anju kurup, walter chapman development of asphaltene deposition tool (adept) houston, tx, april 26,...
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Anju Kurup, Walter Chapman
Development of Asphaltene Development of Asphaltene Deposition Tool (ADEPT)Deposition Tool (ADEPT)
Houston, TX, April 26, 2011
Department of Chemical & Biomolecular Engineering, Rice University
Chemical and Biomolecular Engineering
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
Outline
What are asphaltenes?
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.
Solubility class of components of crude oil Insoluble in low molecular weight alkanes (e.g. n-heptane), Soluble in aromatic solvents (toluene or benzene)
Heaviest and the most polarizable components of the crude oil.
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*.
*Creek, J. L. Energy & Fuels, 2005
http://pubs.acs.org/cen/coverstory/87/8738cover.html
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
Fast facts about Asphaltenes
(a) Condensed aromatic cluster model (Yen et al, 1972), (b) Bridged aromatic model (Murgich at al., 1991)
Uncertainties in literature about
asphaltenes
Model mechanisms by which asphaltenes precipitate, disperse, and deposit.
Ability to model asphaltene phase
behavior as a function of temperature, pressure, and composition.
Predict asphaltene Predict asphaltene flow assurance flow assurance
issuesissues
Differentiate between systems that precipitate and deposit and those that precipitate and do not form deposits in well-bores.
Improve deposition prediction.
Improved Improved operating operating
practices & risk practices & risk mgt.mgt.
Motivation
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.
Literature review
Need for quantitative & qualitative comparison of deposition profile
Deposition Simulator
Thermodynamic Modeling Module
VLXE / Multiflash
Oil & Asphaltene Characterization
P & T
Asphaltene deposition profile & thickness
Flow rate & geometry
Precipitation, Aggregation & Deposition Rates
Translator
Experimental & Field Data
Experimental & Field Data
Simulator Structure
Thermodynamic modeling
Parameters required to characterize each component of the mixture:Segment size ()Number of segments in a molecule (m)Segment-segment interaction energy (/k)
e
m /k
Gross and Sadowski (2001) proposed PC SAFT – successful in predicting phase behavior of large molecular weight fluids – Asphaltene molecules.
Multiflash (Infochem) and VLXE
PC SAFT (Perturbed Chain Statistical Associating Fluid Theory)
Chapman et al., 1988, 1990
Molecules modeled as chains of bonded spherical segments
Thermodynamic modeling
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
100 200 300 400 500
Temperature, F
Pre
ssu
re, p
sia
Live oil fluid A
Stable region
Unstable region
VLE
Precipitation onset
Bubble point
Temperature, °F
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
100 200 300 400 500
Temperature, F
Pre
ssu
re, p
sia
Live oil fluid A
Stable region
Unstable region
VLE
Precipitation onset
Bubble point
Temperature, °F
Exp. Data: Jamaluddin et al., SPE 74393 (2001)
2,000
4,000
6,000
8,000
10,000
12,000
14,000
0 5 10 15 20 25 30
Added Gas, mole %
Pre
ssu
re, p
sia
T = 296 F (147 C)
Asphaltene precipitation onset pressure
Unstable region
Stable region
VLE
Precipitation onset
Bubble point
2,000
4,000
6,000
8,000
10,000
12,000
14,000
0 5 10 15 20 25 30
Added Gas, mole %
Pre
ssu
re, p
sia
T = 296 F (147 C)
Asphaltene precipitation onset pressure
Unstable region
Stable region
VLE
Precipitation onset
Bubble point
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 injectionOil characterization & PC SAFT parameter estimation:
thermodynamic module
Deposition Simulator
Thermodynamic Modeling Module
VLXE / Multiflash
Oil & Asphaltene Characterization
P & T
Asphaltene deposition profile & thickness
Flow rate & geometry
Precipitation, Aggregation & Deposition Rates
Translator
Experimental & Field Data
Experimental & Field Data
Simulator Structure
Wellbore Deposition SimulatorGoal Develop a simulation tool for prediction of
occurrence and magnitude of asphaltene deposition in the well bore.
adve
ctio
n
diffusion
Proposed Model
Accumulation = Diffusion – Convection – Aggregation +
Precipitation – Deposition
Mass balance of asphaltene aggregates in a controlled volume:
PRRC, NMT
Asphaltene Precipitation / Aggregation /
Deposition – first order kinetics
Kp, Ka, Kd
Capillary experiments (NMT)Asphaltene deposition at capillary scale flows
Deposition test-1 Length 3245 cmRadius 0.0269 cmFlow rate 4 ml/hrFlow time 63.2 hrsVelocity 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
(precipitant) 0.74 g/ml
(oil) 0.85 g/ml
(mixture) 0.82 g/ml
(mixture) 3.95 mPa s
Comparison of experimental asphaltene deposition flux with model predictions
0.00E+00
5.00E-08
1.00E-07
1.50E-07
0 0.2 0.4 0.6 0.8 1
Axial length (-)
De
po
sit
ion
flu
x, g
/cm
2 /s
Test1 - Sim
Test1 - Expt
Capillary deposition experimental results from NMT (Dr. Jill Buckley)
0.0E+00
5.0E-08
1.0E-07
1.5E-07
0 0.2 0.4 0.6 0.8 1Axial length (-)
Dep
osi
tio
n f
lux
(g/c
m2/s
)
Expt
Capillary experiments
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
Comparison of experimental asphaltene deposition flux with model prediction
Good qualitative and quantitative agreement
between expt and simulations.
Some discrepancies exist. Overall trend matched.
0.00E+00
5.00E-08
1.00E-07
1.50E-07
0 0.2 0.4 0.6 0.8 1
Axial length (-)
De
po
sit
ion
flu
x, g
/cm
2 /s
Test2 - Sim
Test2 - Expt
0.00E+00
5.00E-08
1.00E-07
1.50E-07
0 0.2 0.4 0.6 0.8 1
Axial length (-)
De
po
sit
ion
flu
x, g
/cm
2 /s
Test1 - Sim
Test1 - Expt
0.0E+00
5.0E-08
1.0E-07
1.5E-07
0 0.2 0.4 0.6 0.8 1Axial length (-)
De
po
sit
ion
flu
x (
g/c
m2 /s
)
Expt
Hassi-Messaoud – Field case 1Thermodynamic 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
Ceq variation along the axial length was computed – input to simulator.
0
2000
4000
6000
0 100 200 300 400
Temperature (oF)
Pre
ssu
re (
psi
)
Ponset-SAFTPsat-SAFTLowP-SAFTP-T curve
Precipitation envelopeP-T operating condition
100
150
200
250
0 0.5 1
Axial length (-)
Te
mp
era
ture
(oF
)0
1000
2000
3000
4000
5000
Pre
ss
ure
(p
si)
TemperaturePressure
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)
Simulation parameters
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
Hassi-Messaoud – Field case 1Hassi-Messaoud – Field case 1Simulator predictionSimulator prediction
Operating and kinetic parameters
5000
5500
6000
6500
7000
7500
8000
8500
9000
9500
0 1 2
Thickness, in
De
pth
, fe
et
1.65 in
Model prediction
SARA - Kabir and Jamaluddin, 1999
*Kabir et al., SPE 71558, 2001
**Data from Chevron
API reported* = 36 to 40PC SAFT = 37. 7
Thermodynamic modeling – PC SAFT
Live oil composition, saturation pressure data from Chevron.
PC SAFT thermodynamic characterization.
Calculated Ceq variation along the length of well bore – input to simulator.
Kuwait Marrat well – Field case Kuwait Marrat well – Field case 22
Asphaltene precipitation envelope
0
4000
8000
12000
16000
70 140 210 280 350
Temperature (oF)
Pre
ss
ure
(p
si)
Psat - Expt* Psat - SAFTP-onset - Expt Ponset - SAFTPsat** LowP - SAFTP-T trace **
L, cm 457200 15000 ft
R, cm 3.49 2.5 inch ID
VZ, cm/s 240.01
Time 2 months
Operating parameters
Kuwait Marrat well – Field case 2Simulator prediction
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
0
0.2
0.4
0.6
3000 4000 5000 6000 7000 8000
Well depth, ft
Th
ick
ne
ss
, in
Development of Asphaltene deposition simulator Development of Asphaltene deposition simulator – I.– I. Thermodynamic module.Thermodynamic module. Deposition module.Deposition module.
Successful application of the simulator to Successful application of the simulator to predict asphaltene deposition in capillary predict asphaltene deposition in capillary experiments.experiments.
Simulator used for deposition prediction in well Simulator used for deposition prediction in well bores.bores. Two field cases studied. Thermodynamic Two field cases studied. Thermodynamic
model of the live oil was developed and model of the live oil was developed and coupled with the deposition module to coupled with the deposition module to predict deposition in well bores.predict deposition in well bores.
A good qualitative and quantitative match A good qualitative and quantitative match between reported field data and simulator between reported field data and simulator predictions has been obtained.predictions has been obtained.
Summary
Microsoft Excel interface for ADEPT
xYZ
Scaling up issues of Scaling up issues of kinetic parameterskinetic parameters
Future ActivitiesFuture ActivitiesProtocol for deposition Protocol for deposition
predictionpredictionSteps to be followed, Steps to be followed,
Tests to be conducted, Tests to be conducted, Parameters to be determined.Parameters to be determined.
Propose set of experiments Propose set of experiments to be performed to obtain to be performed to obtain kinetic parameters used in kinetic parameters used in
the simulation tool.the simulation tool.
Obtain more Obtain more capillary capillary experiment dataexperiment data and and compare simulator compare simulator predictions.predictions.Obtain Obtain field case datafield case data and and compare simulator compare simulator predictions.predictions.
Model improvement Model improvement to address to address limitations of the limitations of the present simulator.present simulator.
Incorporate effect of Incorporate effect of agingaging
Version I to be used in conjunction with flow simulators – Version I to be used in conjunction with flow simulators – sensitivity analysis of operating parameterssensitivity analysis of operating parameters
Operating guidelines to reduce deposition probabilityOperating guidelines to reduce deposition probability
AcknowledgmentsDeepStar
Chevron ETC
Schlumberger
New Mexico Tech
Infochem
VLXE