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Copyright 2005, International Petroleum Technology Conference This paper was prepared for presentation at the International Petroleum Technology Conference held in Doha, Qatar, 21–23 November 2005. This paper was selected for presentation by an IPTC Programme Committee following review of information contained in an proposal submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the International Petroleum Technology Conference and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the International Petroleum Technology Conference, its officers, or members. Papers presented at IPTC are subject to publication review by Sponsor Society Committees of IPTC. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the International Petroleum Technology Conference 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 where and by whom the paper was presented. Write Librarian, IPTC, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.

Abstract It is well known that the deliverability of gas condensate wells can be impaired by the formation of a condensate bank once the bottomhole pressure drops below the dew-point. There have been many excellent laboratory studies on gas-condensate relative permeability that describe this phenomenon, but there are few integrated laboratory-simulation-field studies that compare systematic predictions to field performance.

We present extensive experimental relative permeability data sets on some sandstone reservoirs. These data span the krg/kro and capillary number parameter space. We discuss the experimental procedures, and the design of fluid systems that mimic reservoir fluids, but at lower temperatures. Next we demonstrate various steps involved in our approach by modeling a gas condensate well with field production history. Here we first measured relative permeability data on core samples from the reservoir and fit them to capillary number dependent relative permeability models. Then, we performed detailed single well compositional modeling with realistic geology and boundary conditions. Finally, we compared the predictions to actual production data, and found that the match was quite good. The productivity reduction was found to be in the range of 80%, the majority of which occurred in the initial phases of production. Our ability to reasonably predict the well performance has given us confidence that our approach, including measuring only the relevant portion of the relative permeability curves and using synthetic fluids, may be sufficient. Introduction Gas condensate reservoirs typically consist of single phase gas at initial reservoir conditions. When the flowing bottomhole pressure falls below the dew-point of the reservoir fluid, liquid condensate builds up (“condensate banking”) near the wellbore. Condensate banking leads to reduction in gas relative permeability and loss in well productivity, and this is well documented in several field1-4 and theoretical studies5-6.

Several authors7-10 have designed experiments to measure the critical condensate saturation before condensate can flow, and have reported high values ranging from 20-80%. Kalaydjian et al.8 found similar behavior using model and reservoir fluids; however, Nagarajan et al.11 have recently noted differences in relative permeability behavior. Many investigators12-16 have observed improved relative permeabilities with reduced interfacial tension, typically important in near-critical gas condensate systems. Henderson et al.17-18 have also shown that high velocities near the wellbore can improve the gas relative permeability. Pope et al.19-20 have shown a significant improvement in well productivity when capillary number effects are included in simulations. Blom and Hagoort21 have compared various capillary number options available in the literature for gas condensate systems.

Fevang and Whitson22 have elucidated the physics of condensate banking, and have presented an analytical well deliverability model building on the concepts developed by Muskat23, O’Dell and Miller24, and Jones et al.25. They have also demonstrated that krg = f(krg/kro, Nc) is the underlying relative permeability relationship determining well deliverability of gas condensate reservoirs. They performed rate-time studies of a gas condensate reservoir using two different sets of relative permeabilities with completely different krg(Sg) and kro(Sg) curves, but with an identical krg = f(krg/kro) relationship. Both sets of relative permeabilities yielded identical well performance. Mott26 has further developed the analytical model by accounting for the growth of the two phase flow region. Steady state methods that measure the key relation defining pseudo-steady state flow in gas condensate wells, and do not need measurement of saturation, have also been reported27-30.

In this paper, we present data from steady state relative permeability experiments for three different reservoirs. The three fields are deep sandstone reservoirs located at approximate depth of 12000 feet, with temperature of about 280 °F. The porosity of these reservoirs are around 15%, and the permeability range from 10 – 15 mD. The fluids have similar dew-point pressure; however, the liquid yield varies from 45 STB/MMSCF to 150 STB/MMSCF. We fitted these experimental data sets to the capillary number dependent relative permeability model. We also present simulation results from one of the detailed single well sector models and compare them to the field production data.

Design of Fluids Three different synthetic gas condensate fluids were developed for the laboratory corefloods. Fevang and

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Gas/Condensate Well Deliverability: Integrated Laboratory-Simulation-Field StudyN. Silpngarmlers, SPE, P. Ayyalasomayajula, SPE, and J. Kamath, SPE, Chevron Energy Technology Co.

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Whitson22 have shown the relation between krg/kro and fluid properties is:

krg/kro = (1/Vro -1)*µg/µo………………..(1) where Vro is the relative oil volume from a Constant Composition Expansion (CCE) and µg/µo is the ratio of the gas and oil viscosity of the steady state flowing phases in the near wellbore region.

The synthetic gas condensates were designed with the primary objective of matching the liquid dropout (Vro), viscosity ratio (µg/µo), and interfacial tension of the reservoir gas condensates, while operating the corefloods at a much lower temperature than reservoir temperature. Table 1 lists the composition of the synthetic fluids. Figure 1 through Figure 3 show that the synthetic fluids mimic the relevant reservoir fluid properties.

Figure 4 shows the expected range of krg/kro in the near wellbore region for a reservoir fluid of an example reservoir as calculated using Equation (1). Figure 5 shows a corresponding plot for the synthetic fluid. As expected, the krg/kro behavior of the reservoir fluid and synthetic fluid are similar. It can be seen that the values to be expected near the well range from around 1 to 20 for a typical bottomhole pressure of 1500 psia. The relative permeability experiments are designed to cover the krg/kro values in this range. Experimental Procedure The core samples used in the experiments are representative of the productive regions of the well. The properties of these samples are tabulated in Table 2. The samples were miscibly cleaned, brine saturated, and spun in a centrifuge to the desired Swi.

The experiments have been designed so as to capture the key aspects of the flow near the well. The two most important aspects of the experiments are: 1) They define the krg = f(krg/kro) for the range of krg/kro values that would be expected in a well. 2). They allow for measurements at a range of rates so as to quantify the capillary number effects.

A high pressure core flow apparatus shown in Figure 6 was built to conduct steady-state relative permeabilities measurement of gas condensates. A storage cylinder (II) contains the equilibrium synthetic gas that has been designed as discussed in the previous section. The pump (I) supplies this gas from the cylinder to the inlet of the core (IV) by flashing it across the upstream back pressure regulator (III). The upstream back pressure regulator is held at the reservoir pressure and the downstream backpressure regulator (V) is set to the bottomhole pressure thus resulting in a two-phase condensate flow across the core. The mixture flowing from this system can be varied from a rich (initial) fluid to leaner fluid by varying the pressure of the cylinder. The pressure drop and the flow rate are noted after steady state conditions are achieved, typically after about 10 to 15 pore volumes. The pump rate is then changed and the test is now repeated at a different capillary number. This results in the krg variation with capillary number (Nc) at a fixed krg/kro. The gas in the cylinder is then bled off until the pressure in the tank drops to a lower reservoir pressure and the above procedure is repeated

to yield the same data at a different krg/kro value. Additional experimental details are in reference 30.

Relative Permeability Data and Models Figures 7 through 10 present krg as a function of capillary number at different krg/kro values for the three reservoirs we measured. Literature data is also displayed for reference. As expected, the gas relative permeability increases with capillary number and with increasing values of krg/kro . The data do not show any trend with rock quality.

The experimental data is of the form krg = f(krg/kro, Nc). We fitted it to various capillary number models31 by adjusting the base immiscible and capillary number dependent parameters. Figure 11 shows example of the data fit. The fit is reasonable but it was difficult to fit the entire krg/kro range.

Single Well Simulation Studies We conducted compositional simulations to predict individual well performance and compared the results with historical production data. An example of one vertical well is presented in this paper. The effect of condensate banking was captured by using very fine grids (foot scale) near the well. The single well model was constructed by extracting petrophysical properties from the full field model (FFM). The FFM also provided the appropriate external boundary conditions and the producing rules for the well.

Figure 12 shows the pressure map of the neighboring grid blocks near the well for a particular layer at a given time. These pressure data were provided as external boundary conditions for the single well model. The boundary conditions were specified by introducing wells (injectors/producers) at the edge of the model to mimic the full field pressure depletion as shown in Figure 13. The non-Darcy flow is included in the well productivity index calculations by using high velocity flow coefficient (β) from Forcheimer's equation.

The single well model used the laboratory measured relative permeability data taking into account the capillary number effects in the near wellbore region. The objective of including all the relevant physics of the near wellbore gas condensate flow in the model is to be able to conduct predictive simulations without adjusting any parameters. This is unlike a typical history matching effort where several parameters need to be adjusted to obtain a match to the historical data.

Single Well Model Results. The radial well model properties are given in Table 3. The variation of boundary pressure with time for different layers of the well is plotted in Figure 14. The simulations were performed using rates obtained from smoothing the production rates shown in Figure 15. However, the measured bottomhole pressures (Figure 16) fluctuate representing shut-in and flowing periods, and comparisons of predictions to historical data should be done taking this into account.

The measured bottomhole pressure data for this well is available for a period of one year since the well has been put on production. Figure 16 shows the comparison of the predicted bottomhole pressure and the measured field data for the well. A reasonable match of the bottomhole pressure is obtained. The well PI was also calculated and compared with

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the reported PI as shown in Figure 17. It should be noted that the initial observed PI is not available as the well was put on production at a later time. However, in reference to the single phase PI from the simulations, the productivity loss is in the range of 70-80%. In addition, the absolute PI values from the simulations are in good agreement with the actual well PI.

In our simulations, the major PI loss occurs when the well drops below the dewpoint and the amount of loss is consistent with predictions from condensate banking theory. So, though other factors related to perforations, or other mechanical issues are possible it is likely that banking is the cause for well deliverability loss. Conclusions 1. Our approach of using simpler steady state laboratory

methods with synthetic fluids appears sufficient to reasonably predict gas condensate well performance.

2. Laboratory measured gas relative permeability increases with capillary number and with increasing values of krg/kro. The data do not show any trend with rock quality.

3. The current relative permeability models fit the laboratory data reasonably well, but it is difficult to fit the entire krg/kro range.

4. Carefully designed experiments coupled with fine scale compositional simulation showed that condensate banking appears to be the cause of significant loss in well productivity observed in the field.

Acknowledgments We thank Jack Beroterran for his careful experimental work; Jonathan Sheffield for fluid design and analysis; Eimear Tohill, William Beveridge, Chris Stevens, Stan Franklin, Mel Croft, and Mel Blevens for their support. References 1. Afidick, D., Kaczorowski, N.J., and Bette, S.: “Production

Performance of a Retrograde Gas Reservoir: A Case study of Arun Field,” paper SPE 28749 presented at the 1994 SPE Asia Pacific Oil & Gas Conference, Melbourne, Australia, November 7-10.

2. Barnum, R.S., Brinkman, F.P., Richardson, T.W., and Spillette A.G.: “Gas Condensate Reservoir Behaviour: Productivity and Recovery Reduction Due to Condensation,” paper SPE 30767 presented at the 1995 SPE Annual Technical Conference & Exhibiton, Dallas, October 22-25.

3. Smits, R.M.M., van der Post, N.: “Accurate Prediction of Well Requirements in Gas Condensate Fields,” paper SPE 68173 presented at the 2001 SPE Middle East Oil show, Behrain, March 17-20.

4. Lee, S., Chaverra, M.: “Modeling and Interpretation of Condensate Banking for the Near Critical Cupiagua Field,” paper SPE 49265 presented at the 1998 SPE Annual Technical Conference & Exhibiton, New Orleans, September 27-30.

5. Kniazeff, B.J. and Naville S.A.: “Two-Phase Flow of Volatile Hydrocarbons,” SPEJ (March 1965) 37.

6. Fussell, D.D.: “Single-Well Performance Predictions for Gas Condensate Reservoirs,” JPT (July 1973) 860.

7. Gravier, J.F., Lemouzy, P., Barroux, C., and Abed, A.F.: “Determination of Gas-Condensate Relative Permeability on Whole Cores Under Reservoir Conditions”. SPEFE (February 1986) 9.

8. Kalaydjian, F. J-M., Bourbiaux, B.J., and Lombard, J-M.: “Predicting gas-condensate reservoir performance: How flow parameters are altered when approaching production wells,” paper SPE 36715 presented at the 1996 SPE Annual Technical Conference and Exhibition, Denver, October 6-9.

9. Li, K., Firoozabadi, A.: “Phenomenological Modeling of Critical Condensate Saturation and Relative Permeabilities in Gas/Condensate Systems,” SPEJ (June 2000) 138.

10. Lombard, J-M., Longeron, D., Kalaydjian, F.: “Well Productivity of Gas-Condensate Fields: Influence of Connate Water and Condensate Saturation on Inertial Effects,” paper SCA 9929.

11. Nagarajan, N.R., Honarpour, M.M., Sampath, K., and McMichael, D.: “Comparision of gas-condensate relative permeability using live fluid vs. model fluids,” paper SCA 2004-09 presented at the 2004 International Symposium of the Society of Core Analysts, Abu Dhabi, October 5-9.

12. Bardon, C., and Longeron, D.: “Influence of very low interfacial tension on relative permeability,” SPEJ (October 1980) 391.

13. Asar, H.,: “Influence of Interfacial Tension on Gas-Oil Relative Permeability in a Gas-condensate system,” paper SPE 11740 presented at the 1983 California Regional Meeting, Ventura, March 23-25.

14. Haniff, M.S., Ali, J.K.,: “Relative Permeability and Low Tension Fluid Flow in Gas Condensate Systems,” paper SPE 20917 presneted at the 1990 Europec, The Hague, October 22-24.

15. Boom, W., Wit K., Schulte., Oedai, A.M., Zeelenber, J.P.W., and Maas, J.G.: “Experimental Evidence of Improved Condensate Mobility at Near-wellbore Flow Conditions,” paper SPE 30766 presented at the 1995 SPE Annual Technical Conference and Exhibition, Dallas, October 22-25.

16. Blom, S.M.P., Hagoort, J., Soetekouw, D.P.N.: “Relative Permeability at Near-Critical Conditions,” paper SPE 38935 presented at the 1997 SPE Annual Technical Conference and Exhibition, San Antonio, October 5-8.

17. Henderson, G.D., Danesh, A., Tehrani, D.H., Al-Shaidi, S., and Peden, J.M.: “Measurement and correlation of gas condensate relative permeability by the steady state method,” paper SPE 30770 presented at the 1995 SPE Annual Technical Conference and Exhibition, Dallas, October 22-25.

18. Henderson, G.D., Danesh A., Tehrani, D.H., and Al-Kharusi, B.: “The Relative Significance of Positive Coupling and Inertial Effects on Gas Condensate Relative Permeabilities at High Velocity,” paper SPE 62933 presented at the 2000 SPE Annual Technical Conference and Exhibition, Dallas, October 1-4.

19. Pope, G.A., Wu W., Narayanaswamy G., Delshad M., Sharma M., and Wang, P.: “Modeling Relative Permeability Effects in Gas-Condensate Reservoirs,” paper SPE 49266 presented at the 1998 SPE Annual Technical Conference and Exhibition, New Orleans, September 27-30.

20. Narayanswamy, G., Pope, G.A., Sharma, M.M., Hwang, M.K., and Vaidya R.N.: “Prediciting Gas Condensate Well Deliverability using Capillary and Non-Darcy effects,” paper SPE 51910 presented at the 1999 SPE Reservoir Simulation Symposium, Houston, February 14-18.

21. Blom, S.M.P., and Hagoort J.: “How to Include the Capillary Number in Gas Condensate Relative Permeability Functions?,” paper SPE 49268 presented at the 1998 SPE Annual Technical Conference and Exhibition, New Orleans, September 27-30.

22. Fevang, O., Whitson C.H.: “Modeling Gas Condensate Well Deliverability,” paper SPE 30714 presented at the 1995 SPE Annual Technical Conference and Exhibition, Dallas, October 22-25.

23. Muskat, M.: Physical Principles of Oil Production, McGraw-Hill Book Company, New York City, NY (1949).

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24. O’Dell, H.G., Miller, R.N.: “Successfully Cycling a Low Permeability, High Yield Gas Condensate Reservoir,” JPT (January 1967) 41.

25. Jones J.R., Vo, D.T., and Raghavan R.: “Interpretation of Pressure-Buildup Responses in Gas-Condensate Wells,” paper SPE 15535 presented at the 1986 SPE Annual Technical Conference and Exhibition, New Orleans, October 5-8.

26. Mott, R.: “Engineering Calculations of Gas Condensate Well Productivity,” SPEREE (October 2003) 298.

27. Whitson C.H., Fevang, O., and Saevareid A.: “Gas Condensate Relative Permeability for Well Calculations,” paper SPE 56476 presented at the 1999 SPE Annual Technical Conference and Exhibition, Dallas, October 3-6.

28. Mott, R., Cable A., and Spearing M.: “Measurement and Simulation of Inertial and High Capillary Number Flow Phenomena in Gas-Condensate Relative Permeability,” paper SPE 62933 presented at the 2000 SPE Annual Technical Conference and Exhibition, Dallas, October 1-4.

29. Al-Anazi, H.A., Pope, G.A., and Sharma, M.M.: “Laboratory measurements of condensate blocking and treatment for both low and high permeability rocks,” paper SPE 77546 presented at the 2002 SPE Annual Technical Conference and Exhibition, San Antonio, September 29 – October 2.

30. Ayyalasomayajula, P., Silpngarmlers, N., Berroteran, J., Sheffield, J., and Kamath J.: “Measurement of Relevant Gas Condensate Relative Permeability Data for Well Deliverability Predictions for a Deep Marine Sandstone Reservoir,” paper SCA 2003-33.

31. Ayyalasomayajula, P., Silpngarmlers, N., and Kamath J.: “Well Deliverability Predictions For A Low Permeability Gas Condensate Reservoir,” paper SPE 95529 to be presented at the 2005 SPE Annual Technical Conference and Exhibition, Dallas, October 9-12.

32. Saevareid, A., Whitson, C.H., and Fevang, O.: “An Engineering Approach to Measuring and Modeling Gas Condensate Relative Permeabilities,” paper SCA 9930.

33. Cable, A., Mott, R., and Spearing M.: “Experimental Techniques for the Measurement of Relative Permeability and Insitu Saturation in Gas Condensate Near Well Bore and Drainage Studies,” paper SCA 9928.

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Table 1: Composition of synthetic fluids

Mole % Reservoir 1 Reservoir 2 Reservoir 3

CH4 98.7 97.8 94.0 n-C10 0.98 2.1 5.95 n-C20 0.32 0.1 0.05

Table 2: Properties of core samples

Sample porosity

(%) permeability

(mD) Swi

(%)

1 10.1 51 9

2 13 15 12 Reservoir 1

3 9.5 3 20

1 17.8 3.6 32.5

2 16.9 61.9 26.4 Reservoir 2

3 17.7 35 23.9

1 13.78 22.9 19.62 Reservoir 3

2 16.27 4.9 21.92

Table 3: Properties of the single well radial model

Layer No.

Permeability (mD) Porosity Thickness

(ft) 1 6.4 0.139 21.5 2 1.4 0.081 1.7 3 5.1 0.143 20.3 4 0.1 0.186 10.5 5 13.9 0.153 16.1 6 2.4 0.039 30.0 7 18.8 0.119 41.5 8 6.9 0.120 26.7 9 2.6 0.068 74.1

10 3.7 0.061 141.08 11 6.3 0.031 68.3 12 3.6 0.066 7.6

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Synthetic Fluid @120 F

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/Kro

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4200 psi

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2600 psi

Figure 4: krg/kro as a function of bottomhole pressure for reservoir fluid

0.1

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Figure 5: krg/kro as a function of bottomhole pressure for synthetic fluid Figure 6: Schematic diagram of core flow apparatus for gas condensate systems

0

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1.E-10 1.E-09 1.E-08 1.E-07 1.E-06 1.E-05 1.E-04 1.E-03Capillary Number

Gas

Rel

ativ

e Pe

rmea

bilit

y

Reservoir 1Reservoir 2Reservoir 3SPE 83960SCA 9930SPE 31065SPE 80551

Figure 7: krg as a function of capillary number for krg/kro ratio range of 1.7 - 3.6

0

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0.2

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0.7

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ativ

e Pe

rmea

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yReservoir 1Reservoir 2SPE 83960SCA 9928SPE 31065SPE 80551

Figure 8: krg as a function of capillary number for krg/kro ratio range of 4 - 8.5

0

0.05

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0.2

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ativ

e Pe

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y

Reservoir 1Reservoir 2Reservoir 3SCA 9928SPE 31065

Figure 9: krg as a function of capillary number for krg/kro ratio range of 9.4 - 15

Wat

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IV

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e Pe

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Figure 10: krg as a function of capillary number for krg/kro ratio range of 30 – 45

0

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Nc = 1E-08 (Low)Nc = 5E-08Nc = 1E-07Nc = 1E-06Nc = 2E-06Nc =3E-06

Figure 11: Experimental krg vs. krg/kro data fit

Figure 12: Pressure distribution in the neighboring blocks near the well

Time

Pres

sure

Time

Pres

sure

Time

Pres

sure

Boundary ConditionsWell

656 ft

Time

Pres

sure

Time

Pres

sure

Time

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sure

Time

Pres

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Time

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sure

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Figure 13: Schematic diagram for a vertical well radial model with specified external boundary conditions

0

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4500

0 5 10 15 20 25 30 35 40

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ndar

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re (p

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Figure 14: Variation of boundary pressure with time for different layers

0

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40000

50000

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w R

ate

(MSC

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SCF/

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)

Figure 15: Gas production rate for the well

3521 3487 3486

3366

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3405

3351

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Figure 16: Bottomhole pressure comparison

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Figure 17: Well productivity index comparison

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