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7/31/2019 EAGE VAPEX 2011
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Hadi Zainee (Petronas), Abdullah Alkindi (Petroleum Development Oman) &
Ann Muggeridge (Imperial)
16th European IOR Symposium, 11th-14th April 2011
7/31/2019 EAGE VAPEX 2011
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Outline
What is VAPEX?
Prediction of oil drainage rate
Method
Experimentally validated numerical model
Results
Conclusions
Acknowledgements
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What is VAPEX?
Vapour Extraction Improve heavy oil recovery by injecting vaporised solvent
Horizontal injector and producer
Analogue of SAGD
Oil viscosity reduced by diffusion driven mixing between oil and solvent
More energy efficient than SAGD
Better in thin reservoirs or those underlain by an aquifer
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Prediction of Oil Drainage Rate
Butler & Mokrys (1989), analytical expression for oil rate:
But actual qo >> Butler & Mokrys qo Experimental results including Dunn et al., 1989; Lim et al., 1996; Das and Butler, 1998; Boustani and Maini,
2001; Oduntan et al., 2001; Karmaker and Maini, 2003; Yazdani and Maini, 2005; Kapadia et al., 2006
Explanations include
Increased rate of mixing due to Convective dispersion
Capillary films
Counter-current flow
Higher effective drainage height
sooNSghkq 2
max
min
lnd
)1(c
cs
s
s c
Dc
N
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Prediction of Oil Drainage Rate
Alkindi (2009)
1. performed VAPEX experiments using analoguefluids
Glass bead pack representing 2D section of a reservoir
Producer at bottom & Injector mid-height Could be rotated to give lower aspect ratio
Ethanol (solvent) and glycerol (oil)
All input data measured experimentally includinglongitudinal and transverse convective dispersion
2. Performed numerical simulations (STARS) usingexperimental data as input
To predict experimental behaviour
Injection I
Production
30 cm
15 cm
Injection II
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Prediction of Oil Drainage Rate
Alkindi (2009), results confirmed underprediction of oil rate by Butler &Mokyrs model
Using convective dispersion improves prediction, but still too low
Numerical simulation predicted experimental results
need improved analytic model
Modelheight
cm
Injection ratecm3/hr
Measured oildrainage rate
cm3/hr
Butler and Mokrys drainage rate
Molecular diffusioncm3/hr
Longitudinal dispersioncm3/hr
300.6 0.48
0.320.41
1.2 0.75 0.47
15 0.6 0.41 0.23 0.28
Simulationcm3/hr
0.46
0.75
0.38
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This work
Propose an improved analytic model to predict oildrainage rate
Validate the predictions of oil rate as a function of
Density difference ()
Viscosity ratio (M)
Permeability (k)
Reservoir thickness (h)
Reservoir aspect ratio (h/L)
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Modified Butler-Mokrys model
Original model (Butler & Mokrys, 1989)
=(1-cs)(0-s)
density difference between solvent anddraining solvent-oil mixture
Modified model
=(0-s)
density difference between solvent andundiluted oil
Ns larger,
qo largerWhy?
Butler and Mokrys model assumes VE
Drainage driven by spread of solvent chamber,segregated flow cf. Dietz (1953)
soo NSghkq 2
max
min
lnd)1(
c
c
ss
s cDc
N
max
min
lnd1
c
c
ss cDN
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Methodology
Numerical simulation using experimentally validated model of Alkindi (2009)
Analogue fluid properties (ethanol=solvent, glycerol=oil)
Grid size 60 1 150
Model dimensions (x,y,z) 30 0.5 15 cm
Porosity 0.4
Permeability 43.3 D
Reference pressure 101 kPa
Reference temperature 20C
Longitudinal dispersion coefficient 8.9 10-10
m2/s
Ethanol density (kg/m3) 790
Ethanol viscosity (mPa s) 1.2
Glycerol density (kg/m3) 1261
Glycerol viscosity (mPa s) 1390
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Methodology
Use numerical model validated by comparison between experimental andsimulation results (Alkindi, 2009)
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1 1.2
PVI
Rate,
cm
3/hr
Ex p. 1.2 ml /hr (Mode l he ight 30 cm) Sim. 1.2 ml /hr (Mode l he ight 30 cm)
Ex p. 0.6 ml /hr (Mode l he ight 30 cm) Sim. 0.6 ml /hr (Mode l he ight 30 cm)
Ex p. 0.6 ml /hr (Mode l he ight 15 cm) Sim. 0.6 ml /hr (Mode l he ight 15 cm)
0.01 0.60 1.0 PVI
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Good agreement between modified analytic model and simulation
Original Butler & Mokrys model underpredicts rate
Oil drainage rate increases with solvent oil density difference
Convective dispersion is not important at high density differences
Consistent with experimental results of Alkindi et al. (2011)
0
0.005
0.01
0.015
0.02
0.025
0 0.2 0.4 0.6 0.8 1 1.2
OilRa
te(cm3/min)
PVI
= 118 kg/m3
= 235 kg/m3
= 470 kg/m3
= 940 kg/m3
= 1400 kg/m3
= 2350 kg/m3
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0 10 20 30 40 50 60 70 80
OilR
ate,cm3/min
, kg/m3
Simulation Results
_so and Longitudinal Dispersion
_so and Molecular Diffusion
_sm and Longitudinal Dispersion
_sm and Molecular Diffusion
soo NSghkq 2
max
min
max
min
max
min
lnd1
lnd1
lnd)1(
c
c
sLs
c
c
ss
c
c
ss
s cKNcDNcDc
N
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Results:Viscosity ratio and permeability
Good agreement between modified analytic model and simulation
Original Butler & Mokrys model underpredicts rate
Oil drainage rate
Increases with square root of permeability
Decreases as viscosity ratio (M) increases Analytic model breaks down for M
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Results:Aspect ratio and thickness
No dependency of oil drainage rate on aspect ratio for h/L < 1
Consistent with Butler & Mokrys model
Butler & Mokrys breaks down for h/L > 1
Flow no longer gravity dominated
Oil drainage rate dependency on thickness
Best matched with an exponent of 0.28
Butler & Mokrys and modification both predict an exponent of 0.5
soo NSghkq 2
max
min
max
min
max
min
lnd1
lnd1
lnd)1(
c
c
sLs
c
c
ss
c
c
ss
s cKNcDNcDc
N
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Conclusions
Presented an improved analytic model for predicting oil drainage rateduring VAPEX
Modification of the Butler & Mokrys (1989) model
Use density difference between pure oil and solvent
Investigated the analytic model predictions using numerical simulation
Numerical model originally validated by comparison with experiments ofAlkindi (2009)
The modified Butler & Mokrys model predicts simulation results for
Viscous oils, M> 1000
Thin reservoirs, h/L < 1
Convective dispersion is not important in VAPEX at high density differences
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Discussions and Recommendations
All simulations performed using FCM liquids on the laboratory scale
Numerical model assumptions consistent with Butler & Mokrys derivation
Further work is needed to investigate more realistic conditions
Field length scales
Sub-miscible fluids
A vapourised solvent
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Acknowledgements
We thank
Petronas for funding Hadis MSc studies
PDO for funding Abdullahs PhD studies
CMG for the use of the STARS reservoir simulator