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Combining Reservoir Modeling and Analytical Calculation to Reduce Uncertainty in the Analysis of Enhance Oil Recovery (EOR)

with limited resources

By

Yusni Aditiah Rachman, Tri Firmanto

Abstract As a result of rapidly increasing computer power, these days 3D reservoir modeling is commonly used in the petroleum industry to better understand reservoir behavior and thereby minimize risk and uncertainty. However, integrated reservoir modeling studies require a long time to obtain good quality results, especially if history-matching is required. Also, significant resources (staff, software and hardware) are required. Reservoir modeling studies for secondary and tertiary recovery take even longer than for primary recovery because of the history-matching.

To estimate incremental oil recovery from Enhanced oil Recovery (EOR), an alternative to reservoir modeling is to use analytical calculations, which requires much less time and fewer resources than reservoir modeling. The downside with analytical calculations is the simplifying assumptions mean the results have greater uncertainty.

This paper discusses the procedure used to evaluate incremental oil recovery from EOR from the Melibur oil field using analytically formulae, with the help of the existing reservoir modeling results and limited resources. By using the results of reservoir modeling as an input in analytical calculations, the uncertainty in the result should be reduced. The workflow in this paper covers geoscience and engineering aspects, and considers the economic results.

This method can be used as a screening tool to evaluation alternative EOR techniques for a field, before more detailed studied are performed.

Keyword: Enhance Oil Recovery, Reservoir simulation, remaining oil and recoverable oil, deterministic and probabilistic economic analysis.

Introduction EOR is increasing oil recovery by the injection of materials not normally present in the reservoir. The objective(s) of EOR processes are to improve sweep efficiency and/or to reduce capillary and interfacial forces. There are many EOR techniques, of which the most commonly applied are thermal processes and chemical flood.

Different to primary recovery process, applying EOR requires much investigation and trials on various scales. There is no absolute answer to select the best EOR method for every reservoir. Each EOR method has its specific application. However, taking advantage of published industry experience in analogous fields in SPE papers and others can narrow down the alternatives.

Remaining oil is a key parameter in EOR selection and application, because it determines the potential incremental oil recovery by EOR application which is a key input to estimate the economic value of the EOR project. Many authors have published analytical formulae to quickly estimate incremental oil recovery from the application of EOR. Each formula has advantages and limitations, however, the objective is always the same. This paper estimates incremental oil recovery using Gommas method for a steam flood and the Buckley-Leverett method for water and chemical floods. The input parameters for these formulae came from the output of a history-matched reservoir simulation model, geological parameters and general properties of the Melibur oil field.

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Gomaas Method

One of the important parameters in a steam flood is steam quality. Steam quality at bottom-hole conditions can be estimated using the equation:

=

vR

RStwhbh LT

TTDhxx 1328)310(

)(

Where,

HwHvLv =

Steam quality influences the heating injection rate and the injection period. The theoretical heating injection rate can be calculated using the equation:

)3048.0*4047()1((1000 0hA

HHxhxIq wbhvbhsh+=

=

The effective heat injection rate is lower due to heat loss. The effective heat injection rate can be estimated using the formula:

)1( hvhhe fYqq = Therefore, we can estimate the steam injection period and mass of steam required from the equation below:

)3600*24(hee

i qq

t =

1000/)3600*24(iss tIM = The net recoverable oil by steam flood can be calculated using the equation:

reqFuelrecGrossrecNet ___ =

Where,

RNrecGross om *_ =

1000000/065.0*293.6*_ sMreqFuel =

Water Flood/Chemical Flood

The common formula to estimate oil recovery for a water or chemical flood is expressed by multiplying volumetric and displacement efficiency.

DVAR EEEE =

Where for a 5-Spot pattern, the volumetric efficiency is estimated as:

, [ ])4394.0)123.0ln(3048.0

511.0)0712.0ln(2062.01/(10

0

+++

+=

M

fME wA

XEXEX

EXXEV477.2)ln/43.4()(ln3

62.4)(ln016.0)(ln182.0199.03

2

+

++=

)/(1 ooioioD BSBSE =

Resources

This study was conducted to identify EOR potential in the Melibur oil field in the Malacca Strait PSC. This study used limited resources as follows:

1. One Senior Reservoir Engineer, one Reservoir Engineer and one Geologist.

2. One license for a reservoir modeling software and one license for a black oil simulator.

3. One computer.

The time required for this study was less than one month. This included developing a spreadsheet for EOR calculations, and developing a spreadsheet probabilistic economic model. The 3D reservoir model and history-matched reservoir simulation was developed before this study started.

Getting Input from the Reservoir Simulation As mentioned above, the main parameter in incremental oil recovery calculations is remaining oil. Therefore, the first step was to create a Hydrocarbon Pore Volume (HPV) property for each cell at the time when EOR is to be initiated. The procedure continues as follows:

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1. Sum HPV vertically for each zone

(=

=

nk

mk BoSoVb.. )

2. Select the EOR target area by filtering out cells that contain HPV below the chosen minimum.

3. Calculate the oil in place in the EOR.

4. Calculate the flood area.

5. Determine the bulk volume of the flood area.

6. Calculate the weighted average oil saturation in the flood area using the equation:

= ).()..(

NTGNTGSo

So

7. Determine the weighted average porosity and NTG in the same way as the oil saturation.

8. Calculate gross thickness using the equation:

areafloodVbhgross

_

=

Then use the weighted averages from the reservoir simulation as input for the analytical calculation. The idea is that parameters from a history-matched reservoir simulation would reduce the uncertainty in the analytical estimate of incremental oil recovery.

The HPV property and the selected EOR area are shown in Figure 1 and 2 respectively. Results from the reservoir simulation used as input in analytical calculation are listed in Table-1.

Results

The spreadsheet calculations and results of the analytical estimate of incremental oil recovery for a steam flood, water flood, polymer flood and ASP flood are shown in Figures 3, 4, 5 and 6 respectively.

Incremental oil recovery from a water flood estimated from the reservoir simulation and

from the analytical calculation using this method agreed within 10%.

Using this method, we can quickly estimate the potential incremental oil recovery for selected EOR methods, while reducing uncertainty in the result.

Economic Analysis Both deterministic and probabilistic economic analyses were performed. The assumptions used in the economic analysis are tabulated in Table-2. The deterministic economic results for the medium case are listed in Table-3. The uncertainty tree for the probabilistic economic analysis is shown in Figure 7.

The medium-case deterministic economic analysis suggests that an ASP flood is the most attractive EOR method for the Melibur oil field.

However, the probabilistic economic analysis indicates ASP flood has higher uncertainty compared to a steam flood or polymer flood. As we can see in Figure-8, steam flood and polymer flood has higher chance of making money.

In all cases, it was found that EOR has the potential to increase the economic value of this field.

Future Work

Apply this method to identify the EOR potential of other oil fields in EMP, and extend to other oil fields in Indonesia.

Develop a probabilistic method (Monte Carlo simulation) for the analytical estimation of incremental oil recovery from EOR.

Conclusions The incremental oil recovery estimate from EOR using analytical calculations can be improved by reducing the uncertainty in the input parameters by using the output from a history-matched reservoir simulation model.

The difference in incremental oil recovery estimated from the analytical calculation and

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from the reservoir simulation for a water flood was acceptable at less than 10%.

This method can be used for EOR-screening studies to estimate potential incremental oil recovery from EOR.

Economic analysis is needed to identify whether EOR can increase the economic value of a field.

Acknowledgments We would like to express our thanks to Kondur Petroleum, S.A. for encouraging us and for giving permission to publish this paper, and to Dr. Ezzat E. Gomaa for his advice and assistance during this study.

Nomenclature

whx = wellhead steam quality

bhx = bottom hole steam quality

ST = steam temperature, 0C RT = reservoir temperature,

0C

vL = latent heat of vaporization, kJ/kg

vH = steam enthalpy, kJ/kg

wH = water enthalpy, kJ/kg

hvf = vertical heat loss, W/gross m3 sI = steam injection rate, kg/s hq = heat injection rate, W/gross m3

heq = effective heat injection rate, W/gross m3

sM = total steam mass, ton/pattern

omN = Mobile oil in place, STB

RE = EOR recovery

AE = areal sweep efficiency

VE = vertical sweep efficiency

DE = displacement sweep efficiency M = Mobility ratio

wf = fractional flow A = pattern area, acre h = Gross thickness, ft

bV = Bulk volume, Bbl

= porosity

oS = oil saturation oB = Oil formation volume factor, bbl/STB

NTG = Net to Gross NPV = Net Present Value, US$ IRR = Internal Rate of Return, % CPI = Capital Performance Index SPE = Society of Petroleum Engineers

References

1. Jeff, Jones, Thermal Recovery by Steam Injection, Petroleum Engineering Handbook Vol. V. Chapter 15, SPE, 2007

2. Aurel, Carcoana, Applied Enhanced Oil Recovery, Prentice Hall, 1992.

3. Gomaa, Ezzat, EOR Mechanism, KPSA In-House Training, 2008.

Table-1 Reservoir Simulation Output for Input in Analytical Calculation

Parameter\Zone A B C All Zone

OOIP, MMSTB 14.1 61.4 17.5 92.9

OIP @ Jan-2012, MMSTB 10.0 39.8 15.0 64.8

hgross, ft 5 30 11 46

Ave. Por 0.31 0.32 0.31 0.31

Ave. NTG 0.98 0.94 0.94 0.97

So 0.423 0.451 0.457 0.443

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Table-2 Assumptions for Economic Analysis

Parameter Low Medium High

Oil Price, US$/Bbl

All case 50 60 80

Fix Opex, m US$/year

All case 1 1 1

Capex, m US$

Basecase 0 0 0

Steam 70 50 35

ASP 70 50 35

Polymer flood 70 50 35

Water flood 50 35 20

Facility Capex, m US$

Basecase 0 0 0

Steam 40 30 20

ASP 25 17.5 12.5

Polymer flood 17.5 12.5 10

Water flood 15 10 7.5

Opex, US$/Bbl

Basecase 3 5 10

Steam 15 10 7

ASP 25 17.5 12.5

Polymer flood 20 15 10

Water flood 15 10 5

Table-3 Summary Deterministic economic analysis for medium case

Figure-1 HPV model property from the reservoir simulation output

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Figure-2 Selected EOR area based on remaining oil

Estimating steam requirements

R, % of Nom 0.76Effective heat injected 0.65 Billion J/gross m3Depth 1000 ftDepth 305 mAssumed well head steam quality 0.75Assumed steam injection rate/well 4 kg/s 2174.861 bcwpdWellbore quality gradient (bare tubing) 2.30E-04 /mAssume Injection pressure 800 psia 5520Injection steam temperature 270 CLatent heat of vaporization 1605 kJ/kgBottom hole steam quality 0.70Y, Heat Utilization Factor 0.97Steam enthalpy, Hv 2790 kJ/kgWater enthalpy, Hw 1185 kJ/kgRefference Enthalpy, H0 251 kJ/kgHeat injection rate 5.01 W/gross m3Vertical heat loss, fhv 0.35Effective usefull heat injection rate 3.158805 W/gross m3Injection periods 2382 days 6.53 yearsTotal steam mass 823219 ton/pattern

6.53Oil Recovery Calculation

OIP/pattern 1.296 MMSTB/patternNom/pattern 0.943 MMSTB/patternGross oil recovery 0.717 MMSTB/patternFuel requirements 0.337 MMSTB/patternNet oil recovery 0.38 MMSTB/patternOOIP/pattern 1.859 MMSTB/patternNp @ flood start 0.563 MMSTB/patternUltimate recovery factor 0.688

Total Additional Reserves Calculation

Number of pattern 50Practical number of pattern 70% 35Total additional reserves 13.3 MMSTB

Figure-3 Spreadsheet for Steam flood oil recovery calculation

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WaterfloodFractional Flow of Water 0.96Water saturation behind front 0.6 from Chart fw MeliburOil saturation behind front 0.4Displacement efficiency 0.084Mobility total behind front 0.484Mobility @ intial 0.073Waterflood mobility ratio 6.67M0 6.84Areal sweep efficiency @ bt ( five spot) 0.86Distance netween injector-producer 242 m 795 ftGravity number 0.069Ngh/L 0.0052VDP 0.75X 0.94WOR 24.0Y 6.53Vertical sweep efficiency 0.568Er 0.041

Oil recovery calculationOIP/pattern 1.296 MMSTB/patternNom/pattern 0.596 MMSTB/patternNp @ flood start 0.563 MMSTB/patternOOIP/pattern 1.859 MMSTB/patternOil recovery 0.025 MMSTB/patternUltimate recovery factor 0.316Possible pattern 50Practical number of pattern 35Estimate additional reserves 0.9 MMSTB

Figure-4 Spreadsheet for Water flood oil recovery calculation

PolymerSwp 0.217Water saturation behind front 0.7 from Chart fw MeliburFractional Flow of Water 0.95Oil saturation behind front 0.3Displacement efficiency 0.308Mobility total behind front 0.127Mobility @ intial 0.08Waterflood mobility ratio 1.59M0 0.68Areal sweep efficiency @ bt ( five spot) 0.95Distance netween injector-producer 242 m 795 ftGravity number 0.044Ngh/L 0.0026VDP 0.75X 0.94WOR 19.0Y 31.51Vertical sweep efficiency 0.836Er 0.245

Oil recovery calculationOIP/pattern 1.296 MMSTB/patternNp @ flood start 0.563 MMSTB/patternOOIP/pattern 1.859 MMSTB/patternOil recovery 0.317 MMSTB/patternUltimate recovery factor 0.473Possible pattern 50Practical number of pattern 35Estimate additional reserves 11.1 MMSTB

Figure-5 Spreadsheet for Polymer flood oil recovery calculation

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ASPNvc surfactant 0.0030Swc/Swc max 0.47Sor/Sor max 0.200Swcs 0.120Sorws 0.048Sws -0.414Water saturation behind front 0.85 from Chart fw MeliburFractional Flow 0.95Oil saturation behind front 0.15Displacement efficiency 0.654Mobility total behind front 0.261Mobility @ intial 0.08Waterflood mobility ratio 3.27M0 0.68Areal sweep efficiency @ bt ( five spot) 0.95Distance netween injector-producer 242 m 795 ftGravity number 0.089Ngh/L 0.0052VDP 0.75X 0.94WOR 19.0Y 31.51Vertical sweep efficiency 0.836 from Chart III.20, EOR BookEr 0.519

Oil recovery calculationOIP/pattern 1.296 MMSTB/patternNp @ flood start 0.563 MMSTB/patternOOIP/pattern 1.859 MMSTB/patternOil recovery 0.673 MMSTB/patternUltimate recovery factor 0.665Possible pattern 50Practical number of pattern 35Estimate additional reserves 23.56 MMSTB

From Chart De-saturation curve

Figure-6 Spreadsheet for ASP flood oil recovery calculation

Figure-7 Example of decision tree diagram that used as model for probabilistic economic analysis

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0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%P

rob

ab

. Acu

m.

NPV, US$ M

EOR S-Curve - Melibur Field

ASP Flood-EMV = US$ 11.8 m

Polymer Flood-EMV = US$ 5.87 m

Steam Flood-EMV = 8.2 m

Water Flood-EMV = US$ -9.2 m

Figure-8 S-Curve for each EOR method in Melibur Field