PAPER 2008-464
Effects of Well Placement using MultiSegmented Wells in a Full Field Thermal Model
for SAGD: Athabasca Oil Sands
F. AKRAM
Schlumberger Canada Limited
This paper has been selected for presentation and publication in the Proceedings for the World Heavy Oil Congress 2008.
All papers selected will become the property of WHOC. The right to publish is retained by the WHOC’s Publications Committee.
The authors agree to assign the right to publish the above-titled paper to WHOC, who have conveyed non-exclusive right to the
Petroleum Society to publish, if it is selected.
Abstract
Standing at 2.5 trillion barrels, Canada has the largest
portion of the world’s ultra-heavy oil and bitumen resources1.
While shallow heavy oil reserves are extracted from pit mines,
deeper reserves can only be extracted through wells.
Production requires Steam Assisted Gravity Drainage (SAGD)
and Cyclic Steam Simulation (CSS) methods2. The optimal
placement of wells defines the propagation of steam within the
reservoir and the resulting flow of crude towards the producers.
A full field thermal model was developed using Petrel*
Reservoir Engineering to simulate the SAGD recovery process
for Athabasca Oil Sands.
This study was conducted to observe the behavior of the
SAGD process under a full field environment with multiple
pairs using an advanced well model. This model divides the
wellbore into multiple segments, where each segment acts as an
individual connection to the reservoir. This method allows for
the simulation of complex multi-phase flow effects such as
counter-flow in slowly flowing horizontal wells, fluid fall-back,
variable well-bore storage and friction3.
Sensitivity analysis on parameters, such as well spacing
(inter-well and horizontal) and steam injection rates combined
with the complex well model allowed a true simulation of a
SAGD process in a full field environment. Software programs
including Eclipse* Parallel optimization and the John
Appleyard Linear Solver (JALS) * were used to run multiple
simulations in less time.
This paper describes the process and the sensitivity analysis
used to design various SAGD models. The impact of using
Multi-Segmented Wells and the effects of a full field model on
the steam chambers are also discussed4.
Introduction
Reservoir studies including reservoir simulation for SAGD
have been conducted for the past few years to understand the
effect of steam injection, and the consequent formation of a
steam chamber, on crude recovery5. The dominant issue
throughout this period has been the limitation on thermal
simulation calculations imposed by computing power. Due to
their complex nature, thermal simulations require significantly
more iterations, processing power, and computer memory when
compared to conventional black oil simulations. At the same
time, computing limitations often prevent the use of a small grid
block size in simulations. To minimize artifacts associated with
simulation, a small grid block size is required to model gravity
drainage appropriately. The combination of these factors results
in reservoir studies that are done either on a single well pair or
* Mark of Schlumberger
with large grid block sizes that are inadequate to truly simulate
the gravity effect.
Reservoir simulation can yield a lot of information about
field development and appropriate use of this technology helps
us to see more than what we are accustomed to. With advanced
visualization and the ability to run large reservoir simulation
models over a shorter period of time, we can run sensitivity
analysis on multiple parameters as presented in this document
for optimal well placement and well completion design. This
results in more rigorous reservoir studies yielding better results
which help us to make better business decisions.
In this study, we used multiple grid block sizes to study the
impact on production and on Steam-Oil Ratio (SOR). We
simulated 6 well pairs over two completion scenarios; full and
half tubing. Individual well pair simulations were compared
with the full field model, where the full field model refers to
simulation of all well pairs together. The use of a multisegmented well (MSW) model provided a more accurate
production profile6. We also tested several completion scenarios
and varied horizontal spacing between the well pairs.
This study exploits a hypothetical heavy oil reservoir**. The
well pairs were drilled with a horizontal separation of 80
meters. The injector and the producer were spaced with a 5
meter vertical separation between them. The simulation was run
for a period of five (5) years with four months of preheating.
The virgin reservoir pressure was 16 bar, which was increased
to 30 bar for the first year, and then lowered to 25 bar for the
remaining 5 years. The reservoir temperature was 13°C and the
injected steam was kept at a temperature of 258°C. At the
production wells, the steam trap constraint was used with 7°C
subcooling5.
The multiple sensitivity runs along with their resulting
impact on field development plan are described in the sections
that follow. Schlumberger products Petrel* Reservoir
Engineering and ECLIPSE* Thermal were used for modeling
and simulation.
Simulation Runs
Simulation runs were set up after preparing the geological
model. Use of Petrel Reservoir Engineering* eliminated the
need for export and import; instead the same geological model
was simulated under various sensitivity parameters that are
discussed in this document. The simulations were run as a deadoil case2 with oil molecular weight of 500.
Varying Grid Block Sizes
Three grids were chosen for simulation with different grid
block sizes:
1. 5x50x1
2. 2x50x1
3. 2x25x1
Block sizes are indicated in i,j,k format where: i is distance
of the cross section of the well, j is the length of the well bore,
and k is the thickness of layers. Grid orientation is shown in
Figure 3. The cumulative production, cumulative steam
injection and the resulting SOR for the three different grid block
size simulations can be seen in Figure 4, 5 and 6 respectively.
As depicted in Figure 4 and 5, increasing the grid block size
along the length of the well bore (j) had minimal impact on
production however increasing the grid block size along the
cross-section of the well bore (i) had a significant impact on
cumulative production; production estimates decreased by
1.726 mmbbl. Similarly, the steam injection requirements were
less in the larger grid block models when compared to the
smaller grid block model.
The impact on resulting SOR in the simulation was
interesting. The SOR was found to decrease with the decrease in
grid block size; 2.05 for the 5x50 model compared with 1.88 for
the 2x25 model (Figure 6).
Steam distribution for the 2x50 and the 5x50 simulations is
shown in Figure 7. The results show that the steam chamber
was more gradually connected in the 2x50 model when
compared to the 5x50 model for all 6 well pairs.
The simulations were run on an 8-processor cluster. Using
multi CPU clusters was essential for building accurate thermal
simulation models. Being able to simulate smaller grid block
sizes and to visualize the distribution of steam chamber gave us
more insight and allowed us to get more information to make
better business decisions while preserving the geological
heterogeneity of the reservoir.
Geological Model
To study the impact of well placement and completion
design on a SAGD process, a detailed statistical geological
model was prepared using the principles described by the
Center for Computational Geostatistics7 for Athabasca Oil
Sands. A rectangular grid, measuring 940 meters long by 650
meters wide, was used to build the model. The choice of grid
block size dictated the number of grid blocks in either direction.
There were five rock types introduced in the model as follows:
Facies 1: Mud
Facies 2: Sandy Mud
Facies 3: Muddy Sand
Facies 4: Fine Sand
Facies 5: Sand
The facies were distributed using Sequential Gaussian
Simulation (SGS)8. Facies distribution in the geological model
is shown in Table 1. The oil saturation, porosity and
permeability distribution were computed by assigning the
individual facies a constant value shown in Table 1.
Any number of probable facies distribution models may be
generated using SGS. The realization that was used for
sensitivity analysis is shown in the Figure 1 and the oil
saturation distribution is shown in Figure 2. All saturations were
normalized by So+Sw=1.
Testing Well Completion Designs
The well pairs used in the simulation were 850 meters long
with the last 610 meters of the wells entirely perforated. Three
different scenarios were tested:
1. Full tubing design
2. Half tubing design
3. Half tubing design with 5 infill producer wells
In all three scenarios, both injector and producer wells were
fully cased. The producers also had tubing to the toe of the well.
Packers were placed at the top of the perforation with the oil
flowing alongside the tubing to the toe and then inside the
tubing. In the full tubing completion design, the injectors had
full tubing. In the half tubing design, the injectors had tubing
half way through the perforation. The different completion
designs for the producer and injector wells are shown in Figure
8. Along the length of the well bore, the well was segmented
every 50 meters.
In the third scenario, the half tubing design was used, but 5
infill producers were added. These producers were placed
between the 6 well pairs 40 meters apart to recover incremental
oil. The producers were put on production 2 years into the
** Personal communication with D. Law. 2007. Edmonton, Schlumberger Limited
* Mark of Schlumberger
2
simulation. Cumulative oil production and SOR are compared
for the three scenarios in Figure 9 and 10.
In the simulation, the half tubing design was predicted to
produce 1.61 mmbbl more oil over a period of 5 years
compared to full tubing design. The extra infill producers were
predicted to produce 0.3 mmbbl extra oil. Assuming one barrel
of synthetic oil sells for $60.00, the undiscounted revenue for
the full tubing design was estimated at $340 million and the half
tubing design was estimated at $437 million. The use of half
tubing in the well completion design resulted in two benefits:
first, tubing costs were half that of a full tubing completion, and
second, the design recovered more oil. The addition of infill
producers in the simulation added another $18 million to the
undiscounted revenue. If we assume a cost of one million
dollars per infill well, there would still be a margin of 13
million dollars. The placement of infill producers can be
visualized in Figure 11. Despite the estimate of additional
production shown in the simulation, the placement of infill
producers requires further study. These wells do not seem to
produce significant amounts of oil until the steam chamber has
reached them (Figure 11). Further optimization may result in
even greater recovery and decreased SOR. Yet, even without
optimization, the third scenario still has the better SOR among
the three at 2.00.
These well completion design scenarios could only be
evaluated when we performed a full field simulation run. When
we ran individual well pair runs and compared them with the
full field design, the results were quite different.
Our next step was to increase the perforation for both
injectors and producers for all well pairs, from 610 meters from
the toe to 800 meters from the toe with well pairs 1 and 6 under
the new horizontal placement. The production profile for the
new placement field design was compared with the original
field design and the results were plotted (Figure 16). In this
scenario the steam traveled farther down towards the heel. The
simulation predicted greater oil recovery along the length of the
well bore, but the communication of steam chamber took longer
so estimated production was less for the simulation period. The
SOR value was almost unchanged. Figure 17 shows steam
chamber distribution and Figure 18 shows the impact on SOR.
When examining this over an extended simulation period, the
new design predicted more cumulative oil production since
production rates are steady compared to the old design, where
production is in decline. SOR decreases as simulation continues
beyond 5 years for the new design (not shown).
The simulation showed that communication between the
steam chambers for all well pairs was not complete (Figure 20),
while it was complete for the old design (Figure 19) at the end
of simulation. However, this communication would be
completed over time which would ultimately result in higher
recovery. As a result, we will see reduced SOR with better
sweep efficiency.
As expected, well placements and completion designs have a
huge impact on the overall recovery of the field. These effects
can be studied accurately only with full field simulation models.
Comparison of Individual Well Pair Runs and
their Cumulative with Full Field Run
Conclusions
1.
In order to test the validity of the results from the full field
simulation, we ran individual well pair runs, added their results
and compared them with the full field simulation run (Figure
12).
The simulation showed a difference of 0.39 million barrels
of oil when the cumulative individual well pair production was
compared with the full field model. This difference occurs
because of a simulation artifact known as “double dipping”. The
double dipping effect is essentially counting production of the
same oil twice. Oil between two well pairs is produced once by
the first pair and then again by the second pair. As the results
are cumulated, the amount can be significant.
The double dipping effect is only a concern if there is
communication between well pairs. If the wells don’t
communicate over the simulation period, the individual well
pairs are adequate and the results can be cumulated. If the well
pairs do communicate in a full field environment, it is
imperative to run a full field simulation. More accurate
prediction of cumulative production will allow better planning
of surface facilities.
2.
3.
4.
5.
Varying Well Pair Spacing
We tested the impact on cumulative production by moving
well pair 1 and 6, 120 meters apart from well pair 2 and 5
respectively from original spacing of 80 meters (Figure 13). The
difference in cumulative production in a full field model for pair
1 for the two well pair positions is shown in Figure 14. 120
meter spacing placement predicted less oil production because
of the clay that was present on the west of the new placement
position (Figure 13). When we examined the oil rate for
production well 1 in the original placement the well showed that
it was in decline mode. For the new placement, production well
1 showed a steady rate. It was expected to predict more
cumulative oil for longer simulation period than the original
placement (Figure 15).
6.
The investment in heavy oil reservoirs is enormous
and with the high cost of production, it is paramount
to do reservoir studies that are as true to reality as
possible.
Varying the grid block size resulted in different
cumulative field oil production estimates. Decreasing
the grid block size increases the number of grid
blocks in a simulation run, however it simulates the
physics of gravity drainage better. This difference is
noted only if MSW is used.
Two completion designs were tested. The half tubing
design proved to be the better completion design in
this reservoir simulation. This design required less
tubing resulting in lower completion costs, was more
efficient in steam distribution, and estimated higher
production.
Adding infill producer wells increased recovery.
However, these infill wells need to be optimally
planned to offset their cost with increased
production.
The practice of simulating standalone well pairs and
cumulating their results proved to be inadequate. The
results were optimistic compared to the full field runs
which took into account the communication of steam
chambers across the entire field.
Our full field simulation approach allows for accurate
estimation of different well placement and
completion scenarios.
Acknowledgement
The author thanks David Law, Schlumberger Data and
Consulting Services (DCS) and Paul Naccache, Schlumberger
Information Solutions (SIS) for their advice on this research. He
also thanks his colleagues in Calgary for their support.
3
NOMENCLATURE
SAGD
CSS
JALS
MSW
SOR
SGS
So
Sw
k (md)
φ
%
mmbbl
=
=
=
=
=
=
=
=
=
=
=
=
FIGURES
Steam Assisted Gravity Drainage
Cyclic Steam Simulation
John Appleyard Linear Solver
Multi-segmented Well
Steam-Oil Ratio
Sequential Gaussian Simulation
Oil Saturation
Water Saturation
Permeability (in millidarcy)
Porosity
Percentage
Millions of Barrels of Oil
REFERENCES
1.
2.
3.
4.
5.
6.
7.
8.
Canadian Petroleum Communications Foundation,,
www.pcf.ab.ca/quick_answers/default.asp. Downloaded
14 March 2007.
PRATZ, M., 1986. Thermal Recovery. Monograph
Series, SPE, Richardson, Texas. Vol. 7
ECLIPSE 300 Reference Manual, Schlumberger, 2007.
OBALLA, V., and BUCHANAN, W.L., Single
Horizontal Well in Thermal Recovery Processes; SPE
Paper 37115, 1996.
GATES, I.D., KENNY, J., and HERNANDEZ-HDEZ,
I.L., Steam-Injection Strategy and Energetics of SteamAssisted Gravity Drainage; SPE/PS-CIM/CHOA 97742,
2005.
HOLMES, J.A., BARKVE, T., and LUND, Ø.,
Application of a Multisegment Well Model to Simulate
Flow in Advanced Wells; SPE Paper 50646, 1998.
DEUTSCH, C.V., and MCLENNAN, J.A., Guide to
SAGD (Steam Assisted Gravity Drainage) Reservoir
Characterization Using Geostatistics; Centre for
Computational Excellence (CCG), Guidebook Series
Vol. 3, University of Alberta, April 2003.
Petrel User Manual, Schlumberger, 2007.
Figure 1: Realization of the facies model used for simulation.
TABLES
Facies Statistics
Type Name
1
Mud
2
Sandy Mud
3
Muddy Sand
4
Fine Sand
5
Sand
% fraction
7.23
7.83
14.30
15.27
55.36
So %
0
30
50
65
85
k (md)
100
1250
2500
5000
5000
φ%
1
5
12
25
33
Figure 2: Distribution of Oil Saturation for the simulation
model.
Table 1: Statistics for different facies type
4
Figure 3: Grid orientation used for simulation.
Figure 5: Field steam injection cumulative for varying grid
block sizes.
Figure 6: Field cumulative steam-oil ratio for varying grid
block sizes.
Figure 4: Field oil production cumulative for varying grid block
sizes.
5
Figure 7: Temperature distribution for two cases of grid block
size.
Figure 9: Comparison of cumulative oil production between the
three completion designs.
Figure 8: Completion designs; half tubing on the left and full
tubing on the right.
Figure 10: Comparison of cumulative SOR for the three
completion designs.
6
Figure 11: Infill producers and the distribution of steam
chamber at the end of 5 years and 9 months.
Figure 13: New placement of well pair 1 and 6, 120 meter
apart whereas rest are at original 80 meter spacing.
Figure 12: Comparison of individual 6 well pair runs and their
cumulative with full field simulation run.
Figure 14: Comparison of cumulative oil production for well pair
1 under original and new placement.
7
Figure 15: Comparison of oil production rate for production
well 1 under the original (80m) and new placement (120m).
Figure 17: Steam chamber distribution for the old and new
design. The variation is quite visible. Much better sweep
efficiency for the new design.
Figure 16: Comparison of cumulative oil production for the
entire field between old spacing and completion with new well
spacing and completion.
Figure 18: Comparison of SOR for the old and new field design.
8
May 1, 2007
May 1, 2007
Jan 1, 2008
Jan 1, 2008
Jan 1, 2009
Jan 1, 2009
Jan 1, 2010
Jan 1, 2010
Jan 1, 2011
Jan 1, 2011
Jan 1, 2012
Jan 1, 2012
Figure 19: Evolution of steam chamber for the old design. All
wells are 80 meters apart with 610m long perforation from the
toe.
Figure 20: Evolution of steam chamber for the new design.
Well pairs 1 and 6 are 120 meters apart with new perforation of
800 meters from the toe.
9