SPE 124371

Numerical Investigation of Oil - Base-Mud Contamination in Condensates: From Cleanup to Sample Quality Mayank Malik1,3, Birol Dindoruk2, Hani Elshahawi2 and Carlos Torres-Verdin3 1 – Now with Chevron, 2 – Shell International E&P Inc., 3 – The University of Texas at Austin

Copyright 2009, Society of Petroleum Engineers

This paper was prepared for presentation at the 2009 SPE Annual Technical Conference and Exhibition held in New Orleans, Louisiana, USA, 3-7October 2009. 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 Formation Testers are widely used to determine pore pressure, estimate formation permeability, and detect reservoir connectivity through pressure transient testing after the onset of invasion. Mud filtrate invasion takes place in reservoirs penetrated by a well that is hydraulically overbalanced by mud circulation, or due to capillary forces. In water-base muds (WBM), the invading mud is immiscible with respect to the formation hydrocarbons. Therefore, water can be physically separated from the in-situ hydrocarbons leading to the best estimates of the in-situ PVT properties and thereby formation properties. Oil-base muds (OBM) are partially to completely miscible with the reservoir hydrocarbons, and so OBM contamination causes alteration of fluid properties which becomes even more critical for condensates when changes in fluid viscosity, density, and relative permeability occur. Due to the complexity of partial miscibility with gases and gas condensates, limited work has been done to simulate invasion by OBM. The goals of our work were to: 1. Determine conditions to obtain better samples. 2. Quantify the errors in numerical cleaning methods necessary for obtaining in-situ fluid compositions and properties. 3. Investigate the physics of the clean-up process. 4. Investigate the feasibility of tracers for monitoring contamination. Our results show that for condensates and lean gases, quantifying OBM contamination in terms of the live/bulk fluid alone can be misleading. For such fluids, contamination in the stock tank oil is just as critical as that of the bulk fluid because only the former predicts the errors in saturation pressures and CGR numbers observed in laboratory analyses. Lean fluids can take extremely long times to completely clean up during a formation test or even during a well test. For those fluids, it is more essential than ever to clearly define the primary objectives of the sampling program and to decide which answers are most critical. Introduction Wireline formation testers (WFT) are widely used to measure formation pressure, estimate permeability, perform downhole fluid analysis (DFA), and collect representative fluid samples. The presence of drilling mud in the wellbore at pressures usually higher than formation pressure causes the mud-filtrate to invade the near-wellbore region. When Oil based muds (OBM’s) are used, sampling without adequate cleanup will yield unrepresentative fluid samples for PVT or flow assurance analyses. As OBM is miscible to some degree with all reservoir hydrocarbons, OBM contamination causes alteration of fluid properties. Such alteration becomes even more critical in condensates due to partial/unknown miscibility. Prolonged cleanup times will improve sample quality, but this comes at the expense of increased time, cost, and/or risk. Therefore, as with any optimization problem, the objective is to find the optimal combination of operating parameters that maximizes the value of the obtained formation fluid properties subject to the operating constraints at hand. Partly due to the complexity of modeling partial miscibility in gases, limited work has been carried out to simulate invasion

2 SPE 124371-PP by OBM in gas-bearing formations. McCalmont et al. (2005) performed sensitivity analyses to determine pumpout volume for gas condensates using an immiscible flow formulation. Alpak et al. (2008) used a first-contact miscible flow approach with a compositional equation-of-state (EOS) simulator to compare field examples of sampled fluid GOR to their simulated GOR. They developed an analytical method to compute cleanup time in the presence of invasion however their results were limited to first-contact miscible fluids. Hashem et al. (1999) provided an empirical guideline for predicting fluid cleanup time to obtain a sample of a pre-determined level of contamination when sampling conditions fall within in a given range of filed data, mostly for oil reservoirs. Most recently, Angeles et al. (2009) simulated both conventional and focused sampling probes in highly deviated wells and observed that asymptotic analytical solutions are not accurate for fluid-cleanup prediction although such expressions can be useful for diagnostics. To understand the cleanup with gas condensates, it is essential to use a compositional simulator and model multiple fluid components to accurately replicate the phase behavior. Gozalpour et al. (2007) designed a tracer technique to obtain native fluid composition for lean gas condensates contaminated by OBM. Their experimental technique necessitates partial miscibility between the tracer and the lean gas condensate and requires two different tracers to obtain the native fluid composition. In this work, we studied the effect of different (CGR) gas condensates on fluid cleanup while simulating a formation test. We simulated mud-filtrate invasion and fluid flow in the near-wellbore region using a commercial finite-difference compositional simulator. Fluid properties were modeled using a compositional EOS simulator (CMG-GEM, 2006). Our compositional model consisted of eight pseudo-components: five formation oil components and three oil-base mud-filtrate components, to model the time-space evolution of miscible flow accurately. Hydrocarbon phase compositions were tracked using the Peng-Robinson EOS (Peng and Robinson, 1976 with Peng and Robinson 1978 modification). Phase density was calculated from the EOS to account for variations of fluid density due to pressure and composition changes. Similarly, fluid viscosity was calculated from the Lohrenz-Bray-Clark (LBC) correlation to account for the time-space variations (Lohrenz et al, 1964). The formation test was modeled with a constant flow rate condition at the sand face. No flow boundary conditions exist at the outer radial extent and at the top and bottom of the formation. Subsequently, we performed a parametric sensitivity analysis on gas condensate compositions, fluid and formation properties, and other controlling variables. Results show that fluid sample quality is dependent on the native fluid composition and that leaner fluids can take very long times to adequately clean up. Model Description and Validation All of the simulations in this work were performed using the Computer Modeling Group’s Generalized Equation of State Model (CMG-GEM). The LBC correlation was used to compute the viscosities of oil and gas phases, and the Peng-Robinson EOS was used to characterize the phase compositions of the hydrocarbon components. Our model assumed a vertical well in 3D radial geometry. We neglected physical dispersion, due to convection dominant nature of the problem and temperature variations in the formation, due to the scale that we are interested in. The model did not consider any variation of interfacial tension or rock wettability due to invasion by OBM (i.e., due to potential surface active agents that may be contained in the OBM). The formation tester was located in the middle of the formation as shown in Figure 1, and the outer limits of the reservoir consisted of impermeable zones with no-flow boundary conditions. As explained later, in addition to the above assumptions, we have selected a large outer boundary with respect to the invasion depth in order to isolate near-wellbore physics from the outer boundary conditions.

We simulated OBM invasion using a volume-averaged invasion rate, with invasion occurring axi-symmetrically across the entire formation thickness. Moreover, the formation is assumed homogeneous and anisotropic. In our simulation cases, we varied the radial invasion depth from 0.2 to 2 ft in the formation. The following three-step grid quality check was performed before all runs:

1. Verified analytical solution of single-phase pressure transient flow with GEM using the solution in Earlougher (1977) for

a radial closed boundary system. Both short time and large time responses were matched as expected with high precision. This is important in terms of studying the near saturated systems or system in highly permeable rocks.

2. Scaled numerical saturation profiles with respect to radial distance at different times and compared against analytical solutions (Buckley-Leverett). We felt that this step was necessary due to high thorough-put volumes encountered near the wellbore and thus numerical health of the grids was checked.

3. Compared GEM flash calculation results with EOS at time=0 and at time > 0 (while mimicking the actual samples taken with the probe). The time=0 step was needed to establish the baseline, to make sure that the initialization is correct, and to capture the small amplitude changes in compositions.

SPE 124371-PP 3

Numerical Simulation of Mud-Filtrate Invasion and Fluid Pumpout We simulated invasion by OBMs in the formation as well as the pumpout/sampling sequence through a formation tester probe. Figure 1 shows the location of the tool in a vertical well with respect to the formation. The probe module was arranged to coincide with the center of the formation. The wellbore radius is 0.35 ft. There are 32 nodes in radial direction, 16 nodes in theta direction, and 25 nodes in the vertical direction. The external radius of the reservoir is 202 ft and no-flow boundary conditions exist across it. The formation consists of hydrocarbons in the range of C1 to C19+. The hydrocarbons are lumped in five different components (N2C1, CO2C3, C4C6, C7C18, and C19+) using their pseudo-properties detailed in Table 1. OBM has components between C14 to C18 and is lumped in three components (MC14, MC16, and MC18) detailed in Table 2. Therefore, our compositional simulations consist of eight different hydrocarbon components. Such reduction of components is necessary to speed up the simulation runs. Table 3 shows the binary interaction parameters between the mud-filtrate and the native hydrocarbons. Before the proposed compositional breakdown is accepted, quality check against the measured PVT data was performed and proved that the proposed eight-component fluid model properly accounted for density and viscosity changes (and other key parameters like GOR, and liberated gas volumes) due to variations of component concentrations and as well as pressure and temperature changes. Table 4 lists the geometrical dimensions of the numerical grid and Table 5 shows the formation properties. The relative permeability and capillary pressure curves are displayed in Figure 2. A volume averaged mud flowrate is considered during the process of invasion at the wellbore. The flowrate is based on the flow initiation pressure mentioned in Roy and Sharma (2001) and the mud flowrate in Wu et al. (2002). Mud invasion continues for 2.4 hours with a flowrate of 105 ft3/day, followed by drawdown for 9.6 hours with a flowrate of 14 STB/day (Figure 3). Sample Solutions and Parametric Study A base case was formulated based on the formation, fluid, and geometrical properties outlined in the previous section. The condensate has a GOR of 100,000 SCF/STB and the component mole fractions are illustrated in Figure 4. The dew point of the condensate is 3956.8 psi at reservoir temperature of 213.8 ºF. The formation was invaded with OBM and then fluid flow was carried out based on the flow rate schedule shown in Figure 3. Figure 6 shows the radial back-propagation profile of MC14 component at different times after the onset of production. The r-z surface plot of gas saturation can be seen in Figure 7. At early times, the flow is spherical and at late times, the flow becomes cylindrical. Note that the probe generates drawdown and mobilizes fluid not only from the region in front of it, but also from the entire layer. Compositions of the fluids captured by the probe are shown in Figure 8 and Table 6 summarizes the composition of hypothetical samples collected at various times.

Figure 6 shows the variation of MC14 component (the dominant component in OBM) as a function of radial distance in front of the probe. This helps explain the variation in gas saturation with time seen in Figure 8. The mole and mass fractions of the hydrocarbons vary at the sandface at different times. Eight samples are plotted at progressive snap shots in time. The OBM mole fraction gradually decreases and approaches the original fluid composition. The OBM mass fraction decreases with time, but even at the end of formation testing (34560 sec), the OBM has a mass fraction of 3.7% in the fluid. This is a result of the much larger molecular weight of the OBM components compared to components of the gas condensate.

Figures 9 and 10 show the variation in fluid properties as a function of time at the sandface. At early times, the oil rate is high and it eventually decreases such that gas condensate is being produced. Gas density is high at early times as OBM components are mixing and dissolving in the gas phase. Once the probe production cleans up OBM, gas density decreases and reaches the original gas condensate density. A similar effect can be seen on the gas viscosity. The oil density and viscosity decrease due to mixing with the gas condensate and gas dissolution. A pressure drop~800 psi results at the sandface but throughout, the flowing pressure at the sandface was maintained above the dew point pressure (3956 psi).

Quantification of Contamination We use two criteria to define contamination. The criteria are based on the mass fraction in live fluid and the mass fraction in stock tank oil (STO) at separator conditions of the fluid being produced at the probe as:

)1.(........................................)(

)()(

1

1

∑

∑

=

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cm

z n

jjj

n

iii

Mtz

MtztC

4 SPE 124371-PP

)2........(..............................)(

)()(

1

1

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∑

=

==c

cm

x n

jjj

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Mtx

MtxtC

Figure 9 shows the contamination based on the above criteria. Contamination in the live fluid (Figure 9a) decreases exponentially, and towards the end of sampling, there is less than 4% contamination. However, contamination in the heavy stock tank oil (STO) fraction (Figure 9 b) decreases more gradually, and towards the end of sampling, there is still 37% contamination for the base case of 100,000 SCF/STB. Contamination is inversely proportional to sample quality. Quality increases as a function of time due to cleanup of OBM in the region around the probe due to fluid production. The quality is higher in live fluid than in STO due the lower mass fraction of the gas condensate compared to the STO. In case of a dry gas, the sampling time will have to be increased drastically to get pure gas samples. This is so because even a small fraction of OBM will contaminate the dry gas by introducing heavier hydrocarbon components and will introduce a dew point to the dry gas. In all of the simulations presented in this paper, live fluid and STO contaminations are both listed.

Parametric Study

In order to understand the dependence of sample quality on formation, fluid, and petrophysical properties, we carried out sensitivity analysis on the following parameters:

1. Vertical anisotropy 2. Reservoir porosity 3. Reservoir permeability 4. Reservoir porosity and permeability 5. Well-bore radius 6. Probe angle 7. Probe flow-rates 8. Probe located above or below a shale boundary 9. Volume of mud-filtrate invasion 10. Rock compressibility 11. Gas condensate composition in the formation

Table 6 summarizes what we termed as the cleanup time for each of these cases. The cleanup time is based on gas saturation to reach the maximum gas saturation at the probe location and serves as a useful criterion to compare the sensitivity cases.

Summary and Conclusions We have investigated the OBM contamination problem for condensate gas systems in which the condensate is not first-contact miscible with the OBM. Transfer of components between the condensate and the OBM still requires that interaction of phase behavior and flow be captured correctly. In addition, we have validated the grid quality due to multiple time scales involved in the problem. In order to achieve this, we have considered the single-phase pressure diffusion problem and as well as the two phase front propagation problem (i.e., Buckley-Leverett problem in radial coordinates). In order to understand the dependence of sample quality on formation, fluid, and petrophysical properties, an extensive parametric sensitivity analysis was carried out focusing on the key parameters for fluid sampling. Among these parameters, for a given volume of OBM invasion, porosity had a pronounced effect to the cleanup times, since it controlled the invasion diameter within the formation. For a given total sampling time, cleanup rate also appeared as a critical parameter. Higher rates invariably lead to faster and more effective cleanup. Note that flow rate is indirectly related to mobility for a given allowable drawdown. In other words, it is always advantageous to use the maximum cleanup rate possible as long as the resultant drawdown maintains the fluid in single phase and avoid inducing sanding or plugging, the higher the Since the flow/production times are rather small with respect to the average total drawdown in the radial system considered, compressibility did not have an impact on the level of contamination. Similarly, the probe angle was also found to be an insensitive parameter in our case study. However, it is possible to envision cases in which it could be important, in a deviated well or if the probe angle is near a local heterogeneity or flow boundary that already impacted the local invasion profile. Another way of looking at the compositions is to examine the total OBM content in the fluids sampled. Therefore, from a measurement point of view, one of the most important parameters that we have studied is the quantification of the OBM % in the samples. In order to achieve that goal, we have calculated the OBM percent in the STO as well as in the bulk fluid.

SPE 124371-PP 5 Numerical decontamination methods and as a result; dew point pressure and CGR of the in-situ fluids are impacted mostly by the OBM % in the STO. As expected, gas richness had a large effect on the contamination level. Leaner fluids took very long times to clean up (impractically long for most real-life operations). If the answer desired were gas composition or GOR alone, a moderate level of contamination in the STO may not be a problem. If, on the other hand, accurate quantification of CGR or dew point is critical, even a few wt % of OBM contamination in the live fluid can be too much. For lean fluids, relying on the contamination level in the live fluid alone can be misleading and overly optimistic. For example, 1% contamination in the live fluid can easily be translated to 40-90% contamination in the STO portion of a lean gas. For condensates and lean gas systems, it is more essential than ever to clearly define the primary objectives of the sampling program and to decide which answers are most critical.

Abbreviations CGR EOS

Condensate Gas Ratio Equation of State

FCM First Contact Miscible GOR Gas Oil Ratio OBM Oil Base Mud LBC Lohrenz-Bray-Clark Viscosity Model STO Stock Tank Oil WBM Water Base Mud WFT Wireline Formation Tester Symbols Cx Weight fraction of OBM in (surface)

liquid phase Cz Weight fraction of OBM in overall

sample Mi Molecular weight of component i nc Number of components ncm Number of mud components pdew Dew Point Pressure p Pressure t Time T Temperature xi Mole fraction of component i in

(surface) liquid phase zi Overall mole fraction of component i

Acknowledgement Authors wish to thank Shell International Exploration and Production Inc. for granting permission to publish this manuscript. A note of special gratitude goes to Faruk Omer Alpak for guidance in modeling measurements acquired by formation testers and to Bob Brugman for technical support with CMG-GEM simulator.

References Alpak, F. O., Elshahawi, H., Hashem, M., and Mullins, O.C.: “Compositional modeling of oil-based mud-filtrate clean-up during wireline formation tester sampling,” SPEREE, v. 11, no. 2, p. 219-232, 2008.

Angeles, R., Torres-Verdín, C., Sepehrnoori, K., and Malik, M.: “Prediction of formation-tester fluid-sample quality in highly-deviated wells,” Petrophysics, v. 50, no. 1, 2009.

6 SPE 124371-PP Dong, C., Hegeman, P. S., Carnegie, A., and Elshahawi, H.: “Downhole measurement of methane content and GOR in formation fluid samples,” SPEREE, v. 9, no. 1, p. 7-14, February 2006.

Earlougher Jr., R.C.: “Advanced in Well Test Analysis,” SPE Monograph Series 5, 1977.

GEM manual, CMG Ltd (2006), Calgary CA.

Gozalpour, F., Danesh, A., Todd, A. C., and Tohidi, B.: “Application of tracers in oil-based mud for obtaining high-quality fluid composition in lean gas/condensate reservoirs,” SPEREE, v. 10, no. 1, p. 5-11, February 2007.

Hashem, M.N., Thomas, E.C., McNeil, R.I., Mullins, O.: “Determination of producible hydrocarbon type and oil quality in wells drilled with synthetic oil-based muds,” SPEREE v. 2, no. 2, p. 125-133, 1999.

Lohrenz, J., Bray, B. G., and Clark, C. R.: “Calculating viscosity of reservoir fluids from their compositions,” Journal of Petroleum Technology, SPE 915, p. 1171-1176, October 1964.

Malik, M., Torres-Verdín, C., Sepehrnoori, K., Dindoruk, B., Elshahawi, H., and Hashem, M.: “History matching and sensitivity analysis of probe-type foration-tester measurements acquired in the presence of oil-base mud-filtrate invasion,” Petrophysics, v. 48, no. 6, 2007.

McCalmont, S., Onu, C., Wu, J., Kiome, P., Sheng, J. J., Adegbola, F., Rajasingham, R., and Lee, J.: “Predicting pump-out volume and time based on sensitivity analysis for an efficient sampling operation: prejob modeling through a near-wellbore simulator,” paper SPE 95885, presented at SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 9-12, 2005

Peng, D. Y. and Robinson, D. B.: “A new two-constant equation of state,” Industrial and Engineering Chemistry Fundamentals, v. 15, no. 59, 1976.

Peng, D.-Y., and Robinson, D.B., “The Characterization of the Heptanes and Heavier Fractions for the GPA Peng-Robinson Programs”, GPA Research Report RR-28, 1978.

Roy, R. S., and Sharma, M. M.: “The Relative Importance of Solids and Filtrate Invasion on the Flow Initiation Pressure,” paper SPE 68949, presented at the SPE European Formation Damage Conference, 21-22 May, 2001.

Wu, J., Torres-Verdín, C., Proett, M. A., Sepehrnoori, K., and Belanger, D.: “Inversion of multi-phase petrophysical properties using pumpout sampling data acquired with a wireline formation tester,” paper SPE 77345, presented at SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 29-October 2, 2002.

SPE 124371-PP 7

Table 1: Equation-of-state properties of the in-situ hydrocarbon components used in the base case.

N2C1 CO2C3 C4C6 C7C18 C19+ Tc (K) 185.515 325.345 455.308 619.937 844.227

Pc (atm) 44.4604 57.125 33.906 21.939 12.557

Acentric Factor 0.01053 0.14579 0.2302 0.49041 0.9192

Molar Weight (lbs/lb-moles) 16.6031 36.2292 67.7315 132.793 303.219

Vc (m3/k-mol) 0.09826 0.14096 0.30078 0.62715 1.31898

Parachor 74.434 107.4 221.59 397.279 780.566

Volume Shift -0.1935 -0.1308 -0.0562 0.17156 0.23079

Table 2: Equation-of-state properties of the OBM-filtrate used in the base case.

MC14 MC16 MC18 Tc (K) 674.919 712.322 743.225

Pc (atm) 17.8294 16.3524 15.277

Acentric Factor 0.62575 0.71189 0.78422

Molar Weight (lbs/lb-moles) 190.0 222.0 251.0

Vc (m3/k-mol) 0.83142 0.96283 1.09224

Parachor 503.9 578.78 646.64

Volume Shift 0.07921 0.06664 0.04391

Table 3: Binary interaction parameter between OBM-filtrate and native hydrocarbons for all of the simulations considered in this paper.

Binary Interaction Parameters N2C1 CO2C3 C4C6 C7C18 MC14 MC16 MC18 C19+

N2C1 0.0000

CO2C3 0.0321 0.0000

C4C6 0.0038 0.0296 0.0000

C7C18 0.0226 0.0314 0.0049 0.0000

MC14 0.0000 0.0000 0.0000 0.0000 0.0000

MC16 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

MC18 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

C19+ 0.0223 0.0313 0.0048 0.0000 0.0000 0.0000 0.0000 0.0000

8 SPE 124371-PP

Table 4: Summary of geometrical dimensions of the formation.

Variable Units Value

Wellbore Radius (rw) ft 0.35

External Radius (re) ft 202

Reservoir Thickness ft 34

Number of nodes - radial axis -- 32

Number of nodes - theta axis -- 16

Number of nodes - vertical axis -- 25

Grid cell size - radial axis ft Variable

Grid cell size - theta axis degrees Variable

Grid cell size – vertical axis ft Variable

Table 5: Summary of reservoir rock and formation fluid properties used in the base case.

Variable Units Value

Porosity fraction 0.18

Radial Permeability mD 100

Vertical Permeability mD 100

Water Density @ STP lbf/ft3 64

Water Compressibility psi-1 3E-06

Initial Water Saturation fraction 0.224

Water Viscosity cp 1.0

Formation Compressibility psi-1 1E-9

Production Flow rate Bbl/day 14

Initial Reservoir Pressure psi 5500

Reservoir Temperature ºF 213.8

Table 6: Summary of component mole fraction at different sampling times. Results are shown at time intervals of 5 minutes.

Sample 1 2 3 4 5 6 7 8 Time (secs) 0.17 1.55 2.76 10.02 51.84 316.22 5056 34560 N2C1 0.063019 0.18554 0.34213 0.67493 0.78725 0.83265 0.85664 0.86169 CO2C3 0.007578 0.022493 0.041625 0.078507 0.092892 0.099115 0.10233 0.10297 C4C6 0.001812 0.005454 0.010151 0.017612 0.02139 0.023213 0.024134 0.02431 C7C18 0.00059 0.001809 0.003389 0.00519 0.006522 0.007279 0.007664 0.007738 C19+ 3.82E-05 0.000102 0.000184 0.000225 0.000286 0.000342 0.000373 0.000378 MC14 0.60158 0.50913 0.39089 0.14544 0.059741 0.024364 0.005759 0.001881 MC16 0.19893 0.1684 0.12936 0.047826 0.019577 0.007992 0.001898 0.000626 MC18 0.12645 0.10706 0.082276 0.030265 0.012345 0.005043 0.001203 0.000401

SPE 124371-PP 9

Table 7: Summary of reference cleanup times for all the sensitivity cases. The base case is highlighted in bold font.

Permeability anisotropy

Kv/kh=0.05 Kv/kh=0.5 Kv/kh=0.25 Kv/kh=0.5 Kv/kh=1

Time (secs) 67.3 109.2 183.0 266.6 380.0 Porosity ф=0.05 ф=0.1 ф=0.18 ф=0.25 ф=0.3 Time (secs) 1215.5 784.8 380.0 205.1 147.9 Permeability k=25 mD k =50 mD k=75 mD k=100 mD k=120 mD Time (secs) 833 741.4 588.7 380.0 249.6 Permeability and Porosity

k=25 mD, ф=0.1318

k=50 mD, ф=0.156

k=75 mD, ф=0.17 k=100 mD, ф=0.18

k=120 mD, ф=.186

Time (secs) 951.4 828.9 704.1 380.0 234.4 Radius of wellbore

Rw=0.25 ft Rw=0.3 ft Rw=0.35 ft Rw=0.4 ft Rw=0.45 ft

Time (secs) 694.1 549.6 380.0 254.0 142.3 Probe Angle θ=5 degree θ=10 degree θ=15 degree θ=20 degree θ=30 degree Time (secs) 380.0 341.1 299.6 295.4 239.5 Fluid withdrawal rate

Q=1 Bbl/day Q=2 Bbl/day Q=4 Bbl/day Q=8 Bbl/day Q=14 Bbl/day

Time (secs) 5184.0 2501.2 1251.3 650.1 380.0 Probe above shale boundary

Q=1 Bbl/day Q=2 Bbl/day Q=4 Bbl/day Q=8 Bbl/day Q=14 Bbl/day

Time (secs) 6714.8.0 2673.9 1300.5 628.0 361.8 Probe below shale boundary

Q=1 Bbl/day Q=2 Bbl/day Q=4 Bbl/day Q=8 Bbl/day Q=14 Bbl/day

Time (secs) 2737.5 1541.2 859.6 459.3 288.5 Invasion rate V=8 ft3 V=10.5 ft3 V=15 ft3 V=20 ft3 V=30 ft3 Time (secs) 48.3 380.0 1229.3 2210.8 4204.2 Rock compressiblity

Cr=1e-5 Cr=1e-7 Cr=1e-9 Cr=1e-11

Time (secs) 341.3 376.9 380.0 380.0 Gas composition

Dry Gas GOR=100,000 SCF/Bbl

GOR=20,000 SCF/Bbl

GOR=10,000 SCF/Bbl

Time (secs) 429.0 380.0 894.5 1260.3

,,

,,

SIDE VIEW TOP VIEW

LOCATION OF PROBE

Figure 1: Side and top views of the formation tester probe with respect to the formation and the grid system.

10 SPE 124371-PP

Figure 2: Relative permeability and capillary pressure curves used used in the simulations.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

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Figure 3: Flow rate as a function of time in the formation. Negative flow rate implies OBM invasion in the formation and positive flow rate implies production by the formation tester.

1 20

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Figure 4: Comparison of hydrocarbon components in gas condensate and in OBM filtrate. Sample number “1” represents the native composition of hydrocarbon and sample number “2” represents the OBM filtrate.

Component Mole Fraction in the native HC

N2C1 .86413

CO2C3 .1033

C 4C6 .0244

C7C18 .00777

C19+ .00038

12 SPE 124371-PP

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Time (days)

P vs tP

(ps

i)

GEM

GEMrefined

Analytical

0 50 100 150 200 2500.2

0.3

0.4

0.5

0.6

0.7

0.8

Normalized distance (r2/t)

Oil

Sat

urat

ion

0.5 days

1 days

2 days5 days

10 days

Figure 5: Comparison of analytical solution of single phase pressure transient with GEM radial grid models and the self similarity of the advective flow near the wellbore.

0 0.2 0.4 0.6 0.8 1 1.2 1.40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Radial Distance (ft)

MC

14 C

once

ntra

tion

(fra

ctio

n)

End ofInvasion

Figure 6: Radial back-propagation profile of MC14 component at different times after the onset of production. The bold blue curve shows the radial profile at the end of invasion. In all 32 curves are shown at time intervals of 20 seconds.

SPE 124371-PP 13

a. -1.0 0.1

-1.0 0.1

-0.80-0.70

-0.60-0.50

-0.40-0.30

-0.20-0.10

0.000.10

0.200.30

0.400.50

0.60

-0.7

0-0

.60

-0.5

0-0

.40

-0.3

0-0

.20

-0.1

00.

000.

100.

200.

300.

400.

500.

600.

7

0.00 2.50 5.00 inches

0.00

0.07

0.13

0.20

0.27

0.33

0.40

0.47

0.53

0.60

0.67

0.73

0.80

0.87

0.93

1.00

b. -1.0 0.1

-1.0 0.1

-0.80-0.70

-0.60-0.50

-0.40-0.30

-0.20-0.10

0.000.10

0.200.30

0.400.50

0.60

-0.7

0-0

.60

-0.5

0-0

.40

-0.3

0-0

.20

-0.1

00.

000.

100.

200.

300.

400.

500.

600.

7

0.00 2.50 5.00 inches

0.00

0.07

0.13

0.20

0.27

0.33

0.40

0.47

0.53

0.60

0.67

0.73

0.80

0.87

0.93

1.00

c. -1.0 0.1

-1.0 0.1

-0.80-0.70

-0.60-0.50

-0.40-0.30

-0.20-0.10

0.000.10

0.200.30

0.400.50

0.60

-0.7

0-0

.60

-0.5

0-0

.40

-0.3

0-0

.20

-0.1

00.

000.

100.

200.

300.

400.

500.

600.

7

0.00 2.50 5.00 inches

0.00

0.07

0.13

0.20

0.27

0.33

0.40

0.47

0.53

0.60

0.67

0.73

0.80

0.87

0.93

1.00

d. -1.0 0.1

-1.0 0.1

-0.80-0.70

-0.60-0.50

-0.40-0.30

-0.20-0.10

0.000.10

0.200.30

0.400.50

0.60

-0.7

0-0

.60

-0.5

0-0

.40

-0.3

0-0

.20

-0.1

00.

000.

100.

200.

300.

400.

500.

600.

7

0.00 2.50 5.00 inches

0.00

0.07

0.13

0.20

0.27

0.33

0.40

0.47

0.53

0.60

0.67

0.73

0.80

0.87

0.93

1.00

e. -1.0 0.1

-1.0 0.1

-0.80-0.70

-0.60-0.50

-0.40-0.30

-0.20-0.10

0.000.10

0.200.30

0.400.50

0.60

-0.7

0-0

.60

-0.5

0-0

.40

-0.3

0-0

.20

-0.1

00.

000.

100.

200.

300.

400.

500.

600.

7

0.00 2.50 5.00 inches

0.00

0.07

0.13

0.20

0.27

0.33

0.40

0.47

0.53

0.60

0.67

0.73

0.80

0.87

0.93

1.00

f. -1.0 0.1

-1.0 0.1

-0.80-0.70

-0.60-0.50

-0.40-0.30

-0.20-0.10

0.000.10

0.200.30

0.400.50

0.60

-0.7

0-0

.60

-0.5

0-0

.40

-0.3

0-0

.20

-0.1

00.

000.

100.

200.

300.

400.

500.

600.

7

0.00 2.50 5.00 inches

0.00

0.07

0.13

0.20

0.27

0.33

0.40

0.47

0.53

0.60

0.67

0.73

0.80

0.87

0.93

1.00

g. -1.0 0.1

-1.0 0.1-0.80

-0.70-0.60

-0.50-0.40

-0.30-0.20

-0.100.00

0.100.20

0.300.40

0.500.60

-0.7

0-0

.60

-0.5

0-0

.40

-0.3

0-0

.20

-0.1

00.

000.

100.

200.

300.

400.

500.

600.

7

0.00 2.50 5.00 inches

0.00

0.07

0.13

0.20

0.27

0.33

0.40

0.47

0.53

0.60

0.67

0.73

0.80

0.87

0.93

1.00

h. -1.0 0.1

-1.0 0.1

-0.80-0.70

-0.60-0.50

-0.40-0.30

-0.20-0.10

0.000.10

0.200.30

0.400.50

0.60

-0.7

0-0

.60

-0.5

0-0

.40

-0.3

0-0

.20

-0.1

00.

000.

100.

200.

300.

400.

500.

600.

7

0.00 2.50 5.00 inches

0.00

0.07

0.13

0.20

0.27

0.33

0.40

0.47

0.53

0.60

0.67

0.73

0.80

0.87

0.93

1.00

Figure 7: R-Z layer plot of gas saturation at different times after onset of fluid production, a) before invasion, b) End of invasion, c-h) at time intervals of 5 minutes.

14 SPE 124371-PP

1 2 3 4 5 6 7 80

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Sample Number

Mole fraction at SandfaceM

ole

Fra

ctio

nN

2C

1

CO2C

3

C4C

6

C7C

18

C19

P

MC14

MC16

MC18

1 2 3 4 5 6 7 80

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Sample Number

Mass fraction at Sandface

Mas

s F

ract

ion

N2C

1

CO2C

3

C4C

6

C7C

18

C19

P

MC14

MC16

MC18

Figure 8: Mole and mass concentration measured by the probe at the sandface. Table 5 shows the corresponding sampling times for the sample numbers. Results are shown at time intervals of 5 minutes.

a. 0 0.5 1 1.5 2 2.5 3 3.5

x 104

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time (secs)

Co

ntam

inat

ionZ (f

ract

ion)

Dry GasGOR 100,000GOR 20,000GOR 10,000

b. 0 0.5 1 1.5 2 2.5 3 3.5

x 104

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time (secs)

Co

ntam

inat

ionY

(fra

ctio

n)Dry GasGOR 100,000GOR 20,000GOR 10,000

c. 10

010

110

210

310

40

10

20

30

40

50

60

70

80

Time (secs)

Gas

Rat

e (ft

3 /day

)

Dry GasGOR 100,000GOR 20,000GOR 10,000

d. 10

010

110

210

310

40

5

10

15

Time (secs)

Oil

Rat

e (b

bl/d

ay)

Dry GasGOR 100,000GOR 20,000GOR 10,000

Figure 9: Sensitivity plots for variation in gas condensate composition. The plots show the variation of a) Contamination in live fluid, b) contamination in STO, c) Gas rate, and d) Oil rate as functions of time.

SPE 124371-PP 15

a. 10

310

413

14

15

16

17

18

19

20

21

22

23

Time (secs)

Ga

s D

en

sity

(lb

f/ft3 )

Dry GasGOR 100,000GOR 20,000GOR 10,000

b. 10

010

110

210

310

425

30

35

40

45

50

Time (secs)

Oil

De

nsi

ty (

lbf/f

t3 )

Dry GasGOR 100,000GOR 20,000GOR 10,000

c. 10

310

40.025

0.03

0.035

0.04

0.045

0.05

0.055

Time (secs)

Ga

s V

isco

sity

(cp

)

Dry GasGOR 100,000GOR 20,000GOR 10,000

d. 10

010

110

210

310

40

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Time (secs)

Oil

Vis

cosi

ty (

cp)

Dry GasGOR 100,000GOR 20,000GOR 10,000

e. 10

010

110

210

310

40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time (secs)

Ga

s S

atu

ratio

n a

t pro

be

(fr

act

ion

) Dry GasGOR 100,000GOR 20,000GOR 10,000

f. 10

010

110

210

310

44600

4700

4800

4900

5000

5100

5200

5300

5400

5500

Time (secs)

Sa

nd

face

Pre

ssu

re (

psi)

Dry GasGOR 100,000GOR 20,000GOR 10,000

Figure 10: Sensitivity plots for variation in gas condensate composition. The plots show the variation of a) Gas density, b) Oil density, c) Gas viscosity, d) Oil viscosity, e) Gas saturation, and f) Pressure as functions of time.