Implementation Time of Chemical Flood
and Its Impact on Ultimate Recovery
Authors: Dr. Ali M. AlKhatib and Dr. Amar J. Alshehri
ABSTRACT
INTRODUCTION
During waterflooding processes, injected water can disconnect
oil droplets from the pores/throats of the reservoir as it flows
through. These disconnections are a consequence of capillary
effects, and the mobilization of oil through those pores/throats
is hindered afterward. This capillary trapping makes mobilizing the remaining oil in place by any enhanced oil recovery
(EOR) process very challenging. Chemical flooding has been
identified as an effective EOR method. It is usually implemented in tertiary mode, where field development has reached
a mature level. At this stage, the efficiency of waterflooding
processes in terms of mobilizing any remaining oil has declined,
due to the above described capillary trapping.
Chemical EOR processes, such as surfactant flooding, are
used to reduce this trapping and mobilize the remaining oil.
Surfactants reduce the interfacial tension (IFT), which consequently reduces the capillary pressure effects responsible for
trapping the oil. Although most EOR processes are implemented
in tertiary mode, earlier implementation is more desirable because that makes capillary trapping less prominent. This study
investigates the impact of the timing of surfactant flood implementation after waterflooding, specifically on ultimate recovery and net present value (NPV), given this capillary trapping.
A series of numerical experiments was conducted to test this
effect while accounting for operating expenses associated with
both flooding options. Capillary pressure curves for the waterflood case and the chemical flood case were added to the
model to incorporate capillary trapping effects. Then the
chemical flood implementation time was varied to evaluate its
impact on the ultimate oil recovery. These experiments were
performed on a number of stylized reservoir models with varying field size: a 1D coreflood model, the PUNQ-S3, the SPE10
reservoir model and a synthetic fractured reservoir model that
is analogous to a Middle Eastern carbonate fractured reservoir.
The implementation used an algorithm that was written in
MATLAB and coupled with a commercial reservoir simulator.
Results show that the sooner chemical EOR is implemented,
the higher the ultimate recovery. Due to the relatively large initial investment and operating expenses associated with chemical flooding, however, waterflooding remains more attractive
from an NPV perspective.
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Conventional recovery methods, e.g., waterflooding, usually
recover about 50% of the oil in place1. To increase oil recovery, more sophisticated engineering techniques are implemented, including advanced reservoir management practices
and/or enhanced oil recovery (EOR) methods. Though it is
common to implement such recovery enhancing techniques after exhausting the waterflood option, it has been suggested in
the literature that their earlier implementation might lead to
higher ultimate recovery. The objective of this work is to investigate, using reservoir simulation, the impact of the implementation time of enhanced techniques on ultimate recovery and
net present value (NPV). Surfactant floods were selected as the
advanced method to be tested while varying the injected pore
volumes (PVs) of the preceding waterflood. Two main dynamic
effects were investigated: relative permeability and capillary
pressure.
Using surfactants to reduce oil-water interfacial tension
(IFT) and consequently improve oil recovery has been discussed in the literature since the 1920s2. Surfactant floods
were selected for this investigation because they impact both of
the dynamic effects of interest: relative permeability and capillary pressure. When IFT approaches zero, relative permeability
curves become straight lines with no residual saturations, indicating the complete miscibility of the involved phases, e.g., oil
and water3, 4. Capillary pressure curves are reduced relative to
the magnitude of IFT reduction1.
The impact of implementation time on ultimate recovery
has been reported in the literature on several occasions. In
Smart Waterflooding research, the ionic composition of injected water is tuned to alter rock wettability toward more favorable conditions. It was observed in coreflood experiments
that injecting the Smart Waterflooding slug as early as possible
increases the ultimate recovery. The cores used in these experiments were carbonate cores5, 6. Similar observations have been
made in fractured carbonates7, 8, where surfactant flood experiments were conducted in secondary mode and tertiary mode.
The secondary mode experiment led to low recoveries initially,
but ultimately it recovered 7% more than the tertiary case. AlSofi and Blunt (2014)9 used a streamline-based simulator to
optimize polymer flood design. They found that higher recov-
eries are obtained at the earlier implementations of the polymer flood; a continuous polymer slug was used in this work.
Upon oil reservoir discovery, the oil phase is relatively
closely connected. Continuous waterflooding, however, leaves
behind disconnected droplets that are unswept due to capillary
trapping. As a result, it becomes harder for the tertiary recovery process to displace the remaining oil. Our claim is that the
sooner EOR is implemented, the more oil is recovered. The
basis of this claim is that it is easier to displace large droplets
than small droplets.
Assuming a constant pressure gradient, — P, across a reservoir, the pressure drop, ǵ P, across a droplet with length, L,
becomes:
DP = —P x L
(1)
Equation 1 shows that a larger pressure drop is achieved in
a large droplet than in a small droplet. As a result of that,
large droplets are easier to mobilize and more capable of overcoming entry pressures — capillary pressures — throughout
the porous media.
ness of 0.11 ft. Permeability was assumed to be homogeneous,
and surfactant flooding was initiated after 0, 2, 4, 6, 8 and 10
injected PVs from waterflooding. This was done for the different wettability cases previously shown in Fig. 1.
2D Model: SPE10 Layer 1
This reservoir model is based on layer 1 of the SPE10 model10.
This layer is discretized into a 60 × 220 grid. More details on
the model properties used can be found in Alkhatib et al.
(2013)11. A quarter of a five-spot pattern was assumed, Fig. 2.
3D Model: PUNQ-S3
This model uses the permeability field and the geometry of the
PUNQ-S3 reservoir model12 with a total number of 3,800 (19
× 25 × 8) grid cells, Fig. 3. This example model uses the same
fluid data as the 1D reservoir model. The well pattern used for
the PUNQ-S3 reservoir model is based on placing four producers
METHOD
A series of numerical experiments was performed to investigate the effect of implementation time on ultimate recoveries.
The initiation time of surfactant flooding was varied over a
range of times with respect to the length of the preceding waterflood. These simulation runs were performed for three sets
of relative permeability and capillary pressure curves, based on
wettability conditions, Fig. 1. This workflow was applied to
five different reservoir models to capture the effects of the
same range of implementation times in models having variable
sizes and degrees of heterogeneity. All surfactant-related properties were fixed for the different reservoir models, Appendix A.
Fig. 2. 2D reservoir model based on layer 1 of the SPE10 model.
1D Model: Surfactant Coreflood
This coreflood model is discretized by an 80 × 1 × 1 Cartesian
grid with a length of 0.745 ft, a width of 0.11 ft and a thick-
Fig. 1. Relative permeability and capillary pressure curves for the water-wet
(WW), mixed-wet (MW) and oil-wet (OW) cases.
Fig. 3. The 3D PUNQ-S3 model showing the permeability field and well
placements. Scale is logarithmic.
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on a structurally higher position — a crestal area — within
high permeability streaks and placing one injector at a structurally lower position. The surfactant flooding initiation times
were set at 0, 0.2, 0.4, 0.6, 0.8 and 1 injected PV from a preceding waterflood.
3D Model: Modified SPE10 Reservoir Model (SPE10M)
This reservoir model is based on an upscaled section of the
SPE10 model10. It represents the upper lithology, the Tarbet
Formation, of the original reservoir model; the top 35 layers
are upscaled to a 20 × 55 × 7 grid with a total number of
7,700 grid cells. The original SPE10 model reservoir and fluid
data are used. The well placement pattern used is an inverted
five-spot pattern, Fig. 4. Surfactant flooding initiation times
were set at 0, 0.2, 0.4, 0.6, 0.8 and 1 injected PVs. Simulation
constraints are presented in Appendix A.
For this reservoir model case study, the NPV was calculated
and obtained for all surfactant flooding initiation times. The
NPV was obtained based on calculating the net revenue as:
(2)
where t = 1 is the initial simulation time step and T is the final
simulation time step, Ro is the oil revenue, and Cwi, Cwp and
Cc are the water injection, water production and chemical
costs, respectively. CAPEXC is the additional capital expenditure incurred for the chemical EOR process. Continuous discounting was assumed using a fixed rate of 6%. The oil price
was assumed to vary with time and was modeled as a mean
reverting stochastic process known as the Ornstein-Uhlenbeck
process13. The water production and injection costs were correlated with the oil price — they were assumed to be 2% of
the oil price at the current time step. The surfactant cost was
assumed to follow a uniform distribution with bounds
[2.2,6.6] $/kg. More details on the assumptions and approaches used to model the oil price and the capital expenditure for chemical EOR facilities are found in Alkhatib et al.
(2013)11 and Alkhatib and King (2014)14.
To produce a statistically convergent result for the NPVs
calculated, 1,000 realizations over time were sampled for the
oil price and chemical cost, and the results were obtained by
taking the mean of the NPV over these realizations for each
surfactant initiation time.
Discrete Fracture Network Model
Fig. 4. The modified portion of the SPE10 model, showing permeability and well
placement. Scale is vlogarithmic.
The discrete fracture network (DFN) model was designed to be
analogous to a naturally fractured carbonate reservoir. It was
built using corner point geometry and is comprised of 72 × 78
× 6 layers. The model dimensions were assumed to be 7.2 km
× 7.8 km with a reservoir thickness of 14 m, Fig. 5. A fracture
network was modeled using DFNs. This was performed using
the PETREL software package15. The matrix permeability was
generated using a two-point geostatistical method known as
the moving average method16. Once the DFN is generated in
this model, the permeability, shape factor and porosity are upscaled using the analytical Oda approach17. This upscaling
Fig. 5. The DFN reservoir model showing the well placement pattern. Injector well symbols are marked in white and the producer well symbols are marked in black.
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method is used because it provides reasonable approximations
quickly compared to flow-based methods. It is important to
note, however, that this method is not accurate for grid cells
with low fracture intensity18.
The fracture density was defined as the P32 in the literature,
which is the fracture area divided by the volume. A value of
0.06 was used18. The fracture length was stochastically generated using a Power law with a shape factor of 2.1 and a scale of
25. The maximum fracture length was constrained to 1,000 m.
The fracture orientation was also stochastically generated and
assumed to follow the Fisher model with a mean dip of 90°, a
mean dip azimuth of 40° and a concentration of 8. Finally, the
aperture was also assumed to be random and followed a lognormal distribution of mean 300 μm and a standard deviation
of 50 μm. The production strategy for the carbonate reservoir
modeled here is based on a number of peripheral horizontal
water injectors and producers, while a series of infill inverted
five-spot patterns are used to produce the higher structural
region. The surfactant flooding once initiated was maintained
at a constant concentration of 1 wt%. The injection rate for
the field was controlled at a constant water injection rate of
15,900 m3/day.
RESULTS AND DISCUSSION
Recovery curves were obtained for the different geologic models previously discussed at three sets of relative permeability
and capillary pressure curves. The results are discussed here.
1D Model: Surfactant Coreflood
Figure 6 shows the recovery curves of the 1D model.
2D Model: SPE10 Layer 1
Figure 7 shows the recovery curves of the 2D model. Several
curves are plotted for each wettability condition where each
curve represents a different injected PV from the preceding waterflood. The highest recoveries were achieved in the water-wet
case, with lower recoveries in the mixed-wet case and lowest
recoveries in the oil-wet case. The ultimate recovery for each
wettability state remains constant regardless of the implementation time, but the recovery behavior changes with the implementation time. In this model, more oil is recovered faster with
an earlier implementation of surfactant flood. This behavior is
clearly visible in the oil-wet case, as seen by the separation
between the recovery curves.
Fig. 6. Recovery factor plots for different surfactant flooding initiation times for all wettability cases obtained from the 1D coreflood model.
Fig. 7. Recovery factor plots for different surfactant flooding initiation times for all wettability casesobtained from the 2D SPE10 Layer 1 model.
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3D Model: PUNQ-S3
Figure 8 again shows the recovery curves for the different wettability cases. Ultimate recovery behaved as observed previously, where recoveries were highest for the water-wet case and
lowest for the oil-wet case. Ultimate recoveries achieved for
the different initiation times were similar as the simulation
continued. While more oil is also recovered faster with an earlier implementation of the surfactant flood, this behavior is not
as visible as it was in the preceding SPE10 model.
3D Model: Modified SPE10 Reservoir Model (SPE10M)
Figure 9 shows the results for the modified SPE10 3D model.
Behavior similar to that in the previous models was observed,
where ultimate recoveries were highest for the water-wet case
and lowest for the oil-wet case. The ultimate recoveries for the
set of initiation times in a given wettability case converged after a long simulation horizon. The separation of the curves
once more indicated faster recovery with the early implementation; this behavior is similar in all wettability conditions.
The fact that faster recoveries are obtained with earlier implementation seems very rewarding in terms of NPV. But this
was not the case with the NPV model used for the simulations,
Fig. 10. At all wettability conditions, the greater the delay in
the implementation, the higher the NPV.
DFN Model
Figure 11 shows the recovery results of the DFN model. Similar behavior was observed, with ultimate recoveries highest for
the water-wet case and lowest for the oil-wet case. One difference was that the mixed-wet recovery curves were very similar
to those obtained for the water-wet case. The ultimate recoveries for the set of initiation times in a given wettability case converged after a long simulation horizon. The ultimate recovery
in all wettability conditions here, however, was much lower
than the recoveries achieved in the previous models. This is
mainly a result of the fractures in the system, which take up
most of the injected fluids with minimal imbibition to the matrix. The implementation time showed no effect on the ultimate recovery, but did have an effect, as previously shown, on
the quickness of recovery. This behavior is more visible in the
oil-wet case than in the water-wet case, which will significantly
impact the NPV calculation.
Figure 12 shows the NPV results. The results show a clear
Fig. 8. Recovery factor plots for different surfactant flooding initiation times for all wettability cases obtained from the 3D PUNQ-S3 model.
Fig. 9. Recovery factor plots for different surfactant flooding initiation times for all wettability cases obtained from the modified portion of the SPE10 model.
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Fig. 10. NPV plots for different surfactant flooding initiation times for all wettability cases obtained from the modified portion of the SPE10 reservoir model. Each NPV
curve is obtained as the mean of 1,000 NPV realizations incorporating oil price uncertainty in the time series.
Fig. 11. Recovery factor plots for different surfactant flooding initiation times for all wettability cases obtained from the DFN reservoir model.
Fig. 12. NPV plots for different surfactant flooding initiation times for all wettability cases obtained from the DFN reservoir model. Each NPV curve is obtained as the
mean of 1,000 NPV realizations incorporating oil price uncertainty in the time series.
trend favoring later initiation of surfactant flooding, given oil
price uncertainty in the time series. The NPV curves obtained
for the water-wet and mixed-wet cases were similar, while the
NPV curves for the oil-wet case showed much lower values.
This is in agreement with the lower oil mobility in the oil-wet
case.
Based on the experimental results in the literature and on
the simulation results of this study, it is clear that there is disagreement with the trends observed for ultimate recovery as a
function of initiation time of surfactant flooding. The dynamic
effects of the capillary pressure and the relative permeability
curves must be taken into consideration to capture the varying
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ultimate recovery behavior observed in the experiments in the
literature. Several studies have shown that dynamic capillary
curves should be used in simulations, rather than the static
curves that are often measured and used19-21. Such variations
between static and dynamic measurements will definitely impact the results of fluid-flow simulations.
To demonstrate how this behavior can alter the ultimate recovery and NPV of surfactant flooding, a numerical experiment was designed using the DFN reservoir model and
assuming that the initial behavior is oil-wet and that surfactant
flooding is initiated after 1 PV of water injection. This assumption was used as a proxy for the dynamic behavior of the relative permeability and capillary pressure curves with time as the
reservoir is produced. At this point, three different production
profiles were obtained by using the water-wet, mixed-wet or
oil-wet relative permeability and capillary pressure curves in
the reservoir simulation, Fig. 13.
The results show that in the water-wet and mixed-wet cases,
behavior after surfactant flooding initiation is nearly similar,
while if the oil wettability properties are maintained, recovery
and NPV are less. Although the differences in NPV among the
different wettability systems are more pronounced in Fig. 12
for any surfactant initiation time, with the oil-wet system
achieving markedly lower return, the results here for the dynamic wettability systems show that the NPV is dominated by
the recovery profile prior to the surfactant flooding initiation
point at 1 PV waterflooding, which in this test was based on
an oil-wet system. As a result, a sensitivity measure of when
the wettability changes as a function of the surfactant flooding
timing is required. It is important to mention here that the assumption used is that wettability changes due to waterflooding
prior to surfactant flooding and not due to alteration by surfactant adsorption post-surfactant flooding.
CONCLUSIONS
The objective of this study was to investigate the effect of dynamic capillary pressure and relative permeability on ultimate
recovery for chemical EOR. This was performed on a number
of simulation models ranging from a 1D coreflood model to a
3D fractured reservoir model. Three sets of relative permeability and capillary pressure curves were used to characterize water-wet, mixed-wet and oil-wet systems. For each set, different
implementation times were tested to see how significant the effect of the surfactant flooding implementation time would be.
Results were compared with the experimental results from the
literature.
It was observed from the simulations that a basic varying of
the implementation time does not impact the ultimate recovery, and so does not reflect what several experiments have
shown in the literature. There is an indication that early implementation usually leads to more favorable conditions and
eventually better recoveries. Therefore, it is highly recommended to reevaluate dynamic properties at different initial
conditions and correlate the results to the implementation
time. The newly evaluated dynamic properties are now used in
a reservoir simulator to the corresponding implementation
time.
Initiation time had a different effect on the NPV. In all
cases, the earlier the implementation time, the lower the NPV.
The waterflood scenario by itself is very attractive for several
reasons. Waterflooding does not require a large initial investment for its facilities. It provides an excellent means of pressure maintenance and usually leads to reasonable recoveries. It
is relatively much easier to design and implement for a green
field. It also allows for studying and characterizing the reservoir while it remains in production, and consequently leaves
room for improving the injection plan.
Fig. 13. Recovery factor and NPV plots for different wettability cases obtained from the DFN reservoir model assuming surfactant flooding initiation after 1 PV of
waterflooding.
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Therefore, for a method other than waterflood to be
economically successful, several components need to be
achieved. The cost of injectant and of injection and handling
facilities has to be minimized. Injectants need to be highly effective in mobilizing the remaining oil, reaching values larger
than what can be achieved in a waterflood. Finally, improvements in injectant screening procedures for a green field are
needed to reduce uncertainties in selecting the most efficient
injectant.
ACKNOWLEDGMENTS
The authors would like to thank the management of Saudi
Aramco for their support and permission to publish this article.
This article was presented at the 18th European Symposium
on Improved Oil Recovery, Dresden, Germany, April 14-16, 2015.
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APPENDIX A: CASE STUDIES SIMULATION
CONSTRAINTS AND SURFACTANT PROPERTIES
Constraint
3
Producer Oil Rate (m /d)
Injector Water Rate (m3/d)
Water Cut (%)
BHP min (bars a)
Max Injection Pressure (bars a)
PUNQ-S3
SPE10M
32
280
None
17
300
24
280
None
17
500
Table 1. Simulation constraints for PUNQ-S3 and SPE10M case studies
T
Surfactant Concentration
(kg/m3)
0
0.28
0.57
2.24
Surfactant Water
Viscosity (cP)
0.4
1.1
1.2
1.3
Table
T 2. Surfactant viscosity
Surfactant Concentration
(kg/m3)
0
0.28
0.56
1.42
Surfactant Water/Oil
Surface Tension
(dynes/cm)
20
2
0.70
0.064
Table 3. Surfactant/oil surface tension
T
Log10 (Capillary Number)
Miscibility Function
-9
0
-3
0
2
0
5
1
Table 4. Capillary desaturation curve
T
Surfactant Concentration
(kg/m3)
0
0.28
0.56
2.8
Surfactant Adsorption
(kg/kg)
0
0.00052
0.00052
0.00052
Table 5. Surfactant adsorption
T
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BIOGRAPHIES
Dr. Ali M. AlKhatib is a Reservoir
Engineer currently working with the
Reservoir Engineering Technology
group of Saudi Aramco’s Exploration
and Petroleum Engineering Center –
Advanced Research Center (EXPEC
ARC). His research interests include
enhanced oil recovery, uncertainty quantification and
stochastic optimization. Ali is a member of the Society of
Petroleum Engineers (SPE) and has served on the steering
committees of a number of SPE workshops. He is also a
technical reviewer for the Journal of Computational
Geosciences and the Journal of Petroleum Science and
Engineering.
Ali received his B.S. degree in Chemical Engineering
with Management from the University of Edinburgh,
Edinburgh, U.K., and M.S. and Ph.D. degrees in Petroleum
Engineering from Imperial College London, London, U.K.
He has authored and/or coauthored more than 15
conference and peer-reviewed technical papers.
Dr. Amar J. Alshehri is a Reservoir
Engineer working with Saudi Aramco’s
Southern Area Reservoir Management
Department. He has several years of
experience in areas related to enhanced
oil recovery (EOR), petrophysics,
production engineering, reservoir
management and numerical reservoir simulation.
Amar has been an active member of the Society of
Petroleum Engineers (SPE). He recently served as the
chairman of the 2015 SPE Annual Technical Symposium
and Exhibition pre-event courses and workshops. Amar is
also a technical reviewer for the SPE Journal and the
Journal of Petroleum Science and Engineering, and he has
reviewed several technical papers related to chemical EOR
topics. He has more than 10 technical papers and articles
published in several venues.
Amar received his B.S. degree in Petroleum Engineering
from the Colorado School of Mines, Golden, CO, and his
M.S. and Ph.D. degrees in Petroleum Engineering from
Stanford University, Stanford, CA. During his time at
Stanford, Amar received the Certificate of Achievement in
Mentoring and won the William H. Brigham Memorial
Award.