close

Вход

Забыли?

вход по аккаунту

?

j.jngse.2018.08.008

код для вставкиСкачать
Accepted Manuscript
A correlation to quantify hydrate plugging risk in oil and gas production pipelines
based on hydrate transportability parameters
Piyush Chaudhari, Luis E. Zerpa, Amadeu K. Sum
PII:
S1875-5100(18)30352-4
DOI:
10.1016/j.jngse.2018.08.008
Reference:
JNGSE 2680
To appear in:
Journal of Natural Gas Science and Engineering
Received Date: 21 April 2018
Revised Date:
11 July 2018
Accepted Date: 16 August 2018
Please cite this article as: Chaudhari, P., Zerpa, L.E., Sum, A.K., A Correlation to Quantify Hydrate
Plugging Risk in Oil and Gas Production Pipelines Based on Hydrate Transportability Parameters,
Journal of Natural Gas Science & Engineering (2018), doi: 10.1016/j.jngse.2018.08.008.
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to
our customers we are providing this early version of the manuscript. The manuscript will undergo
copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please
note that during the production process errors may be discovered which could affect the content, and all
legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
1
A Correlation to Quantify Hydrate Plugging Risk in Oil and Gas Production
2
Pipelines Based on Hydrate Transportability Parameters
3
Piyush Chaudhari a, Luis E. Zerpa b,*, Amadeu K. Sum a
a
Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO,
5
6
USA, 80401
b
RI
PT
4
Petroleum Engineering Department, Colorado School of Mines, CO, USA, 80401
* Corresponding author: Luis E. Zerpa, lzerpa@mines.edu, +1 (303) 384-2627
8
Abstract
9
Solid gas hydrate particles may form in oil and gas pipelines in the presence of water at high
10
pressures and low temperatures; typical conditions of subsea hydrocarbon pipelines used in
11
offshore facilities. Gas hydrate particles that form within these pipelines may create blockages
12
following a complex multi-physics phenomenon involving emulsification, hydrate formation and
13
subsequent hydrate particle agglomeration and bedding. Here we present a conceptual model
14
depicting different hydrate plugging risk levels associated with oil-dominated systems,
15
developed based on observations from high-pressure flowloop experiments. Using experimental
16
measurements from these experiments, we develop a mathematical correlation to classify and
17
quantify hydrate plugging risk in oil and gas pipelines. The correlation is based on assessable
18
parameters that govern hydrate transportability in pipelines, such as, liquid loading, mixture
19
velocity, fluid properties, and hydrate amount. A parametric study is performed using the
20
proposed hydrate plugging risk correlation showing the plugging risk increasing with decrease of
21
liquid loading and fluid velocity. The hydrate plugging risk estimation approach using the
22
proposed correlation is illustrated for steady-state and transient operations of a long subsea
23
tieback facility based on numerical transient multiphase flow simulations. The hydrate plugging
24
risk is found to evolve over time as a function of hydrate volume fraction along the pipeline
25
length. The hydrate plugging risk quantification presented, in terms of Hydrate Risk Evaluator,
26
in this study represents an advancement in the area of hydrate risk assessment, as it can be used
AC
C
EP
TE
D
M
AN
U
SC
7
1
ACCEPTED MANUSCRIPT
to assess hydrate plugging risk and consequently operational safety of hydrocarbon transport
2
pipelines from the flow assurance perspective.
3
Introduction
4
In the oil and gas industry, the technical discipline of flow assurance has gained a significant
5
importance not only during the initial subsea facilities design/development stages but also during
6
regular production operations. In the presence of low temperatures and high pressures, contact
7
between gas and water phases triggers the formation of solid clathrate crystalline compounds
8
called gas hydrates, which later could cause an undesirable impact on the oil and gas production
9
(Sloan, Koh, and Sum 2010). The formation of a solid hydrate blockage within a pipeline causes
10
high pressure gradients, thereby posing a great safety concern for safe operation and sometimes
11
even for human lives. As a result, up to 8% of the total estimated cost, which is equivalent to
12
more than US$200 million, is spent by the oil and gas industry annually for the prevention of
13
hydrate-related issues on oil and gas flowlines to maintain uninterrupted production of
14
hydrocarbons (Sloan 2003).
15
In order to overcome hydrate-related issues, the oil and gas industry employs hydrate avoidance
16
and hydrate management techniques given the operational circumstances. In hydrate avoidance,
17
the hydrate formation is prevented by the use of thermal insulation, active heating or chemical
18
inhibition techniques. The latter is considered the most common hydrate prevention technique
19
and involves injection of thermodynamic hydrate inhibitors such as Methanol, Ethanol, or
20
Monoethylene glycol. However, requirement of large amount of such chemicals, high processing
21
and separation cost, low recovery, and subsequent environmental concerns make this option less
22
attractive. On the contrary, hydrate management techniques let hydrates form inside the pipeline
23
during the production with appropriate measures in-lined beforehand, such as injection of Low
24
Dosage Hydrate Inhibitors-Antiagglomerants in order to disperse hydrate particles and avoid
25
potential formation of a hydrate blockage. Thus, the industry paradigm is shifting from applying
26
hydrate avoidance measures to using hydrate management techniques (Sloan 2005).
27
The hydrate management term was first introduced by Statoil in the mid and late 1990s in order
28
to improve the operational practice to tackle the hydrate-related flow assurance issues. In order
AC
C
EP
TE
D
M
AN
U
SC
RI
PT
1
2
ACCEPTED MANUSCRIPT
to establish technically sound and economically viable hydrate management techniques,
2
understanding the risk associated with oil and gas pipelines due to hydrates becomes the key
3
component. Thus, the qualitative and quantitative study of the hydrate plugging risk is of
4
significant importance for optimizing current hydrate management and remediation strategies. It
5
will not only increase the operational safety but also in-turn will assist the future deepwater and
6
ultra-deepwater developments from a safe design perspective.
7
There are various approaches undertaken in the past to understand the hydrate plugging risk of
8
pipelines, which mainly comprise experimental laboratory scale studies and mathematical
9
correlations to probe the hydrate plugging potential of oil-water emulsion systems. While
10
understanding the plugging properties of the oil in the presence of water and hydrates, various
11
experimental techniques (e.g., autoclave, rocking cells, micromechanical force apparatus) have
12
been utilized to show the plugging tendency of the oil phase at varying operating conditions
13
(Braniff 2013). The observations obtained from such type of hydrate plugging analysis are
14
applicable only for the steady state conditions and ultimately depict the end result of the
15
operation. The concept of plugging and non-plugging oils illustrated by Gupta et al. (2011) with
16
the help of experiments performed in a stirred high pressure cell introduced a new term called
17
Hydrate Plug Resistant Oil (HyPRO), which showed that the hydrate plugging tendency
18
increases as a function of water cut in increasing manner (Gupta, Crosby, and Guillory 2011). In
19
addition, the hydrate plugging potential study was performed for MEG and MeOH under-
20
inhibited systems using StatoilHydro flow simulator facility located in Norway, in which the
21
hydrate plugging risk was found to increase with an increase in the inhibitor concentration up-to
22
a concentration of 10-15 wt. % and then decreased at higher concentrations due to the decrease
23
in the capillary forces at higher inhibitor concentrations (Hemmingsen, Li, and Kinnari 2008).
24
Based on experiences from Statoil, the hydrate plugging risk increases with increasing water
25
content, and pipeline length (Kinnari et al. 2014). Autoclave experiments performed by Sohn et
26
al. (2015) with decane showed that moderate water cuts (40 vol. %-60 vol. %) pose high hydrate
27
plugging risk, and it can later be reduced by injection of the hydrate inhibitor (i.e., MEG) (Sohn
28
et al. 2015). The hydrate blockage risk for long transport lines is dependent on the gas to oil ratio
29
(GOR), salinity, and the heat transfer limitations provided by the insulation on the pipeline
AC
C
EP
TE
D
M
AN
U
SC
RI
PT
1
3
ACCEPTED MANUSCRIPT
(Creek et al. 2011). Additionally, various researchers have also presented the deployment of
2
hydrate risk assessment and management approaches in the field, which illustrate the integration
3
of risk management in flow assurance (e.g., hydrates, waxes etc.,) for deep offshore fields
4
(Harun et al. 2008, Camargo, Gon\cc, et al. 2004, Dejean et al. 2005, Zerpa, Sloan, Koh, et al.
5
2012, Camargo, Gonçalves, et al. 2004).
6
Other approach takes into consideration more of a quantitative perspective in order to quantify
7
the hydrate plugging risk as a function of operation variables. The hydrate risk assessment
8
studies performed by Zerpa et al. (2012) quantifies risk based on pressure drop, hydrate volume
9
fraction and relative viscosity, however this approach is limited to only a certain set of geometry
10
and fluids. In the literature, a predictive model for the hydrate risk assessment developed for
11
tetrahydrofuran (THF) and hydrochloroflurocarbon-141b (HCFC-141b) hydrate-formers is
12
presented (Wang et al. 2010). It involves development of a dimensionless number ( C h ), which
13
assesses the competition between agglomeration and separation tendency of hydrate particles
14
versus kinetic energy of the fluids. The dimensionless parameter is given by Equation 1,
M
AN
U
SC
RI
PT
1
2
ρ mix v mix
Ch =
6d pipe Φ critτ
TE
D
15
2
(1)
d particle
where ρ mix is the density of the mixture; d pipe is the pipe diameter; Φcrit is the critical
17
concentration of hydrate particles in the system at which a sudden increase in pressure drop is
18
observed; τ is the sum of the agglomerating forces, which can be used in the form of yield stress
19
and can be obtained from pressure drop data; d particle is the diameter of the hydrate
20
monomer/aggregate; and v mix is the mixture velocity. Though this model can foresee the hydrate
21
risk and quantify flow assurance safety, it is difficult to get hold of reliable yield stress values for
22
natural gas hydrate formers. Additionally, it would not be possible to deploy this model for other
23
fluids because of lack of consideration of interfacial properties. The Hydrate Kinetics
24
Technology (HKT) adapted by Statoil is based on induction time (i.e., time required for
25
nucleation of hydrates) coupled with information on hydrate transportability, water content, and
AC
C
EP
16
4
ACCEPTED MANUSCRIPT
pipeline configuration to understand the hydrate plugging risk associated with a flow system
2
(Kinnari et al. 2014). In other attempt to understand hydrate risk, a framework called Risk Based
3
Flow Assurance Toolkit (RiBFAT) was presented to quantify the risk scenarios for various flow
4
assurance challenges related to hydrates and its application towards the real field developments
5
(Morgan, Zakarian, and others 2015). However, a comprehensive model that quantifies the
6
hydrate risk for natural gas hydrate-formers, which can be applied in-line with current
7
multiphase flow simulators still need to be developed. In this work, an alternative correlation to
8
quantify hydrate plugging risk in oil and gas production pipelines is presented, which is based on
9
system operational parameters that represent the transportability of hydrate particles.
SC
RI
PT
1
1. Experimental Investigations and Conceptual Model
11
In order to develop a platform for quantifying hydrate risk transition in oil-dominated systems,
12
we analyze experiments performed at an industrial-scale flowloop facility. The flowloop facility
13
details, equipment involved, and typical experimental procedure is well explained in the
14
literature (Joshi et al. 2013, Turner et al. 2005, Davies et al. 2010). For the development of the
15
hydrate risk model we utilize pressure drop, pressure, temperature, and hydrate volume fraction
16
measurements obtained from flowloop experiments. The liquid loading, water cut, and mixture
17
velocity varied for all flowloop experiments is shown in Table 1. These experiments were
18
performed in 2011 in presence of Conroe oil as oil phase, which has a viscosity of 0.0031 Pa.s
19
and a density of 800 kg/m3, and water (3.5 wt. % synthetic salt) and methane gas as hydrate-
20
former.
21
We have mainly emphasized on the hydrate volume fraction formed during the experiment as a
22
function of time, flowloop temperature, and the pressure drop across the pump to understand the
23
hydrate plugging risk. The pressure drop behavior experimentally observed for oil continuous
24
systems can be classified primarily in three different regions. Figure 1 presents the pressure drop
25
after the onset of hydrate formation for an experiment performed with 50 vol. % liquid loading,
26
75 vol.% water cut, and 1 m/s mixture velocity. As shown in Figure 1, in region I, a decrease or
27
steadiness in the pressure drop after the hydrate onset is observed and it can be classified as a
28
low risk region. A characteristic of region II is a smooth increase in the pressure drop of the
AC
C
EP
TE
D
M
AN
U
10
5
ACCEPTED MANUSCRIPT
system. This region is classified as an intermediate risk of hydrate plugging. Furthermore, in
2
region III, the pressure drop fluctuations are very large, which implies a high risk or plugging
3
phenomenon. Note that low risk and high risk regions can be noticed and segregated easily.
4
Thus, intermediate hydrate risk (Region II) can be understood as a precursor to the high risk or
5
plugging scenario.
6
Table 1. List of flowloop experiments used to develop hydrate risk model/quantification.
Liquid Loading
Water Cut
(vol. %)
(vol. %)
1
50
15
1
2
50
15
2
3
50
15
2.5
4
50
15
3
5
50
15
1.75
6
75
15
1.75
90
15
1.75
50
90
1
50
90
1.75
50
75
1
8
EP
9
7
velocity (m/s)
AC
C
10
Mixture
SC
TE
D
7
M
AN
U
Experimental Run
RI
PT
1
8
We define three levels of hydrate plugging risk, which correspond to the experimentally
9
observed pressure drop behavior regions, in terms of the hydrate formation and distribution
10
within the pipeline. These levels of hydrate plugging risk are illustrated in Figure 2 in the form of
11
a conceptual model, which holds a significant importance in modeling of the transition of the
12
hydrate plugging risk regions for oil-dominated systems.
6
ACCEPTED MANUSCRIPT
Region I (Low Risk): This region shows an interaction between oil, water, and hydrate particles
2
just after the hydrate onset where the pressure drop of the system does not rise rapidly and
3
remains more or less constant. This stage can be depicted by the presence of small amount of
4
hydrate particles, due to hydrate formation at the interface of water droplets. This region is
5
mainly characterized by a homogeneous dispersion of hydrate particles. An unusual decrease in
6
the pressure drop at the onset of hydrate formation can be explained due to the increase in
7
temperature at the hydrate onset due to exothermic hydrate formation leading to the change in
8
the viscosity and density of the overall system (Li et al. 2013).
EP
9
TE
D
M
AN
U
SC
RI
PT
1
Figure 1. Pressure drop across the pump after the onset of hydrate formation showing the
classification of the plugging risk regions for 50 vol. % liquid loading, 75 vol. % water cut, and 1
m/s mixture velocity;(I) green: low risk (II), yellow: intermediate risk (III), and red: high risk or
plugging.
14
Region II (Intermediate Risk): At this point, more conversion of water droplets to hydrate
15
mass increases the number of hydrate particles present in the system. It results in a smooth
16
increase in the pressure drop, as shown in Figure 1, due to interaction between hydrate particles
17
leading to agglomeration and increasing the slurry viscosity (Aman et al. 2011). Moreover, the
18
system transitions from homogeneous dispersion to heterogeneous dispersion of agglomerated
AC
C
10
11
12
13
7
ACCEPTED MANUSCRIPT
hydrate particles (Joshi et al. 2013). We classify this region as the intermediate risk region, as the
2
flow has not completely ceased at this point. However, the flow conditions during the
3
intermediate risk warn about the high-risk scenario that may arise in the near future. Thus, we
4
call this stage a meta-stable stage.
5
Region III (High Risk or Plugging): This region represents the high risk or hydrate plugging
6
region mainly characterized by the large increase in the pressure drop of the system in the form
7
of large fluctuations shown in Figure 1. These large fluctuations imply that the pump is operating
8
with an intermittent flow with highly dense, viscous fluid, which mainly consists of hydrate
9
agglomerates. The transition from region II to region III, is called fast agglomeration and particle
10
bedding phase. Agglomeration gives rise to bigger chunks or agglomerates of hydrate particles as
11
shown in Figure 2 (III). It may cause an alteration in the flow regime as explained by Rao (2013)
12
(Rao, Sum, et al. 2013) thereby, inducing large liquid slugs. Large agglomerates segregate in the
13
bottom of the pipe forming hydrate beds, which promote the generation of large slugs of liquid
14
flowing in the pipeline. The combination of slugs and hydrate beds results in the large amplitude
15
of fluctuations in the pressure drop shown in Figure 1, and hydrate bedding results in the
16
decrease of the overall mass flow rate as shown by Srivastava et al. (2017). In other scenario, if
17
the fluids do not have enough energy to carry hydrate agglomerates, it is difficult for hydrate
18
agglomerates to remain suspended in the liquid causing the accumulation of hydrate
19
agglomerates in the bottom of the pipeline, as shown in Figure 2 (III).
AC
C
EP
TE
D
M
AN
U
SC
RI
PT
1
8
M
AN
U
SC
RI
PT
ACCEPTED MANUSCRIPT
1
2
3
4
Figure 2. Conceptual picture of the oil-dominated system highlighting different levels of the
hydrate plugging risk with respect to the size and amount of hydrate particles; (I) low risk, (II)
intermediate risk, and (III) high risk.
TE
D
5
2. Hydrate Risk Quantification
7
2.1. Hydrate Volume Fraction Effect on Plugging Risk Transition
8
The hydrate volume fraction at the transition from region I to region II was calculated from the
9
pressure drop and hydrate volume fraction data for all flowloop experiments given in Table 1. A
10
two-fold increase in the pressure drop of the system after the hydrate onset serves the criterion to
11
obtain the hydrate fraction at the risk transition. Table 2 presents the estimated hydrate volume
12
fraction ( Φ hydrate ) at the transition from region I to region II (low to intermediate hydrate
13
plugging risk). However, there is uncertainty associated with the estimated hydrate fraction at the
14
risk transition, as the pressure drop data is not smooth for all experiments. In case of slug flow
15
regime, fluctuations present in the pressure drop data causes the error to be on the higher
16
spectrum. Thus, the uncertainty was calculated using the propagation of error approach. A
AC
C
EP
6
9
ACCEPTED MANUSCRIPT
significantly greater uncertainty was obtained for experimental run #4, due to the high mixture
2
velocity that promoted greater fluctuations (i.e., higher noise) in the measured pressure drop.
3
4
Table 2. Hydrate volume fraction and corresponding uncertainty at Low to Intermediate risk
transition.
Water Cut
(vol. %)
Φ hydrate at
Mixture
velocity (m/s)
Uncertainty
Transition
50
15
1
0.06
0.02
2
50
15
2
0.174
0.043
3
50
15
2.5
0.193
0.032
4
50
15
3
0.191
0.157
5
50
15
1.75
0.061
0.05
6
75
15
1.75
0.063
0.009
7
90
15
1.75
0.074
0.005
8
50
90
1
0.093
0.012
9
50
90
1.75
0.193
0.04
10
50
75
1
0.07
0.009
TE
D
M
AN
U
1
SC
Experimental Liquid Loading
Run
(vol. %)
RI
PT
1
The Buckingham-Pi theorem was applied in order to find dimensionless numbers that play a key
6
role in determining the hydrate plugging risk transition. The result shows that the transition is
7
mainly dependent on the Reynolds number (ratio of inertial to viscous forces, representing flow
8
parameters) and the Capillary number (ratio of viscous forces to surface tension, representing
9
particle cohesion and agglomeration) (Aman et al., 2011) of the system. As a result, the primary
11
AC
C
10
EP
5
form of the correlation is expressed as,
Φ hyd ,transition
Φ hyd , packing
10
= k o Re α Ca β
(2)
ACCEPTED MANUSCRIPT
where Re is the Reynolds number of the hydrate carrier phase, which is oil in case of oil-
2
dominated systems; Ca is the Capillary number; Φ hyd ,transition is the hydrate volume fraction at the
3
hydrate risk transition from low to intermediate or high hydrate plugging risk region. Φ hyd , packing
4
is the packing hydrate volume fraction assumed as 0.52 (Camargo et al. 2000), k o , α , and β
5
are constants involved in the correlation, which are determined by the linear regression
6
performed over the set of experimental data shown in Table 1. After performing a linear
7
regression analysis, the following expression is obtained,
Φ hyd , packing
= 0.002 [Re (1 − LL ) ]
SC
Φ hyd ,transition
8
RI
PT
1
0.52
Ca 0.27
(3)
where LL is the liquid loading of the system. It incorporates the amount of liquid, which is
10
responsible for the transportation of hydrates in the flowline (Grasso 2015). The coefficient of
11
determination (R-squared) for the regression model is 0.63.
12
In this study, Ca ~ 0.1 for Conroe oil and experimental flow parameters. From equation 3,
13
expanding the dimensionless numbers (Re and Ca) by their definition, we develop the Hydrate
14
Risk Evaluator (HRE) index, which takes into account the amount of hydrates formed as a
15
function of time from the onset of hydrate nucleation.
TE
D
EP
 Φ hydrate



1
−
Φ
hydrate


LL
Hydrate Risk Evaluator = 500
0.52
 ρ oil v mix D pipe 
  µ oil voil
(1 − LL ) 

µ oil


  σ oil −water



0.27
(4)
AC
C
16
M
AN
U
9
17
The purpose of the HRE index (Equation 4) is to quantify the hydrate plugging risk for oil-
18
dominated systems. The HRE index is purely based on hydrate-related phenomena occurring in
19
oil-dominated systems. The term in the numerator in Equation 4 is the ratio of hydrates to the
20
hydrate-free liquid with respect to the total amount of liquid present in the system. It implies the
21
capacity or potential of the liquid to carry hydrate particles. The value of the HRE index at the
11
ACCEPTED MANUSCRIPT
plugging risk transition (from region I to II) is 2.25 ± 1σ (σ is standard deviation), based on
2
experimental flowloop data, as shown in Figure 3.
M
AN
U
SC
RI
PT
1
3
Figure 3. Scatter plot of the HRE at the hydrate plugging risk transition for oil-dominated
flowloop experiments (Table 1). Average HRE at risk transition is 2.25.
6
Hydrate Risk Evaluator shown in Figure 4 is calculated for all experimental runs using Equation
7
4, where all parameters required in Equation 4 are shown in Table 2. It can be seen that, hydrate
8
risk evaluator shown in Figure 3 is dependent on mixture velocity and liquid loading.
9
Experimental runs with higher mixture velocity and liquid loading have relatively higher HRE
10
values. For a given system, the amount of hydrates that correspond to an HRE value lower than
11
2.25, are present in the form of homogeneous dispersion in the pipeline. On the other hand, the
12
amount of hydrates corresponding to an HRE value higher than 2.25 imply transition from low to
13
intermediate or higher risk, with a change in the hydrate particle distribution to heterogeneous
14
dispersion. The lower bound of the standard deviation obtained for HRE implies the conservative
15
estimate of the hydrate volume fraction at the hydrate plugging risk transition.
16
There are a few assumptions associated with the overall hydrate risk quantification:
AC
C
EP
TE
D
4
5
12
ACCEPTED MANUSCRIPT
1
•
The HRE correlation is purely based on the oil continuous systems where water is present
2
in the form of dispersed water droplets and is applicable to only these systems. This
3
correlation may not hold true for water continuous or partially dispersed systems.
•
For simplicity, packing hydrate volume fraction is assumed to be 0.52.
5
•
Hydrate deposition is investigated for gas-dominated systems (Rao, Koh, et al. 2013),
RI
PT
4
6
however this phenomenon is not yet completely understood for oil-dominated systems.
7
Thus, hydrate deposition (i.e., hydrate wall growth mechanism) is not considered in the
8
proposed correlation.
10
11
This analysis may not hold true for systems in which the phase inversion occurs at
SC
•
moderate water cuts (lower than 90 vol.% water cut).
•
The drift flux model is employed to calculate the individual phase velocity (i.e., oil phase
M
AN
U
9
12
velocity) (Danielson and others 2012) and it is limited for liquid loadings greater than 30
13
vol. %.
2.2. Validation of the Model
15
The validation of the HRE model is performed by comparing with two flowloop experiments
16
from past studies. We have utilized a flowloop experiment performed at the same industrial scale
17
flowloop facility in 2004 (Turner 2005). This particular experiment was performed with Conroe
18
oil as the oil phase and Methane as the hydrate-former gas. The interfacial tension for Conroe oil
19
and water system in the presence of 3.5 wt. % salt is 25 mN/m. This experiment was performed
20
with 54 vol. % liquid loading, 35 vol. % water cut, 1.3 m/s of mixture velocity, and operating
21
pressure of 1000 psig. Figure 4 shows pressure drop as function of hydrate volume fraction
22
measured in this flowloop experiment, indicating the hydrate volume fraction at the hydrate
23
plugging risk transition predicted by the HRE correlation (Equation 4) compared to the estimated
24
value from the experiment. The risk transition occurs at the hydrate volume fraction value of 0.1.
25
HRE predicts the hydrate plugging risk transition within 0.04 absolute hydrate volume fraction
26
difference. Although, the correlation under predicts the hydrate volume fraction at which the
27
plugging transition occurs from low to intermediate risk, it denotes the conservative estimate of
AC
C
EP
TE
D
14
13
ACCEPTED MANUSCRIPT
the hydrate volume fraction at the hydrate plugging transition, which is acceptable from a safety
2
point of view.
M
AN
U
SC
RI
PT
1
3
Figure 4. HRE validation using an oil-dominated flowloop experiment performed with Conroe
crude oil and methane gas in 2004 (54 vol. % liquid loading, 35 vol. % water cut, 1.3 m/s of
mixture velocity).
7
Similarly, the HRE model prediction was compared to results from a different flowloop
8
experiment performed at the University of Tulsa (TU) flowloop in 2013 (Vijayamohan et al.
9
2015). The fluids used for this particular experiment were different. The oil phase was mineral
10
oil 350T (Crystal Plus 350T-MO350T), with a viscosity and density of 0.00065 Pa.s and 863
11
kg/m3 respectively. The gas phase was Tulsa city gas (mainly methane, ethane, and propane
12
mixture). The composition and detailed experimental procedure can be found in the literature
13
(Joshi 2012). The interfacial tension for MO350T and water is 50 mN/m. The experimental
14
parameters were 70 vol. % liquid loading, 30 vol. % water cut, and 1.7 m/s mixture velocity.
15
From Table 3, it can be seen that the error between the predicted and experimentally measured
16
hydrate volume fraction at the plugging risk transition is very small, thus validating the accuracy
17
of the Hydrate Risk Evaluator model.
AC
C
EP
TE
D
4
5
6
18
14
ACCEPTED MANUSCRIPT
Table 3. Comparison of the hydrate risk transition.
Liquid
Water
Mixture
Φhydrate at Risk
Φhydrate at Risk
Loading
Cut
velocity
Transition
Transition
(vol. %)
(vol. %)
(m/s)
(Observed)
(Predicted)
Oil
Flowloop
2004-XM
Conroe
2013-TU
Mineral
Oil 70T
54
35
1.3
70
30
1.7
RI
PT
Year and
0.14
0.10
0.05
0.055
SC
1
2
3. Parametric Studies
4
Liquid loading, mixture velocity, and interfacial tension are important parameters that govern the
5
hydrate blockage formation. A parametric analysis was performed in order to investigate the
6
effect of the aforementioned parameters using Equation 3.
7
Figure 5(a) shows the effect of the liquid loading on the predicted hydrate risk for oil-dominated
8
systems. By definition, the liquid loading is the amount of liquids (i.e., flowable phase) present
9
in the system. The HRE curves shown in Figure 5(a) stand for varying liquid loadings at constant
10
15 vol. %water cut, and mixture velocity of 1 m/s. It can be seen from Figure 5(a) that the HRE
11
curve for liquid loading of 35 vol. % crosses over the hydrate risk transition at 0.06 hydrate
12
volume fraction, whereas when the liquid loading is increased from 35 vol.% to 65 vol.%, the
13
risk transition cross-over hydrate volume fraction is increased to 0.12. Thus, the amount of
14
hydrates that the flow-system can carry is increased with an increase in the liquid loading of the
15
system. Higher liquid loading refers to higher energy associated with the system, which allows
16
more hydrate particles to remain suspended in the liquid without causing settling or
17
accumulation in the pipeline. Similar effect of liquid loading on hydrate transportability in water-
18
dominated and partially dispersed systems have been observed by previous studies (Joshi 2012,
19
Grasso 2014, Vijayamohan et al. 2015).
20
In addition to liquid loading, other key parameter that governs the dispersion of hydrate particles
21
and consequently the hydrate risk is the mixture velocity of the fluids. For industrial purpose,
AC
C
EP
TE
D
M
AN
U
3
15
ACCEPTED MANUSCRIPT
mixture velocity can also be referred to the overall flow rate of liquids. We can see from Figure
2
5(b) that the mixture velocity has a significant effect on the hydrate risk and subsequently on the
3
flow assurance safety with respect to hydrates. This parametric study is performed with constant
4
50 vol. % liquid loading and 15 vol. % water cut. It can be seen from Figure 5(b) that at low
5
mixture velocity such as 1 m/s, hydrate volume fractions greater than 7 vol. % will shift the
6
system to a higher hydrate plugging risk region.
7
On the other hand, at a fluid velocity greater than 3.5 m/s, hydrates up-to 20 vol. % can safely be
8
transported without any risk of plugging the pipeline. In summary, the system can transport
9
higher amount of hydrates at higher mixture velocity without causing any hydrate blockage
10
issues. At a higher mixture velocity, the transportability of the system increases as the inertial
11
forces dominate over the viscous forces present in the system. Additionally, once the threshold
12
settling velocity for hydrate particles is overcomed by the mixture velocity, it becomes relatively
13
easier for hydrate particles to remain dispersed in the liquid, thus not causing any hydrate
14
accumulation or beds in the pipeline.
15
Furthermore, the capillary number accounts for properties of the oil as well as it reflects the
16
effect of the oil-water interfacial tension on the hydrate risk transition, thereby taking into
17
account the effect of natural surfactants present in the oil phase. As the interfacial tension
18
decreases, the chances of forming larger agglomerates are less due to better dispersion of water
19
droplets and hydrate particles, as a result, the flow-system can transport more hydrates safely at
20
lower interfacial tension as seen from Figure 5(c). The system with lower interfacial tension
21
remains safe in the lower risk region even if the amount of hydrates is higher. However, when
22
surfactants are not present in the oil phase, the presence of a small amount of hydrates can be
23
responsible for transition to a higher risk. The effect of the interfacial tension is also significantly
24
important from the hydrate management perspective where Low Dosage Hydrate Inhibitors
25
(LDHI) such as anti-agglomerants (AA) are injected in order to avoid a hydrate blockage. The
26
inclusion of the interfacial tension in the HRE framework is of great advantage from the
27
perspective of modeling AA injection, as changes in interfacial tension due to the presence of
28
AAs can also be accounted in the model.
AC
C
EP
TE
D
M
AN
U
SC
RI
PT
1
16
(b) Effect of Mixture Velocity
M
AN
U
(a) Effect of Liquid Loading
5
6
7
8
9
AC
C
3
4
EP
TE
D
1
2
SC
RI
PT
ACCEPTED MANUSCRIPT
(c) Effect of Interfacial Tension
Figure 5. HRE approach showing the effect of the mixture velocity, liquid loading and
interfacial tension on the hydrate plugging risk transition. (a) Effect of liquid loading with
constant 15 vol. %water cut, and mixture velocity of 1 m/s. (b) Effect of mixture velocity with 50
vol. % liquid loading and 15 vol. % water cut (c) Effect of interfacial tension with 50 vol. %
liquid loading and 15 vol. % water cut and mixture velocity of 1 m/s,
10
17
ACCEPTED MANUSCRIPT
4. Industrial-Scale Application of Hydrate Risk Evaluator
2
Here we illustrate the application of the proposed Hydrate Risk Evaluator model using the
3
simulation studies performed on a long subsea tieback. Figure 6 shows the integration of the
4
HRE approach with a transient multiphase flow simulator (OLGA®). The HRE shown in
5
Equation 4 requires hydrate-related parameters such as amount of hydrates as a function of time
6
and water conversion, multiphase flow and fluid properties. The hydrate volume fraction is one
7
of the key inputs to the HRE model, which is obtained from the Colorado School of Mines
8
Hydrate kinetic model (CSMHyK, the hydrate formation module of OLGA®, (Zerpa, Sloan,
9
Sum, et al. 2012)). The rest of the parameters are calculated using OLGA®.
SC
RI
PT
1
4.1. Subsea Tieback Case Setup
11
We have used the geometry of a typical Caratinga subsea tieback located in the Campos Basin,
12
Brazil, for the simulation studies. This subsea tieback has been used for a wide variety of
13
hydrate-related simulations previously (Zerpa, Sloan, Koh, et al. 2012). The subsea tieback
14
configuration and relevant geometry can be seen from the schematic shown in Figure 7. Points 1,
15
2, and 3 represent the bottom of the well, wellhead, and platform locations respectively. The oil
16
density and viscosity are 890 kg/m3 and 0.06 Pa.s, respectively, at the wellhead conditions. The
17
interfacial tension between oil and water is 23 mN/m. More details about the Caratinga oil
18
properties are available in the literature (Sjöblom et al. 2010).
19
As shown in Figure 7, the reservoir pressure and temperature at the bottom of the well are 282.3
20
bar and 72 °C. As the well tubing (Point 1- Point 2) is within the geothermal gradient, it does not
21
face any hydrate-related issues due temperatures outside hydrate equilibrium conditions. The
22
flowline-riser configuration connecting the wellhead and the platform is mainly prone to the
23
hydrate formation and plugging issues due to the low subsea temperature (4 °C). The pressure
24
and temperature conditions at the platform are 16 bar and 20 °C in the simulation model.
AC
C
EP
TE
D
M
AN
U
10
18
SC
RI
PT
ACCEPTED MANUSCRIPT
Figure 6. Framework for implementation of the Hydrate Risk Evaluator model with CSMHyKOLGA®.
4
AC
C
EP
TE
D
2
3
M
AN
U
1
5
6
Figure 7. The Caratinga subsea tieback configuration and operating conditions. Notations 1, 2,
and 3 stand for bottom of the well, wellhead, and processing facility or platform respectively.
7
4.2. Steady State Operation
8
The steady state operation refers to the stage during the actual production when the key
9
parameters such as temperature, pressure, and fluid hold-up in the system have stabilized or they
19
ACCEPTED MANUSCRIPT
do not change as a function of time. Based on the flowline-riser configuration shown in Figure 7,
2
a simulation was performed in order to show the hydrate risk behavior calculated by Equation 4
3
in the steady state operation. For this particular case, the liquid loading and water cut are 90 vol.
4
% and 30 vol. %, respectively. The predicted mixture velocity is 0.3 m/s. The mixture velocity is
5
calculated given the difference in the pressure between the wellhead and the platform location.
6
However, the magnitude of the mixture velocity is very low compared to industry operations.
7
The initial temperature and pressure at the bottom of the well are 72 °C and 282.3 bar as shown
8
in Figure 7. The flowline-rise system is initialized with no hydrates present. Based on the
9
flowline-riser length and fluid flow, the simulation case is found to reach steady state conditions
10
after 20 hours. The hydrate plugging risk predicted by the HRE model (Equation 4) along the
11
flowline-riser length is shown in Figure 8. The hydrate risk evaluated along the flowline changes
12
as a function of time until steady state conditions are reached. After 0.5 hour in the production,
13
the hydrate formation event was captured by the model. As a consequence, the HRE correlation
14
predicts the risk associated with the flowline-riser system as a function of the hydrate amount as
15
shown in Figure 8. An atypical very low mixture velocity predicted by OLGA® is the main
16
reason for the hydrate risk observed after 0.5 hours of operation. At this point, a few hydrates are
17
present in the system. A reduction in the transport forces due to low mixture velocity limits the
18
transportation of hydrates.
19
The amount of hydrates and mixture velocity are important parameters that govern the
20
magnitude of the hydrate risk predicted by the HRE model. After 20 hours into the operation, the
21
hydrate mass and the fluid flow reach a steady state, which is also shown by the flat HRE profile.
22
From Figure 8, it is evident that HRE will assist in estimating not only the portion of the flowline
23
length that may fall under higher risk of plugging but also the time required for the risk
24
transition.
AC
C
EP
TE
D
M
AN
U
SC
RI
PT
1
20
M
AN
U
SC
RI
PT
ACCEPTED MANUSCRIPT
Figure 8. HRE along the flowline-riser system for a steady state simulation performed on the
Caratinga subsea tieback. HRE curves at various time-steps show the evolution of the hydrate
plugging risk until steady state conditions are reached (i.e., 20 hours).
5
4.3. Transient Operation (Shut-in and Restart)
6
A transient operation is a combination of fluid flow shut-in and restart that commonly occur in
7
subsea production facilities. Shut-in operation mainly refers to ceasing of the production due to
8
various reasons, which may include operational failures, hurricanes and storms. During shut-in
9
time, the temperature of the system drops as it is exposed to the very low subsea temperatures in
10
the absence of newly produced hot fluids. Upon production restart, the system is more prone to
11
rapid hydrate formation issues that could lead to a blockage compared to shut-in conditions
12
where the fluids are at low temperature and in stagnant conditions.
13
A shut-in simulation is performed using OLGA® on the subsea tieback system shown in Figure 7.
14
The system is kept under shut-in conditions for 48 hours. The temperature of the system drops to
15
5.5 °C during shut-in. Additionally, due to lack of flow in the system, OLGA® predicts the
16
separation of fluids, where the riser and the flowline section get completely filled with the gas
17
and liquid phase respectively over the period of 48 hours.
AC
C
EP
TE
D
1
2
3
4
21
ACCEPTED MANUSCRIPT
The system is set to restart based on a production valve opening schedule over a period of 2
2
hours (Zerpa, Sloan, Koh, et al. 2012). We apply the HRE approach in order to study the hydrate
3
risk during the restart of the operation. The hydrate risk as a function of the time elapsed after the
4
restart along the flowline-riser length is shown in Figure 9.
5
As the fluids are produced from the subsurface well, due to the geothermal gradient, the
6
temperature of the fluids coming into the flowline-riser system is high. During the shut-in
7
operation, the flowline transfer heat to the subsea environment, lowering its temperature. A
8
combined effect of the low fluid temperature and the vigorous mixing of fluids upon restart
9
cause the hydrate onset and subsequent rapid formation of hydrates in the flowline near the
SC
RI
PT
1
wellhead.
11
At 5 minutes, right after the wellhead, the hydrate phase occupies 10 vol. % of the total slurry
12
volume, whereas the hydrates formed along the rest of the flowline are very minimal. The initial
13
section of the pipeline experiences the maximum amount of hydrate formation due to the gradual
14
production ramp-up on restart. Thus, at 5 minutes after the restart, the hydrate plugging risk
15
captured by the hydrate risk model in terms of HRE is very high near the wellhead location as
16
shown in Figure 9(a). On the other hand, towards the downstream locations, hydrate onset is not
17
observed because fluids are more or less stagnant (i.e., very minimal velocity) due to the lack of
18
fluid motion and lack of mixing of the phases. The reason for the high hydrate risk near the
19
wellhead location is the rapid formation of hydrates due to gas flowing from the well that mixes
20
with accumulated water at the initial portion of the pipeline.
21
At 20 minutes after restart, from Figure 9(b), we can see that the flowline-riser system is below
22
the hydrate risk transition region. Even though there is a small amount of hydrate mass present in
23
the flowline, according to the hydrate formation model, the flow rate computed by OLGA® at 20
24
minutes is sufficient enough for the safe transportation of hydrates. The relative viscosity
25
captured by the CSMHyK-OLGA® model is very small, which also confirms low hydrate risk at
26
20 minutes after restart. Note that HRE is a strong function of the fluid velocity; therefore, the
27
velocity required for the dispersion of the hydrates can in-turn keep the system in the low hydrate
28
risk region.
AC
C
EP
TE
D
M
AN
U
10
22
M
AN
U
(a) 5 minutes after restart
(b) 20 minutes after restart
3
4
AC
C
EP
TE
D
1
2
SC
RI
PT
ACCEPTED MANUSCRIPT
(c) 120 minutes after restart
5
6
Figure 9. Hydrate Risk Evaluator and the relative viscosity (from CSMHyK-OLGA®) results
along the flowline-riser system at 5 minutes, 20 minutes, and 120 minutes after the restart.
7
At 120 minutes after restart, the production reaches to the maximum possible capacity. Due to
8
the higher velocity of fluids at 120 minutes, vigorous mixing is achieved, which causes rapid
9
formation of hydrates. The estimated hydrate fraction reaches 20 vol. % at 120 minutes after
23
ACCEPTED MANUSCRIPT
restart. We can see from Figure 9(c) that the upstream region of the flowline-riser system has
2
transitioned to a higher risk region. This transition is evident due to the increased conversion of
3
water and gas to form more hydrate mass in the flowline. The peak captured by HRE shows the
4
higher plugging risk potential at approximately 2 kms. from the wellhead, given the flow
5
conditions. An increase in the relative viscosity also signals the high hydrate risk at the same
6
location at 120 minutes after the restart. In summary, HRE allows one to pin-point the portion of
7
the flowline-riser system that may face hydrate plugging risk as a function of time on the restart
8
after long planned or unplanned shut-ins.
9
5. Conclusions
SC
RI
PT
1
A conceptual model depicting the hydrate plugging risk associated with oil-dominated systems
11
was developed based on observations from high-pressure flowloop experiments, considering
12
various physical phenomena such as emulsification, hydrate formation and subsequent hydrate
13
particle agglomeration and bedding. A mathematical correlation, the Hydrate Risk Evaluator, is
14
developed in terms of dimensionless numbers (Reynolds number and Capillary number) to
15
classify and quantify the hydrate plugging risk in oil and gas pipelines. The hydrate plugging risk
16
is a function of various parameters such as liquid loading, mixture velocity, interfacial tension,
17
and hydrate amount. The Hydrate Risk Evaluator evaluates the magnitude of the hydrate risk
18
associated with the flowline in presence of hydrates. The parametric study performed with the
19
hydrate risk correlation shows that the flow assurance risk increases with decrease in the liquid
20
loading and mixture velocity, on the other hand, it decreases with a decrease in the interfacial
21
tension. The hydrate risk calculation approach is demonstrated using a steady state and transient
22
simulations performed using transient dynamic multiphase flow simulation studies on a long
23
subsea tieback. The hydrate risk is found to evolve as a function of hydrate volume fraction
24
present along the flowline-riser system as a function of time until steady state conditions are
25
reached, whereas during shut-in/restart operations, the hydrate risk predicted by the Hydrate Risk
26
Evaluator increases significantly in short span of time once the production reaches to its
27
maximum capacity.
AC
C
EP
TE
D
M
AN
U
10
24
ACCEPTED MANUSCRIPT
Furthermore, the Hydrate Risk Evaluator can be utilized not only for the prediction of the
2
hydrate plugging risk along the pipeline as a function of time but also for optimizing current
3
hydrate management strategies. It has a potential to model the hydrate inhibition using LDHIs
4
such as AAs, given the interfacial tension component in the mathematical correlation. In the
5
future, we envision the development in the current hydrate risk correlation in terms of
6
incorporating other flow systems such as water-dominated and gas-dominated systems, however
7
to keep all parameters involved in the correlation relatively simple and easily assessable is
8
challenging. In summary, the hydrate risk quantification in this study is not only a great
9
advancement in the area of hydrate risk assessment but also it is an approach to assess the inline
SC
RI
PT
1
hydrate risk and consequently operational safety from the flow assurance perspective.
11
Acknowledgements
12
We would like to thank ExxonMobil for making these flowloop tests possible, in particular Dr.
13
Larry Talley, Jason Lachance, Glenn Cobb, Eddie Hannah and all the staff at ExxonMobil
14
flowloop facility. We acknowledge the support from the CSM Hydrate Consortium (sponsored
15
by BP, Chevron, ConocoPhillips, ExxonMobil, Nalco, Petrobras, Shell, SPT Group, Statoil, and
16
Total).
17
References
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Aman, Zachary M., Erika P. Brown, E. Dendy Sloan, Amadeu K. Sum, and Carolyn A. Koh.
2011. "Interfacial mechanisms governing cyclopentane clathrate hydrate
adhesion/cohesion." Physical Chemistry Chemical Physics 13 (44):19796-19806. doi:
10.1039/c1cp21907c.
Braniff, Martin. 2013. "Effect of Dually Combined Under-Inhibition and Anti-Agglomerant
Treatment on Hydrate Slurries." M.S., Colorado School of Mines.
Camargo, R., T. Palermo, P. Maurel, and R. Bouchard. 2000. "Flow properties of hydrate
suspensions in asphaltenic crude oil." Denmark.
Camargo, RMT, Gon\cc, alves, MAL, JRT Montesanti, CABR Cardoso, K Minami, and others.
2004. "A perspective view of flow assurance in deepwater fields in Brazil." Offshore
Technology Conference.
Camargo, RMT, MAL Gonçalves, JRT Montesanti, CABR Cardoso, and K Minami. 2004. "A
perspective view of flow assurance in deepwater fields in Brazil." Offshore Technology
Conference.
AC
C
EP
TE
D
M
AN
U
10
25
ACCEPTED MANUSCRIPT
EP
TE
D
M
AN
U
SC
RI
PT
Creek, JL, S Subramanian, D Estanga, and Kristian Krejbjerg. 2011. "Project Design Hydrate
Management by Application of Multiphase Flow Simulations Tools with Hydrate
Formation and Transport." Proc. 7th International Conference on Gas Hydrates.
Danielson, Thomas John, and others. 2012. "A simple model for hydrodynamic slug flow." 8th
North American Conference on Multiphase Technology.
Davies, Simon R, John A Boxall, Laura E Dieker, Amadeu K Sum, Carolyn A Koh, E Dendy
Sloan, Jefferson L Creek, and Zheng-Gang Xu. 2010. "Predicting hydrate plug formation
in oil-dominated flowlines." Journal of petroleum science and engineering 72 (3):302309.
Dejean, JP, D Averbuch, M Gainville, F Doux, and others. 2005. "Integrating flow assurance
into risk management of deep offshore field developments." Offshore Technology
Conference.
Grasso, Giovanny A. 2014. "Hydrate Formation in Flowloop Experiments." August.
Grasso, Giovanny A. 2015. "Investigation of hydrate formation and transportability in
multiphase flow systems." PhD, Colorado School of Mines.
Gupta, Arvind, Daniel Crosby, and James Guillory. 2011. "The hydrate plugging tendency of
crude-oils as determined by using high pressure electrical conductivity and transparent
hydrate rocking cell tests." Proceedings of the 7th International Conference on Gas
Hydrates.
Harun, Amrin Fadila, Todd J Blanchard, Muge Erdogmus, and others. 2008. "Managing Hydrate
Risks for a Black Oil Long Subsea Tie-Back When Water Cut Predictions Exceed
Original Design Basis." SPE North Africa Technical Conference \& Exhibition.
Hemmingsen, P\aa, l V, Xiaoyun Li, and Keijo Kinnari. 2008. "Hydrate plugging potential in
underinhibited systems." 6th International Conference on Gas Hydrates, July.
Joshi, Sanjeev. 2012. "Experimental investigation and modeling of gas hydrate formation in high
water cut producing oil pipelines,." PhD, Colorado School of Mines.
Joshi, Sanjeev V., Giovanny A. Grasso, Patrick G. Lafond, Ishan Rao, Eric Webb, Luis E. Zerpa,
E. Dendy Sloan, Carolyn A. Koh, and Amadeu K. Sum. 2013. "Experimental flowloop
investigations of gas hydrate formation in high water cut systems." Chemical
Engineering Science 97:198-209. doi: 10.1016/j.ces.2013.04.019.
Kinnari, Keijo, Jan Hundseid, Xiaoyun Li, and Kjell Magne Askvik. 2014. "Hydrate
Management in Practice." Journal of Chemical \& Engineering Data 60 (2):437-446.
Li, W. Q., J. Gong, X. F. Lu, J. K. Zhao, Y. R. Feng, and D. Yu. 2013. "A study of hydrate plug
formation in a subsea natural gas pipeline using a novel high-pressure flow loop."
Petroleum Science 10 (1):97-105. doi: 10.1007/s12182-013-0255-8.
Morgan, JEP, E Zakarian, and others. 2015. "Development of a Quantitative Approach to Risk
Based Flow Assurance." Offshore Technology Conference.
Rao, Ishan, Carolyn A Koh, E Dendy Sloan, and Amadeu K Sum. 2013. "Gas hydrate deposition
on a cold surface in water-saturated gas systems." Industrial & Engineering Chemistry
Research 52 (18):6262-6269.
Rao, Ishan, Amadeu K Sum, Carolyn A Koh, E Dendy Sloan, Luis E Zerpa, and others. 2013.
"Multiphase Flow Modeling of Gas-Water-Hydrate Systems." Offshore Technology
Conference.
AC
C
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
26
ACCEPTED MANUSCRIPT
37
EP
TE
D
M
AN
U
SC
RI
PT
Sjöblom, Johan, Bodhild Øvrevoll, GunnHeidi Jentoft, Caterina Lesaint, Thierry Palermo, Anne
Sinquin, Patrick Gateau, Loïc Barré, Siva Subramanian, and John Boxall. 2010.
"Investigation of the hydrate plugging and non-plugging properties of oils." Journal of
Dispersion Science and Technology 31 (8):1100-1119.
Sloan, Dendy E., Carolyn Koh, and Amadeu Sum. 2010. Natural Gas Hydrates in Flow
Assurance. Boston: Gulf Professional Publishing.
Sloan, E Dendy. 2003. "Fundamental principles and applications of natural gas hydrates."
Nature 426 (6964):353-363.
Sloan, E Dendy. 2005. "A changing hydrate paradigm - from apprehension to avoidance to risk
management." Fluid Phase Equilibria 228:67-74.
Sohn, Young Hoon, Jakyung Kim, Kyuchul Shin, Daejun Chang, Yutaek Seo, Zachary M.
Aman, and Eric F. May. 2015. "Hydrate plug formation risk with varying watercut and
inhibitor concentrations." Chemical Engineering Science 126:711 - 718.
Srivastava, Vishal, Ahmad A. A. Majid, Pramod Warrier, Giovanny Grasso, Piyush Chaudhari,
E. Dendy Sloan, Carolyn A. Koh, David T. Wu, and Luis E. Zerpa. 2017. "Hydrate
Formation and Transportability Investigations in a High-Pressure Flowloop During
Transient Shut-in / Restart Operations." Offshore Technology Conference, Houston,
Texas, USA.
Turner, D, J Boxall, S Yang, DM Kleehammer, CA Koh, KT Miller, ED Sloan, Z Xu, P
Matthews, and L Talley. 2005. "Development of a hydrate kinetic model and its
incorporation into the OLGA2000® transient multiphase flow simulator." Proceedings of
the 5th International Conference on Gas Hydrates, Trondheim, Norway.
Turner, Doug. 2005. "Clathrate hydrate formation in water-in-oil dispersions." Colorado School
of Mines.
Vijayamohan, P, A Majid, P Chaudhari, AK Sum, CA Koh, E Dellacase, M Volk, and others.
2015. "Understanding Gas Hydrate Growth in Partially Dispersed and Water Continuous
Systems from Flowloop Tests." Offshore Technology Conference.
Wang, Wuchang, Shuanshi Fan, Deqing Liang, and Yuxing Li. 2010. "A model for estimating
flow assurance of hydrate slurry in pipelines." Journal of Natural Gas Chemistry 19
(4):380-384.
Zerpa, Luis E, E Dendy Sloan, Carolyn Koh, Amadeu Sum, and others. 2012. "Hydrate Risk
Assessment and Restart-Procedure Optimization of an Offshore Well Using a Transient
Hydrate Prediction Model." Oil and Gas Facilities 1 (05):49-56.
Zerpa, Luis E., E. Dendy Sloan, Amadeu K. Sum, and Carolyn A. Koh. 2012. "Overview of
CSMHyK: A transient hydrate formation model." Journal of Petroleum Science and
Engineering 98-99:122-129. doi: 10.1016/j.petrol.2012.08.017.
AC
C
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
27
ACCEPTED MANUSCRIPT
1
A Correlation to Quantify Hydrate Plugging Risk in Oil and Gas Production
2
Pipelines Based on Hydrate Transportability Parameters
3
Piyush Chaudhari a, Luis E. Zerpa b,*, Amadeu K. Sum a
a
Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO,
5
USA, 80401
b
6
RI
PT
4
Petroleum Engineering Department, Colorado School of Mines, CO, USA, 80401
* Corresponding author: Luis E. Zerpa, lzerpa@mines.edu, +1 (303) 384-2627
8
Highlights
•
10
Hydrate plugging risk evolves over time as function of hydrate volume fraction along
pipeline length
M
AN
U
9
SC
7
11
•
Hydrate plugging risk decreases with a decrease in the interfacial tension
12
•
Liquid holdup, fluid velocity, and interfacial tension are important parameters that govern
EP
TE
D
hydrate blockage formation
AC
C
13
1
Документ
Категория
Без категории
Просмотров
2
Размер файла
2 837 Кб
Теги
008, 2018, jngse
1/--страниц
Пожаловаться на содержимое документа