flow
Modeling in Horizontal Three-Phase Separators: A Population
Balance Model Approach,” Accepted in American Journal of
Fluid Dynamics.
[8] Feng, J., Chang, Y., Peng, X. and Qu, Z., 2008,
“Investigation of the Oil–Gas Separation in a Horizontal
Separator for Oil-Injected Compressor Units,” Proc. IMechE
Part A: J. Power and Energy, 222, pp. 403-412.
[9] Hansen, E. W. M. and Rørtveit, G. J., 2006, “Numerical
Simulation of Fluid Mechanisms and Separation Behavior in
Offshore Gravity Separators,” Surfactant Science Series,
132, pp. 593-605.
[10] Laleh, A. P., 2010, “CFD Simulation of Multiphase
Separators,” Ph.D. thesis, Department Of Chemical And
Petroleum Engineering, University Of Calgary, Alberta,
Canada.
[11] Laleh, A. P., Svrcek, W. Y. and Monnery, W. D., 2011,
“Design and CFD Studies of Multiphase Separators-a Review,”
The Canadian Journal of Chemical Engineering, 9999, pp. 1-14.
[12] Lee, J. M., Khan, R. I. and Phelps, D. W., 2008,
“Debottlenecking and Computational Fluid Dynamics Studies
of High and Low-Pressure Production Separators,” SPE Annual
Technical Conference and Exhibition, Denver, USA.
[13] Lu, Y., Lee, J. M. and Phelps, D., 2007, “Effect of Internal
Baffles on Volumetric Utilization of an FWKO-A CFD
Evaluation,” Proc. SPE Annual Technical Conference and
Exhibition, Anaheim, California, USA.
[14] Kharoua, N., Khezzar, L. and Saadawi, H., 2012, “Using
CFD to Model the Performance of Retrofit Production
Separators in Abu Dhabi,” Proc. Abu Dhabi International
Petroleum Exhibition and Conference, Abu Dhabi, UAE.
[15] Kharoua, N., Khezzar, L. and Saadawi, H., 2013, “CFD
Simulation of Three-Phase Separator: Effects of Size
Distribution,” Proc. ASME 2013 Fluids Engineering Summer
Meeting FEDSM2013, Incline Village, Nevada, USA.
[16] ANSYS Inc. Fluent User Guide and Fluent Theory Guide,
2011, version 14.0.
[17] O’Rourke, P. J. and Amsden, A. A., 1987, “The TAB
Method for Numerical Calculation of Spray Droplet Breakup,”
SAE Technical Paper 872089.
[18] Karabelas, A. J., 1978, “Droplet Size Spectra Generated in
Turbulent Pipe Flow of Dilute Liquid/Liquid Dispersions,”
AIChE J., 24(2), pp. 170-180.
[19] Angeli, P. and Hewitt, G. F., 2000, “Drop Size Distribution
in Horizontal Oil-Water Dispersed Flows,” Chem. Eng. Sci., 55,
pp. 3133-3143. [20] API SPEC 12J: Specification for Oil and Gas Separators. Eighth edition, 2008. [21] Machado, C. H., Leclerc, J. P., Avilan, E., Landaeta, G.,
Añorga, N. and Capote, O., 2005, “Flow Modeling of a Battery
of Industrial Crude Oil/Gas Separators using 113min Tracer
Experiments,” Chem. Eng. Process., 44 (7), pp. 760-765.
[22] Danckwerts, P. V., 1953, “Continuous Flow Systems-
Distribution of Residence Times,” Chem. Eng. Sci., 2, pp. 1-13.
[23] Viteri, R., Egger, D., Polderman, H., 2006, “Innovative
Gas-Liquid Separator Increases Gas Production in the North
Sea,” Proc. The 85th
GPA Annual Convention, Grapevine, TX.
[24] Mosca, G., Schaeffer, P., Griepsma, B., 2011, “The New
Schoepentoeter Plus: A Step Ahead In the Bulk Separation of
Gas-Liquid Mixtures,” Proc. AIChE AIChE Spring National
Meeting, Chicago, IL.
[25] Wilkinson, D., Waldie, B, Nor, M. I. M. and Lee, H. Y.,
2000, “Baffle Plate Configurations to Enhance Separation in
Horizontal Primary Separators,” Chemical Engineering Journal,
77, pp. 221-226.