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