smart science solutions Measuring the Success

47
smart science solutions Measuring the Success of Waterfloods G. Renouf Saskatchewan Research Council

Transcript of smart science solutions Measuring the Success

Page 1: smart science solutions Measuring the Success

smart science solutions

Measuring the Success of Waterfloods

G. RenoufSaskatchewan Research Council

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AcknowledgmentsAcknowledgments

Petroleum Technology Research Council BP Exploration (Alaska) Inc. Canadian Natural Resources Ltd. Canetic Resources Inc. Husky Oil Energy Ltd. Nexen Inc. Shell International Exploration & Production BV Total E&P Canada Ltd.

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Outline of TalkOutline of Talk

Motivation Recap of previous studies Findings Conclusions and recommendations

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MotivationMotivation

Waterflooding common in heavy oil reservoirs139 in AB and 68 in SK represent 24% of heavy

oil in place Additional recovery ranges from 0.4% to 47%. We need to understand the reasons behind

the extra-successful and failed waterfloods. Extensive body of knowledge on waterflooding

lighter oils … is it applicable to heavy oil waterflooding?

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Previous StudiesPrevious Studies

2004 Study “Heavy Oil Waterflooding Scoping Study”Statistics of 54 heavy oil waterfloods Interviews with 25 engineering & field staff8 abandoned waterfloods examined

2006 Study “Measuring the Success of Western Canadian Waterfloods”Multivariate analysisCompares heavy to medium oil waterfloods

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Multivariate vs Univariate AnalysisMultivariate vs Univariate Analysis

SI=secondary production as % of OIP/#yrs wf

Success Index vs. Production Depth

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

250 350 450 550 650 750 850 950 1050 1150 1250

Production Depth (m)

Suc

cess

Inde

x

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Multivariate AnalysisMultivariate Analysis

General Reservoir and operating parameters are x’s (41 x’s) Success measurements are y’s (8 y’s) Each waterflood is an observation point (168 points)

Details Can include qualitative x’s or y’s Types: PCA, PLS, PCR We used PLS

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0.06 0.29 0.07 0.07 1522 5.18 35 13 1.017 968 14.7 535 1969 14.25

0.26 0.34 0.24 0.09 1604.5 2.82 36 15.3 1.018 965 15.1 491 1970 10.92

0.20 0.27 0.44 0.06 2220 4.52 35 18 1.02 964 15.3 566 1966 38.42

0.17 0.27 0.28 0.05 923 4.15 35 18 1.02 964 15.3 563 1972 30.58

0.16 0.74 0.20 0.12 1749 3.99 32.37 20 1.016 970 14.4 493 1971 13.08

0.43 0.32 0.32 0.32 2229 4.9 30 20 1.023 967 14.8 475 1992 2.67

0.15 1.28 0.15 0.16 874 4.33 35.7 16 1.033 963 15.4 472 1971 20.67

0.28 0.38 0.28 0.03 1263 6.2 30 21 1.031 962 15.6 518 1968 11.5

0.11 0.23 0.10 0.16 971 2.75 35 23 1.033 947 17.9 712 1984 20.33

0.68 0.08 0.67 0.62 518 9.58 30 25 1.031 946 18.1 625 1985 19.08

0.57 0.07 0.58 0.88 420 5.46 30 25 1.031 946 18.1 625 1988 16.17

0.61 0.09 0.93 0.35 227 4.5 30 25 1.031 946 18.1 625 1990 14.58

0.32 0.08 0.23 0.31 227 5.95 30 25 1.036 946 18.1 625 1997 7.58

0.28 0.67 0.41 0.43 987 3.9 34 15 1.017 976 13.5 541 1970 14.58

0.32 0.06 0.42 0.36 1125 4.5 28 20 1.029 970 14.4 737 1988 16.58

0.85 0.11 0.24 0.92 1409 4.75 28 32 1.111 968 14.7 787 1988 16.67

0.21 0.11 0.23 0.29 421 4.1 30 21 1.02 959 16.0 532 1981 22.67

0.33 0.13 0.13 0.27 719 3.72 26.6 25 1.05 978 13.2 823 1969 34.83

0.14 0.10 0.04 0.19 793 6.8 29.6 31 1.039 981 12.7 814 1988 16.58

0.44 0.22 0.34 0.63 259 9.16 30 35 1.039 964 15.3 851 1988 16.5

0.50 0.14 0.48 0.57 2736 4.83 30 35 1.039 964 15.3 851 1988 16.58

0.30 0.12 0.19 0.17 3076 11.46 24.1 31 1.078 982 12.6 823 1959 45.92

0.17 0.20 0.08 0.21 1344 4 30 25 1.039 972 14.1 818 1988 16.33

0.00 0.26 0.00 0.00 502 5.75 32 23 1.063 953 17.0 863 1996 9

1.02 0.14 1.26 0.70 777 7.47 28.5 23 1.063 953 17.0 863 1988 16.58

0.00 0.08 0.00 0.00 190 5.5 31 33.1 1.054 959 16.0 860 2000 4.83

1.13 0.11 1.12 0.79 186 5.72 31 15 1.08 969 14.5 860 1997 8

1.70 0.27 1.99 1.46 1166 6.34 30 20 1.073 960 15.9 900 1995 9

0.57 0.12 0.74 0.31 826 5.9 30 14 1.073 969 14.5 808 1992 12.42

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Scatter PlotScatter Plot

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-2

0

2

4

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

t[2]

t[1]

All WaterfloodsColoured by Province

Series (Settings f or Prov ince)

MissingABSKSIMCA-P 11 - 2/21/2006 11:53:54 AM

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Loading PlotLoading Plot

-0.2

-0.1

-0.0

0.1

0.2

0.3

-0.3 -0.2 -0.1 -0.0 0.1 0.2 0.3

w*c

[2]

w *c[1]

Heavy Oil Waterfloods Loading Plot

R2X[1] = 0.115183 R2X[2] = 0.0468469

XY

Zone(AB)

Zone(SK 1)

Zone(SK 2)

Zone(SK 3)

Zone(SK 4)

Yrs WF

Yrs Primar

VRRCumVRRdeviati

YrsFUFU?(N)

FU?(Y)Viable?(A)

Viable?(V)Company(AD

Company(BACompany(BOCompany(CACompany(CA

Company(CN

Company(CN

Company(CO

Company(CRCompany(CR

Company(EN

Company(EN

Company(EX

Company(GR

Company(GR

Company(HA

Company(HUCompany(IMCompany(MECompany(MU

Company(NECompany(PA

Company(PE

Company(PECompany(PH

Company(PI

Company(PRCompany(PR

Company(QUCompany(RE

Company(SC

Company(SI

Company(TA

Company(TA

Company(WE

DomCoArea

Pay

Por.

WaterFVF

GORInit

Density

TempInit

PressInitDepth

Spacing AccCoredW

AvgGOR

CurGOR

ViscosityViscBB

PermVPerm

H/VPerm

Vis/Perm

VBB/Perm

DP

HALoldHALnew

NoPermMsmtProvince(A

Province(S

InjFrac

Conv'd HP

HDP

HI

HDI

Thruput

Start YrInjRate

GORChange

SI

SI Hyp

SI-FUHarSI-FUExp

SIMCA-P 11 - 5/7/2007 1:12:54 PM

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Variable Importance PlotVariable Importance Plot

0.0

0.5

1.0

1.5

2.0

10 20 30 40 50

VIP

[2]

SIMCA-P 11 - 11/14/2006 11:40:53 AM

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Previous StudiesPrevious Studies

Most important reservoir parameters for medium oils: permeability; heterogeneity; reservoir temperature; porosity

Most important reservoir parameters for heavy oils: viscosity/permeability; formation volume factor; production depth

Comparison between the importance of injection throughput rate and years to fill-up shows pressure maintenance might not be only benefit.

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Previous studies, continuedPrevious studies, continued

Operating parameters also differed for heavy vs medium oil waterfloods

Horizontal & directional production and injection wells very important for heavy oil waterflooding; reducing conversion of producers to injectors

Well spacing important to success of both types Screening criteria not very discriminating Categorization reasonably successful

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Tasks to Improve DatabaseTasks to Improve Database

Add more points (more waterfloods) Add more variables (more reservoir and

operating parameters) Make sure each y-variable (each success

measure) is as accurate as possible

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Add More Points, More VariablesAdd More Points, More Variables

Original plan Incorporate Alaska waterfloodsLooked at 11 Alaskan fields8 of 11 used gas and WAG injection with

waterflooding Actual 2007 tasks

Grew database from 83 to 168 waterfloodsNew category from Alberta EUB data

8 new parameters including: pumping, flowing wells, operating company

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Accuracy of Success Accuracy of Success MeasurementsMeasurements

Two Success measurements proven themselves SI=Secondary Production/OIP/Yrs WF*100% SI-FU=SI calc’d year after fill-up

SI Calculation: After WF start, oil is produced as both primary and secondary, want to fractionate the total Primary Secondary

SIR estimates Coleville 4.6% primary 19.5% enhanced

At WF start, 2.5% OOIP produced, leaving 2.1% primary

Primary = 2.1 2.1+19.5

Secondary = 19.5 2.1+19.5

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Primary Production MeasurementPrimary Production Measurement

Time

Pro

du

cti

on SI Calc’n

assumes constant production rate

Actual production rate declines non-linearly

WF Start

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Decline FittingDecline Fitting

Fit pre-waterflood production data with 3 types of equationsExponentialHarmonicHyperbolic

Production data were individual wells or groupings of similarly-behaved wells

38% of waterfloods could not be decline-fitted Hyperbolic generally best (68% of production

data)

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Decline Calculations:Decline Calculations:Original PlanOriginal Plan

020406080

100120140160180

0 0.5 1 1.5 2

Time

Oil

Rat

e

SISI-FU

SI-DeclineSI-FU Decline

EXPONENTIAL HARMONIC HYPERBOLIC

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Exponential vs HyperbolicExponential vs Hyperbolic vs Harmonic vs Harmonic

772,000

711,000

565,00037%

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Decline CalculationsDecline Calculations

SISI-FU

SI-DeclineSI-FU Decline

SISI-FUSI-ExpSI-HarSI-Hyp

SI-FU-ExpSI-FU-HarSI-FU-Hyp

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Examples of SIExamples of SI

Waterflood SI SI-Hyp

Aberfeldy 0.06 0.15

Senlac 0.85 0.71

Cactus Lake unit 2

0.51 0.31

Mantario North 1.66

Sibbald 0.77 0.78

Taber Glauc K 0.68 0.45

Viking-Kinsella X 1.98 1.27

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Multivariate PLS ModelsMultivariate PLS ModelsDataset Y Variables Q2Cum

All WFs - 2006 SI, WOR 80% 0.545

Heavy - 2006 SI, WOR 5% 0.704

Medium - 2006 SI, WOR 5% 0.532

All SI-Exp, SI 50%, SI-Hyp 10%,

SI-Har 10%

0.507

Heavy - Best SI-Hyp, SI-Exp,

SI-FU Hyp 10%, SI-FU Exp 10%

0.435

Heavy - Used SI-Hyp, SI 50%,

SI-FU-Har 10%, SI-FU-Exp 10%

0.397

Medium SI-Hyp, SI 50%,

SI-FU-Har 10%, SI-FU-Exp 10%

0.505

53 WFs

75 WFs

Better than 2007

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Waterfloods Newly AddedWaterfloods Newly Added to Database to Database

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-2

0

2

4

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7

t[2]

t[1]

Heavy Oil Waterfloods Score Plot

R2X[1] = 0.115183 R2X[2] = 0.0468469 Ellipse: Hotelling T2 (0.95) SIMCA-P 11 - 3/15/2007 11:42:38 AM

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Heavy vs Medium WFsHeavy vs Medium WFs

Importance of Permeability and Heterogeneity

0

0.5

1

1.5

2

2.5P

erm

'y

V P

erm

'y

H/V

Per

m'y

DP

HA

L ol

d

HA

L ne

w

HeavyMedium

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Important Parameters to Important Parameters to Heavy Oil WFsHeavy Oil WFs

Most Important Reservoir Parameters

0

0.2

0.4

0.6

0.8

1

1.2

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1.6

1.8

Pay

Vis

pre

d'd

/Perm

Pre

s

Depth

Por

V/P

erm

Are

a

FV

F

GO

R Init

Vis

c

HeavyMedium

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Effect of Net PayEffect of Net Pay

Heavy y = -0.2578Ln(x) + 0.811

R2 = 0.0756y = 0.9393e-0.2013x

R2 = 0.2613

0

0.5

1

1.5

2

0 2 4 6 8 10 12 14

Net Pay (m)

SI-H

yp

Old WFs

New WFs

Log. (Old WFs)

Expon. (New WFs)

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Viscosity Related ParametersViscosity Related Parameters

Viscosity and Viscosity/Permeability significant to success of heavy oil wfs and insignificant to medium oil wfs

Viscosity data for only 30 of 168 waterfloods Dataset restricted to these 30 waterfloods

Inconsistent results: about same level of importance heavy oil wfs, more important to medium oil wfs

Tested Viscosity predictor for Alaska reservoirs Poor prediction of viscosity Formula was important to medium oil wf success

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Injection Rate ParametersInjection Rate Parameters

Importance of Injection Volume and Rate Parameters

0

0.5

1

1.5

2

2.5

3

Thr

ough

put

VR

R c

um

Yea

rs to

FU

VR

R d

ev

Inj R

ate

FU

?

HeavyMedium

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VRRcum vs VRR Deviation from 1VRRcum vs VRR Deviation from 1

Heavy WFs: 13% VRR > 1.10 Medium: 32% VRR > 1.10 Heavy: 4% VRR > 1.25 Medium: 15% VRR > 1.25

Medium Oil Waterfloods

y = -0.3499Ln(x) + 0.611R2 = 0.0672

y = -0.0791Ln(x) + 0.4433R2 = 0.0277

0

0.5

1

1.5

2

2.5

0 1 2 3 4

VRR

SI-

Hyp

VRR Cum

VRR dev

Log. (VRR Cum)

Log. (VRR dev)Heavy Oil Waterfloods

y = -0.3973x2 + 1.2107x - 0.2857

R2 = 0.1168

y = 0.467e-2.5495x

R2 = 0.1958

-0.5

0

0.5

1

1.5

2

0 0.5 1 1.5 2

VRR

SI-

Hyp VRR Cum

VRR dev

Poly. (VRR Cum)

Expon. (VRR dev)

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Type of WellType of Well

Importance of Well Type

0

0.5

1

1.5

2

2.5

HP

HD

P

HD

I

HI

Con

vert

edIn

j'rs

Inj F

rac'

n

HeavyMedium

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Relationship of Converted Wells Relationship of Converted Wells To Years of WaterfloodingTo Years of Waterflooding

-0.2

-0.1

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0.1

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0.3

-0.3 -0.2 -0.1 -0.0 0.1 0.2 0.3

w*c

[2]

w *c[1]

Heavy Oil Waterfloods Loading Plot

R2X[1] = 0.115183 R2X[2] = 0.0468469 SIMCA-P 11 - 5/9/2007 11:57:14 AM

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Effect of Operating CompanyEffect of Operating Company

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t[2]

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Score Plot for All WaterfloodsHarvest-Operated WFs in Red

R2X[1] = 0.0688541 R2X[2] = 0.0430199 Ellipse: Hotelling T2 (0.95) SIMCA-P 11 - 5/9/2007 1:26:42 PM

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Score Contribution PlotScore Contribution PlotProvost Ll UU (Harvest)Provost Ll UU (Harvest)

-2

0

2

4

6

8

10

12

14

16

10 20 30 40 50 60 70 80 90 100

Sco

re C

ontr

ib(O

bs P

RO

V L

lUU

- A

vera

ge),

Wei

ght=

w*1

w*2

Score ContributionsProvost Lloydminster UU - Average

Mis

sing

SIMCA-P 11 - 5/9/2007 1:43:54 PM

InjectionThroughput

Harvest

HDIHDP

HAL

Spacing

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Prediction of SuccessPrediction of Success

Concept: want to predict waterflooding behaviour for pools currently on primary productionPredict success level Identify waterflood most similar to the primary

pool

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Success PredictionSuccess Prediction

Model has declined in quality Adding Alberta Reds sub-population enlarged

the scatter plot rather than increased the density

Plan to capitalize on sub-populations: make prediction from sub-population rather than whole group

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Heavy Oils – Training Set BlueHeavy Oils – Training Set Blue

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0

1

2

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-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7

t[2]

t[1]

Heavy Oil WaterfloodsTraining Set Shown in Blue

R2X[1] = 0.115183 R2X[2] = 0.0468469 Ellipse: Hotelling T2 (0.95) SIMCA-P 11 - 5/10/2007 9:23:07 AM

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Comparison of Prediction MethodComparison of Prediction Method

Prediction Set 7 WFs Compare Prediction

Blue Training Set vsHeavy WFs Training

Set Blue Training Set:

39-7-18=14 wfs

SI-Hyp predicted vs SI-Hyp

y = 0.4919x + 0.2793

R2 = 0.4538

y = 0.3274x + 0.3999

R2 = 0.0471-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 0.5 1 1.5

SI-Hyp

SI-H

yp p

redi

cted

Training Set 1

Training Set 2

Linear (Training Set 1)

Linear (Training Set 2)

Slope much < 1

Y Intercept > 0

R2 poor

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Primary PoolsPrimary Pools

Suffield Upper Mannville Y2Y (heavy) Chauvin South Lloydminster J (medium)

Barriers:Lack of viscosity dataNo coresAquifer support for Suffield Y2Y

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Primary Pool PredictionPrimary Pool Prediction

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Primary Pool PredictionPrimary Pool Prediction

Chauvin S Lloydminster JBest prediction SI=0.68Analogue WF Provost Sparky D & Edgerton

Lloydminster C&J Suffield Upper Mannville Y2Y

Best prediction SI=1.06Analogue WF Suffield Upper Mannville U

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ConclusionsConclusions

Collected data and measured success of 168 wfs Completed 3 tasks to improve predictability of last

year’s database83 wfs to 168 wfsAdded new parametersRefined success measurements using decline calc’ns

Last year’s database was unusually cohesive

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Conclusions, cont’dConclusions, cont’d

Appears to be poorer predictor

Effect of certain parameters less clear cut

Would be missing large chunk of how certain wfs behave

Approaching truer sense of how heavy oil wfs differ from medium wfs

More confidence in effect of certain parameters when they show impact no matter what database is

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Conclusions, cont’dConclusions, cont’d

Heavy Oil WFs Injection throughput rate Converted injectors Pumping wells Horizontal and

directional producers and injectors

Was fill-up achieved?

Net pay Ratio Viscosity/perm’y Porosity

Medium Oil WFs Injection throughput rate

Permeability Heterogeneity

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Conclusions, cont’dConclusions, cont’d

Prediction is poor I did it anyway:

Chauvin S Lloydminster JSI = 0.7Analogue WF = Provost Sp D or Edgerton Ll C&J

Suffield Upper Mannville Y2YSI = 1.1Analogue = Suffield Upper Mannville U

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Recommendations for Recommendations for Upcoming ResearchUpcoming Research

Fill in blank spots of database Re-examine decline based measurements New parameters to add: injectivity data; pressure

data; geological data Collect field samples - quality injection water;

presence natural surfactants; CO2 injection; salinity of injection water

Revise prediction procedure so PCA is performed first, then PLS

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Thank you for your attention.

Any questions????