SPE 139222 Selection Methodology for Screening Evaluation of Enhanced-Oil-Recovery Methods M. Trujillo, D. Mercado, G. Maya, R. Castro, C. Soto, H. Pérez, V. Gómez and J. Sandoval, Ecopetrol S.A. Copyright 2010, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Latin American & Caribbean Petroleum Engineering Conference held in Lima, Peru, 1–3 December 2010. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract This paper presents a methodology for the selection of the enhanced oil recovery technologies that better applies to some group of fields using screening criteria. The methodology has been integrated in a software in order to make repetitive analysis in an easier way, and has been applied for identifying the technologies whit higher technical potential of application in the Colombian Fields which have the biggest amount of oil in place (approximately 80%). The methodology incorporates oil and rock properties and the reservoir current conditions, besides the specific knowledge of the reservoir generalities and history. In some Colombian fields, processes that use water, gas or steam have been applied; additionally, some other projects using water, gas, chemicals and air are in a design stage at this moment, however, more than 90% of the approximately 280 Colombian fields are still in primary recovery. This is one of the main reasons for having an oil average recovery factor of about 21%, and it also states the need of using methodologies that allow identifying the best investment options. The technologies considered in this analysis were: water injection, lean gas, rich gas, N2, WAG, CO2 (miscible and immiscible), polymer, surfactant – polymer, steam (cyclic and continuous) and some others such as CHOPS, VAPEX, WET VAPEX, SAGD, in situ combustion and electromagnetic heating. The application of the methodology presented in this study allowed to identify the enhanced oil recovery technologies with higher potential for being applied in the Colombian fields with biggest amount of oil in place; it also generated a guide for the construction of every analyzed field development plan, which is presented as an example for the Cocorná heavy oil field. The subject treated in this paper is more important for companies that own an important number of fields, and need to identify those with better characteristics for enhanced oil recovery projects in a quick and easy way; however, it is also very useful for companies that are beginning to develop any specific field. 1. Introduction There is different software applications in the oil industry that, besides the selection of the most technically applicable EOR process, can be used to obtain predictions, recovery factor estimations, etc., some of the mentioned software are: EORgui, allows to apply EOR screening criteria of nine methods to any field, and to quantify the incremental production of the applicable technologies using six different prediction methods. The software is based on the Taber, Martin and Seright screening criteria. Sword, was specially design to make quick evaluation of EOR potential, screening studies and predictions of EOR methods. The screening techniques and prediction methods in the software are based on multi – criteria models, probed analytical solutions, industry experiences, field practices and experts’ knowledge in EOR applications. SelectEORTM executes screening studies taking into account seventeen EOR processes and makes predictions using fourteen methods. It was sent to the market in 2009 for the Alberta Research Council, and it is based on the prior software named PRIzeTM. The screening criteria are based on a complete database which has had good acceptance for its effectiveness in the evaluation of EOR potential around the world.

2 SPE 139222

Screening 2.0 is a software tool developed by the I.C.P. of ECOPETROL S.A., and can apply screening criteria of eighteen EOR methods. This tool considers the screening criteria of Lewin, Farouq Ali, Taber, Seright, NPC, McRee, Iyoho, Stalkup, SSI, E.C. Donaldson, Leonard, Pri – Canada, Ganesh Thakur, William Cobb, Dusseult, Singhal, Geffen, Chu, Poett – Mann, between others. IORSys (IOR – Predictive Software System) is a software tool developed by RIPED (Research Institute of Petroleum Exploration & Development, PetroChina), and its main application is evaluation of EOR potential. The software consists of different modules: data management, screening methods, EOR potential evaluation and prediction. The software tool presented in this paper executes screening criteria of nineteen EOR methods (based on the software Screening 2.0), and allows to obtain the analogs fields of any other supported in a data base of approximately 1000 fields. It also applies the benchmarking methodology developed by Perez et al, and last, it estimates the behavior of water and steam injection. With the use of this tool the engineer could be able of selecting the EOR method that technically applies to any field and / or formation, to identify EOR projects in analogs fields, to determine the probability of success of an specific method and finally to make analytical predictions. The above applications will be shown by an example using the Colombian Cocorná Field, which is part of the Teca Field, and one of the Colombian oil fields with OOIP higher than 500 MMBO. 2. METHODOLOGY The methodology includes 4 main aspects: binary technical screening, analogies, benchmarking and analytical prediction.

‐ Binary technical screening is based on the comparison of certain properties of fluids and reservoir of a field under study with the criteria proposed by diverse authors through time, with the aim of determining which methods of improved recovery are feasible technically to apply in this field. Properties such as porosity, permeability, viscosity, API, So, thickness, depth, reservoir temperature, pressure and lithology are analyzed. Binary technical screening includes a great amount of methods of improved recovery that allow to apply it to different types of reservoirs (light, medium or heavy oil, deep or shallow reservoirs, etc). The contemplated methods are: Injection of water, gas (poor and rich), nitrogen, CO2 (miscible and immiscible), polymer, surfactant-polymer, steam (continuous and cyclic), and others like CHOPS, WAG, VAPEX, WET VAPEX, SAGD, combustion and Electromagnetic Heating.

‐ The analogies are based on an analog model that allows to identify from a data base of approximately 1000 projects

of application of methods of improved recovery if a specific technology EOR has been implemented under properties of fluid and reservoir similar to those in the field under study. Once the analogs fields have been selected, it can be identified the best practices associated with the application of the recovery method and the lessons learned as well as with the problems related to the implementation of this technology.

‐ The benchmarking methodology was developed by Perez et al1 who based it on the characteristics of successful steamflood projects, to develop a model to rank potential reservoirs. They analyzed reservoir data using standard statistical methods for properties, such as: API gravity, initial oil saturation, reservoir temperature, porosity, initial pressure, depth, net pay, viscosity at reservoir condition, initial (at the beginning of steamdrive)- bubble point pressure ratio and average permeability. The statistical model ranked the properties on a standardized score scale. A predicted score near to one hundred indicates a high probability of success1 of the steam injection in the field under study.

‐ The analytical predictions are realized for the methods: water injection and steam injection.

The following explains in more detail each of the components of the methodology. 2.1. BINARY TECHNICAL SCREENING Screening criteria: The screening criteria are the most common, fast and easy tool to use to determine if a field/reservoir becomes a good candidate for implementing an enhanced oil recovery process. In the specialized technical literature are

SPE 139222 3

published a series of screening criteria for different recovery methods, which have been obtained from the experience gained from many worldwide projects. This methodology considers the screening criteria of Lewin, Farouq Ali, Taber, Seright, NPC, McRee, Iyoho, Stalkup, SSI, E.C Donaldson, Leonard, Pri Canadá, ganesh Thakur, William Cobb, Dusseult, Singhal, Geffen, Chu, poett-Mann, among others. Some methods have screening criteria of more than one author and the tool offers the possibility of selecting the set of criteria to carry out the evaluation. The screening criteria are proposed by different authors and at different stages of maturity of a recovery process, therefore, special care must be taken with this aspect when the applicability of a method can not be ruled out if some of the screening criteria proposed by different experts or incorporated into commercial tools are not met2, in this aspect, the analogies and the benchmarking methodology play an important role. Additionally, the knowledge and criterion of the engineer are the most important aspects. Fluid and reservoir properties analyzed: The properties compared with the screening criteria are shown in Table 1. Additional properties are compared, depending on the recovery method being evaluated. The Table 1 shows that the binary screening requires few data, which turns the methodology into a tool easy to apply, because in many occasions the fields do not have sufficient information to realize more detailed studies. After selecting the method or methods of recovery that technically apply to the field/reservoir by means of binary technical screening complemented with analogies and benchmarking methodology, the operating company would initiate the acquisition of the information necessary to carry out a more exhaustive study that can includes experimental evaluations, geological models, numerical simulation, economical analysis, etc, that would finally determine the feasibility of application of a particular method.

FLUID PROPERTIES Viscosity, cp

API Gravity, ºAPI

RESERVOIR PROPERTIES Current Oil Saturation, fraction Thickness, ft Permeability, mD Porosity, fraction Depth, ft Reservoir temperature, ºF Pressure, Psia Lithology

Table 1. Fluid and reservoir properties used to perform the binary technical screening.

Because pressure and fluid saturations change during the productive life of the field, it is important to evaluate these properties to the current conditions of the field/reservoir, to avoid a mistaken selection of the methods to apply to the field under study. Score assignation: Each one of the 10 properties shown in Table 1 are compared with the criteria of screening of the different authors. A score between 0 and 1 is assigned. A score of 1 is assigned when the property is within the range established by Taber-Seright (1997), and zero when it falls in the opposite case. When the property meets only part of the range, a score is assigned proportional to the rate of compliance. The screening criteria of the other authors also are evaluated but are not considered for the assignment of the score. Figure 1 shows the procedure for assigning scores.

4 SPE 139222

FLUID AND RESERVOIR PROPERTIES.FIELD UNDER STUDY (FIELD A)

SCREENING CRITERIA

Vs

SCORE ASSIGNATION

Score for each property

Score of the method

Viscosity, cp 600API Gravity, ºAPI 12Current Oil Saturation, fraction 0.55Thickness, ft 100Permeability, Md 150-300Porosity, fraction 0.28-0.32Depth, ft 2900-3200Pressure, Psia 500Lithology Sandstone (SS)

PROPERTIESTABER-SERIGHT

VsField A

Viscosity, cp 1API Gravity, ºAPI 1Current Oil Saturation, fraction 1Thickness, ft 1Permeability, Md 0.6666Pressure, Psia 1Depth, ft 1Porosity, fractionLithology

0.9524

TABER-SERIGHT (1997)

Viscosity, cp < 100000API Gravity, ºAPI 8-25Current Oil Saturation, fraction > 0.4Thickness, ft > 20Permeability, Md > 200Pressure, Psia NCDepth, ft < 5000Porosity, fraction NELithology NE

Figure 2. Procedure for the score assignation in the binary technical screening.

Screening criteria exist in certain recovery methods that must be met to be technically feasible to apply it on a given field/reservoir. It is the case of the depth in the steam injection, the minimum pressure of miscibility in the miscible gas injection, the temperature in the chemicals injection, among others. This methodology takes into account these special criteria and from not being fulfilled one of them, the method would obtain the lowest score, although the other properties fulfills. The methodology can be applied comparing the properties of a reservoir with the screening criteria of the 19 recovery methods included in it or only the methods that the user wants. This depends on the knowledge that the engineer has about the field and the different methods of enhanced oil recovery. Additionally, results can be analyzed by property and/or author, and it allows different types of graphics which can perform a more complete analysis of them. 2.2. ANALOGIES In many cases the screening parameters alone do not provide the necessary tools to select from a group of technologies which is most suitable to be implemented in a field. That is why it is recommended to rely on the study of projects in fields that although they are not equal to the field in study, it presents certain similarity or analogy. The analogies evaluation allows by means of a reasoning based on the existence of similar attributes between two different fields, to define a potential application of a determined recovery process. This paper proposes a methodology which seeks to select from a database the project that has a greater similarity to the field/reservoir under study. This is done through an expression that quantifies the different between some key properties of the fields in the database with the field that is under study. The expression used for the ranking of the fields is as follows:

1001 ×=∑=

n

FS

n

ii

x (Equation 1)

SPE 139222 5

Where: Sx: Score obtained by a field of the data base with respect to the study field. The major of all these values indicates the most analogous field to the field under study. n: Number of properties to be taken into account when making the analogy. Fi: Similarity factor between the value of a certain property of the field under study with respect to the value of the same property belonging to each one of the fields in the database. This factor indicates how similar are the compared values and takes values between 0 and 1. A value near one indicates greater similarity and close to zero indicate that there is a greater degree of difference. It is calculated as shown in the following equation:

( )iei

ieii pp

ppF

−−

−=max

1 (Equation 2)

Pi: Value of the property to compare and belonging to a field in the database. Pie: Value of the property to compare and belonging to a field under study.

( )max i iep p− The maximum of the differences found in a property by comparing all values of this property of the fields

in the database with the field under study. This is done with the objective that the similarity factor values are always between 0 and 1. For the special case when the value of the property is not a number but a chain of characters, the value of the similarity factor is zero when these characters are not equal. 2.3. BENCHMARKING METHODOLOGY The benchmarking study included in this methodology was developed by Perez et al1 and allows to determine the probability of successful implementation of a particular recovery method in a field. The Perez et al1 study was focused on the LMOSFs (Light/medium oil steamflood) and we extend it to the methods: Injection of water, chemical, steam, combustion and WAG, for which exist enough information in our analog database to apply this procedure. Perez et al1 used successful LMOSF projects to create a database from which they selected certain key variables of the process to develop the study. Because some variables could be more important than others, they developed a model that weighted each variable. The distribution of the variables was performed from the coefficient of variation (CV), which is a dimensionless number. This coefficient allows the determination of how disperse the values are with respect to the average. The larger the CV for a certain property, the more dispersed it is and, hence, its relative importance is diminished. Small values of CV for a property indicate greater "weight" (a greater importance) to this model1. Once the importance of each property was established, they performed a program to determine if the success of LMOSF projects could be predicted based on the previous experience. The program calculates a value called “SCORE”, which varies between zero (0) and one hundred (100). As the SCORE approaches 100, there is a greater probability that the LMOSF will be successful. Values near 50 indicate a possible failure. Values smaller than 50 or near zero indicate a failure, or at least, a bigger risk1. 2.4. ANALYTICAL PREDICTION METHODS The methodology uses the analytical models of Marx y Langenheim, Mandl y Volek, Closmann and the analytical model for heterogenous reservoirs designed by Diana Mercado (ECOPETROL-ICP) to determine the production oil rates and the recovery factor as a result of the steam injection process. The prediction of the behavior of the water injection is performed with CGM method.

6 SPE 139222

3. APPLICATION TO COCORNA FIELD Cocorná field, operated by Ecopetrol S.A., is located in the Middle Magdalena Valley (MMV) basin in Puerto Perales town, department of Antioquia. It was discovered in 1963 by Texas Oil Company. Some general information about the field is shown in Table 2.

FIELD COCORNÁ Sedimentary basin Middle Magdalena Valley Drive mechanism Solution gas and weak water drive Producing formations Transition, A, B and C of the Tune formation OOIP [Mbls] 96 Wells Perforated wells: 57 Active wells: 35 Cored Wells: 5 Average spacing 10 acres Artificial lift Mechanical pumping

Table 2. General information of Cocorná field.

3.1. Binary technical screening: The first step in the implementation of the methodology is the collection of reservoir and fluid data. Table 3 shows this information in the Cocorná field.

FLUID PROPERTIES Viscosity, cp 722 API Gravity, ºAPI 13.1 RESERVOIR PROPERTIES Current Oil Saturation, fraction 0.64 Thickness, ft 132 Permeability, mD 1080 Porosity, fraction 0.2-0.3 Depth, ft 2500 Reservoir temperature, ºF 109 Pressure, Psia 275 Lithology Sandstone (SS)

Table 3. Fluid and reservoir properties of Cocorná field.

After introducing the initial data in the tool, the evaluation of binary technical screening is realized. Fluid characteristics of the field indicate that it is heavy oil, therefore, the criteria of screening for the heavy oil methods were only evaluated: Steam (continuous and cyclic) injection and others like CHOPS, VAPEX, SAGD, in-situ combustion and electromagnetic heating. According to the binary technical screening the methods that technically are feasible to apply in the field are: steam (continuous and cyclic) injection, CHOPS, combustion and electromagnetic heating. Table 4 summarizes the results. These results determine the technical feasibility of implementing certain methods of recovery, however, need further analysis to determine which methods have a greater potential for application. For this, we should analyze factors such as: the impact of implementing certain methods for future implementations of other processes, the influence of certain properties on the performance of each process, availability and management of injection fluids, maturity of technology, among others. This analysis, for the Cocorna field, gives as a result 4 methods of recovery with potential application, and in Table 4 these are emphasized with blue color.

SPE 139222 7

RECOVERY METHODS SCORECHOPS 0.533 VAPEX 0.6 Hybrid VAPEX 0.5 Cyclic steam injection 0.889 Steamflood 0.921 SAGD 0 In-situ combustion 0.891 Electromagnetic Heating 0.5

Table 4. Results of binary technical screening for the Cocorná field.

Figure 2 shows the different possibilities from analysis of the results with binary technical screening through practical graphs, allowing the realization of analysis by author or property. In this case, a graph is shown for continuous steam injection. Additionally, reports with observations are generated about the fulfillment or not of each property.

Figure 2. Analysis of the results for binary technical screening 3.2. Analogies: Given that the results of binary screening showed that the steamflood is the technology with the best expectation of implementation, analogies will be evaluated considering only steam flooding projects. The results are shown in Figure 3. Table 5 shows de main characteristics related to each analog field/process. In the tool, it is possible to access to the information of each of these fields as well as its characteristics and the information related to the injection project.

8 SPE 139222

65 70 75 80 85

Coalinga (Fm.Temblor)

San Ardo (Fm.Aurignac)

Midway (Fm.Potter)

Midway (Fm.Spellacy)

Midway-Sunset (Fm.Monarch)

Placerita (Fm.Lower Kraft)

Midway-Sunset (Fm.Marvic)

Guapo (Fm.Cruse E & F)

Tia Juana (Fm.Lagunillas Inferior)

Fazenda Alegre (Fm.Urucutuca)

72.09

73.68

75.36

76.1

77.67

77.77

79.46

79.89

80.68

81.99

Score

Fiel

d

Figure 3. Results of analogies for the Cocorná field.

Field Operator Country Start date

Area,acres

No. Wellsprod.

No. Wells

inj. Pay zone Prev.

prod. Proj.

matur. Tot.

prod., b/d

Enh.prod.,

b/d Proj.Eval.

Midway Chevron USA 1970 1,200 711 69 Spellacy Prim. HF 9,400 9,400 Succ.

Midway-Sunset Aera Energy USA 1988 68 75 22 Marvic SS HF 1,151 1,138 Succ.

Midway Chevron USA 1964 1,214 2,039 225 Potter C HF 21,000 21,000 Succ.

Coalinga Aera Energy USA 1965 540 392 84 Temblor Prim. HF 5,394 5,394 Succ.

Midway-Sunset Aera Energy USA 1984 15 5 Sub

Lakeview Prim. NC 15 5 Succ.

Tia Juana PDVSA E&P Venezuela 1970 1,692 25 Lagunillas

Inferior HF 5,916 3,815 Succ.

Placerita Berry USA 1987 120 50 58 Lower KraftPrim

/ Cyclic

HF 3,000 2,700 Succ.

Fazenda Alegre Petrobras Brazil 2001 1,255 59 Urucutuca Prim HF 9,500 9,500 Succ

Guapo Petrotrin Trinidad Aug-76 400 80 12 Cruse E &

F Cyclic HF 792 792 Succ.

Coalinga Aera Energy USA 1987 290 85 21 Etchegoin Prim. HF 1,384 1,384 Succ.

San Ardo Aera Energy USA Jun-68 125 28 Aurignac SS NC 304 304 Succ.

Table 5. Some characteristics of the stemflooding processes in the analog fields to Cocorná field.

FUENTE: Oil & Gas Journal Report 2010.

3.3. Benchmarking methodology: The benchmarking process for the Cocorná field was performed by the analysis of the properties: porosity, permeability, current So, temperature, depth, viscosity and API gravity. Figure 4 shows the results obtained. According to the methodology, the score of 79.24, which is close to 100, classifies Cocorná as a potentially successful reservoir to apply steamflood technology.

SPE 139222 9

15%

2%

3%

17%

3%

19%

20%

Cocorná Score : 79.24Porosidad (f racc)

Permeabilidad (md)

Profundidad (f t)

Gravedad API del crudo

Viscosidad del crudo (cp)

Temperatura (F)

Saturación de aceite al inicio del proyecto (f racc)

Figure 4. Results of benchmarking methodology for the Cocorná field.

3.4. Analitical prediction: The figure 5, shows the results of the predictions made with the Marx-Langenheim method.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0 500 1000 1500 2000 2500 3000

Áre

a (A

cres

)

Tiempo (Días)

AREA CALENTADA

0

20

40

60

80

100

120

140

160

0 500 1000 1500 2000 2500 3000

Qo

(B/D

)

Tiempo (Días)

TASA DE ACEITE PRODUCIDO

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

0 500 1000 1500 2000 2500 3000

Qlo

ss (M

MBT

U)

Tiempo (Días)

PÉRDIDAS DE CALOR ACUMULADAS

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 500 1000 1500 2000 2500 3000

Tiempo (Días)

FACTOR DE RECOBRO DE ACEITE

Fact

or d

e re

cobr

o (F

racc

ión)

Figure 5. Analytical predictions for the Cocorná field.

4. CONCLUSIONS The proposed methodology allows to identify in a quick, simple and low-cost way, the technologies that are susceptible to application in any type of reservoir.

10 SPE 139222

The use of screening, analogies and benchmarking together, allows a more accurate perspective of the recovery methods with the greatest potential application, when you have little information of a field / reservoir. The proposed methodology is a useful tool that helps the engineer in making decisions; however, the most important tool is the criterion and engineer's knowledge about their field and different methods of enhanced oil recovery. From the application of the methodology to the Cocorna field it is possible to conclude initially that it is constituted in a good candidate to undergo a process of continuous steam injection. Nevertheless, it should be noted that the thickness of sands of the field will be a critical parameter in the evaluation of the feasibility to implement the continuous steam injection. 5. REFERENCES

1. PEREZ-PEREZ, Alfredo et al. Benchmarking of steamflood field projects in light/medium crude oils. SPE 72137. 2001.

2. MERCADO, Diana Patricia et al. SCREENING DE LOS MÉTODOS DE RECOBRO PARA LOS CAMPOS DE CRUDO PESADO COLOMBIANOS. ECOPTEROL-ICP. 2009.

3. ALVARADO, Vladimir y MANRIQUE, Eduardo. Enhanced oil recovery. Field planning and development strategies. 2010.

4. TRUJILLO, Marta et al. Screening de los métodos de recobro mejorado para los campos colombianos con OOIP > 500 MMBO y aceite remanente > 200 MMBO. ECOPTEROL-ICP. 2009.

5. MANRIQUE, Eduardo y WRIGHT, Jhon. SCREENING METHODS HELP OPERATORS IDENTIFY VIABLE EOR OPPORTUNITIES. The American Oil and Gas Reporter. 2006.

6. MANRIQUE, Eduardo et al. WATER-ALTERNATING-GAS FLOODING IN VENEZUELA: SELECTION OF CANDIDATES BASED ON SCREENING CRITERIA OF INTERNATIONAL FIELD EXPERIENCES. SPE 50645. 1998.

7. JIMENEZ, Ana María. ANÁLISIS E INTERPRETACIÓN DE YACIMIENTOS SOMETIDOS A INYECCIÓN DE QUÍMICOS (SURFACTANTES, POLÍMEROS Y MISCELARES) MEDIANTE ANALOGÍAS. Universidad Industrial de Santander. 2009.

8. JIMENEZ, Robinson. ANÁLISIS E INTERPRETACIÓN DE YACIMIENTOS SOMETIDOS A PROCESOS DE INYECCIÓN ALTERNADA DE AGUA Y GAS (WAG) MEDIANTE ANALOGÍAS. Universidad Industrial de Santander. 2005.

9. ALBAHLANI, A.M. y BABADAGLI, T. Review of the status of SAGD: Where are we and what is next? SPE 113283. 2008.

10. BOBERG, Thomas. Thermal methods of oil recovery. A Exxon monograph. ISBN 0-471-63300-3. USA. 1988. 11. DINGLEY, A.J. The combustión recovery process principles and practices. SPE 1322. 1965. 12. DUSSEULT, Maurice. SPE handbook: Chapter 5. Cold Heavy-Oil Production With Sand. 2006. 13. FANCHI J.R. Feasibility of reservoir heating by electromagnetic irradiation. SPE 20483. 1990. 14. GARB Forrest et al. AC current heats heavy oil for extra recovery. World oil. May 1970. 15. GREEN, Don W y WILLHITE, G. Paul. Enhanced oil recovery. Richardson, TX: Society of petroleum engineers.

ISBN 1-55563-077-4. 16. ISLAM, M.R; CHAKMA, A y ALI, S.M. Farouq. State of the art of in-situ combustion modeling and operations.

SPE 18755. 1989. 17. JAMES, Lesley et al. VAPEX, Warm VAPEX and hybrid VAPEX- The state of enhanced oil recovery for in situ

heavy oils in Canada. JCPT Volumen 47. N°4 .Abril de 2008. 18. KUMAR, Mridul et al. High-mobility-ratio-waterflood performance prediction:Challenges and new insigts. SPE

97671-PA.2008. 19. MCGEE Bruce C.W y VERMEULEN Frederick E. In situ electromagnetic heating for hydrocarbon recovery and

environmental remediation. Volume 39 Nº 8. August 2000. 20. PRATS, Michael. Thermal recovery. 2 ed. New York: Society of petroleum Engineers, 1986. 283 p. (Henry L.

Doherty Series; no.7). ISBN 0-89520-314-6. 21. SHIN, H and POLIKAR, M. Review of reservoir parameters to optimize SAGD and Fast-SAGD operating

conditions. Paper 2004-221- Petroleum society Canadian institute of mining, metallurgy & petroleum. 22. SINGHAL, A.K. et al. Screening of reservoir for exploitation by application of steam assisted gravity drainage/Vapex

process. SPE 37144. 1996 23. SMITH, G.E. Waterflooding heavy oils. SPE 24397. 1992. 24. TOMAS, P et al. Comparison of HASDrive and Sand-Filled multiple communications steam recovery processes for

heavy and extra-heavy oil reservoirs. SPE 18788. 1989. 25. Topic paper#22. Heavy oil. Working document of the NPC global oil & gas study. 2007.

SPE 139222 11

26. IYOHO, A.M. Selecting Enhanced oil recovery process. World oil, November 1978. 27. TABER, J.J. y MARTIN, F.D. Technical screening guides for the enhanced recovery. SPE 12069.1983. 28. TABER, J.J.; MARTIN, F.D. y SERIGHT, R.S. EOR screening criteria revisited-Part 2: Applications and impact of

oil prices. SPE 39234. 1997. 29. STALKUP, Fred. I. Miscible displacement. SPE monograph. New York 1984. 30. LATIL, M. Enhanced oil recovery. Institut Francois de petrole publications. 1980. 31. LAKE, Larry W y WALSH, Mark P. Enhanced oil recovery (EOR), field data, literature research. Technical report.

Department of Petroleum and Geosystems Engineering. University of Texas at Austin. 2008. 32. MANRIQUE, Eduardo et al. Effective EOR decision strategies with limited data: Field cases demostartion. SPE

Reservoir Evaluation & Engineering. 2009. 33. ALVARADO, Vladimir et al. Selection of EOR/IOR opportunities based on machine learning. SPE 78332. 2002. 34. EOR Screening: Examples with PRIze (SelectEOR) & SWORD. 35. Research Institute of Petroleum Exploration & Development. IOR Predictive Software System 1.0 (Introduction and

Getting Started Manual). Jan 2010, cap 3. 36. Alberta Research Council. SelectEORTM, Version 1.0. Fast Methods for Evaluating the Enhanced Oil Recovery

Potential of Petroleum Reservoirs. User Guide. June 2009. 37. Petroleum Solutions EORgui, EOR Screening Analysis Graphical User Interface. User Manual, 2010.