application of asphaltene deposition tool (adept) simulator to field cases yi chen, anju kurup,...
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Application of Asphaltene Deposition Tool (ADEPT) Simulator to Field Cases
Yi Chen, Anju Kurup, Walter Chapman
Houston, April 29 2013
Department of Chemical & Biomolecular Engineering, Rice University
Outline
•Introduction
1. Asphaltene deposition issue2. The ADEPT simulator and application procedure
•Field case studies
•Summary
Asphaltene issue in flow assurance
Flow Assurance Prediction – Operator’s Savings:• Intervention cost to remove solids: ~
300K/well-dry tree, $3,500K / well – wet tree.
• Loosing the well: ~ $50,000K to replace the well with a side track.
• Losses due to downtime: ~ $ 700K /day (for prod. of 7,000bbls/day)
Deposition mechanismad
vecti
on
diffusion
CDaCDarZ
C
Z
C
Pe
Cd
2agp2
21
Precipitation &
Re-dissolution kinetics:
Dimensionless parameters:
Initial & boundary condition:
Kurup, A.S. et al., Energy & Fuels. 2011, 25, 4506–4516 5
z
dd
z
pp
z
agag
z
zeqeq
ff
V
LkDa
V
LkDa
V
LkDa
D
LVPe
L
vt
L
zZ
C
CC
C
CC
C
CC
,,,
,,,0
'
0
'
0
'
0,0,0 )()0,(),0(
LzZZ Z
CCC
pff
eqfpdissdiss
eqfeqfpp
rz
CC
CwhenCCDakr
CwhenCCCDar
,
,
Mathematical model
Thermodynamic module
Depositionmodule
Composition, Liquid density,Bubble point,
GOR, AOP, SARA Asphaltene instability,
Ceq
Depositionprofile,
Thickness,Pressure drop
Kinetic parameters
Operational conditions
Ceq
P-T profile in wellbore/pipeline
6AOP--- Asphaltene onset pressureCeq --- Asphaltene equilibrium concentration
ADEPT simulator structure
Appropriate Parameters
① Characterization / Recombination
②Tuning parameters to match Pb, liquid density, AOP
③Phase behavior prediction
④ Ceq calculation with P-T profile input
MW & mass percentages of all (Pseudo-) components
Asphaltene instability
Asphaltene equilibrium concentration, Ceq
Fluid composition, GOR, SARA
Deposition module
7
Thermodynamic modeling
The kinetic constant of deposition in capillary-scale
⑤ determine kp & kag using reaction model
⑥fitting kd(cap) to reproduce capillary deposition flux
⑦scaling up of kd(cap) to k*d
The kinetic constants of precipitation and aggregation
The kinetic constant of deposition in field-scale
Asphaltene deposition flux, thickness, pressure drop
The asphaltene precipitated amountsThermodynamic module
8
⑧input Ceq , kp , kag , k*d , operational conditions
Deposition modeling
field case 1
9
10
Wellbore pressure loss is approximately 10 psi per day in the first several weeks after wellbore wash;
GOR decreases 60 ScF / STB over 4 months;
GOR increases with gas injection;
GOR sensitivity analysis is needed.
Deepwater Gulf of Mexico wellbore
90 140 190 240 2900
1000
2000
3000
4000
5000
6000
7000
8000
9000onset P @GOR=669
bubble P @GOR=669
lower onset P @GOR=669
P-T trace
onset P- exp. @GOR=669
bubble P-exp. @GOR=669
T / F
P /
Psi
11
Phase behavior prediction(wellbore)
PC-SAFT EoS (VLXE / Multiflash / PVTsim)
90 140 190 240 2900
1000
2000
3000
4000
5000
6000
7000
8000
9000onset P @GOR=1000
onset P @GOR=669
onset P @GOR=549
bubble P @GOR=1000
bubble P @GOR=669
bubble P @GOR=549
lower onset P @GOR=1000
lower onset P @GOR=669
lower onset P @GOR=549
P-T trace
onset P- exp. @GOR=669
bubble P-exp. @GOR=669
T / F
P /
Psi
12
Phase behavior prediction(wellbore)
GOR
GOR
Extract kp & kag
eqfpf
2pag
S
2pageqfp
p
CCkdt
dC
Ckdt
dC
CkCCkdt
dC
Aging Time
(hour)Precipitate amount (g)
0.166667 0.01320.333333 0.0165
0.5 0.01692 0.01674 0.0172
7.5 0.018712 0.018224 0.02
kp / s-1 2.5×10-2
kag / s-1 1.7×10-3
Batch experimental results from NMT
0 5 10 15 20 25 300.2
0.4
0.6
0.8
measured
Predicted
Time, hr
pre
cip
ita
ted
ag
gre
ga
tes
co
nc
en
tra-
tio
n, d
ime
ns
ion
les
s
14Wang, J. X., et al., Dispersion Sci. Technol. 2004, 25, 287–298.
Capillary deposition test
kd(cap) = 2.11×10-3 s-1
Fitting kd (cap) to make the peak of deposition flux curve predicted match the experimental observation.
15
Fitting kd(cap)
0.00 40.00 80.00 120.000.00
40.00
80.00
120.00
160.00
tubing lenth / inch
depo
sition
flux
, g/
m2/
day
Simulation with fitted kd (cap)
Expt
Scale up kd(cap) to k*d
kd (cap) k*d(mom) = 4.31×10-6 s-1
6
1
)(2
KTD
k
D
m
capd
m
ScFkk capdd )(*
1
2
RScF
8/77.62 etmom RD
Kurup, A.S. et al., Energy & Fuels. 2012, 26 (9), pp 5702–5710
17
Deposition flux prediction (wellbore)
0 10,000 20,000-1.0E-03
4.0E-03
Distance ( ft )
CF-C
EQ
(g/m
l)
0 10,000 20,0000.0E+00
4.0E-08
8.0E-08
1.2E-07
Depo
sition
flux
(g/
cm2/
s) I II III
Precipitated particles
Flow out
Aggregation
Deposition
Flow in
CF-CEQ = 0
Re-dissolution starts
0 10,000 20,0000.00
0.04
0.08
0.12
GOR=1000 GOR=669 GOR=549
Distance / ft
Dep
osit
thic
knes
s /
inc
h
18
14 days
Deposit thickness prediction (wellbore)
19
GORSCF/STB
Frictional pressure drop Psi /day
549 9.45669 10.10
1000 10.89≈ 10 Psi / day (Based on 14 days)
25.0
2
Re
316.0
2
f
gg
v
D
LfPfriction
Frictional pressure drop (wellbore)
field case 2
20
21
• Asphaltene problem is reported.
• The total pressure drop in the first 28 days is about 648 psi.
• The asphaltene deposition situation must be estimated.
Pipeline Gulf of Mexico
Pressure 5,284 psi
Temperature 177 ⁰F
Flow rate 13482 bbl/day
Diameter 5.137 inch4.881 inch
length 52389 ft
Field information (pipeline)
22
Phase behavior prediction (pipeline)
Kinetic parameters
0.2
0.4
0.6
0.8
0 5 10 15 20 25 30
Aging Time, hr
Pre
cipi
tate
d am
ount
/ as
pha
l. M
ass
in 1
ml m
ixtu
re
Predicted-Set1 Measured-Set1 Predicted-Set2 Measured-Set2
Simulation with fitted kd (cap)
Expt
kp / s-1 1.32×10-3
kag / s-1 7.29×10-5
kd(cap) = 1.43×10-3 s-1
k*d(mom) 3.25×10-6 s-1
k*d(lar) 1.73×10-6 s-1
k*d(mt) 4.50×10-7 s-1
24
0 10000 20000 30000 40000 50000
-0.1
-2.77555756156289E-17
0.1
0.2
0.3
Kd-Mom
Kd-Lam
Kd-MT
Distance (ft)
Dep
osit
thic
knes
s (in
)
Boundary layer Frictional ∆P(Psi)
Momentum 700
Laminar 605
Mass transfer 519
Field data= 648 Psi (28days)
Simulation results
25
1. ADEPT simulator can successfully predict the asphaltene deposition in wellbore/pipeline.
2. Onset pressure and bubble pressure increases significantly with GOR increases, but the effects on lower onset pressure can be neglected;
3. Deposit location changes with GOR.
Summary
26
• Jeff Creek• Jianxin Wang• Andrew Yen • Sai Panuganti • Jill Buckley • Vargas Francisco
Acknowledgments