-
hDepartment of Chemical and Petroleum Engineering, Schulich
School of Engineering, University of Calgary, Canada
h i g h l i g h t s
In Western Canada, 80% of heavy oil resourc Cold production has
low recovery factor,
-
actively applied in Saskatchewan and Alberta since it is
technicallysimple to implement and has relatively low operating
cost even
Fuelthough incremental oil recovery factors are not signicantly
largerthan primary production.
Solvent-aided thermal recovery methods have also been pro-posed
for bitumen and heavy oil reservoirs. For example, Gates[7]
examined a solvent-aided thermal recovery process for thinoil sands
reservoirs by using optimization. The optimized processhad lower
net energy (both steam and solvent retained in thereservoir) to oil
ratios compared to traditional SAGD. Solvent-onlyprocesses, such as
cyclic solvent injection, have advantages in thatthere are no heat
losses to the surrounding overburden and under-strata. These
methods appear to have promise for use in post-CHOPS reservoirs
[8,9].
Hot water ooding is a relatively low cost thermal oil
recoverytechnique [9] since it only involves sensible heat.
Compared withconventional water ooding, the use of hot water
improves themobility ratio due to a reduction of the oil phase
viscosity arisingfrom it being heated. Furthermore, heating also
reduces the inter-facial tension and residual oil saturation which
both lead to poten-tially higher recovery factor. However, in hot
water ooding, theheated water for injection delivers less heat to
the reservoir com-pared to that with steam due to absence of latent
heat and there-fore it is less effective in reducing oil viscosity.
On the other hand,for thin heavy oil reservoirs, hot water ooding
has advantagesover steam ooding. First, it provides larger
displacement drivethan steam ooding since water viscosity is much
larger than thatof steam [911]. Second, it permits the use of much
higher injec-tion pressure than steam ooding at a given
temperature.Furthermore, higher-pressure injection enables greater
tempera-tures while remaining in the hot water state. Third, due to
smallerreservoir temperature, heat losses to the overburden and
under-strata will be substantially smaller than that encountered in
steamooding. However, less heat losses to the overburden and
under-strata will mean less heat delivery to the heavy oil
interval.Martin et al. [10] describe the results of hot water
injection intoa 57 m thick sandstone reservoir containing oil with
viscosityequal to 600 cP. They found that water injectivity and oil
rateswere signicantly enhanced over that of cold water
ooding.However, although they did not have detailed thermocouple
obser-vation wells, they concluded that 60 percent of the injected
heatwas lost to the overburden and understrata. Thus, there is a
needto design hot water recovery processes for thin reservoirs
thatmanage heat delivery and recovery to and within the
reservoir.
In the study documented here, hot water-ooding strategies
areoptimized by using simulated annealing, a stochastic
optimizationalgorithm. We aimed to understand the effects of
injection pres-sure, water temperature, as well as different
reservoir conditionson the recovery process performance.
2. Models and methods
2.1. Reservoir simulation model
The reservoir evaluated here has properties typical of that of
atypical thin heavy oil reservoir in the Lloydminster area ofused
techniques to raise the overall recovery factor of the
reservoir[4,5]. In heavy oil reservoirs, due to the high viscosity
of the oil ver-sus that of the water, ooding processes may suffer
with respect towater bypassing [46]. In most cases, the viscosity
of the live oilranges from 1000 to 10,000 times that of water which
implieswater ngering occurs. Despite this, water ooding has
been
560 D.W. Zhao, I.D. Gates /Alberta, Canada described in a
previous study [12]. The base casereservoir model is
two-dimensional with two horizontal wellsspaced 50 m apart. The
thickness of the heavy oil interval is equalto 4 m thick. The
models were discretized into a regular Cartesiangrid, displayed in
Fig. 1, with dimensions 1 m in the cross-welldirection, 1000 m in
the down-well direction (into the page) and0.4 m in the vertical
direction. The length of the perforated sectionsof the horizontal
wells in all models is equal to 1000 m. A commer-cial thermal
reservoir simulator (CMG STARS) was used. The com-mercial thermal
reservoir simulator uses the nite volumeapproach. At the top and
bottom boundaries, heat losses were per-mitted and were
approximated by using Vinsome andWestervelds[14] heat loss model.
At the side boundaries of the model, no owand no heat transfer
boundary conditions were applied.
The reservoir simulation model and uid properties are listed
inTable 1. The relative permeability curves, listed in Table 1,
areindependent of temperature. The spatial distributions of
oil/watersaturations (average oil saturation equal to 0.65),
porosity (averageequal to 0.32), and base case horizontal
permeability (averageequal to 3650 mD) are, displayed in Fig.
1(a)(c), respectively.The average oil saturation, porosity, and
horizontal permeabilitieswere derived from core data taken from one
of Devon Canadasheavy oil elds located in eastern Alberta. The
spatial distributionsof the porosity, oil saturation and base case
permeability(described below) were randomly assigned using uniform
proba-bility distributions. Given that the sand is relatively
clean, the ver-tical-to-horizontal permeability ratio is set equal
to 0.8. The initialreservoir pressure and temperature are equal to
2800 kPa and20 C, respectively. The solution gas-to-oil ratio at
original reser-voir conditions is equal to 6.17 m3/m3.
To investigate the effect of permeability and its variations
onthe reservoir performance, ve permeability cases were
optimized(including the base case). These cases were chosen to span
therange of reservoir characteristics that are typical in thin
heavy oilreservoirs in Western Canada.
Case 1: This is the base case reservoir model with
permeabilitydistribution as shown in Fig. 1(c). The average
permeability isequal to 3650 mD. This case represents the expected
permeabilitycase in the study conducted here.
Case 2: In this case, a permeability distribution is created
withthe same average permeability of Case 1 (3650 mD) but
enhancedpermeability at the bottom and lower permeability at the
upperzone, as shown in Fig. 1(d). This vertical permeability prole
wouldbe expected in a reservoir where the sand grains were larger
in sizeat the base of the reservoir with the nest grains at the top
of thereservoir.
Case 3: In this case, a permeability distribution is created
withsame average permeability of Cases 1 and 2, but with
higherpermeability at the upper zone and lower permeability at
thelower part of the reservoir, as displayed in Fig. 1(e). The
verticalpermeability distribution of this case would be expected
wherethe sand grains are largest at the top of the reservoir and
nestat the base of the oil column.
Case 4: The permeability distribution for this case, shown
inFig. 1(f), is created by scaling up the permeabilities of the
grid-blocks of Case 1 universally by a factor of 2. This gives rise
to anaverage permeability of 7300 mD. This case represents the
bestpermeability case examined here and is at the upper limit
ofpermeabilities expect in thin heavy oil reservoirs in
WesternCanada.
Case 5: The permeability distribution of this case, displayed
inFig. 1(g), is created by scaling down the permeabilities of the
grid-blocks of Case 1 universally by a factor equal to 0.6. This
gives riseto an average permeability equal to 2190 mD. This case
representsthe worst permeability case evaluated in this study.
For each of above reservoir model cases, an individual
optimiza-
153 (2015) 559568tion of 800 runs was conducted to determine the
optimumparameter set for each case. The optimization run and
simulationswere executed on a personal computer (3.4 GHz, dual quad
core
-
)
ty (
Fuel(a) Oil Saturation distribution (average = 0.65
(b) Porosity distribution (average = 0.32)
(c) Base case, Case 1: horizontal permeabili
D.W. Zhao, I.D. Gates /with 16 GB memory). Each individual
reservoir simulation took onaverage 2 min and 30 s to execute;
given that 800 simulation runswere done each case, each
optimization run took roughly 34 h tocomplete.
2.2. Optimization algorithm
2.2.1. The simulated annealing methodIn this work, a Simulated
Annealing (SA) algorithm is used for
operating strategy optimization as described in Gates
andChakrabarty [15]. The optimization algorithm is designed to
con-trol the thermal reservoir simulator and execute reservoir
perfor-mance evaluations. Parameters for reservoir simulation
aregenerated by the SA algorithm and then used for generating
thesimulation input le. Then a simulation run based on the
newlygenerated input le is executed by the reservoir simulator.
Oncethe simulation is complete, a computer code is called to
processthe reservoir simulation output data and evaluate the
performanceof the simulated strategy. The evaluation results are
then sent back
(d) Case 2: horizontal permeability (mD) distribureduced
permeability at top (with same overall a
(e) Case 3: horizontal permeability (mD) distribenhanced
permeability at top (with same overal
(f) Case 4: horizontal permeability (mD) distribu
(g) Case 5: horizontal permeability (mD) distrib
Fig. 1. Distributions of the oil saturation, porosity, and
horizontal permeability, scale iwhereas the production well is on
the right side of domain. The spacing between the wethe vertical
and horizontal directions, respectively.mD) distribution (average =
3,650 mD) 153 (2015) 559568 561to the optimizer to generate new
parameter sets and the nextiteration of the optimization algorithm
starts. In the optimizationprocedure, the SA algorithm conducts
random searches thatattempt to lower the value of the cost
function, i.e., the optimumvalue of desired reservoir operating
performance. The parametersof the SA algorithm were the same as
those used in previous stud-ies [15].
2.2.2. Adjustable parameters and cost functionFor optimization,
the adjustable parameters are the injection
pressures and injection water temperature over specied
timeintervals, summarized in Table 2. The pressure and
temperaturesampled during the optimization run ensures that none of
thepressure/temperature combinations are below the steam
sat-uration line. In other words, conditions are maintained such
thatonly subcooled water is injected into the reservoir. In total,
tenpressure parameters with base value of 3000 kPa and
optimizationrange set equal to 20004200 kPa, and ten water
temperature
tion enhanced permeability at bottom and verage permeability as
base case)
ution reduced permeability at bottom and l average permeability
as base case)
tion two times the base case permeability
ution 0.6 times the base case permeability
n (c), of the reservoir models. The injection well is on the
left side of the domainlls is equal to 50 m. The dimensions of the
grid blocks are equal to 0.4 m and 1 m in
-
FuelTable 1Reservoir simulation model and uid properties.
Property Value
Depth to reservoir top (m) 334
562 D.W. Zhao, I.D. Gates /parameters with base value equal to
120 C and range 20250 Care used to optimize the process.
The cost function against which the adjustable parameters
areoptimized is a function of the net present value (NPV). For the
sim-ple economic model used here, the following economic factors
areconsidered: initial capital investment (including well drilling
andeld equipment), operating costs, xed costs, variable costs,
water
Net pay (m) 4Porosity (dimensionless) 0.32 0.02Oil saturation
(dimensionless) 0.65 0.09Solution gas-to-oil ratio (m3/m3)
6.17Horizontal permeability kh (mD) 3650 347kv/kh (dimensionless)
0.8Effective rock compressibility (1/kPa) 14 106Rock heat capacity
(kJ/m C) 2600Rock thermal conductivity (kJ/m day C) 660Reference
pressure (kPa) 2800Reference depth (m) 334Initial reservoir
temperature (C) 20Dead oil viscosity (cP) 20 C 15,21240 C 188480 C
125.4160 C 9.66250 C 3.09Water thermal conductivity (kJ/m day C)
53.5Gas thermal conductivity (kJ/m day C) 5Oil thermal conductivity
(kJ/m day C) 11.5Effective molecular diffusion coefcient of oil
(m2/day) 4.32 106Effective molecular diffusion coefcient of solvent
(m2/day) 4.32 105Methane K-value correlation in oil [13]Kv1 (kPa)
504,547K-value = (Kv1/P) exp(kv4/(T + Kv5)) Kv4 (C) 879.84Kv5 (C)
265.99Oilwater relative permeability curves Sw krw krow
0.15 0.0000 0.99200.2000 0.0002 0.97900.2500 0.0016 0.95000.3000
0.0055 0.72000.3500 0.0130 0.60000.4000 0.0254 0.47000.4500 0.0440
0.35000.5000 0.0698 0.24000.5500 0.1040 0.16500.6000 0.1480
0.11000.6500 0.2040 0.07000.7000 0.2710 0.04000.7500 0.3520
0.01500.8000 0.4470 0.00000.8500 0.5590 0.00000.9000 0.6870
0.00000.9500 0.8340 0.00001.0000 1.0000 0.0000
Gasliquid relative permeability curves Sl krg krog0.1500 1.0000
0.00000.2000 0.9500 0.00020.2500 0.8400 0.00160.3000 0.7200
0.00550.3500 0.6000 0.01300.4000 0.4700 0.02540.4500 0.3500
0.04400.5000 0.2400 0.06980.5500 0.1650 0.10400.6000 0.0930
0.14800.6500 0.0750 0.20400.7000 0.0450 0.27100.7500 0.0270
0.35200.8000 0.0200 0.44700.8500 0.0100 0.55900.9000 0.0050
0.68700.9500 0.0000 0.83401.0000 0.0000 0.9920Table 2List of
optimization parameters.
Parameter Onset time(months)
Base value, allowedrange
1 Injection well pressure 0 3000 kPa, 20004200 kPa
2 Injection well pressure 7 3000 kPa, 20004200 kPa
3 Injection well pressure 13 3000 kPa, 20004200 kPa
4 Injection well pressure 19 3000 kPa, 20004200 kPa
5 Injection well pressure 25 3000 kPa, 20004200 kPa
6 Injection well pressure 31 3000 kPa, 20004200 kPa
7 Injection well pressure 37 3000 kPa, 20004200 kPa
8 Injection well pressure 43 3000 kPa, 20004200 kPa
9 Injection well pressure 49 3000 kPa, 20004200 kPa
10 Injection well pressure 59 3000 kPa, 20004200 kPa
11 Injection watertemperature
0 20250 C
12 Injection watertemperature
7 20250 C
13 Injection water 13 20250 C
153 (2015) 559568treatment costs, and operating revenue. The
following assump-tions formed the basis of our evaluation: well
drilling cost andother initial investment $2,500,000 (for a single
well), discount rateof 10%, variable cost to be 10% of the
operating revenue, heavy oilprice $80.00/bbl [16], natural gas
price $4.4/GJ, thermal efciencyequal to 0.75, and waste water
treatment cost is $2.00/m3. The costfunction (CF) is formally dened
as CF = (6 106 NPV)/1 106.This indexes the value of the CF to
range, in general, between 0and 10 with lower values of the CF
being more optimal.
3. Results and discussion
3.1. Injection pressure and water temperature
Fig. 2 shows the optimized injection pressure and water
tem-perature for all the optimized cases. For Case 1, the results
revealthat the injection pressure remains relatively high,
around4000 kPa, throughout the majority of the operating life of
the pro-cess although a lower injection pressure (2500 kPa) period
existsbetween 1.5 and 2 years of operation. The optimized
injectionpressure for all the other cases generally remains high in
themajority of the operating time before water breakthrough
althoughexhibit stochastic deviations. In Case 4, the high
permeability zoneleads to earlier oil production compared to the
other cases. Theinjecting pressure remains high over the rst two
years and showsa cyclic pattern in the later stages of operation.
In Case 1, the initi-ate water temperature is found to be around
120 C and then jump
temperature14 Injection water
temperature19 20250 C
15 Injection watertemperature
25 20250 C
16 Injection watertemperature
31 20250 C
17 Injection watertemperature
37 20250 C
18 Injection watertemperature
43 20250 C
19 Injection watertemperature
49 20250 C
20 Injection watertemperature
59 20250 C
-
FuelCase 1
D.W. Zhao, I.D. Gates /to 225 C for a period of 6 months. After
this high water tempera-ture period follows a low injecting
temperature period of 1.5 yearswith water temperature ranging from
20 to 50 C. The water tem-perature increases to 175 C and is then
further elevated to 250 Cafter 3 years of operation. The 250 C
injection period persists for ayear before the temperature
decreases to 94 C and then nally to20 C for the last 14 months of
operation. From Fig. 2, one can seethat there is similar pattern
for the optimized injecting water tem-perature. The water
temperature normally starts high and thengives rise to a low
injection temperature period. We could call this
Case 3
Case 5
Fig. 2. Comparison of injection pressure and injection water
temperaCase 2
153 (2015) 559568 563temperature change from high to low an
injection cycle. In the 5cases investigated here, the second cycle
tends to last longer thanthe rst cycle. In Case 5, a third cycle
occurs within the six yearoperation life.
We suggest that low injection temperature enables heat recov-ery
from the reservoir matrix during the process which results inhigher
heat efciency. During the initial period where the tem-perature of
the injected water is relatively high, relatively highheat is
injected into the reservoir and due to heat losses to the
solidmatrix, this results in an elevated matrix temperature. Only a
small
Case 4
ture proles of the optimized strategies of Cases 1, 2, 3, 4, and
5.
-
overall thermal efciency and heat utilization of the
recoveryprocess.
Based on the results of the optimization runs, it is
suggestedthat a high injection pressure is critical to obtain
feasible hot waterooding strategies. Essentially, high injection
pressure promotesrapid uid movement within the oil reservoir which
enhances con-vective delivery of heat to the formation leading to a
greater frac-tion of the heat being delivered to the oil than would
be the casefor low-pressure injection and low injection rate where
conductivelosses to the overburden and understrata would dominate
heattransfer. Similar to the results for optimized SAGD operation
asshown by Gates et al. [17], the optimized process promotes
hori-zontal heat transfer over that of vertical heat transfer. In
the con-text of hot water ooding, this is done within the
constraint ofhot water breakthrough to reduce direct hot water
productionfrom the reservoir. For hot water injection, the results
demonstratethat it is most thermally efcient to adopt a cyclic
pattern control,
564 D.W. Zhao, I.D. Gates / Fuel 153 (2015) 559568Fig. 3.
Comparison of oil production rates of the optimized strategies of
Cases 1, 2,3, 4, and 5.
Table 3Comparison of optimized operating strategies in all the
four cases in terms ofcumulative oil production, cumulative water
produced to oil produced ratio (cWOR),cumulative energy injected to
oil ratio (cEOR), operating time and net present
value(NPV).fraction of the injected heat is produced with the
produced uids.Due to the small thickness of the reservoir pay zone,
a signicantfraction of the heat is lost to the overburden and
understrata.After the hot water injection period, subsequent water
injectionat lower temperature enables heat recovery from the
reservoirmatrix, that is, heat is transferred from reservoir rock
to waterand mobile oil. Furthermore, since the injection
temperature islower than that of the overburden and understrata,
heat recoveryalso occurs from these zones to the reservoir thus
improving the
lower region of pay zone. Case 3 produces 5% more oil than
Case
Case Cumulative oilproduction (m3)
cWOR (m3/m3)
cEOR (GJ/m3)
NPV*
($million)
1 24,366 14.5 6.2 2.82 26,400 14.6 9.9 2.93 25,655 13.5 8.2 2.94
27,319 19.1 7.4 4.75 5396 13.7 3.4 1.6
* The blowdown performance is not considered in the NPV
calculation whichmeans the real NPV could be slightly higher than
the presented values.
(a) after 12 months
(b) after 36 months
(c) after 60 months
Fig. 4. Oil saturation prole1 but used 40% more heat injection
over the total 6 years of opera-tion. The higher permeability
interval at the upper part of reservoircontributes to larger heat
losses to the overburden.i.e. start at high water temperature and
end at low water tempera-ture. Multiple cycles might be benecial
depending on the reser-voir condition.
3.2. Oil production rates and effects of permeability
variations
Fig. 3 shows the oil production rates for Cases 15. The peak
oilproduction rates are found to range from 20 to 25 m3/day for
Cases14. In Case 5, the maximum oil rate seldom exceeds 5 m3/day.
Theresults show that despite the same average permeability value,
thedistribution of the permeability within the pay zone impacts
oilproduction. In Case 2, a higher permeability zone is located
atthe bottom zone of the reservoir. This results in earlier oil
produc-tion than that of Case 3, the case where a higher
permeability zoneis located at the upper part of the reservoir. The
higher permeabil-ity at the lower part of the reservoir causes
faster hot water frontaladvance in the lower part of the reservoir.
This enhances heattransfer (tends to migrate upwards rather than
downwards) tothe oil above the higher permeability zone at the base
of the reser-voir. Furthermore, the accelerated water front speed
leads to moreoil displacement and production. As listed in Table 3,
within thesame operating time of 6 years, Case 2 produced 3% more
oil thanCase 3. On the other hand, 11% more water is produced in
the opti-mized Case 2, which is caused by the higher permeability
of thes of optimized Case 1.
-
Case 2
years of operation for Cases 1, 2, 3, 4, and 5.
FuelIn addition to the effects of the spatial permeability
dis-
Case 3
Case 4
Case 5
Fig. 5. Oil saturation distributions after 4Case 1
D.W. Zhao, I.D. Gates /tribution, the absolute average
permeability value also impactsoil production. As shown in Fig. 3,
the highest permeability case,Case 4, results in the highest oil
production of all cases in the short-est time. On the other hand,
the lowest permeability case, Case 5,has the lowest cumulative oil
production of all cases, only5396 m3 versus 24,366 m3 for Case 1.
It should be pointed out thathigher permeability also leads to
higher water injection and conse-quent production.
Fig. 4 shows the oil saturation distributions after 12, 36,
and60 months of operations for the optimized Case 1. The
confor-mance zone created by hot water ooding is relatively high
dueto the thinness of the pay zone. The water front advances
fasterin the lower part of the reservoir with evidence of water
ngering.Fig. 5 shows the oil saturation distributions of all
optimized casesafter 4 years of operation. In Case 2, as shown in
Fig. 5b, due tohigher permeability at the lower part of the
reservoir, the waterfront moves much faster in the lower part and
breaks through atan early time which lead to overall higher water
cut. In Case 3,as shown in Fig. 5c, the advance of the water front
is relatively uni-form in the pay zone. In Case 4, the high
permeability is found toresult in lowest oil saturation after 4
years of operation (Fig. 5d).However, in Case 5 (Fig. 5e), due to
the low permeability, the waterfront moves at a relatively slow
pace which resulted in the lowestoil production.
3.3. Water injection rates and water production
The water injection rates in all the optimized cases are shown
inFig. 6. For Cases 13, the initial water injection rates are
generallylow in the early stages of oil production but ramp up as
the opera-tion continues. Since the injection temperature drops as
the opera-tions progress, at the later stage of hot water ooding,
waterbreakthrough does not cause substantial heat losses since
lower153 (2015) 559568 565temperature water is injected. In Case 5,
due to the low injectivitydetermined by the low permeability, the
water injection rates arelow and thus the oil production rate is
relatively low.
Fig. 7 shows the water cut of all of the cases studied here.
Thewater cuts are generally larger than 80%. At the later stages,
watercuts rise to above 95%. In Case 4 where reservoir has the
largestpermeability, the water cut rises to 99% by the end of the 6
yearsof operation.
3.4. Temperature distributions, cumulative energy injected to
oil ratio(cEOR), and net present value
Fig. 8 presents the spatial distributions of the temperature
after12, 36, and 60 months of operation in the optimized Case 1.
Figs. 9
Fig. 6. Water injection rates of the optimized strategies of
Cases 1, 2, 3, 4, and 5.
-
12 present the temperature distributions after 12, 36, and60
months in optimized Cases 25. In Case 1, the reservoir tempera-ture
peaks at about 100 C by the end of high temperature waterinjection
period (at the end of 4 years of operation). Due to theuse of cold
water for injection, the temperature of the ooded zonestarts to
decrease and declines to about 50 C. In Cases 24, the
maximum reservoir temperatures during hot waterooding werefound
to be in the range between 107 and 120 C whereas the naltemperature
of the ooded zone was between 50 and 75 C. In Case5, due to low
permeability and therefore low injectivity, the averagereservoir
temperature never exceeded 30 C. In Cases 14, the over-all
reservoir temperature prole versus time reects heat recoveryfrom
reservoir matrix sequestered there during hot water injectionand
recovered during colder water injection.
The cumulative energy injected (as sensible heat in the
injectedwater) to produced oil ratio (cEOR, expressed as GJ
injected energyper m3 of oil produced) versus time for all the
cases is displayed inFig. 14 with results at the end of the six
years of operation listed inTable 3. The cEOR generally starts high
due to heat losses and ini-tial low oil production rate. As the oil
rate increases, the cEORdecreases. By the end of the high oil
production rate period, thecEOR increases until cold-water
injection is started which thenrecovers heat previously stored in
the reservoir matrix. In Case 1,the resulting cEOR is equal to 6.2
GJ/m3, being the lowest valueexcluding Case 5. In Case 2, the
existence of the high permeabilitylayer in the lower part of
reservoir results in relatively early waterbreak through and
therefore greater energy injection, more heatlosses to overburden,
a higher overall reservoir temperature(Fig. 13), and the highest
cEOR equal to 9.9 GJ/m3. Case 5 achievedthe lowest cEOR but also
resulted in the lowest production rate andrecovered oil volume and
therefore had a negative net present
Fig. 7. Water cut of the optimized strategies of Cases 1, 2, 3,
4, and 5.
(a) After 12 months of operation
(b) After 36 months of operation
566 D.W. Zhao, I.D. Gates / Fuel 153 (2015) 559568(c) After 60
months of operation Fig. 8. Temperature (C) distrib
(a) After 12 months of operation
(b) After 36 months of operation
(c) After 60 months of operation
Fig. 9. Temperature (C) distributions of optimized Case 1.utions
of optimized Case 2.
-
Fuel (a) After 12 months of operation
D.W. Zhao, I.D. Gates /value (NPV). This result suggests that
heat losses were reduced inthe low permeability case but oil
production suffers resulting in anuneconomic process. Of the ve
cases studied, the resulting overallcEOR after six years of
operation is under 10 GJ/m3, which indicates
(b) After 36 months of operation
(c) After 60 months of operation
Fig. 10. Temperature (C) distrib
(a) After 12 months of operation
(b) After 36 months of operation
(c) After 60 months of operation
Fig. 11. Temperature (C) distrib
(a) After 12 months of operation
(b) After 36 months of operation
(c) After 60 months of operation
Fig. 12. Temperature (C) distrib153 (2015) 559568 567relatively
good heat utilization efciency. The calculated NPVreveals that hot
water ooding, with the economic inputs usedhere, can be economic in
thin (
-
FuelFig. 13. Average reservoir temperature as function of
operating time in optimizedCases 1, 2, 3, 4, and 5.
568 D.W. Zhao, I.D. Gates /permeability zones at the top or
bottom of the reservoir realizesimilar NPV providing the overall
permeability is similar. Theresults show that Case 4 achieved the
best economic outcome ofthe cases studied here this is a result of
its enhancedpermeability.
4. Conclusions
In the present work, stochastic optimization was conducted
todetermine the optimum injecting pressure and injecting
watertemperature strategies in thin heavy oil reservoir in ve
cases.The key results are as follows.
A high injecting pressure is critical to a success hot water
ood-ing strategy. In the present optimized cases, the injection
pres-sures remain high during the operating process
althoughdeviations present. This promotes larger horizontal heat
trans-fer (convective) than vertical heat losses (vertical
lossesadversely impact process performance due to heat losses
tonon-productive overburden and understrata).
For water injection, the results suggest that starting with
hightemperature injection to lower temperature injection later
onprovides opportunities to recover heat from the reservoir
andoverburden and understrata thus improving the thermal
Fig. 14. Cumulative energy injected to oil ratio (cEOR) of
optimized Cases 1, 2, 3, 4,and 5.efciency of the process. Multiple
cycles of high/low tempera-ture water injection might be benecial
depending on the reser-voir condition.
The permeability distribution is found to affect the
performanceof the hot water ooding process. The existence of
higherpermeability zone at the lower part of the reservoir leads to
ear-lier oil production and water breakthrough. The higher
injectiv-ity and water production also caused higher cEOR.
Theperformance of Case 3, which has higher permeability zone
atupper part of the reservoir, is comparable to that of the Case
1but it used 40% more heat injection.
The absolute overall permeability of the reservoir impacts
per-formance signicantly. Case 4 produced the largest amount ofoil
and water in the early stage of operation. Although Case4s produced
water-to-oil is also substantially higher than theother cases, it
achieved the best economic performance. Thelow permeability of Case
5 led to slow oil production.Although it has the lowest cEOR, the
poor oil production madethe operation process uneconomic.
Acknowledgements
Acknowledgement is extended to the Petroleum TechnologyResearch
Centre (PTRC) for their nancial support and theUniversity of
Calgary for providing nancial and logistical supportas well as
Computer Modelling Group for the use of its thermalreservoir
simulator, STARSTM.
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On hot water flooding strategies for thin heavy oil reservoirs1
Introduction2 Models and methods2.1 Reservoir simulation model2.2
Optimization algorithm2.2.1 The simulated annealing method2.2.2
Adjustable parameters and cost function
3 Results and discussion3.1 Injection pressure and water
temperature3.2 Oil production rates and effects of permeability
variations3.3 Water injection rates and water production3.4
Temperature distributions, cumulative energy injected to oil ratio
(cEOR), and net present value
4 ConclusionsAcknowledgementsReferences