smart science solutions Measuring the Success
Transcript of smart science solutions Measuring the Success
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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
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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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-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
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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
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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
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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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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
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erm
'y
V P
erm
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H/V
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m'y
DP
HA
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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
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d'd
/Perm
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s
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erm
Are
a
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F
GO
R Init
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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
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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
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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
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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
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SI-
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VRR dev
Poly. (VRR Cum)
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Type of WellType of Well
Importance of Well Type
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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
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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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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)
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Sco
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ontr
ib(O
bs P
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lUU
- A
vera
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Wei
ght=
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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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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
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SI-H
yp p
redi
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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????