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Effect of hydrate inhibitors on the accuracy of multiphase flow meters
Conference Paper · July 2015
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International Flow Measurement Conference 2015:
Advances and Developments in Industrial Flow Measurement
1-2 July 2015
Technical Paper
Effect of Hydrate Inhibitors On The Accuracy of
Multiphase Flow Meters
Prafull Sharma, Cranfield University
Hoi Yeung, Cranfield University
ABSTRACT
Multiphase Flow Meters (MPFM) are commonly dependent on accurate
measurement of physical or electrical properties of the produced mixture for the
estimation of phase fractions. However, the accuracy of these estimated fractions
may get influenced by the presence of hydrate inhibitors, which are injected as
part of flow assurance strategies. Thermodynamic inhibitors such as Methanol and
Mono-Ethylene Glycol (MEG) are injected in significant quantities to prevent
hydrate formation. The presence of these inhibitors can significantly influence the
physical and electrical properties of the aqueous phase. Hence, this mismatch
between the assumed and the actual properties can cause bias in the estimation
of phase fraction and flow rate by MPFM.
In this paper, a quantitative assessment of the bias in the watercut due to
presence of hydrate inhibitors, is discussed. Two widely used technologies for
water fraction measurement are discussed a) permittivity based measurement
using microwaves b) density based measurement using gamma densitometer.
Mitigation strategies are also discussed to account for these inhibitors to improve
the accuracy of MPFM.
1
INTRODUCTION
Multiphase Flow Meters (MPFM) are increasingly being used for both subsea and
offshore applications. They are gaining acceptance due to the benefits they bring
over test separators, production control and flow assurance to name a few [1].
Multiphase Flow Meters commonly use measurement of mixture density or
electrical permittivity of the produced mixture for estimation of phase fractions
[1]. Some common examples of such fraction measurements are single or dual
energy gamma densitometer, microwave, capacitance based sensors. However,
accuracy of the measured fractions can be adversely affected by the presence of
injected hydrate inhibitors [2,4,5].
Hydrate formation is a critical flow assurance issue as it can potentially obstruct
the flow or even completely block a pipe potentially leading to well shutdown.
Hydrate formation is commonly suppressed by injection of Thermodynamic
Hydrate Inhibitors (THI) specifically Methanol, Mono-Ethylene Glycol (MEG) or DiEthylene Glycol (DEG) [3]. Low Dosage Hydrate Inhibitor (LDHI) is a relatively
recent and alternative technology to THI albeit their usage is not as widely
adopted yet. LDHIs have two main categories, Kinetic Hydrate Inhibitor (KHI) and
Anti-Agglomerant (AA), where the latter inhibits the aggomeration of hydrate
particles. THIs are injected in significant quantities (upto 60% by volume of
water) in comparison to LDHIs (<3% by volume of water) [3].
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The presence of THI in large proportions can significantly influence the density
and electrical properties of the aqueous phase leading to inaccurate meter
readings. MPFM manufacturers have started addressing the issue [4,5] but still
most of them does not account for the changes in the concentrations of hydrate
inhibitors on the properties of the liquids. This mismatch between the assumed
and the actual properties of liquid, in particular aqueous phase, can cause a
significant bias in the estimates of MPFM.
Figure 1 is a simplistic diagram to represent a possible scenario of THI injection.
In this figure 1, a specific case is shown where MEG is injected from an offshore
platform into a manifold which in turn is configured to inject it into two well
heads, upstream and downstream of their respective MPFM. If a inhibitor is
injected upstream of MPFM, it may affect the accuracy of readings from the
meter.
Fig. 1 - A simple schematic representation of injection of hydrate inhibitor (MEG)
In this paper, a theoretical assessment of the watercut estimation bias due to
injection of hydrate inhibitor is discussed. Since THIs are injected in significant
proportions of water as compared to LDHIs, only Methanol and MEG are
considered in the analysis. Two widely used technologies for watercut
measurement are considered a) permittivity based measurement (eg. microwave,
capacitance or impedance sensors) b) density based measurement (eg. gamma
densitometer). A new formulation is proposed to account for inhibitors in the
estimation of watercut.
2
METHODOLOGY
The methodology used in this assessment involves an estimaton of the absolute
bias in watercut (WC) measurements and it’s relation to the flow rate of water
(Qwater) and oil (Qoil)
Qwater = WC x Qliquid
(1)
Qoil = (1-WC) x Qliquid
(2)
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Technical Paper
the bias in the estimation of watercut (ΔWC) propagates to that in flow rate of
water (ΔQwater) and oil (ΔQoil) as shown in (3) and (4). The bias in flow rates are
also directly proportional to total liquid flow rate (Qliquid).
ΔQwater = ΔWC x Qliquid
ΔQoil = -ΔWC x Qliquid
(3)
(4)
As mentioned earlier, a first principle based analysis of two of watercut
measurements technologies is discussed i.e. 1) permittivity based and 2) density
based sensors.
A conventional formulation of oil-water mixture to estimate watercut is
reformulated as a case of oil-water-THI mixture. Since THIs are miscible in water,
the water+THI phase is classified as an aqueous phase and hence the treatment
of the problem reduces to an oil-aqueous mixture case. The properties of the
aqueous phase is defined by the properties of water, THI and their relative
concentrations.
The watercut estimated from the two formulations, oil-water and oil-aqueous, are
subtracted to obtain an absolute estimate of bias in watercut. This is also in-line
with industry’s practice of specifying water errors in absolute% and not in relative
terms.
Figure 2 shows a conventional oil-water estimation process for a permittivity
based sensor. There are various measurement principles which can be used to
measure mixture permittivity [6]. Capacitance or impedance sensors generally
operate in sub-MHz range while common industrial RF or microwave sensors
typically operate upto 10 GHz and in certain cases even higher frequencies. In
case of oil-water-THI mixture, a conventional approach may treat it as a oil-water
mixture as shown in figure 2 thereby resulting in a bias in the estimation.
Measurement
Transducer Model
(εm)
εm
T
S%
ω
Water Permittivity Model
εw
Mixture model
εo
Oil Permittivity
Watercut
Fig. 2 - Steps to estimate watercut with a conventional oil-water formulation
Figure 2 shows that the sensed parameters are used in a transducer model to
estimate the flow media property i.e. in this case permittivity of liquid mixture
(εm).
εm = Measurement and transducer model
Water permittivity (εw) and oil permittivity (εo) along with liquid mixture
permittivity (εm) are used in a mixture model to calculate watercut. Oil
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Technical Paper
permittivity for various crude oils were analysed by Kjetil et al [7] and were
found to be within the range of 2.0 - 2.5. Water permittivity is calculated using
the model developed by Stogryn [8]. The permittivity of water is dependent on
it’s temperature, salinity and the frequency under consideration.
εo = 2.0 to 2.5 (2.3 in this analysis)
εw = Water Permittivity Model (Temperature, Salinity, Frequency)
The mixture model that is used to relate permittivity of oil, water, oil-water
mixture to water fraction is as developed by Bruggeman and is described in [9].
Since oil-water mixture can exist as a oil continuous or water continuous mixture,
two different forms of Bruggeman mixture models are used. Equation (5) is used
for oil continuous mixture whereas equation (6) is used for water continuous
mixture. In this analysis, watercut 50% is assumed as a transition point of the
mixture type however in reality it is quite uncertain and varies over a broad range
of watercut.
1

3

w  1  w  m   o 
 w   o   m 
w


   
    
o
m
o
w
(5)
1
3


m
(6)
w
WC   w
(7)
Watercut (WC) for oil-water mixture is same as water fraction calculated in (5)
and (6) and represented in (7).
The symbols , ε represents volumetric fraction and permittivity respectively. The
subscripts o, w, m represents oil, water, and mixture respectively.
Measurement
T
S%
ω
Transducer model
(εm)
Water Permittivity Model
εw
Aqua-phase Permittivity
Model
IAR
εm
εa
Mixture model
εo
Oil Permittivity
εI
T
ω
Inhibitor Permittivity Model
Aquacut
Watercut
Fig. 3 - Steps to estimate watercut for a oil-water-THI mixture with a new oilaqueous formulation
Figure 3 shows a new proposed formulation to correctly estimate watercut for oilwater-THI mixture case. Since THI is miscible in water, the water-THI mixture is
considered as a single aqueous phase and the formulation is reduced to a two
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phase oil-aqueous mixture problem. For such a case, a simple oil-water
formulation described earlier is inappropriate to use and will lead to watercut
estimation error. It is proposed that this new formulation be used for the
estimation of watercut to account for the effect of THIs in the mixture.
As shown in figure 3, the oil-aqueous formulation uses a mixture model to relate
the oil permittivity, aqua permittivity along with oil-aqua mixture permittivity
with aquacut. Aquacut is a term used in this formulation to represent volumetric
ratio of aqueous phase (water-THI mixture) with that of total liquid i.e. oilaqueous mixture.
Another
term Inhibitor-Aqua Ratio (IAR) is introduced to represent the
volumetric concentration of THI in aqueous phase. IAR is used to calculate, the
aqueous permittivity as well as to calculate watercut from aquacut as shown
below.
Figure 3 shows that the sensed parameter from the transducer is used to
calculate the oil-water-THI liquid mixure permittivity (εm).
εm = Measurement and transducer model
Aqua permittivity (εa) and oil permittivity (εo) along with mixture permittivity (εm)
are used in the mixture model to calculate aquacut. The aqua permittivity is
calculated using Bruggeman mixture model [9], as shown in (8). Water is
assumed to be the continuous phase in the aqueous medium. The water
permittivity is calculated using the model developed by Stogryn and the inhibitor
permittivity is from literature. In case of Methanol, data provided by Nyshadham
et al [10] is used and for MEG properties data published by Jordan et al [11] is
used. The Bruggeman model in equation (8) is solved inversely in an iterative
manner with an input IAR to obtain aqua permittivity.
εo = 2.0 to 2.5 (2.3 in this analysis)
εw = Water Permittivity Model (Temperature, Salinity, Frequency)
εI = Inhibitor Permittivity Model (Temperature, Frequency)
εa = Inverse Mixture Model (εw , εI , IAR)
i.e. Inverse solution of Bruggeman model
1
3
 
IAR  1   I  a   w 
 I   w   a 
(8)
Bruggeman mixture model is used for oil-aqua mixture to relate permittivity of
oil, aqueous phase and their mixture to aqueous fraction. Here, oil-aqueous
mixture is considered to exist as an oil continuous or aqua continuous mixture,
and two different forms of Bruggeman mixture models are used as shown in (9)
and (10) respectively.
a
a


 1    

   
a
m
a
o


   
    
5
o
m
o
a
1
3
o 

m
(9)
1
3


m
a
(10)
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Equations (11) and (12) are used to relate aquacut (AC) to Aqueous fraction as
represented in (13)
AC 

a
(11)
1
(12)
 o  a
 
o
a
AC   a
(13)
watercut can be calculated from aquacut using (14) and can be derived easily.
WC 
AC .(1  IAR )
(1  AC .IAR )
(14)
Figure 4 pictorially represents the analysis methodology as described by figures 2
and 3 and with equations (5)-(14). The two non-linear curves represent the
mixture model for a conventional oil-water case and oil-aqueous case. The ends
of the oil-aqueous curve are represented by oil and aqua phase. As described
earlier and shown in figure 4, the aqua phase permittivity (εa) is calculated using
water permittivity (εw) and inhibitor permittivity (εI) and Inhibitor-Aqua Ratio
(IAR). It should be noted that figure 4 is a simplified representation of mixture
models and separate curves for oil continouos, water continous and aqua
continous mixtures are not shown as distinct curves.
εw
εa
Bias
εm
WC
(Estimated)
WC
(True)
IAR
εI
Measured εm
εo
x VF
0
AC
WC WC (True)
100
WaterCut (WC), AquaCut (AC)
Fig. 4 – A pictorial represenation of the watercut bias analysis methodology for
permittivity based sensors
For a measured mixture permittivity (εm), estimation of watercut is a one step
process for oil-water case. Whereas it is a two step process for oil-aqueous case
since aquacut is calculated before watercut. ‘VF’ in figure 4 represents a
volumetric factor such that AC= VF.WC and can be derived using equation (14) as
VF = 1/(1-IAR + WC.IAR).
For assessing the bias in watercut estimation, the oil-aqueous formulation is
solved in an inverse manner with an input actual watercut to back calculate a
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Technical Paper
simulated measured permittivity (εm) which is then further used in the oil-water
formulation to estimate erroneous watercut. This estimated value is numerically
subtracted from the actual watercut value to obtain watercut bias in %abs.
Figure 5 pictorially represents the analysis methodology followed for density
based watercut sensors. It can be seen that the methodology followed for density
sensors is very similar to that followed for permittivity sensors. However, the
mixture models are simple linear curves unlike that in the case of permittivity.
ρw
WC
(Estimated)
Bias
ρa
WC
(True)
ρm
IAR
Measured ρm
ρI
ρo
x VF
0
WC
AC
WC (True)
100
WaterCut (WC), AquaCut (AC)
Fig. 5 - Pictorial represenation of the watercut bias analysis methodology for
density based sensors
The component densities used in this analysis are as follows
ρo = 700-1000 Kg/m3 (800 Kg/m3 in this analysis)
ρw = Water Density (Temperature, Salinity) [13]
ρI = Inhibitor Density (Temperature) [12,13]
2.1
Dielectric Properties and Density
The analysis methodology for permittivity based sensors as described in the
previous section requires permittivity of water, Methanol and MEG for use in the
mixture model. It is therefore important to understand the dependence of
permittivitiy on temperature, frequency and water salinity to get a holistric view
of errors.
Figure 6 shows the real part of relative permittivity of water, Methanol and MEG
at 40o Celcius. Both Methanol and MEG have permittivity lower than that of water
across frequencies upto 26GHz. Both Methanol and MEG have strong dielectric
dispersion above 1 GHz where their permittivity rapidly decreases with increasing
frequency. The frequency of 4GHz is used as reference for the analysis since it is
an intermediate frequency range for industrial microwave sensors. Moreover,
distilled water is taken as a refernce case since water permittivity is known to
decrease with increasing salt concentration. Hence distilled water represents a
worst case scenario for watercut estimation error as it’s permittivity difference
with THI is larger as compared to saline water.
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Fig. 6 - Permittivity of Water, Methanol and MEG at 40o Celcius [8,10,11]
Fig. 7 - Permittivity (real part) of Water, Methanol and MEG at 4GHz [8,10,11]
Figure 7 shows the influence of temperature on the permittivity of liquids in
consideration at 4GHz. As can be observed, both Methanol and MEG show a slight
increase in the permittivity from 0oC to 40oC. All the analysis and results
discussed in this paper are at 40oC as that is the highest temperature at which
reliable data could be obtained for Methanol and MEG from open literature.
However, the process temperatures in the field are likely to be much higher than
that and hence an analysis at higher temperature is closer to reality.
From figures 2, 3, 6 and 7, it can be observed that there are five dimensions to
get a complete analysis of the errors 1) Frequency 2) Temperature 3) Water
Salinity 4) IAR 5) watercut. However, in this paper the analysis is simplified and
reduced by choosing an intermediate value of frequency, a temperature and a
fixed salinity for worst case i.e. 4GHz, 40oC and distilled water. IAR and WC were
varied in the range 0-0.5 and 0%-100% respectively.
Figure 8 shows the density of water, Methanol and MEG and the influence of
temperature on them. As can be observed, all of them show a negative sensitivity
to temperature. The density of Methanol is lower than that of water whereas MEG
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have higher density than that of water. Hence the analysis results for density are
expected to be markedly different between Methanol and MEG. It can also be
observed that the density difference between water and THI remains very similar
across the range of temperature 0oC-80oC. Due to this, temperature should not
have much effect on the analysis results and is not considered to be a variable.
Fig. 8 - Density of Water, Methanol and MEG at 4GHz [12,13]
For the density analysis, the reference temperature of 40 oC and distilled water is
chosen thereby maintaining consistency with the permittivity analysis.
3
RESULTS AND DISCUSSION
The results of the analysis are presented in the form of two-dimensional plots as
shown in figures 9 to 14. The colour scale in the plots represents estimated
watercut bias in % absolute scale.
Figures 9 and 10 shows the permittivity based watercut bias for watercut range 0100% and IAR range 0-50% for Methanol and MEG respectively. This was
calculated at 40oC and 4GHz as fixed conditions. watercut of 50% is assumed to
be the transition point between oil continuous mixture and water continuous
mixture and is evident from marked difference in the resulting plot in the two
regions. As discussed earlier, in reality the transition region may be very different
and is quite hard to predict.
Figure 9 shows the bias as calculated for Methanol. It can be seen that oil
continuous region (watercut<50%) shows a positive bias and the water
continuous region (watercut >50%) shows a significant negative bias. The bias in
the oil continuous region is mainly driven by the volumetric factor (VF) for
conversion of aquacut to watercut. Since the influence of aqua phase fraction is
less in oil continuous, the difference in permittivity between aqua phase and
water is not influencial enough in comparison to that of volumetric factor. On the
other hand, in water continous region, the effect of permittivity difference
dominates over the effect of VF due to larger presence of aqua phase resulting in
an overall negative bias in watercut estimation. As can be expected, the bias is
absent when IAR =0% and increases with increasing IAR and watercut. Figure 10
shows the estimated bias for MEG under same conditions as described for figure
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9. The results and trends are very similar to that of Methanol as the permittivity
of MEG is similar to Methanol in comparison to water and oil.
Figures 11 and 12 shows the permittivity based watercut bias for Methanol and
MEG, respectively, in the watercut range 0-100% and Frequency 0.1-10GHz. The
reference temperature is 40oC and IAR is fixed at 50%. Oil continuous shows
positive bias and water continuous shows negative bias due to same reasons as
discussed for figures 9 and 10. In water continuous region, the bias is also
strongly dependent on frequency above 1GHz due to dielectric dispersion of THIs
as discussed in figure 7. In oil continous region, a bias pattern emerges due to
the competing factors of VF and water-aqua permittivity difference across
frequency range.
Figures 13 and 14 shows the density based watercut bias for watercut range 0100% and IAR range 0-50% for Methanol and MEG respectively. This was
calculated at 40oC and pure water as fixed conditions. The transition point
between oil continuous mixture and water continuous mixture should not affect
the mixture density, unless there is any physical or chemical phenomena resulting
in volumetric expansion or contraction of the total mixture.
Figure 13 shows the bias as calculated for Methanol. As can be expected, the bias
is absent when IAR = 0% and increases with increasing IAR and watercut.
Methanol shows a negative bias in watercut. The influence of the volumetric factor
(VF) and the density difference between water-aqueous and THI acts in opposite
directions. This is due to the fact that the density of Methanol is lower than that
of water. The bias in the estimate is highest as ~-50% at WLR=100%, IAR=50%
and lowest as 0% at WLR=0% and IAR=0%.
Figure 14 shows the bias as calculated for MEG. As can be expected, the bias is
absent when IAR = 0% and increases with increasing IAR and watercut. MEG
shows a positive bias in watercut unlike that observed for Methanol. The influence
of the volumetric factor (VF) and the density difference between water-aqueous
acts in an additive manner. This is due to the fact that the density of MEG is
higher than that of water. This also results in a very different bias pattern in
comparison to that of Methanol. The bias in the estimate is highest as ~35% at
WLR=60%, IAR=50% and lowest as 0% at WLR=0% and IAR=0%.
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Fig. 9 - Watercut bias with a permittivity based sensor due to injection of
Methanol as a hydrate inhibitor (at 40oC, 4GHz). The colour scale shows watercut
bias in %abs
Fig. 10 - Watercut bias with a permittivity based sensor due to injection of MEG
as a hydrate inhibitor (at 40oC, 4GHz). The colour scale shows watercut bias in
%abs
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Fig. 11 - Watercut bias with a permittivity based sensor due to injection of
Methanol as a hydrate inhibitor (at 40oC, 50% IAR). The colour scale shows
watercut bias in %abs
Fig. 12 - Watercut bias with a permittivity based sensor due to injection of MEG
as a hydrate inhibitor (at 40oC, 50% IAR). The colour scale shows watercut bias
in %abs
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Fig. 13 - Watercut bias with a density based sensor due to injection of Methanol
as a hydrate inhibitor (at 40oC). The colour scale shows watercut bias in %abs
Fig. 14 - Watercut bias with a density based sensor due to injection of MEG as a
hydrate inhibitor (at 40oC). The colour scale shows watercut bias in %abs
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4
CONCLUSIONS
The effect of injection of hydrate inhibitors on the accuracy of MPFM was studied
specifically for Methanol and MEG as THIs. The concerning condition is when the
THIs are injected upstream of an installed MPFM. Rather than directly quantifying
the error in the water and oil flow rates, bias in the watecut is studied which can
be extended to flow rate errors using simple equations.
A new oil-aqueous formulation is proposed to correctly estimate watercut while
accounting for the injected inhibitors. watercut estimates from this new oilaqueous formulation are compared to that of the conventional oil-water
formulation to quantify bias in the watercut. A new measurement to measure
Inhibitor-Aqua Ratio (IAR) is required to enable the new formulation.
It was observed for both Methanol and MEG that the permittivity based watercut
sensors (eg microwave, impedance, capacitance sensors) shows positive watercut
bias in oil continuous mixture reaching upto ~10% bias for IAR = 50%. For water
continuous mixture, watercut bias is negative and reaching a value upto -25% for
IAR=50% at watercut = 100%. The bias shows an incresing trend with increasing
watercut and IAR. The bias also showed a strong dependency on frequency above
>1GHz. The quantified bias values are limited to analysis conditions of 40oC and
for pure water however they can be calculated for other conditions using the new
proposed oil-aqueous formulation.
For density based watercut sensors (gamma densitometer, differential pressure),
the bias in the estimate shows different behaviour for Methanol and MEG. This is
due to the fact that the density of Methanol is lower than that of water whereas
that of MEG is higher than that of water. Methanol shows a negative bias in the
estimation with maximum bias reaching upto ~-50% at watercut=100% and IAR
=50%. MEG shows a positive bias with maximum value as ~35% at WLR=60%,
IAR=50%. The results shown in this paper are at 40oC however since the density
contrast between water and THIs remain similar across a wide temperature range
(0-80oC), the results are applicable to a wider range of temperature.
This work brings up the issue of errors that can be caused in MPFM and watercut
meters due to hydrate inhibitors and is a small contribution towards improving
the understanding and accuracy of MPFM technology.
5
NOTATION

Phase fraction
Permittivity
Density
Volume flow rate
watercut
aquacut
Inhibitor-Aqua Ratio
Volumetric Factor
ε
ρ
Q
WC
AC
IAR
VF
Subscripts
o
Oil
w
Water
I
Inhibitor
a
Aqueous
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6
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