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Effect of Gas Composition and Gas Oil Ratio on Asphaltene Deposition Ali A. AlHammadi, Yi Chen, Andrew T. Yen, Jianxin Wang, Jefferson L. Creek, Francisco M. Vargas, and Walter G Chapman Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b02313 • Publication Date (Web): 30 Jan 2017 Downloaded from http://pubs.acs.org on January 31, 2017

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Effect of Gas Composition and Gas Oil Ratio on Asphaltene Deposition Ali A. AlHammadi1, Yi Chen2, Andrew Yen3, Jianxin Wang4, Jefferson L. Creek4, Francisco M. Vargas1, Walter G. Chapman1* 1

Dept. of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77005, USA

2

Schlumberger, Houston Formation Evaluation, Houston, TX, 77478, USA

3

Nalco Champion, An Ecolab Company, Sugar Land, TX, 77478, USA

4

Chevron Energy Technology Company, Houston, TX, 77002, USA

KEYWORDS Asphaltene, crude oil, phase behavior, deposition, aggregation, Asphaltene Deposition Tool, Gas Oil Ratio

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ABSTRACT Arterial deposition of asphaltene is a major flow assurance issue in pipelines and wellbores. The numerous compounds constituting crude oils are mutually soluble at reservoir condition, but precipitation can occur with changes in pressure, temperature or composition. As the pressure and temperature changes through the wellbore, asphaltenes can precipitate and potentially deposit. Unfortunately, remediation by solvent-soaks is expensive; hence, the need to forecast the potential risk of asphaltene deposition. In this paper, a previously reported simulation tool, asphaltene deposition tool (ADEPT), is used to predict the magnitude and location of asphaltene deposits in flow lines and wellbores. ADEPT is to be used to gauge the frequency and location of deposits and how often intervention will be needed. The phase behavior of asphaltene is described by the PC-SAFT EOS while the transport equations are coupled with kinetic rates of precipitation, aggregation and deposition. The transport model is simplified resulting in dramatic speed up of the simulator. This paper presents a field case as well as a simulation on the effect of different gases and Gas Oil Ratio (GOR) on asphaltene deposition.


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INTRODUCTION The deposition of asphaltene in the reservoir, well bore and transportation pipelines has been a flow assurance concern. Prediction of asphaltene precipitation is necessary yet not sufficient for prediction asphaltene deposition profiles. Large capital and operating costs are associated with remediation of these deposits creating a need for better predictions to minimize risks. Although thermodynamics enable us to identify the most probable regions of deposition, the magnitude is still of concern. The use of down-hole deposition monitors is very difficult due to high pressure levels and steep inclination of some systems. In addition, these monitors might interfere with hydrocarbons inducing deposition causing blockage and sometimes even the loss of the entire well. Unlike wax and gas hydrates, the ability of asphaltene to deposit even at high temperatures makes the problem even harder. Moreover, paradoxes like deposition are more likely to occur in light undersaturated oils (less than 1 wt% low asphaltene content) than in heavy oils (higher asphaltene content) shows that there are competing mechanisms. In fact, Trbovich and King

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listed eleven causes of asphaltene deposition which are CO2 flooding, rich gas flooding, pH shift, mixing of crude streams, incompatible organic chemicals, simulation, shear, streaming potential, charged bare metals surfaces, pressure and temperature drops. Unfortunately, there are only few publications focused on the prediction of asphaltene deposition in wellbores or pipelines. Most research on modeling particle deposition in pipe flows has mainly focused on particle transport to the wall. Ramirez et al.2 modeled asphaltene deposition in a pipe based on the hypothesis that asphaltene particle transport to the wall is by molecular diffusion. Jamialahmadi et al.3,4 have developed a mechanistic model, which was further applied to predict asphaltene deposition for an Iranian oil field. Besides, Eskin et al.5 have 3 ACS Paragon Plus Environment

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used particle flux mass transfer expressions for turbulent flows to model the deposition process and have shown a quantitative comparison between predicted and measured deposition flux for a field case. Nevertheless, a review of the existing literature reveals that there is a lack of both qualitative and quantitative predictive techniques to generate accurate and reasonable asphaltene deposition rates and deposition profiles in comparison with both laboratory scale and field scale data. This specific need stimulated the development of the asphaltene deposition tool (ADEPT) simulator. The mechanism for ADEPT demonstrated in this paper was proposed by Vargas et al. 6 and later extended by Kurup et al.7. The transport of asphaltene in the wellbore is assumed to be a multistep process where phenomena like precipitation, aggregation, diffusion, advection and deposition are the main factors as summarized in Fig. 1. The model proposes that small asphaltene rich particles deposit, but larger asphaltene aggregates do not deposit due to the flow field. Thus, the rate of asphaltene precipitation is balanced by the deposition rate and by the rate of aggregation and advection. However, the lack of field data makes validation of the simulation difficult. Validating the model requires a combination of laboratory and field data. Results from the model can then be compared with field experiences as has been done successfully in a previous case 8. Hence a systematic data acquisition protocol and a workflow of the simulator application to field scale are required. The objective of this paper is to compare ADEPT output to field observations and to understand in a macroscopic view how asphaltene is transported and deposited in the wellbore and if necessary to revise the existing model, Asphaltene Deposition Tool (ADEPT) 7. 4 ACS Paragon Plus Environment

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METHODOLOGY An important tool to explain the phase behavior of asphaltene is the solubility parameter (δ). For non-polar fluids, the solubility parameter can be readily calculated using the Hildebrand model which provides an estimation of the degree of material interactions. The cohesive energy density is defined as the energy required to remove a molecule from its neighboring molecules to an infinite distance, i.e. from the real state to an ideal state9. Therefore, the extent of miscibility of the solvent and solute is directly linked to closeness of the solubility parameters. At reservoir conditions, asphaltenes are generally stable. However, as pressure and temperature decrease in the wellbore and pipeline or the crude oil composition changes due to gas injection, the crude oil becomes less compatible with asphaltene. This results in separation of the heavy fraction in the form of a high asphaltene content liquid phase due to the decrease in the solubility parameter of the oil. This liquid phase referred to as precipitate or primary particles is represented as small black circles in Fig. 1. These particles will encounter different forces that will direct the deposition process. Due to eddy diffusion in turbulent flows, these particles will either meet up with other particles forming aggregates, known also as secondary particles, or meet up with the wall in which they will adsorb and consequently build up a deposit. However, as the temperature and pressure continues to decrease, the bubble point is reached; the light components leave the mixture raising the solubility parameter of the oil. Once the mixture has high enough solubility parameter, asphaltenes become thermodynamically stable again. The pressure when this occurs is known as the lower onset pressure. Consequently, no more deposition is expected below this pressure; the precipitates will dissolve back to the mixture, and the aggregates will start to re-dissolve slowly. However, since some of these aggregates are large, they might be very slow to completely dissolve. Such particles are responsible for coke 5 ACS Paragon Plus Environment

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formation and fouling in refineries10. The ability to predict the phase behavior and deposition of asphaltene is therefore of great significance. In addition, advection plays a major role as it will carry both the primary and secondary particles through the system to a potentially stable region. In our model, we assume that once the aggregates are large enough, diffusion will be slow and advection will move the aggregates through the system. Thus, the aggregates will not contribute to deposition. Moreover, the shear force from advection can lead to deformation or removal of the deposit especially if the deposit is assumed to be liquid-like 11. The following schematic (Fig. 2) depicts the ADEPT simulator structure. It consists of two modules, thermodynamic module and deposition module. The thermodynamic module predicts when and how much of asphaltene will precipitate from crude oil. Next, the deposition module calculates the rate of deposition, deposition profile and pressure drop due to the deposit. The equilibrium concentration of asphaltene soluble in the crude oil is the main output of the thermodynamic module and is a crucial input to the deposition module. The phase equilibrium is calculated based on the wellbore and flow line PT trace and the fraction of asphaltene precipitated. Consequently, accurate modeling of the phase behavior of asphaltene is key to obtaining accurate representation of the driving force of deposition. In the thermodynamic module, Perturbed-Chain Statistical Associating Fluid Theory Equation of State (PC-SAFT EOS)12 is used because it successfully describes asphaltene stability13. In this work, PC-SAFT EOS is accessible with assistance of commercial thermodynamic software such as VLXE, Multiflash and PVTsim. An automatic PC-SAFT EOS characterization spreadsheet was developed and both PC-SAFT and CPA equations of state capabilities were tested

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details on the PC-SAFT characterization can be found in AlHammadi et al. 14. Table 1 provides a 6 ACS Paragon Plus Environment

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summary of the oil properties for Crude A including SARA, GOR and API. Saturate, Aromatic, Resin and Asphaltene (SARA) analysis a technique that separates crude oil in to four different components according based on polarity. In this study, a modified IP-143 method is used and the asphaltene fraction is defined as hexane insoluble. The rest of the fractions were determined by gas chromatography on the de-asphaltic oil. The mass percent reported were calculated based on whole oil. In the deposition module, a transient material balance equation was employed to describe the transport of the primary particles over a control volume (Fig. 1) of the well bore or pipeline. The main components of the model are composed of transport by advection and dispersion, and the three kinetic processes of precipitation, aggregation and deposition. The mathematical model developed by Kurup et al.7,8 is:

1 =− + + − − 1

where C is the dimensionless concentration of precipitated asphaltene particles, t is dimensionless time, Z is the dimensionless axial coordinate, Pe is Peclet number, Da is the Damkohler number (agg and d stands for aggregation and deposition respectively), and rp is the rate of precipitation. Notice that this equation is dimensionless and the following dimensionless parameters can be defined: =

2

=

2


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=

2

= 2! =

" 2 #

" = 2$ where Uz is the average velocity in the axial direction, L is the total axial length of the pipe, Daxial is the axial dispersion coefficient, and kagg and kd are the aggregation and deposition rate constants, z is the axial length coordinate, , C' is the precipitated asphaltene concentration, and C0 is the initial concentration of asphaltene in the oil. The axial dispersion coefficient can be defined as:

%&'%(

* = ) + 3 48)

where Dm is the diffusion coefficient of the particle and R is the radius of the pipeline. Equation 1 is subjected to the following boundary and initial conditions: ./#, = # 4 .,/# = # 4

|/2 = 0 4


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Equation 4a is an initial condition which specifies that the concentration of asphaltene nanoaggregates initially is C0 throughout the wellbore. The second equation (4b) is a boundary condition specifying that at any time, the concentration of nanoaggregates in the entrance of wellbore is C0. The last one (4c) is a boundary condition specifying that in the vicinity of the exit of the wellbore, there is no change in the concentration of nanoaggregates. In this example, the value of C0 is set to zero for simplicity. A non-zero initial condition indicates the presence of previous deposit in the tubings. The rate of precipitation is dependent on the degree of super-saturation which is the difference between the actual concentration of asphaltene in the solution and the thermodynamic equilibrium concentration of asphaltenes in solution. The larger is the degree of super-saturation, the higher is the value of the precipitation kinetic rate. As more asphaltene is consumed through aggregation and deposition, the actual concentration of asphaltene will decrease until the degree of super-saturation reaches zero. After that point, the mixture is stable again and it is able to carry thermodynamically more asphaltene particles. As a result, the system is under-saturated and the precipitates will start to re-dissolve. Preventing deposition has been a subject of extensive research given the proposed competition between aggregation and deposition, it might be better to enhance aggregation and thus limit the amount of asphaltene available for deposition. However, such an approach might raise issues beyond the wellbore; large aggregates can cause coke formation and fouling in the refineries

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. In addition, it is reasonable to assume that the smaller aggregates are easier to re-

dissolve as shown by Boek et al.15. Aske et al. have demonstrated only partial re-dissolution of the aggregates is observed and the rest takes a long time to re-dissolve 16. They also showed that re-dissolution is typically very slow and depends on the physical state of the system. Schabron 9 ACS Paragon Plus Environment

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and Rovani have found that aggregation can be completely reversible by using different solvents of high polarity17. In addition, Pan and Firoozabadi were able to illustrate that insoluble asphaltene particles can be readily re-dissolved using ultrasonic waves which breaks the aggregates into smaller particles and thus makes it easy for them to re-dissolve 18. Therefore, the rate of precipitation and dissolution can be written as conditional function as follows 7: 4 = D 67 − 89 : if 7 > 89 5 4 = ?'@@ = −?'@@ D A$ 7 < 89 5 where Dap is the Damkohler number of precipitation, kdiss is the dissolution factor, and Cf and Ceq are the dimensionless actual and maximum predicted equilibrium concentrations of asphaltene in the oil phase. For simplicity, the dissolution factor is assumed to be one. The precipitation kinetic constant is fit to laboratory data. The experimental procedure and the fitting are described in later sections. An important observation is that both precipitation and deposition rates are modeled to be pseudo first order for simplicity. The orders can be readily modified as more data become available from the field. On the other hand, aggregation is assumed to be second order following Smoluchowski theory which states that when two particles are within a distance R, they will stick to each other and that the new formed entity will continue to move as one bigger particle19. The model assumes that the asphaltene rich phase must precipitate before forming a deposit. Further, it assumes that a competition exists between aggregation and deposition. Because large aggregates are drawn to the center of the flow field, they are unavailable for deposition. We will assume steady state. The asphaltene deposition process is generally a very slow process.


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Although the deposit is building up in the pipe, the effect on the flow rate is slowly changing and minimal at early stages of deposition. As can be demonstrated by Fig. 3 for Crude B, the difference between steady state and nonsteady state is not significant over a simulated period of a couple of weeks. Moreover, due to the number of iterations, the non-steady state calculation becomes computationally expensive especially as the deposit buildup increases. Simplifying the equation can speed up of the simulator dramatically, enabling quick sensitivity studies7. The kinetic constants are from Kurup et al. 8 and are summarized in Table 2. For crude systems in this and previous studies, the Peclet number is found to be large. The large Peclet number causes the computation to be slow and less stable. We have found that in several cases the term including the reciprocal of Peclet number is very small and may be neglected. In the following, we neglect the axial dispersion term. It is worth noting that the Damkohler numbers for the precipitation, aggregation and deposition are generally very small and thus the system is reaction limited. Such small values indicate that although asphaltene is available to deposit, the small rate of deposition limits the amount of asphaltene deposited. This further simplifies the equation and allows quick sensitivity analysis calculations without sacrificing accuracy. It is worth noting that for the capillary experiments, it might be better to include the full form due to the lower range of Peclet number. Neglecting the axial dispersion term, the deposit buildup is dependent on the precipitation rate, aggregation and deposition kinetic rate. The concentration of precipitated asphaltene available for deposition is calculated from the following equations:

= 6C − DE : − − A$ C > DE 6


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= −GG − − A$ C < DE 6

Unfortunately, despite the simplification, no analytical expression is obtained for the first equation while the second equation can be solved analytically. At steady state, due to the continuous oil flow, the temperature, pressure, and oil composition can be assumed to be a function of position and not time as shown by Fig. 1. Due to the steady state case, one can conclude that the actual concentration of asphaltene entering the unstable section is relatively constant and is depleted by aggregation and deposition. As previously stated, this depletion is controlled by the equilibrium concentration of asphaltenes (Ceq). The concentration of asphaltene dissolved in the oil Cf depends on the rate of precipitation with the driving force (Cf-Ceq):

C = − 6C − DE : 7

Despite the simplicity of the deposition model, it has shown potential in modeling the asphaltene deposition profile. However, the deposition profile depends on the kinetics constants of deposition, precipitation and aggregation of asphaltene. As mentioned earlier, it is difficult to obtain the kinetic constants from limited field data directly, thus laboratory experiments become the only feasible method to acquire them. The precipitation and aggregation kinetic constants can be extracted from laboratory asphaltene batch experiments. The experiment is done by preparing a series of mixtures each with the same oil/n-alkane precipitant ratio. The mixtures are left in an oven at a given temperature to age and at different times, one of the mixtures is removed and filtered to separate asphaltene precipitates. The precipitates obtained at different aging time are then weighed


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yielding a precipitated mass as a function of time. The equations describing the precipitation and aggregation rates of asphaltene are:

!C = − 6C − DE : 8 !′

! = 8 !′

! = 6C − DE : − 8 !′

Based on these models, the kinetic constants for precipitation and aggregation can be simply extracted by fitting the constant to match the precipitated amounts as shown in Fig.4. The kinetic parameters are summarized Table 2 for Crude A. The properties of the oil A are summarized in Table 1. The data in Fig. 4 were obtained for Crude A at oil to heptane ratio of 52/48 in 70oC oven (1mL of oil with 0.92 mL of heptane). At different times, the samples were removed from the oven and filtered using a 0.2 micron filter. We assume that these filtered asphaltene are aggregates. Once the precipitation and aggregation rates are obtained, the model can be used to match the capillary deposition experiments. A key concept of the capillary scale experiment is that not only pressure drop with time must be followed but the profile must also be determined as well as the mass flux of deposition. The experimental setup is a pressure and temperature controlled capillary viscometer. The fluid composition is controlled and adjusted by two different pumps, one containing n-alkane and the second containing the oil, and the complete mixing by ultrasonic bath occurs prior to entering the capillary

20

. Under normal conditions where no deposition 13

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occurs, the pressure drop of a fluid flowing through a capillary tube can be simply calculated using Hagen-Poiseuille equation as follows:

∆ # =

8KL 9 M #N

where µ is the fluid viscosity, Q is the flow rate, L is the length of the tube and r0 is the capillary tube radius. In the previous equation, the radius of the capillary is fixed. However, this is surely not the case when asphaltene starts to deposit. If the deposit is gradual and uniform, the equation can be modified to account for decreasing radius with time as shown in the following equation: ∆ =

8KL 10 M P # − ∆ QN

The relative change of the pressure drop will simply be:

∆ − ∆ # P # − ∆ QRN − #RN = 11 ∆ #

#RN

If the thickness of the deposited layer is small compared to capillary radius, then the previous equation can be approximated 21:

∆ − ∆ # ∆ ≈4 12 ∆ #

#

In addition, the last equation is valid only if the flow rate and the fluid viscosity values are constants. A schematic representation of the setup is shown in Fig. 5. The pressure transducer was used to measure the pressure drop across the capillary tube which was simultaneously recorded using computer. To ensure isothermal conditions, the capillary tube is immersed either in a water bath or an oven. In addition, the total flow was


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maintained within the laminar regime as it is believed that there exists a laminar boundary layer in the pipelines that dictates the deposition process. Prior to each test, toluene is pumped through the capillary tube to measure its actual radius and ensure no deposition is present. At the end of each run, nitrogen was pumped through the capillary to gradually displace any remaining liquid in the capillary tube. An electronic balance can be then used to record the effluent weight with time which is later used to calculate the deposition thickness. Another method is to use some viscous immiscible fluids to be injected and to measure the pressure drop using the transducer. This is a more effective method as it can determine more easily the local effective capillary radius especially in case of non-uniform deposits7. Asphaltene content in crude oils is generally low but still may pose problems. For instance, Hassi-Messaoud oil contains as low as 0.1%, yet asphaltene deposits in the wellbore22. In addition, asphaltene deposition rate is considerably low. Consequently, in the capillary viscometer, a large amount of sample is used (1 L at least) to obtain significant amounts of deposits and therefore, it works as a single pass system. The ability of this experiment to mimic the laminar boundary layer near the wall, its high surface area, and the relatively low expense compared to other methods, makes this experiment an attractive choice to study deposition. The capillary experiment yields the deposition thickness as a function of the axial length. This is later converted to deposition flux. For this case unfortunately, only the pressure drop of the capillary was reported by the operating company which was converted to a deposition flux. The capillary deposition rate constant kd, is obtained by matching the peak of the deposition flux. The field value of the deposition kinetics constant can be obtained by scaling the laboratory parameter to field boundary layer thickness. Three different boundary layers are present in the 15 ACS Paragon Plus Environment

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wellbore which are the momentum, mass-transfer and laminar boundary layers. The momentum boundary layer can be calculated using Prandtl boundary layer theory23: X

TUVU = 62.7 * RY 13 The mass transfer boundary layer is calculated as 23:

TUGG =

14 Zℎ

The laminar boundary layer can be calculated as following 5:

TU\] = 5

^C 15 KC _ ∗

where Dt is the diameter of the pipeline, Re is Reynolds number, ρf and µf are the density and viscosity of the fluid respectively, u* is the friction velocity, and Sh is the Sherwood number. A summary of the kinetics constants is provided in table 2. The deposition kinetic is scaled based on the boundary layers:

= a

2T ∅ 16 * ∅+1

where

∅ =

U 1 17 T a

A quick look on the parameters from these two cases illustrate that the kinetic parameters are within similar range. The kinetic coefficient of precipitation for both cases is quite close.


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Moreover, the ratio for the aggregation and deposition kinetic constants between Crude A and B seems consistent. As the three kinetic parameters are crucial for a quantitative prediction, a sensitivity analysis was carried out to understand their effects on asphaltene deposition and pressure drop. It was found that the magnitude of their effect varies from case to case but the overall trend is similar. The kinetic rate of precipitation can typically determine the shape of the deposition profile. A higher precipitation constant means a steeper profile with sharp peak. However, its effect on the pressure drop is not as pronounced. This is because after a certain value of precipitation constant, the system seems to show instantaneous precipitation. A more reasonable value for precipitation rate is to be fast compared to deposition and aggregation but not instantaneous. This is more representative of the polydisperse nature of asphaltene. The aggregation kinetic rate is another important parameter. Higher aggregation rate means less asphaltene available for deposition. Therefore, a higher value of aggregation rate is equivalent to lower asphaltene deposition flux and hence deposition. However, the dependence is almost linear with a slight negative slope. The change in the pressure drop is also very slight. In comparison, the kinetic rate of deposition seems to have the most effect. The effect on the asphaltene deposition and pressure drop seems to be exponential. An increase in the kinetic rate of deposition means the system is able to deposit asphaltene much faster. Since the system seems to be reaction limited, an increase of the deposition rate is critical and can have pronounced consequences on deposition thickness and pressure drop.


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RESULTS Accurate modeling of the phase behavior is essential to represent the driving force of precipitation. The PC-SAFT EOS is used to model the system and calculate the equilibrium concentration of asphaltene soluble in the crude oil. The difference between the actual asphaltene concentration present in the system and the equilibrium concentration drives the deposition behavior. Figure 6 represents the phase behavior of the crude oil at a GOR of 669 scf/stb. Data points for the bubble point and the asphaltene onset pressure are matched by PC-SAFT (curves). Also shown is the pressure-temperature trace from reservoir conditions up the wellbore. Asphaltenes are unstable below the onset pressure. As the pressure continues to decrease, the bubble curve is reached and gas starts to leave the mixture. The largest driving force for asphaltene deposition is at the bubble point pressure. Once the pressure is below the bubble curve, the solubility of the oil raises again leading to asphaltene becoming stable again at the lower onset. The input to the deposition simulator includes the pressure and temperature profile, all kinetic constants, the asphaltene equilibrium concentration from the thermodynamic module, and operation conditions such as pipeline length and diameter, flow rate, and initial dimensionless asphaltene concentration in oil phase. The deposition simulator uses this information to predict the profile of asphaltene deposition along the well bore or pipeline. Figure 7 illustrates the deposition thickness along the wellbore length. No deposition occurs before the onset condition is reached. The traditional way to determine the deposition profile is to use calipers. However, sometimes this option is not possible especially in subsea pipelines; alternatively, pressure drop is measured continuously. The occurrence and the magnitude of the additional frictional pressure 18 ACS Paragon Plus Environment

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drop indicate the presence and thickness of asphaltene deposit. In this field case, there are no direct deposition profile measurements to compare against but a frictional pressure drop was measured in the first 14 days after pipeline wash. To compare with this value, the frictional pressure drop was calculated through the Darcy−Weisbach formula 24 and shown in Table 3. The operator reported that the frictional pressure drop due to deposit is approximately 140 psi in the two weeks after a wellbore wash. It is found that the predicted results are very close to field data measured. This demonstrates that the ADEPT simulator is able to predict the pressure drop due to asphaltene deposition quantitatively based on laboratory experimental results. Effects of GOR on asphaltene deposition in well bore Since Gas Oil Ratio (GOR) can initially decrease with oil production or increases due to a change in producing horizon, the asphaltene stability and deposition in the well bore can be affected accordingly. Thus, one well bore (Crude A) from the Gulf of Mexico having asphaltene deposition problem and decreasing GOR was studied through the ADEPT simulator to understand the effects of GOR on asphaltene phase behavior and deposition. In this case, the original GOR (zero flash) of the reservoir fluid is 669 Scf/stb. The operator reported that GOR decreased about 60 Scf/stb over four months. GOR would increase to some extent due to change in producing horizon. Hence, we consider two other gas oil ratios; 549 Scf/stb and 1000 Scf/stb were set as the desired conditions. Figure 8 depicts asphaltene phase behavior predicted at different gas oil ratios. The straight solid line corresponds to the P-T profile in the well bore. The square and triangle points are experimental results for asphaltene precipitation and bubble points respectively. The blue, red and green curves are prediction results. It is found that both asphaltene onset pressure and bubble 19 ACS Paragon Plus Environment

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pressure increase significantly with increasing GOR, but the effect on the lower onset pressure curve is minimal. This is because gas components are precipitant for asphaltene, thus the oil with higher GOR becomes unstable at higher pressure. It means even less depressurization (high pressure) can result in asphaltene precipitation for higher GOR system. Regarding bubble point shift, it can be explained by the gas dissolution capability. Higher GOR system with more gases needs more pressure to keep gas components dissolved in the oil phase. For the unchanged lower onset curves, the reason is the oil composition with different gas oil ratios are almost the same when the gas evolves below the bubble point. Those light gases escape from the oil phase making it gradually a good solvent for asphaltenes. Consequently, the precipitated asphaltene can re-dissolve back to the oil. Note that only in the system with GOR of 1000 Scf/stb, the oil is unstable to asphaltene initially. Such system is likely to have asphaltene deposition in the reservoir. As shown by Fig. 9, at higher GOR, the solubility parameter of the oil is lower and reaches the asphaltene instability solubility at much higher pressure causing the asphaltene to phase separate (upper onset). This continues until the bubble point is reached at which the gases escape causing the solubility to rise. The lower onset is reached when the solubility parameter is above the asphaltene instability solubility. Notice that the upper onset for different GOR is at various pressures compared to the lower onset which is relatively at much narrower range as can be further explained by Fig. 9. Figure 10 shows the deposit thickness predicted with respect to different gas oil ratios in two weeks (14 days). The two dashed curves (GOR = 669 and 549 Scf/stb) show the similar trend with the solid curve (GOR=1000 Scf/stb) but are still in the initial part of Stage I. Even though


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the decrease in the deposition for both GORs is not shown, it is expected that it will appear beyond the well bore at some downstream location under much lower pressure. In this specific case, the magnitudes of deposit thickness in the three systems were found to be similar. In other words, the deposit thickness is not sensitive to GOR. The appearance of the deposit shifts to the lower location (higher pressure) with increasing GOR. This is because oil having higher GOR can become unstable even at high pressure, in turn, asphaltene starts to precipitate and deposit at that location. According to the discussion above, it can be concluded that GOR can significantly affect the asphaltene phase behavior and change the deposit location strongly. Such effects should be taken into account before GOR is increased. Additionally, the deposition flux was predicted and the result is shown in Fig. 11. The curve indicates that deposition rate grows fast initially, and then it slows down until it reaches the maximum (stage I). After that, it starts decreasing slightly until a critical point (stage II), and then drops sharply (stage III). This curve shows the typical trend of deposition rate along the well bore or pipeline as long as the pressure of well bottom or pipeline inlet is in the asphaltene instability region. This trend can be explained through asphaltene precipitation driving force (Fig. 12) and kinetic effects. Deposition rate is a function of the concentration of asphaltene primary particles (generated from precipitation) which is strongly dependent on precipitation (primary particle formation) and aggregation and deposition (primary particle consumption). The difference between the amount precipitated and consumed is what drives an increase in the deposition amount. 21 ACS Paragon Plus Environment

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In stage I, asphaltenes precipitate right after the oil enters the pipeline according to phase behavior prediction results. Moreover, the largest precipitation driving force results in the fastest increase rate of the asphaltene precipitated amount and deposition flux. Later in the stage I, with the consumption of the asphaltene concentration in oil phase, the precipitation driving force gradually decreases to some extent and lead to the deposition flux increases slower even though the particle amount accumulates. The precipitation driving force continues to decrease to even lower level right below the bubble point. Thus, the slight decrease of deposition rate in stage II can be attributed to the limited accumulation of asphaltene particles due to the co-effects of all processes. The start point of stage III is the beginning of the re-dissolution based on our model. That means no precipitation occurs any more. Meanwhile, the particle re-dissolving causes the primary particle concentration to decrease immediately. Therefore, the deposition rate decreases very fast in stage III. Effects of Gas Injection on asphaltene deposition in well bore Another interesting issue is the effect of gas injections on asphaltene deposition. A touch on this issue is given briefly in the previous section where adding gas to the system results in higher GOR. Consequently, asphaltene becomes less stable leading to higher deposition range. On this section however, the effect of individual gas components is examined. Surely, adding gas to the oil will result in a higher GOR. Nonetheless, the increase in the GOR is not comparably large, and the most prevalent cause of the deposition is the interaction of these components with the oil and the consequent effect on asphaltene phase behavior. Figures 13 and 14 illustrate the effect of methane and nitrogen on asphaltene phase behavior and deposition respectively.


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The deposition thickness of the various injections reflects the phase behavior and the position of PT trace. A larger instability region leads to larger deposition area which is the case of methane. Nitrogen on the other hand, gives a narrower but larger deposition thickness. This is because the oil with Nitrogen injection has a lower equilibrium concentration resulting in a fast depletion of asphaltene precipitated particles over a smaller area25. It is worth mentioning that in all of these simulations; it was assumed that asphaltenes were stable prior to the start of the simulation. CONCLUSION Our prediction technique, the ADEPT simulator, can help the oil industry not only to identify the conditions that can cause asphaltene problems, but also to improve production planning and wellbore or pipeline design. Furthermore, with laboratory data, ADEPT can help distinguish the crude that will have deposition issues from those only having precipitation phenomenon during production operations. ADEPT would be applied to estimate the frequency of remediation for a given well or development during the economic evaluation of whether or not to go forward with a prospective project. If it requires wellbore intervention too frequently, the project could become uneconomic. In this paper, a summary of the procedure of application the ADEPT simulator to field cases is presented through a wellbore case from the Gulf of Mexico. Based on the current research, by fully relying on the laboratory experimental measurements, the current ADEPT simulator is able to provide a good quantitative agreement with field observations. This application of the ADEPT simulator should therefore be followed by benchmarking against field examples with known fluid properties and deposition problems. The simplified 23 ACS Paragon Plus Environment

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mathematical model gives the same accuracy with a 1st order ODE as opposed to the 2nd order PDE and is much faster computationally. Moreover, an analytical expression for the actual concentration and for the dissolution of asphaltene can be obtained. In addition, GOR shows significant effects on asphaltene phase behavior and deposition location in the well bore generally. Higher GOR and gas injections (Methane and Nitrogen) lead to higher asphaltene deposition amount and earlier deposition due to shift in onset conditions. ASSOCIATED CONTENT S Supporting Information The compositional analysis of the crude oil used in this study. This material is available free of charge via the Internet at http://pubs.acs.org/ AUTHOR INFORMATION Corresponding Author *Email: [email protected] Notes The authors declare no competing financial interest. ACKNOWLEDGEMENTS Ali A. AlHammadi gratefully acknowledges Abu Dhabi National Oil Company (ADNOC) support through the PhD scholarship. Yi Chen and Walter G. Chapman acknowledge financial support from the Deepstar consortium. The authors are thankful to Deepstar consortium for


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granting the approval to present this paper. The authors thank Andrea Gutierrez, Sai Panuganti, Le Wang and Mohammed I.L. Abu Taqiya for their helpful discussions.


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REFERENCES 1. Trbovich, M.; King, G. In Asphaltene deposit removal: Long-lasting treatment with a cosolvent, SPE International Symposium on Oilfield Chemistry, Society of Petroleum Engineers: 1991. 2. Ramirez-Jaramillo, E.; Lira-Galeana, C.; Manero, O., Modeling asphaltene deposition in production pipelines. Energy & fuels 2006, 20 (3), 1184-1196. 3. Jamialahmadi, M.; Soltani, B.; Müller-Steinhagen, H.; Rashtchian, D., Measurement and prediction of the rate of deposition of flocculated asphaltene particles from oil. International Journal of Heat and Mass Transfer 2009, 52 (19), 4624-4634. 4. Soltani Soulgani, B.; Rashtchian, D.; Tohidi, B.; Jamialahmadi, M., A Novel Method for Mitigation of Asphaltene Deposition in the Wellstring. Iran. J. Chem. Chem. Eng. Vol 2010, 29 (2). 5. Eskin, D.; Ratulowski, J.; Akbarzadeh, K.; Pan, S., Modelling asphaltene deposition in turbulent pipeline flows. The Canadian Journal of Chemical Engineering 2011, 89 (3), 421-441. 6. Vargas, F. M.; Creek, J. L.; Chapman, W. G., On the Development of an Asphaltene Deposition Simulator†. Energy & fuels 2010, 24 (4), 2294-2299. 7. Kurup, A. S.; Vargas, F. M.; Wang, J.; Buckley, J.; Creek, J. L.; Subramani, H., J; Chapman, W. G., Development and application of an asphaltene deposition tool (ADEPT) for well bores. Energy & fuels 2011, 25 (10), 4506-4516. 8. Kurup, A. S.; Wang, J.; Subramani, H. J.; Buckley, J.; Creek, J. L.; Chapman, W. G., Revisiting Asphaltene Deposition Tool (ADEPT): Field Application. Energy & fuels 2012, 26 (9), 5702-5710. 9. (a) Hildebrand, J.; Scott, R., The solubility of nonclectrolytes 3rd edition. New York: Reinhold 1949, 488; (b) Hildebrand, J. H.; Scott, R. L., Regular solutions. Prentice-Hall: 1962. 10. Wiehe, I. A., Process chemistry of petroleum macromolecules. CRC Press: 2008. 11. Vargas, F. M. Modeling of asphaltene precipitation and arterial deposition. Ph.D Dissertation, Rice University, Houston, 2009. 12. Gross, J.; Sadowski, G., Perturbed-chain SAFT: An equation of state based on a perturbation theory for chain molecules. Industrial & engineering chemistry research 2001, 40 (4), 1244-1260. 13. Panuganti, S. R.; Vargas, F. M.; Gonzalez, D. L.; Kurup, A. S.; Chapman, W. G., PCSAFT characterization of crude oils and modeling of asphaltene phase behavior. Fuel 2012, 93, 658-669. 14. AlHammadi, A. A.; Vargas, F. M.; Chapman, W. G., Comparison of Cubic-PlusAssociation and Perturbed-Chain Statistical Associating Fluid Theory Methods for Modeling Asphaltene Phase Behavior and Pressure–Volume–Temperature Properties. Energy & fuels 2015. 15. Boek, E. S.; Ladva, H. K.; Crawshaw, J. P.; Padding, J. T., Deposition of Colloidal Asphaltene in Capillary Flow: Experiments and Mesoscopic Simulation†. Energy & fuels 2008, 22 (2), 805-813. 16. Aske, N.; Kallevik, H.; Johnsen, E. E.; Sjöblom, J., Asphaltene aggregation from crude oils and model systems studied by high-pressure NIR spectroscopy. Energy & fuels 2002, 16 (5), 1287-1295. 17. Schabron, J. F.; Rovani, J. F., On-column precipitation and re-dissolution of asphaltenes in petroleum residua. Fuel 2008, 87 (2), 165-176. 26 ACS Paragon Plus Environment

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18. Pan, H.; Firoozabadi, A., Thermodynamic micellization model for asphaltene precipitation inhibition. AIChE journal 2000, 46 (2), 416-426. 19. Lang, R.; Xanh, N. X., Smoluchowski's theory of coagulation in colloids holds rigorously in the Boltzmann-Grad-limit. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete 1980, 54 (3), 227-280. 20. Wang, J.; Buckley, J. S.; Creek, J. L., Asphaltene deposition on metallic surfaces. Journal of Dispersion Science and Technology 2004, 25 (3), 287-298. 21. Broseta, D.; Robin, M.; Savvidis, T.; Féjean, C.; Durandeau, M.; Zhou, H. In Detection of asphaltene deposition by capillary flow measurements, SPE/DOE Improved Oil Recovery Symposium, Society of Petroleum Engineers: 2000. 22. Ali, M.; Islam, M., The effect of asphaltene precipitation on carbonate-rock permeability: an experimental and numerical approach. SPE Production & Facilities 1998, 13 (03), 178-183. 23. Deen, W. M., Analysis of transport phenomena (topics in chemical engineering). Oxford University Press, New York: 1998; Vol. 3. 24. Whitaker, S., Introduction to Fluid Mechanics. Krieger Publishing Company: Malabar, FL, 1968. 25. Juyal, P.; McKenna, A. M.; Fan, T.; Cao, T.; Rueda-Velásquez, R. I.; Fitzsimmons, J. E.; Yen, A.; Rodgers, R. P.; Wang, J.; Buckley, J. S., Joint industrial case study for asphaltene deposition. Energy & fuels 2013, 27 (4), 1899-1908. 26. Colebrook, C.; White, C., Experiments with fluid friction in roughened pipes. Proceedings of the royal society of london. series a, mathematical and Physical sciences 1937, 367-381.


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Figure 1. Asphaltene phase transitions as the oil flows up the wellbore 6. The black dots represent asphaltene particles resulted from phase separation. Due to flow forces, asphaltene will either meet up with each other forming large enough aggregate which are carried by the flow or diffuse to the wall building up a deposit.


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Figure 2. Schematic of the ADEPT simulator structure. The simulator consists of two modules: thermodynamic and deposition modules. The thermodynamic module provides the location and the amount available to deposit whereas the deposition module forecast the magnitude of the deposit.


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Steady State

Non-Steady State

0.25

Deposition thickness (in)

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0.2 0.15 0.1 0.05 0 0

0.2

0.4

0.6

0.8

1

Pipeline length (-) Figure 3. Asphaltene deposition in steady and non-steady state over 4 weeks period. The difference is not significant. Consequently, for quick design estimate, the steady state model is recommended. The kinetic parameters used in this case is from Kurup et al. 8 and are summarized in Table 2.


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0.70

Asphaltene precipitated (-)

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0.65 0.60 0.55 0.50 0.45 0.40 0

5

10

15

20

25

30

Time (hr) Figure 4. Precipitated mass of asphaltene through 0.2 um filter as a function of aging time for Crude A. These data are used to determine the kinetics constants of precipiation and aggregation. 1 mL of oil is mixed with 0.92 mL of heptane. The kinetic constants of precipitation and aggregation are summarized in Table 1. See Table 1 for crude properties. The Y-axis is dimensionless as it shows the ratio between the amount precipitated to the initial amount of asphaltene present in the oil.


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Figure 5. Schematic representation of the capillary deposition experiment 20.


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Upper AOP

9000

Bubble Pressure

PT Trace

8000 7000

Pressure (Psi)

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6000 5000 4000 3000 2000 1000 0 90

140

190

240

Temperature (oF)

290

Figure 6. Asphaltene phase behavior of the system. The PT trace demonstrates that at the start the system is stable but at one point along the wellbore, the onset condition is passed and a liquid-liquid phase separation occurs. This continues until the bubble curve is reached.


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0.15

Deposit thickness (in)

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0.1

0.05

0 0

0.2

0.4

0.6

0.8

1

Wellbore Length (-) Figure 7. The deposition thickness along the wellbore length. No deposition occurs when the system is stable, i.e. above the onset condition.


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Figure 8. Asphaltene phase behavior at different gas oil ratios. (■- onset pressure tested; ▲- bubble pressure tested; black line- PT trace; blue line- asphaltene upper onset pressure; red line – bubble pressure; green line- asphaltene lower onset pressure). (Solid line - predictions at GOR = 669 scf/stb; Dashed line – predictions at GOR = 1000 scf/stb; Dotted line – predictions at GOR = 549 scf/stb)


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13.4

GOR 669 GOR 549

13.2

Solubility Parameter

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GOR 1000 Asphaltene Instability

13

12.8

Bubble Pressure

12.6 12.4 12.2 12 11.8

0

1000

2000

3000

4000

5000

6000

7000

8000

Pressure (Psi) Figure 9. Solubility parameter of the crude oil as a function of pressure at fixed temperature (T=190 oF). Higher GOR reaches the asphaltene instability solubility at higher pressure causing asphaltene to be unstable over wider range of pressures. GOR unit is scf/stb


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GOR 669

GOR 1000

GOR 549

0.15

Deposit thickness (in)

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GOR = 1000 SCF/STB

0.1

0.05 GOR = 669 SCF/STB GOR = 549 SCF/STB

0 0

0.2

0.4

0.6

0.8

1

Wellbore Length (-) Figure 10. Asphaltene deposit thickness at different gas oil ratios. Higher GOR leads to higher deposition thickness.


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Figure 11. Asphaltene deposition flux prediction at GOR =1000 Scf/stb (simulation).Asphaltene deposition flux prediction. The deposition flux goes through different stages.


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Figure 12. Precipitation driving force in the system with GOR =1000 Scf/stb (simulation). Large driving force initially leads to large deposition flux. The driving force quickly weakened. CF and CEQ are Cf and Ceq as defined in Equations 5a and 5b. The Ceq value is 0.367.


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12000 20% Nitrogen Injection Onset

20% Methane Injection Onset

10000 8000

Pressure (Psi)

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Onset at no gas injection

6000 4000 2000 0 90

140

190

240

290

Temperature (oF) Figure 13. Asphaltene phase behavior due to 20% Methane and Nitrogen injection at GOR = 669 Scf/stb. The lower onset pressure is very close to the bubble curve. Thus, the bubble curve serves as an indication of the proximity of dissolution. (■- onset pressure tested; ▲- bubble pressure tested; blue line- asphaltene upper onset pressure; red line: bubble pressure). (Solid line – predictions at no gas injection; Dashed line – predictions at 20 mol% methane injection; Dotted line - prediction at 20 mol% nitrogen injection)


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Figure 14. Asphaltene deposition thickness due to different gas injections. Nitrogen injection leads to large deposition thickness over narrower lengths.


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Table 1. Oil properties of crude A. More data of the oil are available in the supporting information.

Saturates (Wt%)

63.05

Aromatics (Wt%)

16.99

Resins (Wt%)

16.22

Asphaltene (Wt%)

3.74

Gas Oil Ratio (SCF/STB)

669

API Gravity

32.3


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Table 2. Kinetic parameters obtained from the batch and capillary laboratory experiments for Crudes A and B. The kinetic constants for Crude B are from Kurup et al.8. The laminar boundary layer was used.

Crude

kp, (s-1)

kag, (s-1)

kd, (s-1)

A

8.05×10-4

7.61×10-4

2.17×10-5

B

1.32×10-3

7.29×10-5

2.47×10-6


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Table 3. The frictional pressure drop prediction of Crude A using the Darcy-Weisbach formula. The roughness value used is 0.18 which was calculated from the Colebrook-White equation 26. The laminar boundary layer was used for this calculation.

GOR

Frictional pressure drop

SCF/STB

Psi

669

122.8 Field data ≈ 140 Psi


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