A publication of
CHEMICAL ENGINEERING TRANSACTIONS
VOL. 35, 2013
The Italian Association
of Chemical Engineering
www.aidic.it/cet
Guest Editors: Petar Varbanov, Jiří Klemeš, Panos Seferlis, Athanasios I. Papadopoulos, Spyros Voutetakis
Copyright © 2013, AIDIC Servizi S.r.l.,
ISBN 978-88-95608-26-6; ISSN 1974-9791
Simulation of Waterflooding, Carbon Dioxide Flooding and
Water-Alternating-Carbon Dioxide: Comparative Evaluation
Wantanee Teerasukakula, Farshid Torabib, Chintana Saiwana*
a
b
Petroleum and Petrochemical College, Chulalongkorn University, Bangkok 10330, Thailand
Petroleum Technology Research Centre, University of Regina, SK, Canada S4S 0A2
Chintana.sa@chula.ac.th
The statically review of world energy indicates that the global oil production is still not enough for current
global oil consumption levels. This problem leads to consideration of unconventional resources, including
heavy oil. This paper presents a comparative evaluation of three oil recovery processes: waterflooding,
carbon dioxide (CO2) flooding, and water-alternating-carbon dioxide (CO2-WAG) to enhance heavy oil
recovery based on laboratory experiments and simulations. The experiment was designed by using two
different sand packs which were used to acquire different permeabilities of 12 and 40 darcy. Eleven
experiments were operated at a low injection pressure of 345 kPa and low temperature of 25 C on two
types of oil with a viscosity of 440 cp and 1500 cp. The hybrid grid system from IMEX CMG was used and
the grid refinement option was applied in order to model the radial flow parallel to the horizontal well. From
the experiments indicated that waterflooding can produce the highest recovery factors for both types of oil
and sand packs. Oil viscosity had a greater effect of oil recover than the absolute permeability for all types
of oil recovery methods. The simulation results had the same trends of oil production as the experimental
data.
1. Introduction
Energy from petroleum production still plays a significant role in industries and communities all around the
world. A statistical review of world energy indicated that global oil consumption had grown 0.7 % to reach
88 million barrels per day from the previous year, while oil production has increased by 1.1 million barrels
per day, or 1.3%. Thus, the global supply of oil production is less than the global oil demand. Conventional
light oil is always the first choice, if possible, due to low production costs. Consequently, new supplies from
exploitation of unconventional resources are necessary. The unconventional resources including oil sands,
extra-heavy oil, biofuel, coal-to-liquid, gas-to-liquid, and shale oil grow on an average of 4.6 % per year
(Rajnauth, 2012). In this study heavy oil is considered because of the increasing importance of supplying
energy in Canada. Heavy oil properties include viscosity ranging from 1,000 to 10,000 cp, low API gravity,
high carbon residues, low hydrocarbon to carbon ratio and high content of asphaltene, nitrogen, heavy
metals and sulphur. Typically, the formation of heavy oil occurrences are around 30% porosity, high
permeability, shallow depth less than 1,000 m, unconsolidated sand deposit, thickness ranging from 15 m
to 300 m (Ali and Abad, 1976, Selby et al., 1989, Brook and Kantzas, 1998, Mai and Kantzas, 2010).
Traditionally, the production of oil reservoirs happens in three stages: primary, secondary, and tertiary.
Primary recovery is the production of oil from the reservoir under natural force, and recovers approximately
5 - 20% of the original oil in place (OOIP). Secondary recovery, which refers to gas, water, and a
combination of both as an injection fluid to raise or maintain the reservoir, could be increased up to 30 –
40% of OOIP. (Hadia et al., 2011, Harrasi et al., 2012, Winoto et al., 2012) In this study, the secondary
recovery of waterflooding, carbon dioxide flooding, and water-alternating-gas (WAG) were considered in
order to improve the recovery of the original oil in place.
Reservoir simulation has been developed into the flexible and widely used tools in reservoir engineering.
The reservoir simulation program is used to predict the future performance of oil and gas reservoirs over a
wide range of operating conditions. Simulation is faster, cheaper, and more reliable than other methods for
prediction of performance. A reservoir model was divided into a number of grid blocks. Each block
corresponded to a designated location in the reservoir condition which included porosity, permeability,
relative permeability, etc. The IMEX module of the CMG reservoir simulator was used for simulation this
study. The objective of this study was a comparative evaluation of three types of oil recovery methods in
heavy oil reservoirs.
2. Experimental
2.1 Data Collection
The collection of experimental data included core dimension, oil viscosity (o), permeability, pore volume
(PV), porosity, connate water saturation (Swc), initial oil saturation (Soi), residual oil saturation (Sor), and
recovery factor (RF). The core dimensions from the experiment are shown in Table 1 and the 11
experimental data are shown in Table 2.
Table 1: Core dimensions
Dimension
Length (cm)
Diameter (cm)
Area (cm2)
Volume (cm3)
Core Flood
9.55
2.57
5.18
49.47
Table 2: Experimental data
Test
1
2
3
4
5
6
7
8
9
10
11
Method
Waterflood
Waterflood
Waterflood
CO2 flood
CO2 flood
CO2 flood
WAG(1:1)
WAG(1:1)
WAG(1:1)
WAG(1:2)
WAG(2:1)
µo
(cp)
440
440
1500
1500
440
1500
440
1500
1500
1500
1500
Permeability
(Darcy)
39.90
38.60
11.40
41.51
38.60
11.40
43.00
40.60
12.60
41.95
42.76
PVs
(cm3)
17.41
18.40
19.40
17.41
18.40
19.40
18.40
17.41
19.40
20.39
19.40
Porosity
(%)
36.44
38.53
40.61
36.44
38.53
40.61
36.10
36.44
40.61
40.01
38.05
Swc
Soi
Sor
Recovery Factor
8.07
12.50
5.13
16.12
26.09
20.60
2.18
3.48
0.49
10.74
13.12
91.93
87.50
94.87
83.88
73.91
79.40
97.82
96.52
99.51
89.26
86.88
37.78
41.71
44.73
43.23
53.12
59.11
51.49
57.88
59.94
66.57
65.09
58.91
52.33
52.85
48.46
28.13
25.55
47.36
40.02
39.77
25.41
25.07
In the previous experiment, two types of oil, with a viscosity of 440 cp and 1500 cp, and two sand pack
permeability of 11 to 43 μm3 were used to operate in this experiment. All of the experiments were
constantly operated at 25 °C and 345 kPa. The waterflooding injection rate of 0.112, 1.124 and 5.62 cm3/
min was used continuously for 1.5 PV. The injection rate for CO2 flooding was 1 cm3/ min for 4.5 PV. The
injection rate for water-alternating-carbon dioxide (CO2-WAG) performed at 1 cm3/ min for 4.5 PV for
processes that used a 1:1 CO2/water slug ratio and processes that used a 1:2 and 2:1 CO2/water slug
ratios were used an injection period of 2.5 pore volume (PVs). The water and gas were injected from one
side of the sand pack and oil was produced from the other side.
2.2 Conversion of Oil Viscosity to API Gravity
In order to create API gravity for input into builder CMG, the empirical correlation equation was used
 od  10 ( 0.71523API  22.13766) T ( 0.269024API 8.268047)
(1)
where µod is viscosity of dead oil (cp), T is temperature (°F) and API is American Petroleum Institute
gravity. The oil viscosity of 440 cp, with a temperature of 77 °F (25 °C) gave API gravity of 19. The oil
viscosity of 1500 cp, with a temperature of 77 °F (25 °C) gave API gravity of 16.
2.3 Simulation Model
In this study, a compositional simulation model was built by using software from CMG Computer Modeling
Group Limited, Canada; (Version 2009.11). Implicit-Explicit (IMEX) module is a three-phase black oil
reservoir simulator for modeling primary depletion and secondary recovery processes in oil and gas
reservoirs. In this study, the IMEX module was used for modeling the secondary recovery process in
heavy oil reservoirs.
A cartesian grid block model consisted of only one block with the three dimensions of length (J), width (I)
and depth (K). The corresponding dimension of the cartesian grid block model is 9.552.572.57 cm. The
shape of the sand packs was cylindrical; however, it was converted to rectangular area. Homogeneous
porosity and permeability were assumed for all directions. The hybrid grid option in the IMEX module was
used to refine the cartesian grid block in order to model the radial of sand pack to be parallel to the
horizontal well. The hybrid grid option was refined by using 4 grids in θ-direction, 10 grids in r-direction,
and 40 grids in j-direction. This refinement simulation model consisted of 1600 grid blocks. Figure 1 shows
the refined model in two dimensions of width and depth (2D-IK), two dimensions of length and depth (2DJK) and three dimensions (3D).
(b)
(a)
(c)
!
Figure 1: Schematic of simulation model shows the refined model in hybridization (a) 2D-IK view
(b) 2D-JK view (c) 3D view.
In the simulation models, the experimental data, such as oil viscosity, porosity, and permeability were
input. Two types of oil, with a viscosity of 440 cp and 1500 cp were used. Additionally, the two sand pack
permeabilities were used to obtain the varying permeabilities ranging from 11 to 43 μm3. All of the
simulation models were constantly operated at 25°C and 345 kPa. The injection rate for waterflooding
consisted of 0.112, 1.124 and 5.62 cm3/ min, which was used continually for 1.5 pore volume (PVs). The
injection rate CO2 flooding performed at 1 cm3/ min for 4.5 pore volume (PVs). The injection rate for wateralternating-carbon dioxide (CO2-WAG) performed at 1 cm3/ min for 4.5 PV for processes that used a 1:1
CO2/water slug ratio and processes that used a 1:2 and 2:1 CO2/water slug ratios were used an injection
period of 2.5 PV. From the experimental procedure, water and gas were injected from one side of the sand
pack and oil was produced from the other side. Thus, one producer and one injector were considered
perforated for simulation model at blocks (1,1,1/1,1,1/1,1,1) as a producer and (1,1,1/1,10,1/1,1,4) as an
injector. The producer in the experiments had a well constraint of minimum bottom hole pressure of 200
kPa.
2.4 Error between Experimental Result and Simulation Result.
A comparison between the experimental results and simulation results were calculated using average
absolute relative error (AARE). The error percentage was calculated for each test and a different value of
recovery factor (RF) was obtained for each 0.2 pore volume (PV). Equation 2 was applied to calculate the
error percentage.
AARE 

1 N  Expi  Simi
 100 


N i 1 
Expi

(2)
3. Result and Discussion
3.1 Waterflooding
A plot of RF and PV injected was used for comparative evaluation. Figure 2 shows comparison of three
waterflooding simulations with different oil viscosities and sand pack permeabilities. Test 1 and test 2
compared two oil viscosities. Test 2 and test 3 compared two sand pack permeabilities (table 2). The
waterflooding with the lowest viscosity and highest permeability had the highest recovery factor. The effect
of viscosity and sand pack permeability demonstrated that increasing oil viscosity from 440 cp to 1500 cp
decreased the recovery factor to  12% and increased the sand pack permeability from 11.4 darcy to 38.6
darcy, with had no major change in recovery factor.
Figure 2: Comparison of three waterflooding
simulations with different oil viscosities and sand
pack permeabilities.
Figure 3: Comparison of three carbon dioxide
flooding simulations with different oil viscosities
and sand pack permeabilitie
3.2 Carbon dioxide flooding
Figure 3 shows the comparison of three CO2 flooding simulations with different oil viscosities and sand
pack permeabilities. Test 4 and test 5 compared two oil viscosities. Test 5 and test 6 compared two sand
pack permeabilities (Table 2). All graphs displayed S-shaped behaviour. This behaviour can be explained
by CO2 diffusion into the oil within the porous media, which caused a lower recovery rate at initial after CO2
achieved the equilibrium condition between CO2 and oil. Oil recovery increased continuously until it
reached oil breakthrough. The results showed that the CO2 flooding test with low viscosity and high
permeability had the highest recovery factor. Moreover, it showed that the acceleration of CO2 diffusion
rate could produce more oil recovery. The effect of viscosity and sand pack permeability demonstrated that
increasing oil viscosity from 440 cp to 1500 cp decreased the recovery factor by  40 % and increased
sand pack permeability from 11.4 darcy to 38.6 darcy, with had no major change in recovery factor.
Water-alternating-carbon dioxide flooding
Four simulation tests were performed with 1500 cp oil viscosity and the other with 440 cp oil viscosity
(Figure 4). Test 7 and test 9 was performed at a carbon dioxide/water slug ratio of 1:1 while test 10 and
test 11 was performed at CO2/water slug ratios of 1:2 and 2:1, respectively. Test 7 and test 8 compared
two oil viscosities. Test 8 and test 9 compared two sand pack permeabilities. Test 8, test 9 and test10
compared three different CO2/water slug ratios of 1:1, 1:2, and 2:1. The CO2-WAG of the low viscosity and
high permeability test had the highest recovery factor. Moreover, it showed that the CO2/water slug ratio of
1:1 had the highest recovery factor compared to CO2/water slug ratios of 1:2 and 2:1. The effectiveness of
viscosity and sand pack permeability test demonstrated that increasing oil viscosity from 440 cp to 1500 cp
decreased the recovery factor to  20 % and increased sand pack permeability from 12.6 darcy to 40.6
darcy, with had no major change in recovery factor.
Figure 4: Comparison of five CO2-WAG simulations with different oil viscosities, sand pack permeabilities
and CO2/water slug ratio.
3.3 Error between experimental and simulation results
The AARE calculated results are shown in Figure 5. The error between experimental and simulation
results for test 6 had the lowest AARE with a 4.29 % error rate and for test 3 had the highest AARE with a
13.09 % error rate. The maximum AARE error rate was less than 8 % error in every test except test 3.
Figure 5: AARE calculated for each tests and different recovery factor (RF) at each 0.2 PV.
The different enhanced oil recovery (EOR) methods, based on the simulation results of this study, shows
that waterflooding had the highest recovery factor followed by CO2-WAG and CO2 flooding. The trends of
the simulation results were similar to experimental data.
In this simulation, it was found that waterflooding is more effective than other flooding processes because
water has a higher viscosity than carbon dioxide. The mobility ratio between oil and water was less than
the mobility ratio between oil and CO2; therefore, waterflooding created more favorable displacement than
CO2 flooding.
The main notable carbon dioxide flooding in heavy oil is reduction in oil viscosity. The viscosity of oil
saturated with carbon dioxide is a function of temperature and pressure. In general, the lowest
temperature of heavy oil is approximately 25 °C and the lowest pressure is approximately 700 kPa
(Metwally, 1998, Naylor et al., 2000, Miller et al., 2003). However, the pressure in this simulation was
much lower than the regular pressure in a heavy oil reservoir. At this pressure carbon dioxide was not be
able to mobilize the oil as positively as the water. The main notable characteristic of water-alternatingcarbon dioxide in heavy oil is the reduction in oil viscosity by using carbon dioxide slug. Moreover, this
process uses the water displacement mechanism to produce more oil recovery.
As a result, it should not be consider that carbon dioxide flooding and water-alternating-carbon dioxide
process are necessarily less effective than waterflooding. Hence, different operating conditions should be
tested to obtain the appropriate conditions for enhancing heavy oil recovery.
4. Conclusions
Three different enhanced oil recoveries (EOR), based on the previous experimental data, were performed.
The results showed that waterflooding produced the highest amount of recovery factor followed by wateralternating-carbon dioxide and carbon dioxide flooding. The results from all simulation models showed that
the low viscosity of heavy oil had a higher recovery factor than the high viscosity of heavy oil and sand
pack permeability had no major change in recovery factor. The trends from the simulation results were
similar to the experimental data.
5. Acknowledgements
I would like to thank University of Regina, Petroleum Technology Research Centre for providing funding
and acknowledge the contribution of B.A. Paquin and N.J. Rumpel for performing the experiments. Also
thanks to Petroleum and Petrochemical College and Petroleum and Petrochemical Research Unit Center
for Petroleum, Petrochemicals, and Advanced Materials, Chulalongkorn University, Thailand.
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