Anju Kurup, Walter Chapman

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

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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)

• 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.

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Asphaltenes - Flow Assurance Context
http//pubs.acs.org/cen/coverstory/87/8738cover.ht
ml

• 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.
Creek, J. L. Energy Fuels, 2005
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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-2-
asphaltenes.pdf
Uncertainties in literature about asphaltenes
(a) Condensed aromatic cluster model (Yen et al,
1972), (b) Bridged aromatic model (Murgich at
al., 1991)
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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.

• 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.
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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
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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
Precipitation, Aggregation Deposition Rates
Experimental Field Data
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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
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Thermodynamic modeling
Gonzalez, Ph.D. Dissertation, 2008
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)
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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
Precipitation, Aggregation Deposition Rates
Experimental Field Data
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Wellbore Deposition Simulator
Goal ? Develop a simulation tool for prediction
of occurrence and magnitude of asphaltene
deposition in the well bore.
advection
diffusion
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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
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Capillary experiments (NMT) Asphaltene deposition
at capillary scale flows
Deposition test-1 Deposition test-1  
Length 3245 cm
Radius 0.0269 cm
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 7624 v/v
Oil properties (M1) 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
Comparison of experimental asphaltene deposition
flux with model predictions
Capillary deposition experimental results from
NMT (Dr. Jill Buckley)
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Capillary experiments
Good qualitative and quantitative agreement
between expt and simulations.
Comparison of experimental asphaltene deposition
flux with model prediction
Deposition test-2 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
Some discrepancies exist. Overall trend matched.
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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
P-T operating condition
Ceq variation along the axial length was computed
input to simulator.
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Hassi-Messaoud Field case 1
Simulator prediction
Simulation parameters
Operating and kinetic parameters
L 335981 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.

Qualitative and Quantitative agreement
Model prediction
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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.

Kabir et al., SPE 71558, 2001 Data from
Chevron
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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.

Kabir et al., SPE 71558, 2001
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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.

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Microsoft Excel interface for ADEPT
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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
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
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Acknowledgments
DeepStar Chevron ETC Schlumberger New Mexico
Tech Infochem VLXE