Experimental and Numerical Simulation of Oil ... - ACS Publications

3976

Energy & Fuels 2008, 22, 3976–3985

Experimental and Numerical Simulation of Oil Recovery from Oil Shales by Electrical Heating Berna Hascakir,†,‡,§ Tayfun Babadagli,*,‡ and Serhat Akin† Department of Petroleum and Natural Gas Engineering, Middle East Technical UniVersity, Ankara 06531, Turkey, and School of Mining and Petroleum Engineering, Department of CiVil and EnVironmental Engineering, UniVersity of Alberta, Edmonton, Alberta T6G 2W2, Canada ReceiVed May 23, 2008. ReVised Manuscript ReceiVed July 24, 2008

The recovery characteristics of four different oil shale samples were tested experimentally using the retort technique. To accomplish efficient temperature distribution, the thermal conductivity of the oil shale samples was increased by the addition of three different iron powders. The doses of iron powders were optimized for each oil shale sample based on the highest oil production value experimentally. The experiments were then modeled using the electrical heating option of a commercial reservoir simulator. The viscosity-temperature relationship was obtained by matching the experimentally obtained temperature distribution in the cores and production data to the numerical ones. After the other parameters needed for the numerical model were collected and compiled, field-scale simulations were performed and a parametric analysis was performed for different oil shale cases. The experimental and numerical results show that field-scale oil recovery from oil shales by electrical heating could be technically and economically viable.

1. Introduction To ensure current energy needs and meet future expectations, new techniques for efficient use of unconventional resources, exploration of new reserves, and evaluating the potential alternatives have to be considered together.1,2 Oil shales are one of the alternative fossil fuel resources. Shales are known as finegrained sedimentary rocks that yield significant amounts of oil through pyrolysis, and these resources have been used since ancient times.3-5 The modern industrial use of oil shales for oil extraction dates to the mid-19th century.6 Although information about many oil shale deposits is rudimentary and much exploratory drilling and analytical work needs to be performed, the potential resources of oil shales in the world are enormous.6 The total proven oil shale reserves of the world are about 80 000 million tons.7 Therefore, it is important to investigate the recovery characteristics of oil shale resources.8 * To whom correspondence should be addressed. Fax: +1-780-492-0249. E-mail: [email protected]. † Middle East Technical University. ‡ University of Alberta. § Visiting Ph.D. student at the University of Alberta. (1) Greene, D. L.; Hopson, J. L.; Li, J. Have we run out of oil yet? Oil peaking analysis from an optimist’s perspective. Energy Policy 2006, 34, 515–531. (2) Ivanhoe, L. F. Future world oil supplies: There is a limit. World Oil, Nov, 1995; pp 91-94. (3) Tissot, B. P.; Welte, D. H. Petroleum Formation and Occurrence; Springer: Verlag, Germany, 1984. (4) Farouq Ali, S. M. Heavy oilsEvermore mobile. J. Pet. Sci. Eng. 2003, 37, 5–9. (5) Moody, R. Oil and gas shales, definitions and distribution in time and space, the history of on-shore hydrocarbon use in the U.K. Abstracts, April 20-22, 2007. (6) Altun, N. E.; Hicyilmaz, C.; Hwang, J.-Y.; Bagci, S. Evaluation of a Turkish low quality oil shale by flotation as a clean energy source: Material characterization and determination of flotation behavior. 2006, 87, 783791.

Oil shale is a general expression usually used for a finegrained sedimentary rock, containing significant amounts of kerogen, from which liquid hydrocarbons can be obtained.9,10 Kerogen is a mixture of organic chemical compounds that make up a portion of the organic matter in sedimentary rocks. Because kerogen has a huge molecular weight of its component compounds, it is insoluble in normal organic solvents. Bitumen and/or prebitumen, which are known as the soluble portion of kerogen may also exist but in relatively lower amounts. If it is heated to the right temperatures in the Earth’s crust, some types of kerogen release oil or gas, collectively known as hydrocarbons (fossil fuels). If such kerogens are present in high concentration in rocks, such as shale, and have not been heated to a sufficient temperature to release their hydrocarbons, they may form oil shale deposits.11 The key to produce oil from these resources is to reduce oil viscosity, and that is best accomplished by heating these resources up to 500 °C, which is known as the pyrolysis process, which is known also as the decomposition of the kerogen.4,12 Oil retorting can be considered as the most effective method for extracting oil from oil shales.6 The easiest way to increase the efficiency of this method is to increase the thermal conductivity of the system or increase the reduction of the oil viscosity by using some additives. Metallic additives cause (7) World Energy Council (WEC). Survey of energy resources: Oil shale. WEC, London, U.K., 2001, also available at http://www.worldenergy.org/ wec-geis/publications/reports/ser/shale/shale.asp. (8) Campbell, C. J.; Laherrere, J. H. The end of cheap oil. Sci. Am. 1998, March, 78–83. (9) Tissot, B. P.; Welte, D. H. Petroleum Formation and Occurrence; Springer-Verlag: Berlin, Germany, 1984; p 699. (10) Hutton, A. C. Petrographic classification of oil shales. Int. J. Coal Geol. 1987, 8, 203–231. (11) Weber, G.; Green, J. Guide to oil shale. National Conference of State Legislatures, Washington, D.C., 1981; p 21. (12) Johannes, I.; Kruusement, K.; Veski, R. Evaluation of oil potential and pyrolysis kinetics of renewable fuel and shale samples by Rock-Eval analyzer. J. Anal. Appl. Pyrolysis 2007, 79, 183–190.

10.1021/ef800389v CCC: $40.75  2008 American Chemical Society Published on Web 09/05/2008

Oil RecoVery from Oil Shales by Electrical Heating

Energy & Fuels, Vol. 22, No. 6, 2008 3977

Table 1. Properties of Oil Shale Samples14 deposita

calorific value (kcal/kg)

oil shale resource (106 tons)

OS1 OS2 OS3 OS4

1390 774 860 851

66 360 122 130b

a

OS ) oil shale. b Probable reserve.

changes in the nature and the amount of fuel formed during in situ combustion. These changes appear to depend upon the type of oil used. Various crude oils are affected differently by different additives.13 In this study, the recovery characteristics of four different oil shale samples obtained from different oil shale deposits in Turkey were investigated experimentally at laboratory conditions by using the retort technique. To enhance the oil production by reducing oil viscosity and achieving the effective temperature distribution, three different iron powders and their three different doses were used. The experimental results were simulated using a commercial reservoir simulator, where the data required for field-scale simulation were obtained through history matching of production data and temperature distribution inside the core. Note that the deposits were considered to be deeper than the minable range and, therefore, in situ recovery techniques are needed for oil recovery. Finally, field-scale application of electrical heating was simulated, and a technical and economic evaluation of the applicability of the method was presented. A few parameters such as viscosity-temperature variation, molecular weight, and relative permeabilities that are needed in the simulation of oil recovery from oil shale studies are highly difficult to obtain (or not practically measurable) at laboratory conditions unlike conventional reservoirs. The accuracy of the simulation results, however, strongly depends upon these parameters. Experimental verifications and supports are critical in this regard, and we introduced a new approach in the fieldscale simulation of the process based on the laboratory-scale modeling of the process experimentally and numerically. This would provide realistic data needed for simulation and, therefore, increase the accuracy of modeling studies.

Figure 1. Retort setup.22

muscovite, illite, and smectite.18-20 Geochemical analysis revealed that the organic content of this oil shale was mainly derived from algae, pollen, and planktonic algae, as well as bacteria. Also, traces of liptodetrinite and humic organic material were observed.16,20 It contains a relatively high amount of ash (≈70%); however, the total sulfur content seldom exceeds 1.5%, with an average of less than 1%.16,19 The OS4 deposit underlies conglomeratic rocks, and the average thickness of the oil shale bed is 13 m. The average calorific value and oil content of the deposit are 851 kcal/kg and 13.7%, respectively.14,21 Experimental Setup and Procedure. Retort experiments were conducted with a setup that consists of a stainless-steel cylindrical body that houses the samples with an inner diameter of 10 cm and height of 20 cm, respectively. The cylindrical body was wrapped with a band heater (1000 W), and this heater was connected to a temperature controller to increase the temperature of the system by using commercial software. A thermocouple was placed at the center of the sample holder and connected to the temperature controller to record temperature values continuously by using the same software. Then, the sample holder was placed in another cylindrical cell, which has a 20 cm inner diameter and 25 cm height. To minimize the heat losses, the space between two cylinders was filled by crushed perlite [thermal conductivity of 75 °F (24 °C), 0.27-0.41 Btu in. h-1 ft-2 °F-1 (0.04-0.06 W m-1 K-1)]. From the help of software and the band heater, the temperature of the system could be increased to desired temperatures (Figure 1).

2. Experimental Methodology Samples and Characterization. Four different oil shale samples (OS1, OS2, OS3, and OS4) from four different oil shale deposits were used. The properties of those samples are given Table 1. The OS1 basin is of the Neocene age. Its seam consists of more than 50% liptinite, 20-50% huminite, and 0-20% inertinite maceral groups and is characterized by its high organic content.15 The origin of the organic matter is mainly algae and plants.16 The OS2 deposit is of the Paleocene-Eocene age. It contains around 80% liptinite, 5-10% bituminite, and 5-10% huminite. The high liptinitic content shows that the organic matter originated mainly from hydrogen-rich organic remains of algae and pollen. Calcite, dolomite, quartz, and smectite are the major inorganic constituents.17 OS3 is rich in quartz, dolomite, calcite, and clay minerals, such as (13) Castanier, L. M.; Brigham, W. E. Upgrading of crude oil via in situ combustion. J. Pet. Sci. Eng. 2003, 39, 125–136. (14) S¸engu¨ler, ˙I. Bituminous shales: Their origin, usage and importance. Bull. MTA Nat. Resour. Econ. 2007, 3 (in Turkish). (15) S¸ener, M.; Gu¨ndogdu, M. N. Geochemical and petrographic investigation of Himmetogjlu oil shale field, Go¨ynu¨k, Turkey. Fuel 1996, 75 (11), 1313–1322. (16) Putun, E.; Akar, A.; Ekinci, E.; Bartle, K. D. Chemistry and geochemistry of Turkish oil shale kerogens. Fuel 1988, 67, 1106–1110. (17) S¸ener, M.; Sengu¨ler, I. Geological, mineralogical, geochemical characteristics of oil shale bearing deposits in the Hatıldagj oil shale field, Go¨ynu¨k, Turkey. Fuel 1998, 77 (8), 871–880.

3. Experimental Results To determine the optimum heating periods, eight different experiments were carried out with OS4. Optimum heating and soaking periods were selected according to the highest oil production. These eight experiments were summarized in Table 2. Because thermal cracking, which is also known as pyrolysis, takes place over 500 °C, for all of these experiments, 500 °C was selected as last temperature value, to produce shale oil.13 (18) Hufnagel, H. Investigation of oil shale deposits in western Turkey. Technical Report Part 2, Project 84.2127.3, BGR, Hannover, Germany, 1991. (19) S¸ener, M.; Sengu¨ler, I.; Ko¨k, M. V. Geological considerations for the economic evaluation of oil shale deposits in Turkey. Fuel 1995, 74, 999–1003. (20) Ko¨k, M. V.; Sengu¨ler, I.; Hufnagel, H.; Sonel, N. Thermal and geochemical investigation of Seyito¨mer oil shale. Thermochim. Acta 2001, 371, 111–119. (21) Altun, N. E. Beneficiation of Himmetogjlu and Beypazari oil shales by flotation and their thermal characterization as an energy source. Ph.D. Thesis, Graduate School of Natural and Applied Sciences, Middle East Technical University, 2006; pp 5-29. (22) Hascakir, B.; Demiral, B.; Akin, S. Experimental and numerical analysis of oil shale production using retort technique. 15th International Petroleum and Natural Gas Congress and Exhibition of Turkey, Ankara, Turkey, May 2005.

3978 Energy & Fuels, Vol. 22, No. 6, 2008

Hascakir et al.

Table 2. Determination of Optimum Heating Period for Retort Experiments22 first period heating

second period soaking

heating

soaking

number of experiments

Da (s)

Tb (°C)

D (s)

T (°C)

D (s)

T (°C)

1 2 3 4 5 6 7 8

300 1800 1200 1200 3600 600 1800

100 100 100 100 100 100 100

3600 3600 2700 1800

100 100 100 100

5400 3600 6000

100 100 500

9000 5400 6000 900 6000 5400 12600

500 500 500 580 520 500 350

a

D (s)

T (°C)

4500 6000

500 500

petroleum production (cc) 3 4 0.2 1 1 0.2 0 3

D ) duration. b T ) reached temperature values at the end of the heating or soaking period.

Figure 2. Temperature profile determined to be the optimum operation periods.22

Figure 3. Retort experiment results for oil shale samples. Table 3. Density and the API Gravities of the Produced Shale Oil sample OS1 OS1 OS2 OS2 OS3 OS3 OS4 OS4

plus 0.1% Fe plus 0.1% Fe2O3 plus 0.5% Fe plus 0.1% Fe2O3

oil density (g/cm3)

API gravity

0.97 1.06 0.89 0.99 1.03 1.02 0.89 0.92

14.94 2.57 27.49 12.01 5.88 7.40 27.40 21.97

Because the highest production was obtained after the second experiment, heat and soak periods belong to this experiment were selected as optimum periods (Figure 2). The other experiments were accomplished by using this optimum condition. There are two different soaking periods determined as the optimal experimental operation period (Figure 2). The first one

Figure 4. Grid sizes used in simulations.

starts at 100 °C. This soaking period helps to vaporize water. The second soaking period begins at 500 °C, which is the pyrolysis temperature required for the decomposition of the kerogen. To vaporize the water or decompose the kerogen needed, time is given with the help of soaking periods. One can infer from experiment 7 that, below the pyrolysis temperature, it is impossible to produce oil from oil shale samples. Although it is possible to produce oil from oil shale when the pyrolysis temperature is reached, the oil produced would not be much. To enhance the oil production efficiencies, effective temperature distribution and viscosity reduction of the shale oil should be accomplished together. Therefore, three different iron powders and their three different doses were tested on the shale oil production. The iron powders used are Fe,



Figure 5. Water-oil relative permeability curves used in simulation for oil shale samples.25

Figure 6. Gas-liquid relative permeability curves used in simulation for oil shale samples.25

Figure 7. Temperature match for all oil shales (I, experimental study results; II, numerical study results).

Fe2O3, and FeCl3, and their doses are 0.1, 0.5, and 1% by weight. The average particle size of Fe and Fe2O3 is 10 µm. FeCl3 was crushed by a porcelain mortar, and its average size is slightly greater than this value. A total of four experiments were conducted for raw oil shale samples, and 36 experiments were conducted to find the optimum types and doses of iron powders for each oil shale sample. Oil production, after retorting of raw

oil shale samples and after retorting of samples containing optimum types and doses of iron powders, is summarized in Figure 3. Because iron powders help increase the thermal conductivity of the system, heat transfer was accomplished more efficiently, yielding increased oil production at laboratory conditions. Also, iron additives have a catalytic effect that increases the reaction


Hascakir et al.

Figure 8. Production match for all oil shales (I, experimental study results; II, numerical study results).

Figure 9. Viscosity variations of all shale oils.

speed. The chemical reactions between iron powders and shale oil help to break the chemical bonds by increasing the temperature and magnetic effect of iron powders on the reduction of oil viscosity, which caused an increase in the oil production after the addition of iron powders.13,23,24 API gravity and the oil densities with and without iron powder addition were obtained experimentally and are given in Table 3. 4. Numerical Simulation All simulation studies were performed using the electrical heating option of the CMG-STARS (steam, thermal, and advanced processes reservoir simulator) commercial simulator (CMG). The domain was discretized into 20 × 1 × 10, 3D radial blocks of varying size in the r direction and constant size in the z direction. The dimensions of the reservoir for the field case and grid size for both laboratory and field cases are given in Figure 4. The required input data for simulation are porosity, permeability, thermal conductivity, rock heat capacity, rock compressibility, viscosity, and relative permeabilities. The output data (23) Kershaw, J. R.; Barrass, G.; Gray, D. Chemical nature of coal hydrogenation oils part I. The effect of catalysts. Fuel Process. Technol. 1980, 3-2, 115–129. (24) Odenbach, S. Ferrofluids/magnetically controlled suspensions. Colloids Surf., A 2003, 217, 171–178.

of the simulation is time-dependent temperature distribution and oil production data, which were also determined experimentally. All required input data, except the viscosity-temperature relationships, were taken from the literature. All of the input data (porosity, permeability, rock properties, etc.), except the temperature-viscosity relationship, were taken from the literature for the numerical simulation runs. These data were compiled from the literature about the oil shales studied in this study and the common oil shale fields around the world. The viscositytemperature relationship was obtained through matching numerically obtained temperature distributions and production data to the experimental output. Another critical parameter in addition to the viscosity-temperature data is the relative permeabilities. It was observed that the effect of relative permeabilities is trivial compared to the viscosity-temperature data. Relative permeability data used in the study were taken from another oil shale field (Figures 6 and 7). Hence, relative permeabilities typically suggested for these types of simulations in the literature were adapted and used. Because shale oil is too viscous and the amount of produced oil at the laboratory conditions is very little, it was very difficult to determine the viscosity of the shale oil experimentally. Therefore, the only way to obtain this critical data was the experimental matching exercise. Another critical point that



Figure 10. Temperature matches for all oil shale after the addition of optimum doses of iron powders (I, experimental study results; II, numerical study results).

Figure 11. Production matches for all oil shale after the addition of optimum doses of iron powders (I, experimental study results; II, numerical study results).

entails this type of approach is to determine the level of heating and corresponding temperature to have oil become flowable. Using the shale oil viscosity values and literature data, the process was then simulated at field scale. In these numerical simulation studies, the power of the system, operation time, and the number of heaters were optimized by considering both oil production and economics of the project. 5. Numerical Simulation Results During the experiments conducted with oil shale samples, temperature and production data were recorded continuously. As mentioned above, produced oil from oil shale is highly viscous and very little in amount at the laboratory conditions. Thus, shale oil viscosities are difficult to measure. Moreover, there is little knowledge about oil shale properties in the literature. In the simulation studies, literature data given in Figures 5 and 6 were used. Using the viscosity as the adjustable parameter, the simulation outputs were matched to the experimentally obtained temperature and production data. These matches are given in Figures (25) Sarkar, A. K.; Sarathi, P. S. Feasibility of steam injection process in a thin, low-permeability heavy oil reservoir of ArkansassA numerical simulation study. U.S. Department of Energy Assistant Secretary for Fossil Energy, 1993; p 42.

7 and 8. The viscosity-temperature relationship obtained through this exercise is shown in Figure 9. After 400 °C, a linear relationship was observed between temperature and shale oil viscosity on a semi-log plot, which means that oil starts to flow as a result of reaching the pyrolysis temperature. This type of trend observed after 400 °C is a typical relationship between viscosity and temperature during pyrolysis.4,12 Simulation studies were also performed for the verification of the experimental studies carried out after the addition of optimum iron powder types and doses. These simulation matches for all oil shales containing optimum doses and types of iron powders are given in Figures 10 and 11 for temperature and production, respectively. The viscosity-temperature relationship obtained through the matching exercise is shown in Figure 12. As can be inferred from Figure 12, the iron powder addition decreases the shale oil viscosity considerably. The viscosity-temperature data obtained through matching the experimental results to the numerical output and the porosity, permeability, rock compressibility, rock heat capacity, rock thermal conductivity and the molecular weight of oil values (as given in Table 4) previously used for the simulation of laboratory experiments were also used to simulate the field cases. When the data were kept the same as the laboratory simulation data, the operation time, the power needed in the field


Hascakir et al.

Figure 12. Viscosity variations for all oil shale after the addition of optimum doses of iron powders. Table 4. Data Used for the Simulation of Laboratory and Field Cases4,6,26,27 oil shale OS1

OS2

OS3

OS4

parameter

unit

raw

0.1% Fe

raw

0.1% Fe2O3

raw

0.5% Fe

raw

0.1% Fe2O3

porosity permeability rock compressibility rock heat capacity rock thermal conductivity molecular weight laboratory pressure laboratory temperature reservoir field pressure reservoir field temperature reservoir depth initial water saturation in the reservoir formation thickness

(%) (md) (kPa-1) (J m-3 °C-1) (J m-1 day-1 °C-1)

45 5000 0.001 6.4 × 105 1.44 × 105

45 5000 0.001 6.53 × 105 2.06 × 105

45 5000 0.001 1.37 × 106 1.44 × 105

45 5000 0.001 1.29 × 106 2.06 × 105

45 5000 0.001 6.5 × 105 1.7 × 105

45 5000 0.001 6.9 × 105 4.5 × 105

45 5000 0.001 8.9 × 105 1.44 × 105

45 5000 0.001 9.2 × 105 2.06 × 105

(g mol-1) (kPa) (°C) (kPa, ×103) (°C)

650 101 21 5-10 50

650 101 21 5-10 50

600 101 21 5-10 50

600 101 21 5-10 50

600 101 21 5-10 50

597 101 21 5-10 50

600 101 21 5-10 50

600 101 21 5-10 50

(m) (%)

500-5000 25

500-5000 25

500-5000 25

500-5000 25

500-5000 25

500-5000 25

500-5000 25

500-5000 25

(m)

30

30

30

30

30

30

30

30

application, and the number of heaters were optimized. For this purpose, an operation time of 60 and 90 days, the power values of 46 296, 34 722, and 23 148 W, and 5 or 10 heaters were used. Furthermore, sensitivity analyses were conducted by using two different reservoir depths, i.e., 500 and 3000 m, at three different reservoir pressure values, i.e., 5000, 9000, and 10 000

Figure 13. Simulation results for OS1 (field case).

kPa, and three different bottom hole pressures, i.e., 1000, 1800, and 2000 kPa. Because the simulation results yielded very similar oil production results (not shown here) for different reservoir depths, pressures, and bottom hole pressures (even at extreme values of those reservoir properties), it can be stated





that electrical heating of oil shale reservoirs are not critically affected by reservoir depth, pressure, and bottom hole pressure values. In the simulation runs, oil was produced continuously while heating the reservoir in the same well. When the soaking period was applied in the simulations, it was observed that oil production did not change significantly but the operation time increased, which resulted in an increase in the cost of the process. For OS1 and OS2, three different field simulations were conducted and the results were summarized in Figures 13 and 14, respectively. While in the first runs, the operation time was 60 days, in the second and third runs, operation times were increased to 90 days. The number of heaters for the third run was reduced from 10 to 5, so that the power exerted for these operations was decreased. For both cases, the third run yielded the optimum oil production (highest recovery/power ratio). It is observed that, while the iron powder addition caused an increase in oil production for OS2, the opposite was observed for OS1 when the power was reduced. The results for OS3 are shown in Figure 15. It was observed that the second run gave better oil production for the raw oil shale. However, after the

addition of 0.5% Fe, the oil production values increased sharply because of the effect of viscosity variation after the addition of 0.5% Fe.23,24 All of the runs were performed for 60 days and with 10 heaters. For OS4, four different runs were carried out and the third run yielded the highest oil production (Figure 16). Also the difference between the raw and 0.1% Fe2O3 cases were very similar for the first three runs; the fourth run showed no significant change in production when Fe2O3 is added. For the first two and fourth runs, the operation times were 60 days; it was 90 days for the third run. A total of 10 heaters were used for the first three runs, and for the forth run, the number of heaters was decreased to 5. While keeping the number of heaters (i.e., 10) and the operation times (i.e., 60 days) constant, different powers were applied (i.e., 34 722 and 46 296 W) in the first and second cases. The oil production increased from 49 to 109 bbl for raw oil shale and from 148 to 250 bbl for additional oil shale. The second and third runs compare the cases with different operation times. While the power (i.e., 46 296 W) and the number of heaters (i.e., 10) were kept constant for the second and third runs, the oil production increased because of the


Hascakir et al.


Figure 17. Cumulative oil production results for the optimum operation conditions of oil shales (I, raw oil shale; II, oil shale containing optimum types and doses of iron powders). Table 5. Economic Evaluation of the Studya

sample name OS1 OS1 OS2 OS2 OS3 OS3 OS4 OS4 a

addition 0.1% Fe 0.1% Fe2O3 0.5% Fe 0.1% Fe2O3

number of heaters

total operation time (days)

oil production (bbl)

cost of the study ($/bbl)

5 5 5 5 10 10 10 10

90 90 90 90 60 60 90 90

296 119 145 180 9 55 262 420

15.55 38.68 30.46 24.62 502.55 79.85 33.78 21.07

The cost of electricity ) 0.088 US$/1 kWh.28

increasing of operation time, from 60 to 90 days. The increase in the oil production for the raw production case was 240%, and this value turned out to be 168% for the 0.5% Fe2O3 cases. 6. Economics

Incorporated Company (TEDAS).28 Table 5 summarizes the economic evaluation of this study for only the best oil production results. Economic evaluation of this study shows that the recovery of oil from these reserves by the retort technique can be applicable for three oil shale reservoirs (OS1, OS2, and OS4) but not OS3. Note that only the heating cost was considered in this analysis and the CAPEX was not included. Finally, Figure 17 shows the time dependency of the cumulative oil production for the raw oil shales and oil shales after the addition of iron powders. The best result was obtained for the OS4 case with iron powder. Over the time period of investigation (90 days), the increasing trend was still obvious and the plateau region had not been reached unlike the other seven cases. It is interesting that its “raw version” also showed a similar trend but much less oil production. Note that OS4 has significantly higher rock heat capacity compared to the other three samples.

Economic evaluation was carried out by considering the cost of electricity determined by the Turkish Electric Distribution

7. Conclusions

(26) Bechtel SAIC Company, LLC. Heat capacity analysis report. U.S. Department of Energy Office of Civilian Radioactive Waste Management Office of Repository Development, 2004; pp 6-23-6-24.

(1) Viscosity-temperature relationship as data to the simulator was observed as the most critical parameter, but it is not easily measurable. Therefore, an experimental study was


performed to obtain this input data by numerically simulating the experiments and matching the history for temperature distribution and production. (2) After 400 °C, the relationship between viscosity and temperature was observed as linear on a semi-log plot. This is the pyrolysis temperature. (3) Introducing iron powder into the reservoir for practical applications is a critical issue, but we are not aware of any application or suggestion in the literature in this regard. The addition of iron powder could be achieved by injecting iron powders into the reservoir, after mixing them with petroleum-based fluids, such as light oils or solvents. If the field is shallow enough for surface mining, a better solution would be adding the iron powders during the extraction process. (4) The technical and economic feasibility analyses showed that electrical heating is still a (27) Lide, D. R. CRC Handbook of Chemistry and Physics, 88th ed.; CRC Press: Boca Raton, FL, 2007-2008. (28) http://www.tedas.gov.tr (accessed on Jan 2, 2008).


marginal application, but the results proved that it is in an applicable range. (5) All oil shales showed different recovery trends. The production rate and the ultimate recovery from the oil shale case of OS4 were remarkably higher compared to the other three cases. This could be attributed to significantly higher rock heat capacity of this particular sample compared to the other three samples. Acknowledgment. This work was funded by The Scientific and Technological Research Council of Turkey (TUBITAK) and the Faculty Development Program-Middle East Technical University (OYP-METU). The numerical modeling part of the study was performed during the stay of the first author (B.H.) at the University of Alberta as a visiting Ph.D. student. We gratefully acknowledge these supports. We also thank the Computer Modeling Group (CMG) for providing the simulation software package and its electrical heating option for this research. EF800389V

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