International Journal of Multiphase Flow 37 (2011) 671–694
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International Journal of Multiphase Flow
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / i j m u l fl o w
Review
Wax formation in oil pipelines: A critical review
Ararimeh Aiyejina a, Dhurjati Prasad Chakrabarti a,⇑, Angelus Pilgrim a, M.K.S. Sastry b
a
b
Department of Chemical Engineering, The University of the West Indies, Trinidad and Tobago
Department of Electrical and Computer Engineering, The University of the West Indies, Trinidad and Tobago
a r t i c l e
i n f o
Article history:
Received 23 December 2010
Received in revised form 9 February 2011
Accepted 20 February 2011
Available online 27 February 2011
Keywords:
Waxy crude oil
Oil-pipe
Solid–solid transition
Solid–liquid equilibrium
Wax precipitation
wax removal
a b s t r a c t
The gelling of waxy crudes and the deposition of wax on the inner walls of subsea crude oil pipelines
present a costly problem in the production and transportation of oil. The timely removal of deposited
wax is required to address the reduction in flow rate that it causes, as well as to avoid the eventual loss
of a pipeline in the event that it becomes completely clogged. In order to understand this problem and
address it, significant research has been done on the mechanisms governing wax deposition in pipelines
in order to model the process. Furthermore, methods of inhibiting the formation of wax on pipeline
walls and of removing accumulated wax have been studied to find the most efficient and cost-effective
means of maintaining pipelines prone to wax deposition. This paper seeks to review the current state of
research into these areas, highlighting what is so far understood about the mechanisms guiding this
wax deposition, and how this knowledge can be applied to modelling and providing solutions to this
problem.
Ó 2011 Elsevier Ltd. All rights reserved.
Contents
1.
2.
3.
4.
5.
6.
7.
8.
9.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Detection of deposited wax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.
Detecting blockages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.
Detecting wax deposits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wax deposition mechanisms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.
Molecular diffusion mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.
Soret diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.
Brownian diffusion mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.
Gravity settling mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.
Shear dispersion mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.6.
Shear stripping mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.7.
Nucleation and gelation kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.8.
Deposition in two-phase flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Effect of emulsified water on gelation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cloud point, pour point and gel point correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Review of some existing wax deposition models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.
Thermodynamic models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2.
Hydrodynamic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wax aging models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.
Counter diffusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2.
Ostwald ripening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Correct analogies for correlated heat and mass transfer in turbulent flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Inhibition of wax deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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⇑ Corresponding author. Address: Dept. of Chemical Engineering, The University of The West Indies, St. Augustine, Trinidad and Tobago. Tel.: +1 868 6622002x4001; fax: +1
868 6624414.
E-mail address: dhurjatiprasad@yahoo.co.in (D.P. Chakrabarti).
0301-9322/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ijmultiphaseflow.2011.02.007
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A. Aiyejina et al. / International Journal of Multiphase Flow 37 (2011) 671–694
9.1.
9.2.
Chemical inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Types of chemical inhibitors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2.1.
Ethylene copolymers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2.2.
Comb polymers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2.3.
Wax dispersants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2.4.
Polar crude fractions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2.5.
Short-chain alkanes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.3.
Surfaces that prevent wax deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.4.
Cold flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.
Wax removal methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.1.
Pigging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.2.
Inductive heating. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.3.
Biological treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.
Restart of gelled pipelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.1.
Time-dependent gel degradation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.2.
Examples of restart models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12.
Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction
Wax build-up is a complex and very costly problem for the
petroleum industry, widely reported and studied by researchers
in decades past (Reistle, 1928, 1932; Bilderback and McDougall,
1963; Haq, 1978). For subsea pipelines, in particular, it has become
especially important to solve the issue of wax build-up, as largescale oil production in colder regions will be faced with more
severe wax precipitation (Smith and Ramsden, 1978; Asperger
et al., 1981).
Wax precipitation within pipelines at and below the Cloud
Point or Wax Appearance Temperature (WAT) can lead to gelling
that inhibits flow by causing significant non-Newtonian behaviour
and increasing effective viscosities as the temperature of a waxy
crude oil approaches its Pour Point (Pedersen and Rønningsen,
2003). Alternatively, when just the pipeline wall is below the
WAT, this promotes the deposition of a layer of paraffin molecules
that can grow over time, constricting flow. This is especially problematic for pipelines in deep-sea environments, as, even in relatively warm climates, the water temperature will be on the order
of 5 °C (Azevedo and Teixeira, 2003).
Some researchers, such as Carmen García et al. (2001) and
Carmen García and Urbina (2003), have studied correlations between the properties of crude oils and their flowing properties,
including the precipitation and deposition of wax during flow.
Models have been developed to predict the onset of wax precipitation and the deposition of wax along pipeline walls. However,
accurately modelling deposition in pipelines can be a complex
and difficult undertaking, because, while precipitation is mainly a
function of thermodynamic variables such as composition,
pressure and temperature, deposition is also dependent on flow
hydrodynamics, heat and mass transfer, and solid–solid and surface–solid interactions (Hammami et al., 2003). Only recently has
a model been developed that incorporates correct analogies for
heat and mass transfer.
This paper reviews cases where researchers have studied
ways to model wax deposition and the aging of wax deposits
in pipelines; methods of measuring wax build-up in pipelines;
methods of inhibiting this deposition; wax removal methods;
and restart procedures for pipelines gelled with waxy crude. In
doing so, this paper, as one goal, seeks to show how our understanding of these mechanisms has developed, to highlight areas
where further understanding of these mechanisms is still needed,
and to show how well our current correlations can be applied to
the accurate prediction of wax deposition. Furthermore, this paper seeks to highlight the progress that has been made in devel-
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oping methods to mitigate and treat the formation of paraffin
layers in pipelines.
2. Detection of deposited wax
2.1. Detecting blockages
In order to experimentally explore wax deposition in the field or
to determine the locations of particularly large wax deposits or
even complete plugs, methods are needed for detecting the extent
of wax deposition at different points in a pipeline or of detecting
the location of plugs. Pressure echo techniques can be used to find
the location of a blockage by measuring the time for a pressure
wave to be reflected back along the pipeline from the point of
blockage (Chen et al., 2007). Alternatively, the pipeline could be
pressurized and then a special tool with a calliper and video camera on a remotely-operated submersible could be used to measure
the external diameter of the pipeline. Upstream of the blockage,
but not downstream of it, an appreciable difference in the diameter
can be detected when the pipeline is pressurized (Sarmento et al.,
2004).
2.2. Detecting wax deposits
Traditional experimental methods for measuring the extent of
wax deposits include direct methods such as pigging and the
‘‘take-out’’ method, in which a section of pipe is removed and
the volume of wax inside measured. Additionally, pressure drop
and heat transfer methods can be used to measure wax deposits
indirectly without down time (Chen et al., 1997). Zaman et al.
(2004) explored alternative methods of measuring wax deposition
in pipelines. Firstly, they experimented with measuring light
absorption through crude oil using a light source and a detector
circuit mounted within a pipe. They found that, in laboratory tests,
this detector circuit proved capable of detecting contamination
even with a very small percentage present. The use of ultrasound
for solid detection, also explored by Zaman et al. (2004) proved
very successful in detecting extremely small solid grains. Finally,
they were able to use a strain gauge to detect very small changes
in pipeline weight associated with wax deposition. However, all
of these methods were only tested with small-scale laboratory representations of actual systems. Practical methods for application of
these tools to actual subsea pipelines would still need to be designed. Zaman et al. (2006) have also experimented with the use
A. Aiyejina et al. / International Journal of Multiphase Flow 37 (2011) 671–694
of a laser spectroscope to detect paraffin in paraffin-contaminated
oil samples.
3. Wax deposition mechanisms
The behaviour of waxy crudes is usually approximated by modelling them as Bingham-like fluids. Different mathematical models
have been proposed ranging from a general one-dimensional model of a waxy crude oil to models that describe crude oils depositing
wax in closed flow loops. For example, Fusi (2003) and Fasano et al.
(2004) delineate many models of differing complexity for the representation of waxy crude oils. In order to fully model the flow of
these crude oils, the mechanisms governing the deposition and removal of solid wax must be incorporated into the model. Then
models can be developed, informed by a theoretical understanding
of the mechanisms at play and the properties of the mixtures under study. However, the question arises of which mechanisms
are actually relevant.
Investigations in this area have been ongoing for decades by
researchers such as Hunt (1962); Burger et al. (1981), and Leiroz
and Azevedo (2005). Azevedo and Teixeira (2003)did a critical review of wax deposition mechanisms, starting with wax deposition
by molecular diffusion as described by Burger et al. (1981). In this
review it is acknowledged that, in most models of wax deposition,
molecular diffusion is treated as the dominant mechanism, and it is
also argued that experimental evidence suggests that gravity settling and shear dispersion play no significant role in wax deposition. However, Azevedo and Teixeira point out that shear
dispersion may play a role in wax deposit removal, which would
affect the rate at which wax accumulates. Other authors, such as
Solaimany Nazar et al. (2005b) and Correra et al. (2007), have
incorporated wax removal mechanisms involving shear forces
(sloughing, ablation) into their wax deposition models. Other
mechanisms including thermo phoresis, the Saffman effect and
turbophoresis have also been considered in modelling wax deposition (Merino-Garcia et al., 2007).
3.1. Molecular diffusion mechanism
It is assumed that, for the flow of crude oil in the turbulent regime, the turbulent diffusivities of momentum, chemical species
and temperature will lead to a uniform distribution of velocity,
temperature and concentration profiles in a pipe cross-section.
Therefore, the transport of wax will be controlled by the gradients
prevailing at the laminar sub-layer close to the wall (Azevedo and
Teixeira, 2003). In a subsea pipeline in which the walls are cooled
below the cloud point, there will be a radial temperature gradient
and wax crystallization will occur in cooler regions nearest to the
wall. Thus, solid wax crystals will exist in equilibrium with the liquid phase. Since wax solubility decreases with temperature, there
will also be a concentration gradient established by the temperature gradient within the pipeline, with the cooler regions near
the wall having the lowest concentration of wax in the liquid
phase. This is what leads to the molecular diffusion of wax from
the bulk fluid to the walls of the pipeline.
Azevedo and Teixeira (2003) suggested that the mass flux of the
wax be estimated by Fick’s Law as
dmm
dC
¼ qd Dm A
dt
dr
ð1Þ
Here mm is the mass of deposited wax, qd is the density of the solid
wax, Dm is the diffusion coefficient of liquid wax in oil, A is the surface area over which deposition occurs, C is the concentration of
wax in solution (volume fraction), and r is the radial coordinate.
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3.2. Soret diffusion
Soret diffusion or the Soret effect refers to thermal diffusion,
which accounts for mass separation caused by the existence of a
temperature gradient within the pipeline (Ekweribe et al., 2009).
Some researchers, such as Merino-Garcia et al. (2007), have classified its effect in wax deposition as negligible. However, expressing
diffusion in terms of molecular and thermal diffusion allows for a
wax deposition model to more correctly account for thermal effects in diffusion (Banki et al., 2008). Thus, total mass flux would,
ideally, have to be represented as a combination of Fick’s Law, in
terms of Dm and the concentration gradient, and transport by the
Soret effect, in terms of a thermo diffusion coefficient, DT, and
the temperature gradient.
3.3. Brownian diffusion mechanism
This would occur when wax crystals that have precipitated out
of the oil solution collide with excited oil molecules. The use of this
mechanism in modelling deposition was also explored by Azevedo
and Teixeira (2003). This diffusion mechanism can also be represented by Fick’s Law as shown in equation.
dmB
dC
¼ qd DB A
dt
dr
ð2Þ
Here mB is the mass of wax deposited by Brownian motion, DB is the
Brownian motion diffusion coefficient of the solid wax crystals and
C is the concentration of solid wax out of solution.
Azevedo and Teixeira (2003) acknowledge that many authors
dismiss Brownian diffusion as a relevant mechanism for wax deposition. However, they conclude that there is not enough evidence
to warrant this, citing an argument used by Majeed et al. (1990),
which suggests that Brownian diffusion flux will be away from
the wall, where the solid concentration would be highest. They dismiss this argument, because if the wax crystals are trapped in the
immobile solid layer at the wall, the concentration of solid crystals
in the liquid at the wall is zero, or nearly zero, allowing for Brownian diffusion toward the wall. The review concludes that Brownian
diffusion remains a possible contributing mechanism for wax
deposition.
3.4. Gravity settling mechanism
Azevedo and Teixeira (2003) classify gravity settling as insignificant in contributing to wax deposition, citing experimental evidence from Burger et al. (1981), which showed that the settling
velocities of wax crystals under typical conditions do not contribute significantly to deposition. This was further supported by
experimental evidence from Burger et al., which demonstrated that
deposition under horizontal and vertical flow is identical within
the limits of experimental error.
3.5. Shear dispersion mechanism
Shear dispersion could contribute to wax deposition through
the lateral motion of particles immersed in a shear flow. Some
authors, such as Fusi (2003), include deposition in terms of a shear
dispersion coefficient in the modelling of wax deposition. Also,
Fasano et al. (2004) claim that, based on the literature, for temperatures much lower than the cloud point and for moderate heat
fluxes the dominant process is shear dispersion, while for slightly
higher temperatures the dominant process is molecular diffusion.
However, Azevedo and Teixeira (2003) claim that shear dispersion
does not contribute to deposition, because experimental evidence
shows no deposition of wax under conditions of zero heat flux,
when it would only be possible if driven by a flow-induced
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mechanism, such as shear dispersion. However, Azevedo and
Teixeira concede that shear forces can still contribute to the
removal of wax deposits. Regardless of conflicting theories with
regard to the role of shear dispersion in wax deposition, the importance of this mechanism in the overall accumulation and aging of
wax deposits cannot be ignored.
3.6. Shear stripping mechanism
Removal of wax deposits by shear forces becomes especially
important under turbulent conditions when the rate of removal
will be significantly higher compared to laminar flow. Therefore,
in order to accurately model wax deposition, especially for turbulent flow, it is necessary to incorporate shear stripping effects into
the model. Additionally, modelling wax removal by shear forces
could help in the design of flow improver chemicals, as some of
these may act by causing the formation of softer gel structures that
are more susceptible to removal by shear forces. Some researchers,
such as Matzain (1999), have tried to represent this effect as an
empirical correlation for the reduction in rate of deposit formation
caused by shear forces.
3.7. Nucleation and gelation kinetics
The crystallization of waxes is a kinetic process, the onset of
which can be described by classical homogeneous nucleation theory (Paso, 2005). While much work has been done to approach wax
deposition as a thermodynamic problem, modelling based on the
kinetics of deposit formation has not been widely explored
(Merino-Garcia et al., 2007). Paso (2005) sought to address the
insufficient understanding of the crystallization and gelation processes, as well as the assumption that paraffin precipitation kinetics does not limit deposition rates; an assumption that could lead
to the prediction of wax deposition in cases where a stable gel
cannot form. He used model fluids consisting of n-paraffin components dissolved in petroleum mineral oils, and applied homogenous nucleation and crystallization theory, along with differential
scanning calorimetry to measure the onset of crystallization and
the crystallization rate.
Paso (2005) compared experimental and equilibrium crystallization rates to show that there were three regimes in the crystallization process at low cooling rates. The first is a nucleation lag
period starting at high-temperature conditions. The second is a
supersaturation growth period, driven by the supersaturation
established during the nucleation lag period as well as by decreasing solubility conditions, and during which the crystallization rate
can spike well above the equilibrium crystallization rate. The third,
meanwhile, is an equilibrium growth period, which starts when
the supersaturation ratio is diminished and the crystallization rate
converges with the equilibrium predictions of the van’t Hoff relation. One thing noted by Paso about these regimes was that the
temperature span of the supersaturation growth regime was
independent of the model fluid viscosity, providing evidence of
the absence of transport limitations in the crystallization rate.
Through the application of the van’t Hoff solubility model within the framework of classical homogeneous nucleation theory, Paso
(2005)demonstrated that nucleation represents the primary
kinetic limitation associated with the crystallization of n-alkanes
in organic solution at low cooling rate conditions, with crystallization rate limitations becoming significant at high cooling rates. He
also highlighted that the initial nucleation event is dependent upon
the solubility behaviour of the highest fraction of n-alkane components in the fluid, and that the introduction of chain-length variations effects a reduction in the critical nucleus surface energy by
co-crystallization of dissimilar chain-length paraffins.
Paso (2005) also investigated the mechanical properties of waxy
model fluids at constant cooling rates using controlled-stress rheometric measurements, applying an oscillatory upon the fluid samples in order to characterize their mechanical properties during
gelation. The crystal structure in samples was also studied via
microscopy and, furthermore, Paso applied an extension to an
established three-dimensional analytical percolation approximation to wax–oil gel systems. This allowed for the prediction of theoretical gelation via the percolation threshold, the fractional
volume of the solid crystalline phase at which it forms a continuous, domain-spanning path connected by crystal–crystal interactions. For this purpose, paraffin crystals were represented by
ellipsoidal geometries with spherical rotational volume of interaction. The primary and secondary ellipsoidal aspect ratios of the
crystals, a1 and a2, were related to the solid phase fraction at the
percolation threshold, ug, by equation.
/g ¼ hp
1 1
a1 a2
ð3Þ
Here hp = 0.295 represents the spherical percolation threshold.
While this would give a prediction of the formation of a crystal percolation network, it was noted that this will lead to gelation only if
the number density and strength of the crystal–crystal interactions
are sufficient to impart solid-like properties to the fluid (Paso,
2005).
Overall, Paso (2005) noted that the gel point of a waxy petroleum fluid is dependent on the morphologies and surface characteristics of the randomly oriented paraffin crystals, and that
aspect ratios on the order of 100 allowed mechanical gels to form
from these oils with paraffin content as low as 0.5%. Also, that
mono disperse crystals exhibited ordered surfaces and sharp edges,
providing minimal crystal–crystal contact and weak interactions,
while polydisperse n-alkane crystals exhibited nano-scale surface
roughness, which provides contact points for strong crystal–crystal
interactions, allowing for mechanical gelation at smaller wax contents. Additionally, Paso concluded that percolation threshold
models provide accurate gel point predictions for physical gelation
systems that exhibit strong crystal–crystal interactions, while under-predicting the solid fraction necessary to induce gelation in
weakly-interacting particle systems.
Other recent studies that approached the subject of nucleation
and gelation kinetics include those by Lopes-da-Silva and Coutinho
(2007) and Ekweribe (2008). They analyzed gelation kinetics with
the phenomenological Avrami model and noted an apparent
dependence of nucleation and crystal growth mechanisms and
rates on the degree of supercooling below the WAT at which crystallization is occurring. Lopes-da-Silva and Coutinho (2007) also
noted an apparent predominance of heterogeneous nucleation
and diffusion-controlled growth, especially at higher supercooling
and/or higher oil complexity composition and molecular weight.
These results and those of Paso (2005) and further studies should
prove invaluable in the development of more robust wax deposition models, which take kinetic considerations into account. They
can also be useful in determining mechanisms by which wax gelation can be inhibited or wax deposits weakened by wax crystal
modification.
3.8. Deposition in two-phase flow
Analyzing and modelling liquid–liquid two-phase flow has previously been explored by many researchers as well as present
authors (Raj et al., 2005; Chakrabarti et al., 2006, 2007). Deposition
in two-phase flow shows some characteristics similar to liquid–
liquid two-phase flow. Matzain et al. (2002) found that the
thickness, hardness and profile of wax deposition in two-phase
gas–oil flow show dependence on flow patterns. They used a closed
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flow loop and the liquid displacement–level detection (LD–LD)
technique, proposed by Chen et al. (1997), to measure wax deposits under different conditions. For horizontal flow, the thickness of
deposits varied around the circumference of the pipe depending on
flow pattern, as shown in Fig. 1.
Matzain et al. (2002) account for these distributions by describing how, in stratified flow, only the lower part of the wall will be in
contact with the oil phase, and the heat transfer rate will be highest at the bottom of the pipe and will decrease upward, resulting in
decreasing deposit thickness in a crescent shape. In the case of
wavy stratified flow, the wavy gas–oil interface is cooled because
of the waves, increasing heat transfer rate and, thus, deposit thickness along the interface. With intermittent flow, the passing of liquid slugs induces high shear force and stress along the bottom of
the test pipeline and shearing of wax deposits, resulting in thinner
deposits at the bottom of the pipe. With annular flow, the wax
thickness is uniform around the circumference, as oil is uniformly
in contact with the entire wall surface.
The results of Matzain et al. (2002) also showed changes in
hardness of the wax deposits for different flow patterns. Stratified
flow gave a soft deposit at the bottom of the pipe, with harder and
thicker deposits along the edge of the wavy gas–liquid interface.
Intermittent flow resulted in a hard deposit, with increasing hardness from the top to the bottom of the pipe. Lastly, annular flow resulted in a very hard deposit, uniform across the circumference of
the pipe. Their results for vertical two-phase flow, on the other
hand, showed very uniform thickness distribution in the different
flow regimes, with very hard deposits for annular flow, deposits
of medium to high hardness for intermittent flow, and hard deposits for bubbly flow with high superficial velocity.
4. Effect of emulsified water on gelation
Crude oil emulsions, in particular, can pose significant flow
assurance risks and, with the increase in multiphase production
in offshore environments, it has become important to evaluate
the impact of emulsified water on crude oil gelation (Visintin
et al., 2008). The presence of water over a threshold value can promote gel formation and viscous wax–oil gel emulsions. These
emulsions may be stabilized by the presence of polar compounds
such as asphaltenes and resins, and can have water cuts as high
as 70% (de Oliveira et al., 2010). Paso et al. (2009c) attributed the
stability of waxy emulsions to the stabilizing effect of asphaltene
particles on oil–water interfaces. They also suggested that, at
low-temperature conditions, molecular asphaltene adsorption
onto precipitated wax crystals may increase the water wettability
of the crystals, thus promoting adsorption at the oil–water
interface.
Visintin et al. (2008) hypothesized that the solid paraffin stabilizes the emulsion by being strongly adsorbed at the liquid–liquid
interface forming Pickering emulsions. They suggested that, by
means of the strong interaction between wax crystals and the drop
surface, growth of the gel network involves the droplets themselves, forming a volume-spanning wax crystal network with
Stratified
Smooth
Stratified
Wavy
entrapped dispersed water, as shown in Fig. 2. They observed a
sharp increase in shear viscosity, yield stress and pour point for
waxy crude oil emulsions with above 25–30% volume of dispersed
water, as demonstrated in Fig. 3.
It was similarly noted by de Oliveira et al. (2010) that these viscous emulsions can increase gel strength and hinder pipeline restart by increasing the magnitude of the rheological properties of
the waxy crude oil gel. They attributed this change to the network
developed by the aggregation of the waxy crystals and water. Paso
et al. (2009c) also noted drastic increases in fluid viscosities and
shear thinning rheological behaviour due to the presence of emulsified water. These observations show the significance of considering the effect of emulsified water on gelation, and Visintin et al.
(2008) note the importance of accounting for the impact of emulsified water during field development studies. Water fraction produced by a well generally increases over its lifetime (Lockhart and
Correra, 2005; Visintin et al., 2008). Thus it would be very useful to
account for the increasing impact of emulsified water on gelation
and gel rheology during continued operation.
5. Cloud point, pour point and gel point correlations
Some authors have focused on developing correlations between
measurable properties of crude oils, such as the pour point, and the
conditions under which disruptive wax deposition will occur.
Work such as this may help in predicting if and when fatal wax
deposition would occur in pipelines carrying particular crudes. Li
et al. (2005) cited the results of Holder and Winkler (1965) as indicating that 2 wt.% precipitated wax is sufficient to cause gelling of
virgin waxy crudes. Li et al. thus started with previously developed
correlations and tried to develop their own correlation linking the
temperature at which this 2 wt.% precipitation would occur, Tc (2
wt%), and the pour point, Tpp, and gel point, Tgp, of various waxy
crude oils., represented graphically by Figs. 4 and 5. These results
and future research could be useful in both determining the tendency for different waxy crudes to gel and harden at particular
temperatures, and in devising chemical means of inhibiting this
occurrence.
6. Review of some existing wax deposition models
Many different authors have proposed models for the flow of
waxy crude oils and the associated deposition of solid wax within
pipelines, including Farina and Fasano (1997), and Fusi and Farina
(2004). Additionally, there are commercial software codes
developed to describe these processes, such as those compared
by Bagatin et al. (2008). Fasano et al. (2004) reviewed various
mathematical models for the flow of waxy crude oils in laboratory
experimental loops, in which the oils are assumed to behave like
non-Newtonian Bingham fluids, a common assumption for modelling these fluids. Torres and Turner (2005) approached the problem
by developing a method of straight lines for solving a Bingham
problem for modelling the flow of waxy crude oils.
Intermittent
Annular
Fig. 1. Approximation of wax thickness distribution for various horizontal flow patterns (as described in Matzain et al., 2002).
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Fig. 2. Schematic representation of the gelation of waxy crude oil emulsions. Paraffin crystals that precipitate after a decrease of temperature below the WAT can adsorb on
droplet surface (A) or cover it (B), and stabilize the emulsion. Flocs of solid paraffin continuously grow on drops of water or between them (C). Dispersed water is entrapped
by a wax crystal network (D): the system spans the entire volume and the gelation is complete (Visintin et al., 2008).
Fig. 4. Tc (2 wt%) vs. ASTM pour point (Li et al., 2005).
Fig. 3. Pour point of waxy crude oil emulsion with increasing water content
(Visintin et al., 2008).
concentration can be determined using the chain rule. This is a
problem that has been corrected in more recent deposition models
such as the one used in the Michigan Wax Predictor developed by
Hyun Su Lee.
6.1. Thermodynamic models
The earlier models presented here incorporate the wax deposition processes for pipelines containing waxy crude oils, and consider cases where either molecular diffusion or shear dispersion
is considered the dominant mechanism involved in wax deposition. However, one of the mistakes commonly introduced to wax
deposition models is the assumption that the temperature and
concentration gradients are independent, and that, therefore, wax
Many researchers have studied the thermodynamics of wax
deposition in hopes of creating a model that accurately describes
the process. In one example of earlier work, Lira-Galeana et al.
(1996)developed a thermodynamic framework for calculating
wax precipitation in petroleum mixtures as several distinct solid
phases. Solaimany Nazar et al. (2005a) later developed a
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Fig. 5. Tc (2 wt%) vs. gel point (Li et al., 2005).
multi-solid phase thermodynamic model for predicting wax precipitation in petroleum mixtures, by using the Peng–Robinson
equation of state to evaluate the phase behaviour of both liquid
and vapour phases. The model is solved for equilibrium, in which
the fugacity of each component is equal in every phase, using Eq.
(4), proposed by Prausnitz et al. (1986).
"
!
!
s
DHfi
f
T
DHtri
T
1
1
¼
exp
fl i
RT
RT
T tri
T fi
#
Z Tf
Z f
i
1
1 T i DC pi
þ
dT
DC pi dT þ
RT T
R T
T
ð4Þ
Here fis is the solid phase fugacity, DHri is the enthalpy of solid–
solid transition between different solid phases, T fi is the temperature of fusion, Ttr is the transition temperature, Cp is the heat capacity, and R is the ideal gas constant.
Table 1 shows a comparison of experimentally determined
WATs for five synthetic paraffin systems and those predicted by
the model of Solaimany Nazar et al. (2005a) and a UNIQUAC model
developed by Coutinho (1998). The synthetic systems were each
composed of decane and a bimodal paraffin distribution. It should
be noted that with this and other models which use experimentally
determined cloud points to validate the model, there is a limit to
how accurately cloud points can be measured which is highly
dependent on the particular oil mixture, as discussed by Coutinho
and Daridon (2005) and Hammami et al. (2003). Therefore, agreement with experimental data may not prove definitively the accuracy of a model, especially as far as its applicability to a wide range
of wax–oil mixtures.
Wuhua and Zongchang (2006) also developed a more recent
thermodynaamic model, based on the equality of fugacities at
equilibrium, which estimates solid precipitation as a function of
temperature and composition. For this study, Eq. (5) was used for
the condition of equal fugacities in the solid and liquid phases.
xSi
cL f L
¼ iS iS exp
L
xi
ci fi
Z
0
P
V Li V Si
dP
RT
!
and liquid phase respectively. For their model, there was an added
level of specificity for modelling particular n-alkane species. Different correlations were used for the fusion enthalpies of n-alkanes
and for their enthalpies of solid–solid transition based on both carbon number and whether that number is odd or even. Similarly,
transition enthalpies were calculated for different components
based on chain lengths.
Table 2 shows a comparison of experimentally determined
WATs for three crude oils and those predicted by the model of
Wuhua and Zongchang (2006) and a similar model developed by
Leelavanichkul et al. (2004) and Fig. 6 compares the predictions
of the two models to experimental data for wax precipitation as
a function of temperature. The data indicates that refinement of
thermodynamic correlations, as performed by Wuhua and
Zongchang, can increase model accuracy in predicting precipitation as a function of temperature.
Further studies, for example, by Edmonds et al. (2008), have
also explored ways of representing the wax phase in order to more
accurately model wax deposition. Edmonds et al. modelled the
wax phase as a continuous distribution of n-alkane components,
showing how this eliminated physically unrealistic artefacts found
in the predictions of models that lumped n-alkanes into pseudo
components. Edmonds et al. carried out simulations with numbers
of components approaching 100 and, in order to increase the computational speed, converted phase equilibrium and physical property data into empirical expressions, fitted to the rigorous model.
They also noted the importance of considering the deposit limiting
mechanism of wax shearing in order for both their model and others from the literature to more accurately agree with the limited
field data available from actual pipelines.
6.2. Hydrodynamic model
Ramírez-Jaramillo et al. (2001) also developed a multi-solid
phase thermodynamic model for predicting wax deposition. In
addition, Ramírez-Jaramillo et al. (2004) developed a multicomponent liquid-wax hydrodynamic model for simulating wax
deposition in pipelines, which treated molecular diffusion as the
dominant mechanism. Fig. 7a shows the computational domain
Table 2
Experimental WAT data and model predictions for crude oils (Wuhua and Zongchang,
2006).
Sample
Experimental
results
Leelavanichkul
model
Crude
Oil A
Crude
Oil B
Crude
Oil C
298.2 K
298.8 K
295.2 K
294.2 K
Deviation
Present
model
Deviation
0.4 K
301.3 K
3.1 K
293.4 K
1.8 K
295.4 K
0.2 K
296.0 K
1.8 K
297.8 K
3.6 K
ð5Þ
Here x is the mole fraction, c is the activity coefficient, V is volume, P is pressure and the S and L superscripts indicate the solid
Table 1
Comparison of the WAT between experimental data, UNIQUAC model and Solaimany
Nazar et al. model (Solaimany Nazar et al., 2005a).
WAT (K)
UNIQUAC
This model
Bim 0
Bim 3
Bim 5
Bim 9
Bim 13
308.75
307.05
308.45
309.65
307.55
309.05
310.37
308.47
309.55
311.33
309.63
310.7
312.81
311.41
312.75
Fig. 6. Wax precipitation as a function of temperature for crude oil A (Wuhua and
Zongchang, 2006).
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Fig. 7. (a) Computational domain for a model pipe. (b) Sections of a model pipe with concentric layers (Ramírez-Jaramillo et al., 2004).
used by Ramírez-Jaramillo et al. (2004), consisting of a model pipe
of length, L, and radius, r, along which a mixture of hydrocarbons
flows. The pipe was divided to form a computational mesh, with
boundary conditions applied at the ends and along the exterior
surface of the pipe, and finite differences were used in the solution
of differential equations.
Ramírez-Jaramillo et al. (2004) modelled the fluid as consisting
of n hydrocarbon components in thermodynamic equilibrium, with
mole fractions, in both the liquid and solid phases, that are functions of pressure and temperature. They considered the wax deposition rate to depend on oil composition, oil temperature, external
temperature around the pipe, flow conditions, pipeline size and
pressure. The model assumed wax deposition by molecular diffusion and removal by shear forces, which would be especially significant at high Reynolds numbers [( quDh /l), where q = density,
u = velocity, l = dynamic viscosity, Dh = hydraulic diameter]. In
addition, the model included aging by the diffusion of wax into
and within the gel-like deposit, which is discussed later in this
paper. The mass flux was calculated for all components in the
system and summed to give the total flux.
Ramírez-Jaramillo et al. (2004) used mass, momentum and
energy balances, shown in Eqs. (6)–(8) and assumed mixture
incompressibility and quasi-steady state for all rate processes
concerning mass, momentum and energy.
@ qm
þ r qm m ¼ 0
@t
qm
@m
þ m rm ¼ rP þ r s þ qm g
@t
qm C v
@T
þ m rT ¼ kr2 T
@t
ð6Þ
ð7Þ
ð8Þ
Here P, s and g are the pressure, stress tensor and gravitational
constant; Cm and k are the heat capacity and thermal conductivity
(which is assumed constant), respectively; and m is the average
macroscopic velocity of the mixture. They expressed the total
amount of deposited wax, M(t,z), in terms of the deposited mass
A. Aiyejina et al. / International Journal of Multiphase Flow 37 (2011) 671–694
of each component due to molecular diffusion, MMDi(t,L), the mass
removed by shear forces, MSR(t,L), and the mass of wax molecules
diffusing into the gel deposit, MGD(t,L), as shown in equation.
Mðt; zÞ ¼
n
X
M MDi ðt; LÞ M SR ðt; LÞ MGD ðt; LÞ
ð9Þ
i¼1
Ramírez-Jaramillo et al. (2004) solved for the total deposition
rate, @M/@t. The output of their model included solid fractions, density, viscosity, radial mass flux and deposited mass calculations.
The model showed reasonable agreement with previously developed models and experimental data for a binary mixture reported
by Cordoba and Schall (2001), as shown in Fig. 8 Ramírez-Jaramillo
et al. found that the Peclet number and Reynolds number parameters had a significant impact on the amount of wax deposited.
7. Wax aging models
7.1. Counter diffusion
Researchers have also explored the properties of wax crystals
and wax deposits formed from crude oils. Nautiyal et al. (2008)
studied the crystal structure of n-alkane paraffins crystallized from
crude oil. Other studies have specifically looked at the way wax
deposits change after the initial formation. This is important because, in addition to understanding the mechanisms involved in
the deposition of wax in pipelines, in order to fully model the flow
of crude oil and accumulation of wax, it is vital to understand the
mechanisms that govern the aging of wax deposits. These deposits
are not simply static and unchanging. Rather, after a layer of wax
has formed along a pipeline wall, its composition gradually
changes. The crystalline wax deposit actually behaves like a porous
medium with oil trapped within its three-dimensional network
(Singh et al., 2000, 2001a). The wax content of this deposited gel
can therefore increase with time by diffusion. As this happens,
hardness, melting point and heat of fusion of the deposit can
change, which could affect decisions about the appropriate method
of wax removal to employ in a pipeline.
Singh et al. (2000) studied this phenomenon by use of food
grade wax dissolved in a mineral oil–kerosene mixture, which
was pumped through a closed flow loop setup. Their experimental
procedure involved heating a wax–oil mixture to 30–35 °C in a
stirred tank and maintaining the temperature of this vessel above
679
the cloud point, while pumping the wax–oil mixture through the
flow loop. The flow loop consisted of a 5/8 in. OD steel tubing test
section, which was cooled by a heat exchange jacket, and an identical but non-cooled reference section. Pressure taps connected to
pressure transducers were used to measure the increase in differential pressure during operation in order to determine the thickness of the deposit within the test section. The bulk fluid inlet
temperature, tb, and wall temperature, ta, were also monitored.
Singh et al. (2000) determined that a counter diffusion phenomenon, in which wax molecules diffuse into the gel deposit and oil
molecules diffuse out of the deposit, is responsible for the aging
of the deposit. They furthermore determined that the rate of aging
is dependent on oil flow rate as well as the pipeline wall temperature. In their experimental setup with oil in a closed flow loop with
cooled walls, there was a rapid decrease in internal radius measured over the first day followed, which then plateaued. Similarly,
the increase in the measured weight fraction of wax slowed after a
rapid change in the first day. The wax content (determined using
high-temperature gas chromatography, HTGC) of the gel deposit
also changes over time, with the proportion of lighter components
decreasing after the first day, while the proportion of heavier components increases. The data recorded by Singh et al., showed that
the wax content of the deposit continued to increase even after
the thickness stabilized, and that waxes of chain length higher than
29 diffused into the deposit while the ones with lengths less than
29 diffused out. 29 is the critical carbon number, CCN, for the given
operation conditions; a value that could be useful in determining
what inhibitors to use in a particular well or pipeline, based on
whether or not they can inhibit crystallization of waxes above
the CCN (Paso and Fogler, 2003).
Singh et al. (2000) were able to develop a mathematical model
to describe the wax deposition process in a laboratory flow loop by
solving numerically a coupled system of differential and algebraic
equations of heat and mass transfer inside and outside the gel deposit. Eq. (10) shows the mass balance they used to relate the rate
of change of wax in the gel deposit to the radial convective flux of
wax molecules from the bulk of the fluid–gel interface.
d
½pðR2 r 2i ÞF w ðtÞLqgel ¼ 2pr i Lk1 ½C wb C ws ðT i Þ
dt
ð10Þ
Here R is the original internal radius of the pipe, ri is the internal
radius during deposition (average radius available for flow of oil),
F w is the weight fraction of solid wax in the oil, L is the length of
pipe, qgel is the density of the gel deposit (considered constant),
k1 is the mass transfer coefficient, Cwb is the bulk concentration
of wax, Cws is the solubility of the wax in the oil solvent derived
in terms of Ti, and Ti is the interfacial temperature, which was obtained from the energy balance shown in equation,
2pr i hi ðT b T i Þ ¼
2pke ðT i T a Þ
2pri k1 ½C wb C ws ðT i ÞDHf
lnðR=r i Þ
ð11Þ
where hi is the interface heat transfer coefficient, ke is the effective
thermal conductivity of the gel, and DHf is the heat of solidification
of the wax. The heat and mass transfer coefficients were obtained
using Hausen, Seider and Tate correlations.
Eq. (12) shows the deposit growth equation derived by Singh et
al. (2000), by relating the rate of addition of wax to the gel deposit
in the flow loop to the radial convective flux of wax molecules from
the bulk to the fluid–gel interface and the diffusive flux into the gel
at the gel interface.
2pr i F w ðtÞqgel
Fig. 8. Dimensionless wax thickness distribution vs. time. Comparison of model
predictions with experimental data for the 30:70 (cyclo C6C19:C8) ratio (RamírezJaramillo et al., 2004).
dr i
dC w
¼ 2pr i k1 ½C wb C ws ðT i Þ 2pri De
dt
dr i
ð12Þ
Here De is the effective diffusivity of wax inside the gel deposit.
Coupled differential equations from Eqs. (10) and (12) were solved
by Singh et al. throughout the length of the pipe at each time
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r¼
Fig. 9. Kinetic growth of crystals for oil sample from X-ray diffraction analysis at
10 °C (Coutinho et al., 2003).
instant using Runge–Kutta algorithms, along with equations for
dTi/dr. This system of equations was used to obtain the trajectories
of thickness and wax content at each location in the pipe. This
model showed excellent agreement with experimental data. However, Lee (2008) has shown that the mass-heat transfer correlations used by Singh et al. incorrectly assume independent heat
and mass transfer, and were successfully applied only because of
a high degree of supersaturation in the laminar boundary layer.
Other researchers, such as Hernandez et al. (2004), modelling
wax deposition in pipelines has begun incorporating wax aging
into their models. Additionally, Singh et al. (2001b) were able to
develop a thermodynamic model to predict both cloud point temperatures and CCNs of wax–oil mixtures, where CCN is a function
of the mixture composition as well as the wall temperature. This
model also showed good agreement with experimental data, predicting the cloud points and the CCNs of model oils with good
accuracy.
7.2. Ostwald ripening
It must be noted that the diffusion mechanism used by Singh
et al. (2000, 2001a,b) is not the only possible mechanism for
explaining the aging process. In fact Continuo et al. (2003) found
that aging of wax deposits takes place even for samples kept under
isothermal conditions. The diffusion mechanism for aging cannot
account for this as it is driven by temperature-composition gradients. Coutinho et al. reported broadening of peaks on X-ray diffraction and Cross Polar Microscopy (CPM) images showing an increase
in the crystallite’s size. Fig. 9 shows an example of their results
from X-ray diffraction analysis, for which the crystallite size, r, is
related to a shape factor K, and the measured peak position, h,
and breadth, b, by equation.
Kk
b cos h
ð13Þ
Coutinho et al. (2003) noted an increase in crystal size observed
by CPM at a temperature in the neighbourhood of the pour point.
They reported an increase from 6.4% to 15.3%, over 110 h, for the
fraction of a CPM image occupied by crystals. Furthermore, they
obtained Differential Scanning Calorimetry (DSC) thermograms
under the same conditions, which did not show detectable heat
effects associated to this change in the crystal size, as seen in
Fig. 10. They noted that this can only occur when the heat of crystallization released is used by the melting of an equivalent mass of
crystals. This indicates that wax deposits in crudes suffer recrystallization. Coutinho et al., thus, conclude that Ostwald Ripening is
also a mechanism responsible for the aging of wax deposits.
8. Correct analogies for correlated heat and mass transfer in
turbulent flow
Many existing wax deposition models assume that heat and
mass transfer can be related by the chain rule, which assumes that
the system is at thermodynamic equilibrium (which may not be
true), or use mass-heat transfer analogies, such as the Chilton–
Colburn analogy, which are valid only when the temperature and
concentration fields are independent. Venkatesan and Fogler
(2004) noted that such heat-mass transfer analogies are not applicable for predicting the mass transfer rates in turbulent flows,
where the concentration field is correlated to the temperature field
and the concentration boundary layer and temperature boundary
layer thicknesses are not independent. They showed that use of
the Colburn analogy in this case would result in a significant
over-prediction of wax deposition. They also proposed a method
for estimating the convective mass transfer rate using the Nusselt
number and the experimentally obtained solubility curve. However, this method would only be valid for thermodynamic equilibrium in the mass transfer boundary layer, when precipitation
kinetics are not limiting.
For the development of more rigorous and accurate models, it
has been necessary for researchers to explore the correct relationship between heat and mass transfer. Lee (2008) investigated the
combined heat and mass transfer phenomenon under laminar
and turbulent flow conditions using the finite difference method.
He developed a model based on that of Singh et al. (2000), which
could be applied for any precipitation kinetics. For turbulent flow,
Lee showed that the solubility method proposed by Venkatesan
Fig. 10. Thermogram for oil C (thick line). The isothermal region above 5000 s shows that there are no detectable heat effects related to the aging of the wax (Coutinho et al.,
2003).
A. Aiyejina et al. / International Journal of Multiphase Flow 37 (2011) 671–694
and Fogler (2004) under-predicts deposition by assuming that the
concentration profile in the mass transfer boundary layer follows
the thermodynamic equilibrium limit between temperature and
concentration at every point. This was contrasted with the overprediction of the Chilton–Colburn analogy, which gave maximum
supersaturation. The comparison showed that these two approaches constitute the limiting cases for deposition, and that
the actual concentration profile, which is dependent on the precipitation kinetics, falls between those calculated by the two methods.
Instead of using those two limiting cases, Lee (2008) employed
a computational approach for calculating the Nusselt numbers
[(hL/kf), where L = characteristic length, kf = thermal conductivity
of the fluid, h = convective heat transfer coefficient] and Sherwood
numbers [(KL/D) where L is a characteristic length, D is mass diffusivity, K is the mass transfer coefficient] according to following
equations.
Nu ¼
Sh ¼
ð2ri Þ@T
@r
rr i
Tb Ti
ð2r i Þ@C
@r
rri
Cb Ci
¼
ð2r i Þhi
k
ð14Þ
¼
ð2r i ÞkM
Dw0
ð15Þ
The temperature and concentration gradients at the fluid-deposit interface, needed for these calculations, were obtained by
solving mass and energy balance equations, as shown in following
equations.
mz
@C 1 @
@C
kr ðC C ws Þ
¼
rDwo
@z r @r
@r
ð16Þ
mz
@T 1 @
@T
bðC C ws Þ
¼
r aT
@z r @r
@r
ð17Þ
Here vz is the axial velocity, Dwo is the molecular diffusivity of
wax in oil, kr is the thermal conductivity, aT is the thermal diffusivity, and the precipitation term b(C–Cws) is considered negligible.
Lee first did this for laminar flow. Using a discretized form of the
mass-heat transfer equation along with their appropriate boundary conditions, Lee wrote the governing equations in matrix form.
Then by inverting these matrices to give the radial temperature
and concentration profiles, and numerically marching from the inlet of the tube to the exit he could obtain the complete set of temperature and concentration profiles with respect to the radial and
axial position.
From this, Lee (2008) showed how the Sherwood number
profile as a function of axial distance would change for different
precipitation rate constants. This showed that if there was no precipitation in the boundary layer, the heat and mass transfer rates
become independent of each other, resulting in a supersaturation
curve. However, as the precipitation rate constant increases the
Sherwood number is decreased, because wax molecules would
not reach the oil–deposit interface, and would instead flow down
to exit as solid particles.
To obtain the Sherwood and Nusselt numbers under turbulent
conditions, Lee (2008) used the same procedure with governing
equations modified for turbulent flow to include the turbulent
axial velocity profile and the thermal and mass transfer eddy diffusivities. The wax concentration profiles in the turbulent boundary
layer obtained this way showed that heat and mass transfer
become independent as the precipitation rate constant approaches
zero, resulting in the Chilton–Colburn analogy-derived concentration profile. Conversely, as the precipitation rate constant
increases, precipitation in the boundary layer increases, with concentration approaching the solubility limit for thermodynamic
equilibrium.
681
In his model, after calculating the Sherwood and Nusselt numbers, Lee (2008)could then solve the growth and aging governing
equations from Singh et al. (2000)’s model to solve for deposit
thickness and wax fraction at each time step in his computational
procedure. Lee (2008)showed that there was excellent agreement
between the results of his model and lab-scale laminar flow loop
experimental data. There was also good agreement with turbulent
lab-scale results, though there was significant discrepancy for early
times at higher volumetric flow rates, possibly due to sloughing.
The results of the computational model also closely matched
large-scale flow loop data. The results obtained by Lee (2008) show
that this model is applicable for varying precipitation kinetics, and
provides a robust and rigorous way of predicting wax deposition
under a range of turbulent conditions.
9. Inhibition of wax deposition
The most effective way of dealing with the problem of wax
deposition in crude oil pipelines would be to prevent it from occurring in the first place. Thus, researchers have investigated different
methods of inhibiting the deposition process. These include the
heat insulation of subsea pipelines to actually inhibit precipitation
by keeping pipeline temperatures as high as possible (Quenelle and
Gunaltun, 1987), the internal coating of pipelines with plastics
(Patton, 1970; Bummer, 1971), and also methods of preventing
wax deposition on pipeline walls, such as the use of chemical
inhibitors, which will be discussed in more detail in this paper.
9.1. Chemical inhibitors
Many researchers have studied the efficacy of different inhibitors of wax deposition and the mechanisms by which they inhibit
this deposition, including Jorda (1966), Mendell and Jessen (1970),
Fulford (1975), Addison (1984), Newberry and Barker (1985),
Fielder and Johnson (1986), Singhal et al. (1991), Jang et al.
(2007), and Tinsley et al. (2007). The efficacy of commercially
available inhibitors tends to be limited, and has to be evaluated
on a case-by-case basis. Wang et al. (2003), for instance, found,
when testing some wax inhibitors, that the inhibitors they had
studied reduced the total amount of deposition, but had only limited success in suppressing the deposition of the high molecular
weight paraffin components (C35 and above). This resulted in harder wax deposits than in the absence of an inhibitor. They also found
that inhibitors most able to depress the WAT were more likely to
be superior products for decreasing total wax deposition, and that
the addition of the corrosion inhibitor, oleic imidazaline (OI), significantly increased the efficacy of deposition inhibition. Fig. 11
shows some of their results, where PIE is the paraffin inhibition
efficiency, the amount of wax deposited with inhibitor as a wt.%
of amount deposited without it.
Bello et al. (2006) also studied the efficacy of commercial wax
inhibitors, particularly on Nigerian crude oils. They found that
the use of a trichloroethylene–xylene, TEX, binary system as an
additive was actually more effective and economically feasible
than the use of commercial inhibitors. Other researchers have
noted the need to tailor inhibitor treatments to particular crudes
in order to maximize efficacy. Manka and Ziegler (2001), for instance, found that additives work best when matched to the paraffin distribution in the crude oil being treated. Similarly, Carmen
García (2001) noted a strong relationship between a specific paraffin inhibitor’s efficiency and the crude oil composition, which
would require case-by-case consideration for selecting inhibitors
for use in the field.
Additionally, there is the consideration of the environmental
conditions under which a wax inhibitor is to be used, since, for
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Fig. 11. Effect of wax inhibitors (100 ppm) and oleic imidazoline, OI, (200 ppm) on paraffin deposition from a mixture of paraffin wax in C10 solution (Wang et al., 2003).
operations at particularly low temperatures, the inhibitor formulation must be winterized to allow effective delivery under those
conditions (Manka et al., 1999; Jennings and Breitigam, 2009).
Also, while work continues towards developing new, more effective wax inhibitors, it remains the case that inhibitors typically
do not provide 100% inhibition, and so are used in conjunction with
remediation methods such as pigging (Jennings and Breitigam,
2009; Kelland, 2009).
9.2. Types of chemical inhibitors
There are different mechanisms by which chemical inhibitors
can prevent wax deposition or gelling in pipelines. They can lower
the WAT or pour point or can modify the wax crystals so as to prevent their agglomeration and deposition (Kelland, 2009). The
chemicals that modify the WAT are usually referred to as wax
inhibitors or wax crystal modifiers, while those that affect the pour
point are known as pour point depressants (PPDs) or flow improvers; although there is a great deal of overlap in terms of the chemistry and mechanisms of these two classes (Kelland, 2009). Some
detergents or dispersants that act as wax inhibitors, such as polyesters and amine ethoxylates, may act partly by modifying the surface of the pipe wall, rather than just the wax crystals, to prevent
adhesion (Pedersen and Rønningsen, 2003), and many effective
wax inhibitors create weaker deposits that are more easily removed by shear forces (Manka et al., 1999; Kelland, 2009). The
main types of wax inhibitors and PPDs include ethylene polymers
and copolymers, comb polymers and assorted other branched
polymers with long alkyl groups, such as alkyl phenol–formaldehyde, which are not as effective as comb polymers when acting
on their own as flow improvers (Kelland, 2009).
9.2.1. Ethylene copolymers
This group includes ethylene/small alkene copolymers, ethylene/vinyl acetate (EVA) copolymers, and ethylene/acrylonitrile
copolymers (Kelland, 2009). More specifically, examples of these
polymers used in wax inhibition studies include poly (ethyleneb-propylene) and poly(ethylene butene) polymers (Tinsley et al.,
2007; Kelland, 2009). Random, low molecular weight EVA copolymers, illustrated in Fig. 12, are widely used and investigated as wax
inhibitors (Kelland, 2009). The effectiveness of the EVA copolymer
as an inhibitor is influenced greatly by the percentage of vinyl acetate in the copolymer. The, more polar, vinyl acetate content aids
solubility and lowers crystallinity and so is necessary for the
depression of the WAT, whereas the polyethylene content is necessary to allow for co-crystallization with structurally similar wax,
but, on its own, has little effect on crystallization (Kelland, 2009).
9.2.2. Comb polymers
Comb-shaped polymers, illustrated in Fig. 13, have been studied
extensively as wax inhibitors by researchers such as Duffy and
Rodger (2002), Duffy et al. (2004), Jang et al. (2007), and Soni et
al. (2008). They are usually made from (meth)acrylic acid or maleic
anhydride monomers, or both, and generally provide improved
wax inhibition compared to the ethylene copolymers (Kelland,
2009). One proposed mechanism for their action as PPDs is that
Fig. 12. Ethylene/vinyl acetate (left) and ethylene/acrylonitrile copolymers (right) (Kelland, 2009).
A. Aiyejina et al. / International Journal of Multiphase Flow 37 (2011) 671–694
683
Fig. 13. Traditionally depicted structure of a comb polymer (left). X is a spacer group. The structure looking down the helical backbone (right) (Kelland, 2009).
comb polymers reduce the ability of wax crystals to agglomerate
into a gel structure by introducing defects or repulsive forces (Jang
et al., 2007; Soni et al., 2008; Kelland, 2009). As illustrated by Figs.
14 and 15, they can accomplish this by providing nucleating sites
for wax crystals on their paraffin-like pendant chains while a polar
backbone impedes the formation of an interlocking wax network
(Soni et al., 2008).
In selecting the most effective comb polymers for use with a
particular crude oil, researchers have found that the length of the
side chains plays an important role. For example, Manka and
Ziegler (2001) found that matching the average pendant chain
length of comb polymer PPDs with the paraffin distribution of a
crude oil provided the greatest pour point depression. Also, Jang
et al. (2007) obtained results which suggested that using comb
structures with side arms of such length as to interact favourably
with the fraction of oil most likely to crystallize into the hard
wax phase provided the best wax inhibition. This creates a problem for especially long-chained waxes, for which it would be difficult to introduce a comb polymer (or ethylene copolymer) of
sufficient length to provide efficient inhibition, and also makes it
important to have a range of comb polymers available for treatment of different crudes (Kelland, 2009).
9.2.3. Wax dispersants
These are surfactants that adsorb onto pipe surfaces and reduce
the adhesion of waxes to those surfaces, possibly by changing the
wettability of the pipe surface to water-wet, or by creating a weak
layer from which wax crystals are easily sheared off, or by adsorbing onto the wax crystals and reducing their tendency to stick
together (Kelland, 2009). Some researchers have worked on developing their own dispersant formulations. Groffe et al. (2001), for
instance, developed their own inhibitor that shows wax dispersant
behaviour and anti-sticking properties. They suggest that this
chemical, referred to as P5, interferes with the wax crystal growth
mechanism by preventing the formation of a three-dimensional
network, and thus reduces the pour point and improves the flow
characteristics of crude oils. Fig. 16 shows the effectiveness of P5
in preventing the adherence of wax from crude oils to a steel
surface.
Typical, low-cost wax dispersants include alkyl sulfonates, alkyl
aryl sulfonates, fatty amine ethoxylates and other alkoxylated
products, but these dispersants have shown limited effectiveness
in the field when not blended with polymeric wax inhibitors (Kelland, 2009). Dispersants, however, have been used successfully to
support the functions of polymeric flow improvers because of their
ability to hinder wax settling and deposition (Al-Sabagh et al.,
2007).
9.2.4. Polar crude fractions
It has been found that polar extracts from crude and distillate
oils, which can be extracted using super critical gases such as carbon dioxide or ethylene, and which contain asphaltenes, resins and
aromatics, can be a potential source of low-cost flow improvers
(Kelland, 2009). Venkatesan et al. (2003) studied the effects of
Fig. 14. Characteristic structure of a comb polymer PPD (Soni et al., 2008).
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Fig. 15. Prevention of interlocking of wax crystals by polymer additives by (a) providing nucleating sites to asphaltene as well as wax molecules; (b) polar parts hinder the
co-crystallization of both wax as well as asphaltenes (Soni et al., 2008).
Fig. 16. Effect of 500 ppm of P5 on wax adherence to a steel surface exposed to three different crude oils (Groffe et al., 2001).
asphaltenes on the formation of paraffin gels in crude oil. They
found that the addition of asphaltenes depressed the gelation temperature of model wax–oil mixtures, as summarized in Figs. 17 and
18; although they found that beyond certain thresholds, further
addition resulted in macroscopic phase separation of the mixture,
attributable to gravity settling.
Kriz and Andersen (2005) also studied the effect of asphaltenes
on wax crystallization in crude oils. They found that this effect
A. Aiyejina et al. / International Journal of Multiphase Flow 37 (2011) 671–694
685
certain degree. These are all properties associated with PPDs and
proposed mechanisms for their action, which include co-precipitating with waxes and hindering crystal network growth or coating
wax crystals to prevent agglomeration. Thus, as also suggested by
the observations of Kriz and Andersen (2005), it stands to reason
that asphaltenes affect wax precipitation by the same mechanisms
as other flow improvers such as comb polymers.
Fig. 17. Gelation temperature depression (cooling rate of 1 °C min1) for a foodgrade paraffin wax (Wax 1) and a laboratory-grade paraffin wax (Wax 2) by
addition of asphaltene (Venkatesan et al., 2003).
Fig. 18. Depression in yield stress of Wax 1 system (at temperature, Tys, below the
gelation temperature) upon asphaltene addition (Venkatesan et al., 2003).
depends strongly on the degree of asphaltene dispersion or flocculation more than on the asphaltene type or origin. They reasoned
that the asphaltenes, when well dispersed at very low concentrations, are easily accessible for any kind of interaction with the paraffins and can be fully incorporated into the wax structure. They
noted a delay in crystallization, which indicated that building the
asphaltene molecules into this structure would require a higher
driving force because of asphaltene–paraffin spatial interference.
This would suggest that the asphaltenes are acting by some of
the same mechanisms proposed for inhibition by polymeric
inhibitors.
In agreement with the results of Venkatesan et al. (2003), Kriz
and Andersen (2005) also saw a depression in yield stress and
WAT, which they accounted for by suggesting that asphaltene molecules flocculate together when over a critical concentration, with
possible co-precipitation with waxes, resulting in an unorganized
asphaltene–paraffin composite rather than a proper wax network.
They note, though, the need for further understanding of the way
asphaltenes and waxes interact during wax crystallization, and another study by Yang and Kilpatrick (2005) indicated that asphaltenes and waxes do not co-precipitate in solid organic deposits.
In accounting for the observed flow improver properties of
asphaltenes, Venkatesan et al. (2003) noted that asphaltenes have
polar groups as well as alkane chains and are soluble in oil up to a
9.2.5. Short-chain alkanes
Senra et al. (2008) analysed how n-alkanes impact the crystallization of one another, and Senra et al. (2009) studied the gelation
characteristics of long-chained n-alkanes in a short-chained
n-alkane solvent, looking at the inhibition of gel formation caused
by the addition of other crystallizable n-alkanes to long-chained
n-alkanes, which are the primary component of wax deposits. As
is the case with polymeric inhibitors, the results obtained by Senra
et al. (2009) indicate that the ability of a particular short-chained
n-alkane to inhibit gel formation by a longer-chained one depends
on the particular pairing. The trend of this inhibition was found
to depend on the extent of differences in size and solubility characteristics between the long-chained n-alkane and the added
shorter-chained one as demonstrated by the results in Figs. 19
and 20.
Senra et al. (2009) found that, for a given wax percent of a longchained n-alkane, polydispersity and co-crystallization weaken the
gel formed in spite of the fact that more crystallizable wax is present in solution. In cases where co-crystallization was possible, such
as in a C36/C32 system, they witnessed a noticeable decrease in
pour point and gelation temperature with the addition of small
amounts of the shorter n-alkane. This, they accounted for by the
defects in the crystal structure that would be required to accommodate the C32 crystals that co-crystallize with the C36. This would
make the formation of large crystals and a volume-spanning
network gel more difficult, in the same way that the inclusion of
polymeric flow improvers into wax crystal structures inhibits
aggregation and gel formation. The addition of increasing concentrations of the shorter-chained co-crystallizing n-alkane, however,
resulted in a minimum pour and gel point followed by an increase.
This was accounted for by a limit to how much the addition of the
shorter-chained n-alkane can decrease crystal size, beyond which
further addition only adds more material to form wax crystals.
On the other hand, with n-alkanes of similar size which did not
co-crystallize, such as in a C36/C28 system, Senra et al. (2009) saw a
very different trend. In this case, low concentrations of the shorterchained n-alkane had no effect on the pour and gel points, until a
Fig. 19. Effect of varying the wax percent of C28 and C32 on the pour points and gel
points of 4% C36 solutions in dodecane (Senra et al., 2009).
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C32/C24 system, where co-crystallization does not occur, the C24
was seemingly too small to influence the crystal structure and
impact C36 gelation, and so simply acted like a solvent. These
results show that an understanding of how oil composition affects
wax–oil gel formation can help significantly in implementing
inhibition measures.
9.3. Surfaces that prevent wax deposition
Fig. 20. Effect of varying the wax percent of C28 and C30 on the pour points and gel
points of 4% C32solutions in dodecane (Senra et al., 2009).
concentration at which a sharp decrease was witnessed in both
followed by a gradual increase. This was accounted for by the fact
that, at low concentrations, the more soluble shorter-chained nalkane will not crystallize out and will not be present in high
enough concentration to disrupt the crystallization of the longerchained n-alkane, so will have no effect. Then, at a high enough
concentration, the association of the shorter-chained n-alkane
molecules with the longer-chained n-alkane crystals would disrupt
gel formation. Then gelation will occur as the more soluble shorterchained alkane is added in high enough concentration to crystallize
sufficiently to form a gel. Senra et al. supported this analysis with
the results of cross-polarized microscopy experiments.
Furthermore, for a C32/C30 system, Senra et al. (2009) noted that,
due to the very similar chain length and solubility characteristics,
there was only a slight initial decrease in pour point due to the formation of co-crystals, which would have relatively few vulnerable
points since the two n-alkanes are so similar. Beyond that, the C32/
C30 system behaved much like a monodisperse system. Also, for a
There is an obvious appeal to developing wax-repellent surfaces
for use in oil pipelines as this would limit or eliminate the need for
wax inhibition and removal measures to maintain normal operation. With a proper understanding of the mechanisms by which
waxes adhere to oil pipeline walls it would be possible to create
pipelines in which the nature of the walls makes adhesion unfavourable. Paso et al. (2009a) performed a comprehensive review
of the use of non-stick and anti-adhesive coatings for inhibiting solid–liquid deposition phenomena, including the use of metal surface treatments and synthesized polymers. The classes of
materials that they found promising included fluoro-siloxanes, fluoro-urethanes, oxazolane-based polymers and hybrid diamondlike carbon and polymer coatings.
Fig. 21 shows some of the reported surface free energies of surfaces for paraffin control investigated by Paso et al. (2009a), which
gives an indication of the ability for waxes to interact with those
surfaces, and thus the potential of these surfaces for preventing
wax deposition. Further study of the mechanisms involved in
wax adhesion, hopefully, will result in even more effective surface
treatments in the future.
9.4. Cold flow
Heating or insulation of subsea pipelines can be used to try to
prevent cooling of the pipeline wall below the WAT. However, a
Fig. 21. Surface energy reduction possible with novel surface technologies (Paso et al., 2009a).
A. Aiyejina et al. / International Journal of Multiphase Flow 37 (2011) 671–694
very different method of inhibiting wax deposition on pipeline
walls, discussed by Merino-Garcia and Correra (2008), is the use
of Cold Flow technology. This approach suggests that it might be
possible to prevent deposition on pipeline walls by reducing the
bulk temperature within the pipeline to be equal to the temperature of the sea water around it, thus eliminating the temperature
gradient. This would allow for the waxes to be transported as a solid dispersion within the bulk fluid. While wax deposition may, in
fact, become negligible in the case of zero heat flux, even below the
WAT, much work would still be required to develop the technology
required for effectively cooling the bulk fluid to this condition and
for transporting the resulting cold slurry over long distances. Ilahi
(2005) also discussed SINTEF and NTNU Cold Flow technology.
Additionally Haghighi et al. (2007) and Azarinezhad et al. (2010)
proposed a wet cold flow-based concept, termed HYDRAFLOW,
for preventing gas hydrate agglomeration, with the potential
benefit of wax inhibition. Gas hydrates are the solid solutions of
gas components and water. Hammerschmidt (1934) discovered
the formation of hydrates in natural gas systems. Hydrates like
waxes have concerned deep-water production at seafloor depths
of 1–3 km and temperatures between 2 and 4 °C (Gudmundsson,
2002), conditions which encourage hydrate plug formation. Several
studies have been done on kinetics of hydrate formation. Those
previous studies can be categorized into two main subjects:
nucleation and growth. In contrast to previous studies, gas pipelines hydrate agglomeration plays an important role. After the
break-up of the hydrate film along the interface, hydrate particles
agglomerate to form a hydrate plug (Lingelem et al., 1993) like
wax. Herri et al., 1999 analyzed the particle size distribution of
hydrate particles with the particle balance equations and a mass
transfer model. However it is difficult to describe agglomeration
from experimental observation. The particles start to agglomerate
just after the nucleation process (Mersmann, 2002). The observed
particle size distribution is a result of kinetic contributions such
as nucleation, growth, agglomeration, breakage, and attrition.
Viscosity is also a contributory factor to particle agglomeration
(Mersmann, 2002).
10. Wax removal methods
If wax deposition cannot be prevented, then it is imperative to
regularly remove accumulated wax from the inside of pipeline
walls in order to prevent the total blockage of the line. Several
methods have thus been developed for the removal of wax deposits, including complete blockages of pipelines. Traditional methods
of wax removal in the petroleum industry have always had problems and limitations, and they include mechanical removal, the
use of bottom hole heaters, the use of exothermic reactions such
as that between magnesium bars and hydrochloric acid, and the
use of paraffin solvents (Woo et al., 1984). Research continues to
be done to find the most efficient, cost-effective and safe methods
of removing wax deposits and blockages. Furthermore, some
researchers have worked on modelling the operating conditions
necessary for the successful and safe restart of gelled pipelines,
in which gelled waxy crude needs to be displaced using applied
pressure.
10.1. Pigging
The practice of pigging is a way in which wax removal is commonly accomplished in the field. With this method, deposited wax
is mechanically removed by launching a pipeline pig along the line
to scrape wax from the walls as it is forced along by the oil pressure. This, however, poses the risk of forming a wax plug downstream from the pig as the scraped wax accumulates and is
687
compressed ahead of the pig. In such an event the pipeline could
be lost. The use of bypass pigs tries to address this problem. When
the differential pressure across such a pig becomes too high, because of the accumulation of solid wax and debris ahead of it,
the bypass pig allows liquid to flow through it and disperse the
accumulated solid ahead. However, there is always the danger that
if pigging has to be temporarily suspended due to mechanical failure, or that if the pigging frequency for a pipeline is not correctly
optimized, that the result will be a stuck pig and sizable production
losses (Fung et al., 2006).
Wang et al. (2008) studied the use of regular and bypass pigs in
the removal of wax from pipelines in a laboratory system. The test
facility used consisted of a 20 ft test section of carbon steel pipe, a
mineral oil tank, a pump to push the pig with liquid as in real pigging operations, and a receiving tank to observe the structure of
the pigged materials. Four pressure transducers were installed to
monitor pressure change along the test section during pigging
operation. Candle wax with different oil contents was cast as a film
or plug for measuring wax breaking force or plug transportation
force, respectively. After casting, the waxy spool pieces were
mounted on the test section and the pig was pushed through the
pipe by oil from the pump, removing the wax film or plug while
the pressures at four locations along the test section were
recorded.
They concluded that the wax breaking force increases with the
decrease in oil content and the increase in wax layer thickness;
transportation force per unit plug length is affected by oil content;
transportation force decreases with the presence of oil due to
lubrication effects; and bypass pigs exhibit a very similar breaking
force behaviour when compared with regular pigs. Other studies
have focused on determining the optimal frequency of pigging to
maintain a pipeline and avoid plug formation.
10.2. Inductive heating
Another possible wax removal process, studied by Sarmento et
al. (2004), is the use of inductive heating of a plugged section of
pipe. They proposed this as an alternative to the use of chemicals
that react exothermically at the wax blockage to melt it, for cases
when the pipeline is completely blocked in a horizontal section so
that it is impossible to flow chemicals to the blockage. They tested
this method using the experimental setup shown in Fig. 22. They
found that the steel layers which compose commercial flexible
lines can be heated by induction and the heat transferred to a solid
wax plug in the interior of the line. They also found that their
mathematical model, which agreed well with available experimental results, suggested that the power levels required for large-scale
inductive heating might be feasible for removing wax blockage in
field applications with undersea pipelines.
10.3. Biological treatment
Biological wax removal methods have also been studied in recent years by researchers such as Rana et al. (2010), who developed
systems of paraffin-degrading bacterial consortiums with nutrient
supplements and growth enhancers for controlling paraffin deposition in the tubular and well bore region and in surface flow lines.
Their results showed that their systems were highly effective,
eliminating the need for repeated scrapings of wax over a period
of several months. These methods are especially important because, if successfully implemented, they have the benefit of providing continuous control of wax deposition in pipelines through
constant biodegradation, rather than just providing a very temporary fix.
Etoumi et al. (2008) studied the use of Pseudomonas bacteria for
the reduction of wax precipitation in waxy crude oils. Their results
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Fig. 22. Schematic view of experimental test section for wax removal by inductive heating (Sarmento et al., 2004).
showed the ability of Pseudomonas species to emulsify immiscible
hydrocarbons such as kerosene, toluene, xylene and crude oil, an
effect also studied by others, such as Sifour et al. (2007). The observed overall effect of Pseudomonas treatment on crude oil
showed a reduction in the concentration of long-chain hydrocarbons (C22+). Etoumi et al. concluded that Pseudomonas species
may be an efficient species for reducing paraffin deposition, and
that the speed of the biochemical action on crude oil is faster within the first 7 days. They also concluded that an observed reduction
in viscosity and WAT is indicative of the conversion of long-chain
alkenes to short ones.
Additionally, He et al. (2003) determined through field tests,
that two Bacillus species and a Pseudomonas species showed good
paraffin removal properties in test wells, increasing oil production
and eliminating the need for more expensive wax removal processes. Thus, biological wax removal methods may prove to be
quite effective and economically beneficial and warrant further
study. If a biological system can be successfully and cheaply applied under the conditions in subsea pipelines then it will provide
an extremely effective method of controlling wax deposition.
11. Restart of gelled pipelines
In subsea pipelines carrying waxy crude oils that have to be
shut down temporarily for operational or emergency reasons, the
oil will eventually cool below its gel and pour points resulting in
the formation of a gel throughout the pipeline consisting of precipitated wax in a viscous matrix (Chang et al., 1999). This occurrence
complicates the restart procedure, as the gelled oil would need to
be displaced in order to resume normal operations. Numerous
researchers have addressed this problem, including Smith and
Ramsden (1978), Chang et al. (1999), Davidson et al. (2004),
Frigaard et al. (2007), and Vinay et al. (2007). Chang et al. (1999)
modelled the isothermal restart of gelled pipelines by the application of higher than normal operating pressures. In this start-up
scenario, oil is pumped into the gelled line at high enough sustained pressure to overcome the static yield stress of the gel, thus
breaking up the blockage and clearing the line.
The viscoplastic nature of waxy crude oils and their timedependent behaviour complicate modelling. In order to describe
the breakdown of the gel structure along with a decrease in viscosity, Chang et al. (1999) defined the static yield stress of the gel, ss,
as the critical shear stress value for determining whether the start
of a flow from a rest state will occur. Furthermore, they defined the
dynamic yield stress, sd, as the parameter for describing the relationship between shear stress and shear rate in a flow state after
yielding. However, the description of this yielding behaviour has
seen many variations and disagreements among different authors.
Many studies have been published regarding the rheology of
waxy crudes and their gels, the dependence of gel properties on
shear and thermal histories, and how they yield (Wardhaugh
et al., 1988; Chang et al., 1998, 2000; Lopes-da-Silva and Coutinho,
2007; Lee et al., 2008; Oh et al., 2009). Wardhaugh and Boger
(1991), for instance, defined yield stress as ‘‘the shear stress at
which the gelled oil ceases to behave as a Hookean solid,’’ and referred to bulk yielding phenomena, when gross yielding behaviour
is observed, as the yielding stress or yielding point. Houwink
(1958) described a transition from elastic behaviour to plastic
behaviour and then to viscous flow, distinguished by a lower and
a higher yield stress. Meanwhile some researchers, such as Barnes
(1999), who noted the high degree of variation in the definition of
yield stress, maintained that no real yield stress exists, even for
very non-Newtonian liquids. They argue this because these liquids
continue to flow or creep even below an apparent yield stress.
Barnes notes, however, that the concept of a yield stress is useful
for describing behaviour over a limited range.
11.1. Time-dependent gel degradation
Time-dependent gel-degradation is one of the important complications in modelling the restart of gelled pipelines. Ongoing efforts to model the time-dependent rheology of gels, in order to be
able to model gel breakdown under stress, draw on research such
as that of Cheng and Evans (1965), Petrellis and Flumerfelt (1973),
and Rao et al. (1985). Recent studies of the time-dependent rheological behaviour and breakdown of wax–oil gels include that done
by Paso et al. (2009b), in which the mechanical behaviour of a
model wax–oil gel was examined under various shear rates. Paso
et al. observed a convergence of shear stress values for different
shear rates at absolute strain magnitudes greater than 0.1, as
shown in Fig. 23. This was indicative of gel strength following a
path-independent function of the absolute strain imposed on the
gel, and further indicated that the gel structure is a point function
of the absolute strain. Based on this they concluded that, in
A. Aiyejina et al. / International Journal of Multiphase Flow 37 (2011) 671–694
689
their model wax–oil gel at constant shear rate conditions. They
concluded that a well-defined mechanism controls the rupture of
wax crystal–crystal network linkages, and that the rheological
modelling framework based on the structural parameter, k,
provides an appropriate physical representation of the breakage
process, even for crude oils in the field with a variety of hydrocarbon and additive components that may cause a deviation from
third order degradation kinetics. They also proposed a model for
describing shear stress responses associated with changing shear
rates during gel degradation by applying a time-dependent
Bingham constitutive equation to experimental stress–strain data
obtained while increasing the shear rate.
11.2. Examples of restart models
Fig. 23. Measured shear stress during breakage of a wax–oil gel at shear rates
ranging from 105 s1 to 1 s1 (Paso et al., 2009b).
modelling the breakdown of a wax gel at low shear rates, the entire
shear history could be represented by a single dimensionless variable in the form of the absolute strain.
Paso et al. (2009b), furthermore, determined that the maximum
shear stress did not provide a useful parameter to characterize the
gel structure. Thus, in order to define a structural parameter, k, representing the fraction of unbroken crystal–crystal linkages remaining in the gel structure at a given shear stress, they did so in terms
of the experimental stress near the convergence point. They then
used this structural parameter in an nth order degradation model
to describe the gel breakage model, as shown in following
equation.
1
1
ðk ke Þ1n
ðk0 ke Þ1n ¼ ac_ b t
1n
1n
ð18Þ
Here k0 and ke are the initial and equilibrium structural parameter
values, and the degradation rate parameters, n and ac_ b , were determined by fitting experimental values of k to equation (18) via a least
squares minimization procedure, with ke assumed to be 2 103.
Paso et al. (2009b) were able to obtain good model fits to experimental values, as shown in Fig. 24. Their fitted degradation order
for different shear rates ranged from 2.7 to 3.33, indicating that a
third order degradation mechanism controls the breakdown of
Fig. 24. Comparison of experimental and fitted k values at a shear rate of 103 s1.
The optimized reaction rate order is 3.07, with a rate constant of 0.131 s1 (Paso
et al., 2009b).
Chang et al. (1999) went onto use a three yield stress model
proposed by Kraynik (1990), which added a dynamic yield stress
for describing behaviour after yielding to the model put forward
by Houwink (1958). The three yield stress model utilized an elastic-limit yield stress, se, described as denoting the materials limit
of reversibility; a static yield stress, ss, described as the minimum
shear stress required to cause the deformation of a material that
may be described as yielding; and a dynamic yield stress, sd, described as the shear stress at zero shear rate, extrapolated from
the flow curve. Chang et al. used this model to describe the three
possible outcomes of applying constant pressure to a gelled pipeline in terms of the relationship between the wall shear stress,
sw, applied to the pipeline and the initial gel strength of the oil:
Start-up without delay (sw > ss) – Flow begins immediately with
three different regions, as shown in Fig. 25, where R is the total
radius of the pipeline and rf and rc denote the boundaries of the
regions:
– Flow area – The outermost region (R > r > rf), consisting of a
sheared annulus. Local stress is higher than the static yield
stress (s(r) > ss). The gel structure in this region is immediately broken down and the oil becomes liquid-like, displaying a dynamic yield stress.
– Creep area – Middle region (rf > r > rc). Local stress is lower
than static yield stress, but higher than elastic-limit yield
stress (ss > s > se). Gel structure in this region begins to
degrade with a viscoelastic deformation.
– Elastic deformation area – Innermost region (r < rc). Local
stress is lower than elastic-limit yield stress (s < se). Solidlike core where oil only undergoes elastic deformation. Will
initially move with creep region as an unsheared plug of
radius, r, until the gel in the creep region degrades from
the outside in, leaving only the core as the plug.
Start-up with delay (ss > sw > se) – Flow begins after a delay
time, tdelay. Exterior creep region and interior elastic deformation area exist and, initially, no flow occurs. Flow only begins
once gel in the creep region has sufficiently degraded, starting
at the wall, allowing for movement of an unsheared plug with
uniform velocity through the pipe. The size of the plug (r) will
decrease as degradation in the creep region continues.
Unsuccessful start-up (sw < se) – Flow will not start under this
condition. Oil only deforms elastically and gel structure is unaffected by shear.
Chang et al. (1999) noted that for a successful start-up, the
gelled oil in a cross-section of pipe will become heterogeneous because of differences in the rate of structural breakdown caused by
differences in local shear stress. Therefore, their model takes into
account the time-dependent rheology of the waxy crude oils. A
time-dependent Bingham-style equation, shown in Eqs. (19a)(19c) was used for an approximation of the time-dependent,
690
A. Aiyejina et al. / International Journal of Multiphase Flow 37 (2011) 671–694
Fig. 25. Schematic diagram of start-up without delay (sw>ss when t = 0)(Chang et al., 1999).
non-Newtonian behaviour of a gelled waxy crude oil under controlled stress conditions.
s ¼ sy ðtÞ þ gðtÞc_ ; s > sy ðtÞ
sy ðtÞ ¼
sy ð0Þ sy ð1Þ
1 þ kt
þ sy ð1Þ
gðtÞ ¼ constant
ð19aÞ
ð19bÞ
ð19cÞ
Here g is the plastic viscosity, c_ is the shear rate, sy is the apparent yield stress governing the behaviour of the oil, and k is a rate
constant. sy(0) and sy(1) are the apparent yield stress at times
t = 0 and t = 1, respectively. sy(0) would be equivalent to ss(0),
with an initial wall stress above this value resulting in an instantaneous finite flow rate, and sy(1) coincides with se(0), with wall
stresses below this value resulting in reversible deformation and
no possible flow.
The basic physical model used by Chang et al. (1999) to describe
the start-up process was the pumping of an incoming fluid (ICF)
into a pipe of length, L, and inside diameter, D, to displace the outgoing fluid (OGF), as shown in Fig. 26. Here Z(t) is the length of the
pipe occupied by the ICF, r I ðtÞ and r o ðtÞ are the unsheared plug radii of the ICF and OGF respectively at time, t, P1 is the inlet pressure, P2 is the exit pressure, and Pz is the interface pressure. The
radius of the unsheared plug in the flow was given by equation,
r ðtÞ ¼ R
sy ðtÞ
swo ðtÞ
ð20Þ
where swo(t) is the wall shear stress in the OGF at time t.
For the case of start-up without delay (or start-up with delay at
time, t > tdelay), Chang et al. (1999) define the initial wall shear
stress in terms of the pressure drop. To model the time- and position-dependent changes in the flow properties of the OGF, Chang
et al. (1999) used a finite differences method. M time intervals
were used to divide the duration of the flow from start-up
(Dt = ti–ti1), and the flow was treated as approximately steady
in each time interval for sufficiently small Dt. The sheared annulus
(r < r R) was divided, for each instant, ti, into N radial elements of
thickness, Dr, and distance, rj, from the centre of the pipe
(rj = rj1 + Dr = r + jDr). The volumetric flow rate, Qi, at time ti
was thus given by following equation,
Qi ¼
N
X
Q j þ Q plug
ð21Þ
j¼1
where Qplug is the flow rate of the unsheared plug and Qj is the
volumetric flow rate of the jth annular element. In a later work,
However, Davidson et al. (2004) maintained that the finite differences method was unnecessary, because of the quasi-steady state
assumption for the OGF, which was represented as a Bingham fluid
that would have apparent yield stress and plastic viscosity independent of the shear rate and thus the radial position.
Using their model, Chang et al. (1999) could calculate the plug radius, r, for each time interval using equations (19b) and (20) with a
known wall stress. The flow rate could then be computed from j = N
at the pipe wall inward to the unsheared plug at j = 0. The onset of
turbulence was predicted by calculation of a critical Reynolds number, with the appropriate adjustment to the friction factor used in
the model. For these calculations the pipe dimensions (L and R),
Fig. 26. Schematic diagram of two-fluid displacement model. (a) True interface; (b) simplified interface (Chang et al., 1999).
A. Aiyejina et al. / International Journal of Multiphase Flow 37 (2011) 671–694
pump pressure (DPc), properties of the OGF (sy(0), sy(1), g(t), k and
qo), and properties of the ICF (sB, gB and qI) need to be known. Here qI
and qo are the fluid densities of the ICF and OGF; sB is the Bingham
yield stress; and gB is the Bingham plastic viscosity. Therefore, the
accuracy of this model in predicting the time-dependent flow properties during start-up and the time needed to clear a blockage depended greatly on comprehensive knowledge of the system,
requiring accurate experimental measurements.
Davidson et al. (2004) developed another model for the restart of
gelled pipelines. This model extended the one developed by Chang
et al. (1999) to account for the compressibility and inhomogeneity
of the gelled oil and displacing fluid. In this model, when the inlet
pressure creating a wall stress in excess of the static yield stress is
applied to the gelled oil, initially only a narrow region of length Lf deforms and breaks down under stress. This yielded region is compressed by the entering ICF, which is also compressed. Eventually,
at time t = t0 the entire gelled oil plug will yield and move together
with the ICF at the same mass flow rate, as shown in Fig. 27.
For the calculations in this model from Davidson et al. (2004),
the bulk mass flow rate, G, is first guessed (can use value from previous time step). Then the frictional factor, fk, is calculated by iteration for each longitudinal ICF and OGF segment at current time
step using the Buckingham–Reiner equation for pipe flow of a
time-independent Bingham fluid, and empirical relationships
developed by other authors for calculating the frictional factor in
laminar and turbulent flow. Equations (22) and (23) are used to
evaluate the mean velocity and shearing time, tsk,
qk Q k ¼ G
t sk ðtÞ ¼ t
ð22Þ
k1
t0
M1
ð23Þ
691
where k is the current subdivision out of M initial subdivisions of
the gelled oil used in the calculations; and qk is the dimensionless
density and Q k the dimensionless volumetric flow rate of the current subdivision. The pressure drop over each segment was calculated using following equation.
swk ¼
fk qk t2k
Pk
¼
4DLk
2
ð24Þ
In the calculation procedure used by Davidson et al. (2004), the
length and density of each segment of oil is then updated to
account for the displacement of the OGF from the length of pipe
and the increase in the length of the ICF within the pipe. The ICF
rheology is assumed to be time-independent and it is therefore
separated into segments of equal length in each time step, with
the number of segments increasing by one with each time step.
The length of an ICF segment, DLICF, in time interval, i, at time
t t 0 is given by following equation.
DLICF ¼
P
OGF
LICF
DL m
k¼1 Lk
¼
K þi
K þi
ð25Þ
Here K is the number of ICF segments chosen at time t = t0, m is
the number of remaining OGF segments within the pipe, and DLOGF
k
is the length of the k th gelled oil segment in the OGF for time
t t 0 . Davidson et al. (2004) also calculated DLOGF
and the average
k
density for the k th segment in dimensionless form. Next in their
calculation procedure, the location of each segment and the ICF–
OGF interface is determined relative to the downstream end of
the OGF plug. Then the pressure drop over each ICF and OGF
segment is summed to give the overall pressure drop, and the
difference between this value and the applied pressure drop
Fig. 27. Schematic of compression flow (Davidson et al., 2004).
692
A. Aiyejina et al. / International Journal of Multiphase Flow 37 (2011) 671–694
calculated and mass flow rate adjusted accordingly. This overall
process is iterated until the difference is negligible. G iterates to
zero in the case that the applied pressure is not high enough to
start flow at a given time, and the calculated pressure drop becomes the minimum required for start-up. The results of this model were significantly different from those of the earlier model
developed by Chang et al. (1999), indicating the importance of fully
understanding the mechanism by which the gelled oil yields and is
displaced, and of determining the most realistic assumptions that
can be made during modelling.
Other researchers have also tackled understanding, modelling
and optimizing the restart of gelled lines. Borghi et al. (2003)
developed a model focusing on solid-like fracture propagation, viscous dissipation and compression of the broken gelled oil. Ekweribe et al. (2009), for instance, studied the effect of system pressure
on the restart of gelled subsea pipelines. They determined that
higher system pressures in subsea pipelines could lead to the formation of a weaker gel with lower yield strength, which would
mean that the necessary applied pressure for displacing it would
be more easily and cheaply achieved than might be predicted.
12. Conclusions
Contention still remains as to the specific mechanisms that govern wax deposition in pipelines. However, the importance of
molecular diffusion is generally accepted and shear dispersion is
usually not dismissed, at least due to the involvement of shear
forces in the removal of wax deposits, the accounting of which
has been shown by some authors to have a great impact on the
accuracy of wax deposition models. Many models have been developed based on the importance of these mechanisms, for which the
approach to a realistic representation of the solid phase wax components has a significant impact on accuracy. Recently, a correct
heat-mass transfer analogy has been introduced into the modelling
of wax deposition, allowing for more accurate prediction across the
range of possible precipitation kinetics. In the future even more
accurate and robust models will be possible by combining this
new approach with an increased understanding of the mechanisms
involved in wax deposition and gelation and of the impact of other
species present in crude oil, such as asphaltenes and emulsified
water.
Understanding wax aging mechanisms is also very important to
fully understanding the process of the formation of wax deposits in
pipelines. Furthermore, understanding these mechanisms and predicting the CCN of particular crude oils would be helpful in determining what chemical inhibitors would be most effective for
preventing wax build-up in pipelines carrying those oils. The
continuing research into methods of inhibiting wax deposition
and removing deposits has the potential of making the maintenance of crude oil pipelines significantly easier, as it becomes easier to optimize pigging frequency, to determine the minimum
pressure required to restart gelled lines, or even to avoid the need
for constant wax removal procedures by finding a way to costeffectively implement a promising method of control such as the
use of polar crude oil fractions or biological removal measures.
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