Chemical and Biomolecular Engineering
Development of Asphaltene
Deposition Tool (ADEPT)
Anju Kurup, Walter Chapman
Department of Chemical & Biomolecular Engineering, Rice University
Houston, TX, April 26, 2011
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
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. nheptane), Soluble in
aromatic solvents (toluene
or benzene)
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.
Asphaltenes - Flow Assurance Context
http://pubs.acs.org/cen/coverstory/87/8738cover.html
Asphaltenes affect oil production
Deposition in
 Reservoirs – near well bore
region – alter wettability.
 Well bore.
 Other production facilities –
separator, flow lines, etc.
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*.
 Poison refinery catalysts.
*Creek, J. L. Energy & Fuels, 2005
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).
(a) n-C5 asphaltenes
(b) n-C7 asphaltenes
http://baervan.nmt.edu/Petrophysics/group/intro-2asphaltenes.pdf
(a) Condensed aromatic cluster model (Yen et al, 1972),
(b) Bridged aromatic model (Murgich at al., 1991)
Uncertainties in literature
about asphaltenes
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.
Improved operating
practices & risk mgt.
 Differentiate between systems
that precipitate and deposit and
those that precipitate and do not
form deposits in well-bores.
 Improve deposition prediction.
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., 2010 - 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 2004 - Surface
deposition, pore throat plugging and re-entrainment of deposited solids.
 Boek et al., 2008, in press, SRD simulations considering asphaltenes as
spherical molecules.
Need for quantitative & qualitative comparison of deposition profile
Simulator Structure
Experimental
& Field Data
Oil & Asphaltene
Characterization
Translator
Thermodynamic
Modeling Module
VLXE /
Multiflash
P&T
Flow rate &
geometry
Precipitation,
Aggregation &
Deposition Rates
Experimental
& Field Data
Deposition
Simulator
Asphaltene
deposition
profile &
thickness
Thermodynamic modeling
PC SAFT (Perturbed Chain Statistical Associating Fluid Theory)
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
Parameters required to
characterize each component
of the mixture:
Segment size ()
Number of segments in a
molecule (m)
Segment-segment interaction
energy (/k)

m
e/k
Thermodynamic modeling
Gonzalez, Ph.D. Dissertation, 2008
10,000
Precipitation
onset
12,000
8,000
Stable region
7,000
6,000
5,000
4,000
3,000
2,000
100
Asphaltene precipitation onset
pressure
Stable region
Live oil fluid A
Pressure, psia
Pressure, psia
9,000
14,000
Unstable
region
10,000
8,000
Unstable
region
Precipitation
onset
6,000
Bubble point
4,000
Bubble point
VLE
T = 296 F (147 C)
VLE
2,000
200
300
400
500
Temperature,
°F
Temperature, F
0
5
10
15
20
25
30
Added Gas, mole %
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 74393 (2001)
Simulator Structure
Experimental
& Field Data
Oil & Asphaltene
Characterization
Translator
Thermodynamic
Modeling Module
VLXE /
Multiflash
P&T
Flow rate &
geometry
Precipitation,
Aggregation &
Deposition Rates
Experimental
& Field Data
Deposition
Simulator
Asphaltene
deposition
profile &
thickness
Wellbore Deposition Simulator
advection
Goal  Develop a simulation tool for prediction of occurrence
and magnitude of asphaltene deposition in the well bore.
diffusion
Proposed Model
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
PRRC, NMT
Capillary experiments (NMT)
Asphaltene deposition at capillary scale flows
Deposition test-1
Length
3245
Radius
0.0269
Flow rate
4
Flow time
63.2
Velocity
0.4888
Capillary stainless steel 316
cm
cm
ml/hr
hrs
cm/s
T= 70o C
Precipitant= C15
Oil: precipitant= 76:24 v/v
1.50E-07
1.5E-07
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
Test1 - Sim
Test1 - Expt
Expt
22
Deposition
(g/cm
Depositionflux
/s
flux,
g/cm /s)
Oil properties (M1)
1.00E-07
1.0E-07
5.00E-08
5.0E-08
0.00E+00
0.0E+00
0 0
0.4 0.6
0.6 0.8
0.8
0.20.2 0.4
Axial
length
(-)(-)
Axial
length
11
Comparison of experimental asphaltene deposition flux with model predictions
Capillary deposition experimental results from NMT (Dr. Jill Buckley)
Capillary experiments
1.50E-07
1.00E-07
1.0E-07
5.00E-08
5.0E-08
0.00E+00
0.0E+00
00
Test1 - Sim
Test1 - Expt
2
Deposition flux, g/cm /s
Test2 - Sim
Test2 - Expt
Expt
22
Deposition
Depositionflux
/s
flux,(g/cm
g/cm /s)
1.5E-07
1.50E-07
1.00E-07
5.00E-08
0.00E+00
0.2
0.2
0.4
0.6
0.4
0.6 0.80.8
Axial
Axiallength
length(-)
(-)
1 1
Comparison of experimental asphaltene deposition flux
with model prediction
Deposition test-2
Length
3193
cm
Radius
0.0385
cm
Flow rate
11.68
ml/hr
Flow time
35.9
hrs
Velocity
0.6967
cm/s
0
0.2
0.4
0.6
0.8
Axial length (-)
Good qualitative and
quantitative agreement
between expt and
simulations.
Some discrepancies exist. Overall trend matched.
1
Hassi-Messaoud – Field case 1
Thermodynamic modeling
PC SAFT
Live oil composition – Haskett and Tartera (1965), SARA – Minssieux (1997)
Density prediction = 0.8096 g/cm3
Reported = 41.38 = 0.8185 g/cm3
Precipitation envelope
250
4000
2000
5000
4000
200
3000
2000
150
Temperature
Pressure
1000
100
0
0
0
Pressure (psi)
Temperature (oF)
6000
Pressure (psi)
P-T operating condition
Ponset-SAFT
Psat-SAFT
LowP-SAFT
P-T curve
0.5
1
Axial length (-)
0
100
200
300
400
Temperature (oF)
Ceq variation along the axial length was computed – input to simulator.
Hassi-Messaoud – Field case 1
Simulator prediction
Simulation parameters
Operating and kinetic parameters
Thickness, in
0
1
2
5000
5500
6000
Depth, feet
335981 cm
11000 ft
R
5.715 cm
4.5 in dia
VZ, cm/s
179.36
 Input from thermodynamic model,
duration – 25 days (average of
reported time intervals), thickness
of deposit matched.
6500
7000
 Spread of deposit ~ 2000 ft while
reported ~ 1000 ft.
7500
 Depends on P-T operating curve Changes as production continues.
8000
8500
Asphaltene
deposition profile as
reported in (Haskett
and Tarterra, 1965)
L
1.65 in
9000
9500
Model prediction
 Paper – P-T curve for one well bore
while deposit measurements are
after the asphaltene mitigation
treatment utilized in the paper.
Qualitative and Quantitative
agreement
Kuwait Marrat well – Field case 2
Thermodynamic modeling –
PC SAFT
Asphaltene precipitation envelope
16000
Psat - Expt*
P-onset - Expt
Psat**
P-T trace **
Pressure (psi)
12000
Psat - SAFT
Ponset - SAFT
LowP - SAFT
API reported* = 36 to 40
PC SAFT = 37. 7
SARA - Kabir and
Jamaluddin, 1999
 Live oil composition,
saturation pressure
data from Chevron.
8000
 PC SAFT
thermodynamic
characterization.
4000
0
70
140
210
Temperature (oF)
*Kabir et al., SPE 71558, 2001
**Data from Chevron
280
350
 Calculated Ceq
variation along the
length of well bore –
input to simulator.
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
Time
240.01
2 months
For 2 months: thickness
matched, 1 and 3 month
kd changes respectively.
P-T curve with axial
length has impact on
precipitation start and end
zone.
*Kabir et al., SPE 71558, 2001
0.6
Thickness, in
With appropriate choice
of dissolution kinetics and
other kinetics a good
qualitative and
quantitative agreement is
obtained.
0.4
0.2
0
3000
4000
5000
6000
Well depth, ft
7000
8000
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.
x
Y
Z
Microsoft Excel interface for ADEPT
Future Activities
Protocol for deposition prediction
Steps to be followed,
Tests to be conducted,
Parameters to be determined.
Propose set of experiments to
be performed to obtain
kinetic parameters used in
the simulation tool.
Scaling up issues of kinetic
parameters
Obtain more capillary
experiment data and compare
simulator predictions.
Obtain field case data and
compare simulator
predictions.
Model improvement to
address limitations of
the present simulator.
Incorporate effect of
aging
Version I to be used in conjunction with flow simulators –
sensitivity analysis of operating parameters
Operating guidelines to reduce deposition probability
Acknowledgments
DeepStar
Chevron ETC
Schlumberger
New Mexico Tech
Infochem
VLXE