On Hot Water Flooding Strategies for Thin Heavy Oil Reservoirs----

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










11 Injection watertemperature

0 20250 C


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


25 20250 C


31 20250 C


37 20250 C


43 20250 C


49 20250 C


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



(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



Fig. 10. Temperature (C) distrib




Fig. 11. Temperature (C) distrib




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

Documents

On Hot Water Flooding Strategies for Thin Heavy Oil Reservoirs----