1. Development of Asphaltene Deposition Tool (ADEPT)
Anju Kurup, Walter Chapman
Department of Chemical & Biomolecular Engineering, Rice University
Houston, TX, April 26, 2011

2. Outline Introduction / Motivation
Asphaltene deposition simulator structure
Thermodynamic module
Deposition module
Results and discussion
Capillary scale experiments
Field cases – Thermodynamic modeling & deposition simulator predictions
Conclusions
Future work
Acknowledgements
This is the outline of the talk. We will start about learning some of the facts about asphaltene, the problems associated with them and the motivation of this project. This will be followed by a brief review of the work done in the field of modeling asphaltene deposition in well bores. A brief overview of the deposition simulator structure developed so far will be given followed by the results and discussion where we will see the comparison of our simulator predictions with the capillary scale experiment results from NMT. We will also see some sensitivity analyses of the parameters in the model. We have also used the simulator to predict a the deposition in one of the field case and we will see that comparison as well followed by conclusions.

3. What are asphaltenes?
Heaviest and the most polarizable components of the crude oil.
Solubility class of components of crude oil Insoluble in low molecular weight alkanes (e.g. n-heptane), Soluble in aromatic solvents (toluene or benzene)
So what are these asphaltenes that we are making such a big deal about. Asphaltene in crude terms is the black sticky stuff that we use to surface roads and these are the heaviest and …they are defined based on their solubility as the components…During the oil production one of the important problems operators face is the blockage of the well bores which is economically very detrimental as we will be seeing in the later slide. These blockages are sometimes caused by deposition of compounds such as waxes, gas hydrates and asphaltenes. Of these three..as we will be seeing later..asphalten…
Arterial blockage in oil well-bores – waxes, gas hydrates and asphaltenes.
Asphaltenes – Special challenge - not well characterized, form a non-crystalline structure, deposition can occur even at relatively high temperatures.

4. Asphaltenes - Flow Assurance Context
Asphaltenes affect oil production
Deposition in
Reservoirs – near well bore region – alter wettability.
Well bore.
Other production facilities – separator, flow lines, etc.
Poison refinery catalysts.
Intervention costs –
USD 500,000 for on-shore field to USD 3,000,000 or more for a deepwater well along with lost production that can be more than USD 1,000,000 per Day*.
As mentioned in the previous slide asphaltenes affect oil production tend to deposit in reservoirs, well bore other facilities and can even poison catalysts in downstream refining and hence they are notoriously known as the cholesterol of petroluem. Needless to say that some of the intervention costs are extremely high especially when we are considering the offshore facilities.
*Creek, J. L. Energy & Fuels, 2005

5. Uncertainties in literature about asphaltenes
Fast facts about Asphaltenes
Polydisperse mixture.
Deposition mechanism and molecular structure are not completely understood.
Behavior depends strongly on P, T and {xi} (addition of light gases, solvents and other oils in commingled operations or changes due to contamination).
Some quick facts about asphaltenes. Asphaltenes are polydisperse mixture. The deposition mechanism of asphaltenes are still not clearly understood making it difficult to model them. These two figures are two different proposed structures of asphaltenes bu different researchers in this area. The behaviour of asphaltenes in the oil depend very strongly on the operating conditions such as pressure and temperature and the composition. For example a change in composition of oil can be changed by addition of light gases or other solvents typically used during production or during commingling operations or even contamination can affect stability of asphaltenes in oil. These two pictures obtained from NMT show how different asphaltenes are in terms of their structure and their color just when the precipitating agents are different. All this gives an indication of the amount of uncertainty involved in the modeling of asphaltene behavior in crude oil.
(a) n-C5 asphaltenes
(b) n-C7 asphaltenes
Uncertainties in literature about asphaltenes
(a) Condensed aromatic cluster model (Yen et al, 1972), (b) Bridged aromatic model (Murgich at al., 1991)

6. Motivation Predict asphaltene flow assurance issues
Ability to model asphaltene phase behavior as a function of temperature, pressure, and composition.
Model mechanisms by which asphaltenes precipitate, disperse, and deposit.
So the main motivation of our project is driven by the fact that since asphaltenes affect oil production we should be able to predict the scenarios where asphaltenes can cause flow assurance issues by depositing in the well bores. For that we should be first able to model their phase behavior in oil as a function of temperature, pressure and composition of the oil but that is only part of the problem. We should also be able to predict how they disperse and finally deposit on the wall of the well bore. The idea is to basically be able to distinguish between systems where asphaltenes precipitate and then deposit and systems where asphaltenes precipitate but sometimes do not deposit on the surface. This will help us improve our predictions which can allow operators to change operating procedures or to work around the conditions which can cause deposition causing economical operations.
Differentiate between systems that precipitate and deposit and those that precipitate and do not form deposits in well-bores.
Improve deposition prediction.
Improved operating practices & risk mgt.

7. Literature review
Well bore modeling
Ramirez-Jaramillo et al., 2006, - Molecular diffusion along with shear removal model to describe deposition (SAFT-VR – therm model).
Jamialahmadi et al., 2009, - Mechanistic model - flocculated asphaltene concentration, surface temperature and flow rates – parameters fit to expt. Soulgani et al., 2009 – model of Jamialahmadi et al., with Hirschberg model (thermodynamic modeling) to predict well shut down time and compared with field data.
Vargas et al., 2010 – Conservation equations with proposal to couple with PC SAFT (therm model).
Eskin et al., Uses particle flux expressions from literature for particle suspended in turbulent flows to describe diffusion and turbulent induced particle transport, use population balance model to compute particle size distribution in the oil phase, Model parameters obtained by fitting to expt data obtained from Couette flow device.
Reservoir modeling / formation damage modeling
Leontaritis 1997, Nghiem and Coombe 1998, Kohse and Nghiem 2004, Wang and Civan 1999, 2001, 2005, Almehaideb Surface deposition, pore throat plugging and re-entrainment of deposited solids.
Boek et al., 2008, in press, SRD simulations considering asphaltenes as spherical molecules.
Modeling of asphaltene deposition in reservoir has been addressed for quite few years as late as 1997 by Leontaritis who was one of the first in this area..this was followed by several groups in Alberta and University of Oklahoma and recently Boek from London has also performed molecular simulations. However, modeling asphaltene deposition in well bores has been not so extensively addressed and there are only a few papers in the recent years in the literature that deal with this. Ramirez from Mexico uses molecular diffusion model to describe deposition. Jamialahmadi in their work have developed something called as a mechanistic model based on three parameters they hv identified in their system and used expt results to fit the parameters with these three variables. This model was further used by Soulgani to predict asphaltene deposition for a Iranian oil field. Vargas proposed a simulator based on species conservation equations coupled with thermodynamic modeling of oil with PC SAFT. The result shown in this presentation are continuation of this work. One of the most recent work is a conference proceeding by Eskin from Schlumberger Edmonton Canada where they have used particle flux mass transfer expressions for turbulent flows, model parameters are fit with their expt Couette flow device results. They have shown a quantitative comparison with one field case. There is lack of both qualitative and quantitative comparisons of asphaltene deposition profile in the literature.
Need for quantitative & qualitative comparison of deposition profile

8. Thermodynamic Modeling Module
Simulator Structure
Experimental & Field Data
Translator
VLXE / Multiflash
Oil & Asphaltene Characterization
P & T
Thermodynamic Modeling Module
Asphaltene Solubility
CA*
Flow rate & geometry
Deposition Simulator
Asphaltene deposition profile & thickness
This slide shows the structure of the simulator developed. Heart of the simulator lies the thermodynamic modeling module that works in tandem with the commercial softwares such as Multi..or VLXE here we use PC SAFT equation of state. Various physical and thermodynamic properties are needed to characterize the oil such as the saturation pressures and asphaltene onset conditions. With this data available and with pressure and temperature in the well bore we can calculate asphaltene solubility in the oil. This goes as an input to the deposition simulator which again requires other operating conditions such as flow rate and other kinetic parameters such as precipitation, aggregation and deposition rates which are obtained from the capillary or other experiments and with this data the simulator can predict the asphaltene deposit thickness and profile which can be benchmarked with available field data and simulator can be used for further predictions for other conditions. Let us first quickly take a peek at the thermodynamic module.
Precipitation, Aggregation & Deposition Rates
Experimental & Field Data

9. Thermodynamic modeling
PC SAFT (Perturbed Chain Statistical Associating Fluid Theory)
Parameters required to characterize each component of the mixture:
Segment size ()
Number of segments in a molecule (m)
Segment-segment interaction energy (/k)
Chapman et al., 1988, 1990
Molecules modeled as chains of bonded spherical segments
Gross and Sadowski (2001) proposed PC SAFT – successful in predicting phase behavior of large molecular weight fluids – Asphaltene molecules.
Multiflash (Infochem) and VLXE e  m
/k
As I have mentioned before we use PC SAFT EOS to describe the behavior of asphaltene in crude oil. PC SAFT stands for ….the SAFT was first proposed by my advisor in the late 80 s according to SAFT molecules are modeled..PC SAFT was a modification proposed by …and has been successfully used for predicting large molecular weight molecules and Dr. Chapmans group has used it to model asphaltene molecules..and we need these three parameters to characterize..PC SAFT is available in ….

10. Thermodynamic modeling
Gonzalez, Ph.D. Dissertation, 2008
These are few results borrowed from the work of Doris previous phd student of Dr. Chapman shows PC saft does a good job in reproducing the saturation pressure obtained from experiments over this temperature range. It also compares well with the experimentally obtained onset pressure for asphaltene. The expermental data points are from work of ..this figure shows PC SAFT does a very good job in reproducing the effect of increases nitrogen gas on the onset pressures and the bubble point of the oil. Many more of these validations have been reported in the publications of Doris, david ting and Vargas.
P-T diagram: Comparison of experimental bubble point and asphaltene onset curves with PC SAFT predictions
Comparison of experimental bubble point and asphaltene onset curves with PC SAFT predictions for increased nitrogen gas injection
Oil characterization & PC SAFT parameter estimation: thermodynamic module
Exp. Data: Jamaluddin et al., SPE (2001)

11. Thermodynamic Modeling Module
Simulator Structure
Experimental & Field Data
Translator
VLXE / Multiflash
Oil & Asphaltene Characterization
P & T
Thermodynamic Modeling Module
Asphaltene Solubility
CA*
Flow rate & geometry
Deposition Simulator
Asphaltene deposition profile & thickness
So now that we have covered the thermodynamic module let us focus on the deposition simulator
Precipitation, Aggregation & Deposition Rates
Experimental & Field Data

12. Wellbore Deposition Simulator
Goal  Develop a simulation tool for prediction of occurrence and magnitude of asphaltene deposition in the well bore.
advection
As I have mentioned before our overall goal has been to develop a tool …this is a schematic representation of the well bore which can be divided into these various axial segments. In each of these segments depending on the pressure, temperature conditions the oil is either stable or unstable to asphaltenes. And if it is unstable they precipitate, they further aggregate or deposit on the wall of the pipe as the oil is flowing up in this direction.
diffusion

13. Proposed Model Accumulation = Diffusion – Convection
Mass balance of asphaltene aggregates in a controlled volume:
Accumulation = Diffusion – Convection
– Aggregation + Precipitation
– Deposition
Asphaltene Precipitation / Aggregation / Deposition – first order kinetics
Kp, Ka, Kd
The proposed model in simple terms is a mass balance of asphaltene aggregates in a cell and is governed by diffusion, convection and the kinetics of precipitation aggregation and deposition. We use first order kinetics to describe these three.
PRRC, NMT

14. Capillary experiments (NMT)
Asphaltene deposition at capillary scale flows
Deposition test-1
Length
3245 cm
Radius
0.0269
Flow rate 4
ml/hr
Flow time
63.2
hrs
Velocity
0.4888
cm/s
Capillary stainless steel 316
T= 70o C
Precipitant= C15
Oil: precipitant= 76:24 v/v
Oil properties (M1)
Saturates
62.9 wt%
Aromatics
21.4
Resins
13.28
Asphaltenes
2.42
r (precipitant)
0.74 g/ml
r (oil)
0.85 g/ml
r (mixture)
0.82 g/ml
m (mixture)
3.95 mPa s
Let us see some of the results that we have obtained from the model. These are capillary experiments performed at NMT Dr. Buckleys group. They use a capillary of these dimensions and flow a mixture of known amount of oil and precipitant. These are the oil proeprties and this is the experiemntaly obtained asphaltene deposition flux along the capillary length. We use our mathermatical model and with these operating conditions we use these parameters and to obtain this match against the experimentally observed trends.
Comparison of experimental asphaltene deposition flux with model predictions
Capillary deposition experimental results from NMT (Dr. Jill Buckley)

15. Capillary experiments
This is another experimentally observed deposition flux with the same oil but different operating conditions. So we used the kinetic parameters from the earlier result since it is the same oil and predicted the deposition flux for this case and this is the comparison we have. We can see that there is a good match…although there are some discrepancies in these region however the simulator does a good job in predicting in this change in profile due to change in these operating parameters.
Comparison of experimental asphaltene deposition flux with model prediction
Good qualitative and quantitative agreement between expt and simulations.
Deposition test-2
Length
3193 cm
Radius
0.0385
Flow rate
11.68
ml/hr
Flow time
35.9
hrs
Velocity
0.6967
cm/s
Some discrepancies exist. Overall trend matched.

16. Hassi-Messaoud – Field case 1
Thermodynamic modeling PC SAFT
Live oil composition – Haskett and Tartera (1965), SARA – Minssieux (1997)
Density prediction = g/cm3
Reported = = g/cm3
Precipitation envelope
P-T operating condition
With the model being validated for capillary scale experiments we decided to use it to predict field cases. First is the most commonly known Hassi-Messaoud oil wells. We first used PC SAFT EOS to thermodynamically model the oil with oil composition and SARA from these publications and matched the density of the oil we obtained the precipitation envelope of the oil with this oil and with pressure and temperature conditions known along the well bore axial length we can compute the asphaltene sulubility in the oil which goes as an input to the simulator.
Ceq variation along the axial length was computed – input to simulator.

17. Hassi-Messaoud – Field case 1 Qualitative and Quantitative agreement
Simulator prediction
Simulation parameters
Operating and kinetic parameters L cm
11000 ft R
5.715 cm
4.5 in dia
VZ, cm/s
179.36
Asphaltene deposition profile as reported in (Haskett and Tarterra, 1965)
Input from thermodynamic model, duration – 25 days (average of reported time intervals), thickness of deposit matched.
Spread of deposit ~ 2000 ft while reported ~ 1000 ft.
Depends on P-T operating curve - Changes as production continues.
Paper – P-T curve for one well bore while deposit measurements are after the asphaltene mitigation treatment utilized in the paper.
This is the reported asphaltene deposition profile in the paper by …this is the depth of the well bore in feet this line has been reported as 1.65 in scale and the deposit thickness therefore is little more than 1/3 rd of 1.65 inch roughly around 0.6 inch. With these operating and kinetic parameters we get this prediction with our model. The duration of the deposit was not very clear so we used an average of the different time intervals that are mentioned between each of these profile measurements which is around 25 days and the thickness of deposit is matched. However, we see that the simulator predicts a much broader deposit spread across 2000 ft compared to the reported trend which is spread probably around 1000 ft. In the paper it was reported that as the well production continues initialy a long thin deposit is formed which spreads around 2000 ft and as the thickness of the deposit increases it acts as a choke to the oil flowing increasing the frictional loss and causing the bubble pressure to go down the depth and making the deposit shorter as it grows thicker due to the modified flow regimes. Since currently we do not consider these effects in the model the simulator predicts the same deposit spread as it what started with. Also pressure temperature data that we are using in the simulator. It is well known that the p-T changes with time as production continues and we do not have information about the PT exactly for this deposit formation. These measurements were taken after the asphaltene mitigation scheme that they have proposed in the paper so may differ. Still we can say that we have a good qualitative and quantitative agreement with what has been reported inspite of all the uncertainties in the data involved.
Qualitative and Quantitative agreement
Model prediction

18. Kuwait Marrat well – Field case 2
Thermodynamic modeling – PC SAFT
API reported* = 36 to 40
PC SAFT = 37. 7
Asphaltene precipitation envelope
SARA - Kabir and Jamaluddin, 1999
Live oil composition, saturation pressure data from Chevron.
PC SAFT thermodynamic characterization.
Calculated Ceq variation along the length of well bore – input to simulator.
That was field case 1 let us see field case 2 which is Kuwaiti marrat oil well for which we have the deposit profile data available. Again like in the previous case we use PC SAFT to thermodynamically model the oil and have matched the density. Dr. Creek provided us with some new data about the live oil composition and saturation pressure of this oil. We used SARA data from one of the publications..this paper also contained onset and saturation pressure data for asphaltenes as shown by these solid points. These data points are the sat pressure from Chevron which match what has been reported in this paper. So we perform the thermodynamic characterization of this oil with this information avaible and these thick solid lines show the predictions of PC SAFT against the expt and their simulation results…. A PT trace for one of the oil well was also provided from Chevron shown by this red curve. With this thermodynamic model and by assuming a linear variation of pressure and temperature along axial length …
*Kabir et al., SPE 71558, 2001
**Data from Chevron

19. Kuwait Marrat well – Field case 2
Simulator prediction
Operating parameters
L, cm
457200
15000 ft
R, cm
3.49
2.5 inch ID
VZ, cm/s
240.01
Time
2 months
For 2 months: thickness matched, 1 and 3 month kd changes respectively.
With appropriate choice of dissolution kinetics and other kinetics a good qualitative and quantitative agreement is obtained.
P-T curve with axial length has impact on precipitation start and end zone.
Let us see the simulator predictions. For operating conditions in this table the reported deposition profile is shown in this figure taken from work of Kabir. We are looking at this blue curve shown here. The deposit thickness is around 0.5 inch. With these parameters and for a period of 2 months this is the deposition profile we obtain with the simulator. The time was not given so we assumed two months however for 1 and 3 months the kd changes accordingly to get the same deposition thickness. With appropriate choice of kinetic parameters especially the dissolution rates and if we take the P-T data from Chevron as is we can get this shape of the deposit. The position in terms of depth depends on the PT variation along axial length same like we observed in HM. Our predictions show deposit higher in the well bore compared to what has been reported. By slight variation on pressure vs position data we can match the exact location of what has been reported. Again Pressure temperature data can change as the production continues.
*Kabir et al., SPE 71558, 2001

20. Summary Development of Asphaltene deposition simulator – I.
Thermodynamic module.
Deposition module.
Successful application of the simulator to predict asphaltene deposition in capillary experiments.
Simulator used for deposition prediction in well bores.
Two field cases studied. Thermodynamic model of the live oil was developed and coupled with the deposition module to predict deposition in well bores.
A good qualitative and quantitative match between reported field data and simulator predictions has been obtained.
To summarize…

21. Microsoft Excel interface for ADEPTY Z
We have developed an interface for the easy usage of this tool which is originally a fortran alogorithm. So basically we have built a dynamic link library using the fortran algorithm which is then accessed through Visual basic editor in excel which enables calculation of the deposition profile in excel with respect to changes in these input parameters which are length diameter flow rate and so on..so if we enter the values here and then hit run the dep flux gets updated for diff axial position and this is the plot.
Microsoft Excel interface for ADEPT

22. Future Activities Protocol for deposition prediction
Steps to be followed,
Tests to be conducted,
Parameters to be determined.
Obtain more capillary experiment data and compare simulator predictions.
Obtain field case data and compare simulator predictions.
Propose set of experiments to be performed to obtain kinetic parameters used in the simulation tool.
Model improvement to address limitations of the present simulator.
Incorporate effect of aging
Slide shows the proposed upcoming activities and ongoing activities.
Scaling up issues of kinetic parameters
Version I to be used in conjunction with flow simulators – sensitivity analysis of operating parameters
Operating guidelines to reduce deposition probability

23. Acknowledgments DeepStar Chevron ETC Schlumberger New Mexico Tech
Infochem
VLXE