Guarinarenas2010.pdf

SPE 138934 A New Inflow Model for Extra-Heavy Crude Oils: Case Study Chichimene Field, Colombia F. Guarin Arenas and C.A. Garcia, Ecopetrol; C.A. Diaz Prada and E. Cotes Leon, Corporacion NATFRAC; and N. Santos, Universidad Industrial de Santander

Copyright 2010, Society of Petroleum Engineers This paper was prepared for presentation at the 2010 SPE Latin American and Caribbean Petroleum Engineering Conference held in Lima, Peru, 1–3 Dec 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

Chichimene field is a heavy oil field located in the little deformed sector known as the "foreland 'basin plains of Colombia. This field is located in Castilla La Nueva in the Meta Department in the east part of the country. Chichimene field was discovered by Chevron in 1969 with the drilling of Chichimene-1 well (CH-1), but it only started production in 1.985. Since 2.000 Ecopetrol S.A has been in charge for field operation. Chichimene field produces a medium crude oil (approximately 20 API) from cretaceous structures named K1 and K2, In 2.001 an extra heavy crude oil (range between 7 – 9 API) from a terciary (T2) formation named San Fernando started production with the CH-18 Well. Despite of the high density presented, the T2 crude oil is mobile at reservoir conditions due to an abnormally elevated reservoir temperature The interest formation corresponds to an elongated northwest direction tertiary anticline layer, the crude oil discovered in this structure is extra-heavy produced by severe biodegradation and evaporative fractionation processes, the depositional model is associated to a distal marine Cretaceous rock. Nowadays the San Fernando formation produces about 14,000 BOPD using electric submersible pumps (ESP) as artificial lift method. This paper reviewed the conditions of the reservoir (petrophysical model, PVT properties, Oil, water and gas production behavior), the physical property of the produced fluids and fluid characteristics of foamy oil properties, to establish an appropriate mathematical expression for modeling the performance of these extra-heavy oil wells.

Introduction

The target proposed by ECOPETROL S.A for extra heavy crude production in the San Fernando (T2) formation is quite ambitious; therefore the production development plan was defined to contribute with 100,000 barrels per day to the overall´s company production at the end of 2.011. To scale up this production levels, it is expected to complete 40 kbopd from T2 due to the incorporation of 50 new wells tin 2.010. The development plan will be completed in 2.011 with the drilling of 84 new wells. At the end of this drilling campaign it is expected to complete a total of 145 new wells including 11 wells drilled in 2.009. The development plan contemplates a distribution of wells in the field as follows (Figure 1): The formation of interest (San Fernando) is located at an average depth of 7900 feet, making it one of the deepest extra-heavy oil reservoirs in the world. To develop this field petrophysical and fluid models have been made to understand the behavior of the reservoir. Production data show a behavior of this crude oil with the characteristic of foamy oil as several authors have reported in areas in Canada and Venezuela

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Fig. 1 Planned Chichimene Field T2 formation development wells Distribution

The depositional model for the formation T2 Chichimene field, and the was developed petrophysical study of the reservoir was developed by ECOPETROL (N Tyler et al. Tyler, 2010). The Chichimene field has abundant core information, hence to develop the study the existing cores from Chichimene T2 (San Fernando) formation were evaluated. Where used Cores for wells CHSW3, CH22, CH25, CH27, CH28 and CH29. See Figure 2.  

Fig. 2. Chichimene wells with core information.

The depositional and petrophysical analysis in the field showed that there are four types of rocks, which impact the formation flow capacity. These rock types are associated according to the permeability of the reservoir as follows: • • • •

RT_1: K>2 Darcies RT_2: 200
Relativity permeability curves were done in laboratory in order to establish the porous media flow permeability for liquid phase. Figure 3, Shows relative permeability curve for CH- 22 well. In addition to the petrophysical model, in this study foamy Oil theory was incorporated for the analysis of the fluid, along with correlations for calculation of PVT properties developed for heavy and extra heavy crude Oils. Foamy Oil theory has been studied by several authors for some years to now. A study conducted by Sahni et al , found that in PVT tests analysis developed constant volume depletion for heavy and extra heavy crude, the mechanism of gas in solution in heavy crude Oils is a no equilibrium process. That means that the gas release during production stages is neither complete, nor instantaneous as is the case of normal black oil reservoirs. This trapped gas behavior in the oil, impacts the rheological properties of the oil and the volumetric properties of Bo and Rs. These results were confirmed later by Benion et Al in 2001 and in 2005 Kantza et Al.

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Relative Permeabilities  CH ‐22 1,0

Base Permeability  = 4.629 mD

0,9 0,8

Krw

Kr (fraction)

0,7

Kro

0,6 0,5 0,4 0,3 0,2 0,1 0,0 0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

Water Saturation (Fraction)

Fig 3. Relative permeability curve for CH- 22 well.

This specific behavior of gas in the foamy oil crude type, promotes an increase in the productivity of these reservoirs, compared with common type solution gas reservoirs. Kamp et al conducted a work to check the effect of trapped gas in the viscosity and density variables governing equations of fluid flow in porous media. Methodology Used The methodology used is as follow: 1. 2. 3. 4. 5.

PVT data quality control Evaluation of PVT Modeling equations. Fitting of PVT information. IPR models evaluation with production data. Outflow validation

PVT Evaluation According to the characteristic of the fluid one of the most important challenges that have guided this work has been on the hydraulic fluid properties characterization. Various calculation models were used in order to obtain the best fit of the data obtained from performed PVT analysis. Two fluids studies have been developed in this field, the first study was done in 2.006 and corresponds to a surface fluids sample in the CH-18 well. Subsequently a new sample from bottom well fluid was analyzed for the CH-26 well in 2.009. Results of the analysis made to both samples of crude oil are shown in the Figure 4.

PVT data quality control Based on the PVT obtained for crude oil from San Fernando (T2) formation, results shown that gas solubility and viscosity of oil obtained from the two PVT tests are quite similar and have not significant variation, however the behavior of formation volume factor and oil density are higher in the sample taken from surface (well CH-18) that the sample obtained bottom hole (well CH-28). In order to be rigorous in the fluid analysis, the PVT obtained from the bottom hole sample was taken to validate the fluid correlations. This sample was considered more representative of the original fluids conditions of T2 formation.

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PVT Data : San Fernando Crude Oil Density

PVT Data : San Fernando Crude Oil Viscosity 1000

0,990

900

0,985

Reservoir Oil Density (gr/cc)

Reservoir Oil Viscosity (cp)

800 700 600 500 400 300 200

PVT CH 18

100

PVT CH 28

0,980 0,975 0,970 0,965 0,960

PVT CH 18

0,955

PVT CH 28

0,950

0 0

500

1000

1500

2000

2500

3000

3500

0

4000

500

1000

1500

PVT Data : Formation Volumetric Factor San Fernando Crude Oil

2500

3000

3500

4000

PVT Data : Gas Solubility San Fernando Crude Oil

1,09

80,00 PVT CH 18

1,08

70,00

PVT CH 28

Rs (SCF/ STB)

FVF ( res BBL/ STB)

2000

Reservoir Pressure (Psia)

Reservoir Pressure (Psia)

1,07 1,06 1,05

60,00 50,00 40,00 30,00

1,04

PVT CH 18

20,00

1,03

PVT CH 28

10,00

1,02

0,00 0

500

1000

1500

2000

2500

3000

3500

4000

0

500

1000

1500

Reservoir Pressure (Psia)

2000

2500

3000

3500

4000

Reservoir Pressure (Psia)

Fig 4. PVT data Wells CH-18 and CH- 26 PVT modeling equations In this study we used several models for characterization of fluid properties (PVT), these models included conventional correlations to calculate properties of black oil, also correlations developed in order to calculate the properties of heavy and extra heavy crude were used (De Ghetto et al , 1995). Finally a model to characterize Foamy Oil PVT was evaluated (Romero et al, 2001). Figure 5 show the results of PVT modeling with different correlations. Gas Solubility  (Rs)  P
Measured Vázquez TOTAL AGIP

70 60

L B / 50 C  P ,F ° 6 40 8  1 t a  30 s R

Standing Glaso Kartoatmodjo TOTAL ADJ

622, 68,3

414, 47,3

64, 25,7

20 10

15, 8,3

0 0

100

200

300

400

500

600

700

Pressure, psi 

Dynam ic Oil  Viscosity 

Oil Formation Volumetric Factor (FVF)  Bbls/STB

1400

1,09

1200

1,08

1,07

1,03

622, 1,052 915, 1,048 1315, 1,044

414, 1,042

1515, 1,042

p  c, F° 6 800 8  1 ta y t is o cs 600 i V

64, 1,026

Measured

1,02

Kartoatmodjo

Chew

Beggs

Kartoatmodjo

AG IP

15, 795 100, 728 300, 587 3314, 469

500, 454

400

15, 1,020

Beal

Vázquez

Foamy Visc

1000

B 1,06 ST /l B   , F° 1,05 6 8 1  t   a  1,04 F V F

Medido

622, 380 700, 385

Standing

1000, 394

1500, 408

2000, 424

2500, 444

3000, 460

2500

3000

200

Vázquez

Glaso

TOTAL

Kartoatmodjo

1,01

0

0

200

400

600

800

Pressure, psi

1000

1200

1400

1600

0

500

1000

1500

2000

Pressure, psi

Fig 5. PVT properties calculated with different correlations

3500

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The results of the evaluation of the different models showed that in the case of extra-heavy crude from the San Fernando Formation (T2) Chichimene field, the correlation proposed by TOTAL, was the one that best fits variables as Formation Volumetric Factor (βo), Solution Gas (Rs), Bubble Pressure (Pb) with the real data obtained for the fluid analysis of CH-26 Well. In the case of Viscosity, Chew and Conelly correlation gave the best results. Foamy Oil PVT modeling Equations Several authors have been working on modeling of Foamy crude Oils. In this work review of several models was done, in order to establish the most appropriate model to work with the San Fernando crude Oil. The work done by Romero (Romero et al, 2001), based on a methodology to determine the extra-Fluid thermodynamic characterization of the Orinoco belt, was selected. The equations applied to modeling the foamy oil behavior were defined according to the pseudo-bubble pressure theory, which require adjust of the (K-values) to determine the equilibrium between gas and oil phases in order to represent the stability of solution gas. K values at different pressure were calculated from CH-26 PVT data. Gas chromatography and simulated distillation information were load in a computacional model developed in the software ASPEN HYSYSTM. Peng Robinson Equation of state (EOS) was used to obtain equilibria conditions. The obtained phase envelope of the live crude oil can be seen in Figure 6. Phase Behavior Live San Fernando Crude Oil 1800 1600

Pressure (Psia)

1400

Bubble Point DewPoint

1200 1000 800 600 400 200 0 ‐400

‐200

0

200 Temperature (F)

400

600

800

Fig 6. Phase Envelope San Fernando Crude Oil. IPR prediction models Different analytical, empirical and semi-analytical models have been proposed to determine the behavior of inflow (IPR) in wells where gas and oil flowing together, In the case of two-phase flow where the flowing pressure is below the bubble point pressure it is recommended to use quasi-analytical or empirical models. Of these models the most widely known is the Vogel IPR model (Vogel et al, 1968). Vogel's model for multiphase flow has a parabolic shape, compared with Darcy singlephase flow model that presents a linear behavior. In the Vogel model the IPR curve can be generated, taking the maximum flow rate (Qo max), the average reservoir pressure (reservoir or static) and a pair of bottom hole pressure and flow rate at this condition. Fetkovich, (Fetkovich, 1973) based on Vogel´s work derived an equation that relates the flow to a quadratic function of pressure, based on the pseudosteady state theory, and assuming the existence of a linear relationship to model the mobility of oil in the reservoir. Later Klins (Klins, 1.993) presented an equation based on the Vogel´s work, in this equation there is a new parameter that most be calculated based on the flowing pressure and the buble pressure. Recently Choi (Choi Suk et al, 2008) presented a comparison study of different analytical models developed by different authors for the determination of the IPR in vertical and horizontal. The following table shows the different models that have been developed for different flow conditions, geometry and / or disposition of the well. Table 1.

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Table 1. Comparison of analytical models for calculation of IP / IPR (Choi Suk et al, 2008) Recent works have been developed for heavy oil applications. The authors Gasbarri et Al (Gasbarri et al, 2.009) developed a modification to the adjustment term in the Vogel model, applied to the heavy crude Oils in the Orinoco Belt. On the other hand Kumar (Kumar & Mahadevan, 2.008) performed a theoretical study of the IPR in which they involve the influence foamy crude oil heavy crude in the development of an analytical productivity index model (IPR) for heavy crude oils based on Darcy's law. For the development of this work Darcy, Vogel, Klins and Gasbarri models were selected to evaluate the performance of the inflow of extra heavy oil wells applied to fit a model of IPR in the case of San Fernando(T2). crude Oil. All the methods used in this study were based in the form of the Vogel method. Vogel method is based on dimensionless equation that relates the well flow capacity as function of a “V” parameter with the bottom hole flowing pressure and the reservoir static pressure. The parameters for Vogel model are V = 0.8 and n = 2. The equation form is as follows:

Where : Qo = Actual Oil or Liquid flow Qomax = Maximun flow at Pwf = 0, also denominated AOF. Pwf = Bottom Hole flowing pressure PR = Reservoir Static Pressure V and n are parameters for each model Vertical Flow Correlation In order to validate the most appropriated model to predict the behavior of San fernando Extra Heavy crude Oil (T2), with real production data, a computational model that predicts inflow and tubing outflow performance was developed in commercial software. According with a production log test (PLT) run in CH-15 well, the Oil, gas and produced water data acquired from CH-15 well, as well as, the information recorded from the ESP artificial system, Pump Intake Pressure (PIP), Tubing Head Pressure (THP), Production Rate (Ql), Well Static Pressure (Pws), were used to adjust a computational model in the Nodal Analysis Software PIPESIM TM. Discussion of Results In order to validate results obtained with different IPR models used in this work, one set of production data from wells of San Fernado formation were available, also production log test (PLT) run in November of 2.009 to the CH-16 well. Figure 7 presents the results of calculating IPR for wells in different field locations with the selected models. Results shown what is expected in a Extra heavy crude oil, related with a linear behavior in the zone where bottom flowing pressure (Pwf) is above the bubble pressure, under bubble pressure; models shown diverse performance. Gasbarri model presents good fit when water content is high, in cases where the water content is low, there is a particular behavior in the

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model because the curve concavity trends upward, this trend does not seems physically feasible based in the fact that the crude oil at this conditions must be in a two phase zone. Based on the relative gas and crude oil permeabily, the expected effect causes a reduction in the liquid production opposite that the Gasbarri model calculates. Table 2. Shows production data from some T2 (San Fernando) Wells. Inflow Performance  Well CH‐2 3.500

Pwf Calc Gasbarri Vogel Klins & Clark Darcy

2.500

Pwf Calc Gasbarri Vogel Klins & Clark Darcy

3.000

Bootom Pressure (Psia)

3.000 Bootom Pressure (Psia)

Inflow Performance  Well CH‐5 3.500

2.000 1.500 1.000

2.500 2.000 1.500 1.000 500

500

0

0 0

500

1.000

1.500

2.000

2.500

3.000

3.500

0

4.000

500

1.000

1.500

Inflow Performance  Well CH‐18

2.500

3.000

3.500

Inflow Performance  Well CH‐22

4.000

3.500

Pwf Calc Gasbarri Vogel Klins & Clark Darcy

3.000

Pwf Calc Gasbarri Vogel Klins & Clark Darcy

3.000

Bootom Pressure (Psia)

3.500 Bootom Pressure (Psia)

2.000

QL (BPD)

QL (BPD)

2.500 2.000 1.500 1.000

2.500 2.000 1.500 1.000 500

500

0

0 0

500

1.000

1.500

2.000

2.500

0

500

1.000

QL (BPD)

1.500

QL (BPD)

Fig 7. Inflow performance with selected models Wells Tests Production Data Well

API

Reservoir Pressure (psia)

CH-2

8,6

3242

CH-5

8

3200

CH-18

9

3430

CH-22

7,8

3080

Qo (BPD)

Qw (BPD)

Ql (BPD)

BSW

Pwf (psia)

734 693 802 802 978 1.526 376 356 386 335 254 584 584 383 458 840 769 736 1.522

22 22 35 35 62 594 1.928 1.714 1.619 1.621 2.289 506 507 734 1.534 73 23 15 269

756 715 837 837 1.040 2.120 2.304 2.070 2.005 1.956 2.543 1.091 1.091 1.117 1.992 913 792 751 1.790

3% 3% 4% 4% 6% 28% 84% 83% 81% 83% 90% 46% 46% 66% 77% 8% 3% 2% 15%

2.131 2.128 2.233 2.233 2.060 472 1.039 1.031 997 1.018 495 1.945 1.749 1.687 493 1.937 1.837 1.936 485

Table 2. Wells test production data.

2.000

2.500

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In the next table, the maximum fluids production at open flow (AOF) defined when the bottom hole flow pressure (Pwf) is equal to zero, was calculated. The results were as follows:

Calculated AOF (BFPD) Darcy Vogel Klins & Clark Gasbarri CH-2 2.501 2.416 2.175 2.622 CH-5 3.061 2.956 2.657 2.904 CH-18 2.304 2.231 2.022 2.256 CH-22 2.143 2.067 1.848 2.251 Table 3. AOF Calculated for different IPR Models. Well

Using multivariable optimization and linear regression techniques models, the IPR models used in this study were adjusted to tune the production data, using least square model, results are shown in the next table.

Least Squares Model 22.168 Gasbarri 292 Gasbarri Mod Vogel 1.879 Vogel Mod 1.701 Klins & Clark 5.428 Klins & Clark Mod 1.806 Table 4. Models Error comparison with “Least squares model” . IPR Model

According with the error calculation method, Gasbarri original model presented the highest deviation respect to the real data, and Vogel model was the model that shown the less error respect the original production data, nevertheless when a multivariable optimization technique is used to tune the V parameter in each model, the results obtained from this tunning shown that the Gasbarri method can be better ajusted than other IPR methods. In table 4 Gasbarri modificated model presented a dramatical drop in comparison with Vogel and Klins and Clark modificated methods. In Figure 8, a crossplot between calculated and real production data with the different models is shown.

QL Predicted vs QL Real Crossplot 3.500 3.000 ) 2.500 D P B (  d 2.000 e ta l u lca 1.500  C L Q 1.000 500

Gasbarri

Gasbarri MOD

Vogel Normal

Vogel Mod

Klins & Clark

Klins & Clark mod

0 0

500

1.000

1.500

2.000

2.500

3.000

3.500

QL Real (BPD)

Figure 8. Production data comparison with different models To incorporate into a predictive IPR behavior model of wells in the field Chichimene, a relationship between petrophysical properties of the formation and physical fluid properties and petrophysical was proposed. Based on the Gasbarri model a new term related with relative permeability (Kro), physical properties of the fluid (βo, μo) and the net pay of the production zones in the well was incorporated.

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Gasbarri Original model is established as follows:

V factor is defined:

And the terms are:

Where: WC = Water Fraction API = API gravity Pwf = Bottom hole flowing pressure PB = Bubble Pressure PR = Reservoir Static Pressure Qo max = Maximum flowrate at AOF.

The proposed new model based on the Gasbarri model, for extraheavy crude oils in the Chichimene Field, San Fernando Formation, incorporates the term Kh* [Kro/(μo βo)] in the V factor calculation , where K = Base permeability (md) H = Net pay in the well. (fts) Kro = Oil relativy permeability (md) μo = Viscosity (cp) βo = Formation volumetric factor Therefores the new V factor is:

And the terms are:

The proposed model was tested compared with other models and it gaves better match with the production data. In Figure 9 there is a comparison between original and modified models respect to the proposed T2 model. There is a issue that must be consider relating to the α term, because it was specifically obtained for the fluid and petrophysical properties of the San Fernando (T2) Formation, so for applications in other fields it must be recalculated in order to avoid miscalculations.

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100000

Least Squares Model

10000 1000 100 10 1 Gasbarri

Gasbarri   Mod

Vogel

Vogel Mod

Klins &  Klins &  T2 Model Clark Clark Mod

Figure 9. Models comparison with least squares method. Validation of the model competence to predict the behavior of San fernando Extra Heavy crude Oil (T2) production, with real data, was developed a computational model in commercial software in order to predict inflow and tubing outflow performance. Information from fluids production, and the information recorded from the ESP artificial system, (Pump Intake Pressure (PIP), Tubing Head Pressure (THP), Production Rate (Ql), Well Static Pressure (Pws)), that information was introduced into a model in the Nodal Analysis Software PIPESIM TM. Table 5 shows information feed in the PPESIM model. Formation  T2  Pws (psia)  3012  Reservoir  Temperature (°F)  172  IP (BPD/psi)  0.93  PMP (ft)  7794  Intake  (ft)  4494,76  Pump Manufacturer  Centrilift   Artificial Lift System  Model  P23  Hz  60  Stages  164  API  9,8  Viscosity @150°F (cp)  4000  Viscosity @180°F (cp)  1100  BFPD (Bbl)  908  BOPD (Bbl)  893  BWPD (Bbl)  Fluid Model  15  Gas KPC   505  Water S.G  0,9756  Gas S.G  0,765  Cut water   2,00%  GOR (SCF/STB)  565,0  THP (psia)  52  Measured Data  PIP (psia)  1116  WHT (°F)  100  Table 5. Information for PIPESIM modeling of CH-16 Well

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Finally, a Productivity Index (PI) map for the T2 formation was obtained with this new Model , Figure 10 presents the PI map.

Figure 10. Productivityt Index map obtained with the T2 model.

Conclusions •

A new model was defined to be applied in the San Fernando Formation (T2) Chichimene oil field, based on the model proposed by Gasbarri. The results of the mathematical validation of the model with production data, presented an error less than that obtained with the original model proposed by Gasbarri.

•

PVT data and petrophysical properties were incorporated to the model developed in this study in order to obtain a particular model for the San Fernando (T2) Formation

•

Several PVT correlations were used in order to simulate the fluid proerties, in the particular case of San Fernando Crude Oil, the results of the evaluation of the different models showed that in the case of extra-heavy crude from the San Fernando Formation (T2) Chichimene field, the correlation proposed by TOTAL, was the one that best fits variables as Formation Volumetric Factor (βo), Solution Gas (Rs), Bubble Pressure (Pb) with the real data obtained for the fluid analysis of CH-26 Well. In the case of Viscosity, Chew and Conelly correlation gave the best results.

References 1. 2. 3. 4. 5. 6.

BEGGS H. D., Production Optimization using NODAL Analysis. OGCI Publications. BENION, B et AL. Predicting Foamy Oil Recovery. SPE 68860, Califormia, 2001 CHOI SUK et Al, A comprehensive study on analitycal PI / IPR Correlations, SPE 116580, Denver 2008. KAMP, A. M. et Al. Modeling Foamy Oil in Porous Media”. International Journal of Multiphase flow, 2002. KANTZAS, A et AL. Novel Techniques for Measuring Heavy Oil fluid properties. SPE 97803, Alberta, 2005 R. KUMAR et Al., Well performance relationships in heavy foamy oil Reservoirs SPE 117447. Calgary Alberta, 2008. 7. ROMERO D et AL. Thermodynamic Characterization of a PVT of Foamy Oil, SPE International Thermal Operations and Heavy Oil Symposium, SPE 69724 Margarita Island, Venezuela March, 2001, 8. S. GASBARRI et AL , Inflow predictions for heavy crude Oils, SPE 122292, Cartagena, LACPEC 2009. 9. SAHNI, A ET AL. Experiments and analysis of Heavy Oil Solution Gas Drive. SPE 71498, New Orleans, 2001.