Uso de modelos de simulação para estimar fator de recuperação … · 2017. 4. 3. · Uso de...
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Uso de modelos de simulação para estimar fator de recuperação Use of reservoir simulation models to estimate recovery factor Denis J. Schiozer March – 2017 [email protected] UNICAMP/CEPETRO/UNISIM
Transcript of Uso de modelos de simulação para estimar fator de recuperação … · 2017. 4. 3. · Uso de...
Uso de modelos de simulação para estimar fator de recuperação
Use of reservoir simulation models to
estimate recovery factor
Denis J. SchiozerMarch – 2017
UNICAMP/CEPETRO/UNISIM
2
1) Recovery factor (RF) study
□ literature review
2) Model based decisions
□Closed loop
□NPV x RF (performance indicators)
3) Importance of R&D
Outline
3
Based on study - literature review
Hard to find reliable data
□Partial description of reservoir characteristics
(specific places/regional/ missing data)
Definition RF
□ RF = Np / VOOIP
□ RF max = (Np + Expected production) / VOOIP
Recovery factor study
4
Maximum reported FR
Recovery factor study
R 2 = 0.6304
0%
10%
20%
30%
40%
50%
60%
70%
80%
0% 10% 20% 30% 40% 50% 60% 70% 80%
F R má x imo
F R
L inear (F R )
5
66 companies
Recovery factor study
R2 = 0.0075
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
1 11 21 31 41 51 61 71
E mpresa
F R
L inear (F R )
6
Place
Recovery factor study
R2 = 0.0169
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
0 2 4 6 8 10 12 14 16
L oc a l
F R
L inear (F R )
Código Local(Região)
1 Alaska 8 Colorado
2 Texas 9 Mar do Norte/Noruega
3 Califórnia 10 Golfo do México
4 Louisiana 11 México
5 Wyoming, Montana 12 Canadá
6 New México 13 Illinois
7 Arkansas 14 Argélia
7 Florida, Alabama, Kansas, Mississipi 15 Angola
7 Oklahoma 16 Nigéria
7
Year of initial production
Recovery factor study
R2 = 0.0021
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
1890 1905 1920 1935 1950 1965 1980 1995 2010 2025
Ano
F R
L inear (F R )
8
Reported original oil in place
Recovery factor study
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
0 3 5 8 10 13 15
B ilhõesOrig ina l-Oil-In P la c e E stima do (bbl)
F R
L inear (F R )
R2 = 0.0512
9
Onshore / Offshore (water depth)
Recovery factor study
R2 = 0.013
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
0 50 100 150 200 250 300 350 400
L â mina d´á g ua (m)
F R
L inear (F R )
10
Average porosity
Recovery factor study
R2 = 0.0165
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
0 10 20 30 40 50
P orosida de (%)
F R
L inear (F R )
11
Average permeability
Recovery factor study
R2 = 0.0158
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
0 1000 2000 3000 4000
P ermea bilida de (mD)
F R
L inear (F R )
12
API gravity
Recovery factor study
R2 = 0.0758
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
5 15 25 35 45 55
AP I
F R
L inear (F R )
13
Estimation of recovery factor is complex
□Reservoir (rock/fluid) properties
□Economic model
□Tax regime
□Investments / objective
Literature examples:
□high variability
□hard to find strong correlations
Important to consider particularities of each case
Remarks - Part 1
Closed Loop Reservoir Management and
Development (model based decisions)
Simulation
models
High fidelity
models
Decision
Analysis
Data assimilation
algorithms
Geology, seismic,
well logs, well
tests, fluid
properties…
Data measurement
noise
Operational Noise Prod. Strategy Production
Fluid Movement
Predicted
output
Measured
output
PastFuture
Objective
Prod.
Strat.
Slide 15
Big loop
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Objective (Objective-function)
Project variables (G1)
□ Production system (platform, wells, ...)
Control variables (G2)
□ Field Operation
Field revitalization variables (G3)
□ IOR/EOR
It is important to
□ IOR/EOR in the development phase
□ consider uncertainties (rock/fluid/economic/operational …)
□ Integration with economic studies
□ Integration with production facilities (MIP)
Reservoir Development / Managment
17
NPV x RF – average economic model
Best tested NPV strategy (14 wells)
Best tested RF strategy (22 wells)
Example 1 – NPV vs RF
-100.0
0.0
100.0
200.0
300.0
400.0
500.0
0.40 0.42 0.44 0.46 0.48 0.50 0.52
FR
VP
L (
Mil
hõ
es U
S$)
18
3 economic oil prices (normalized)
Example 1 – NPV vs RF
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0.0 0.2 0.4 0.6 0.8 1.0 1.2
FR norm
VP
L n
orm
alto
médio
baixo
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Influence of Investment on NPV and RF
Example 1 – NPV vs RF
0.40
0.42
0.44
0.46
0.48
0.50
0.52
800 900 1000 1100 1200
VPi (Milhões US$)
FR
(M
ilh
õe
s m
³)
-100
0
100
200
300
400
500
VP
L (
Mil
hõ
es
US
$)
Np
VPL
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Production strategy - polymer flooding
Optimization considering 3 approaches
A. Using NPV as objective-function;
B. Using RF as objective-function;
C. Using NPV as OF to select Investments and RF to optimize production
Optimization process divided in 2 phases
1. variables with influence in investment (project variables)
2. variables without influence in investment (control variables)
Example 2 – Polymer Flooding
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Study 2 – Polymer Flooding
Approach Objective
Function
NPV
(106 USD)
Np
(106 m³)
Wp
(106 m³)
RF Winj
(106 m³)
NProd NInj
Total
Investments
(106 USD)
A NPV 1456 58 59 0.19 77 11 4 2457
B RF -607 84 337 0.27 398 45 8 5322
C NPV+RF 1275 60 97 0.20 112 11 4 2491
Approach A
Phase 1 Phase 2
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Study 2 – Polymer Flooding
Additional informationEOR planned since the beginning yields much better results
Very different solutions
Example 3 - Uncertanties
Uncertainties 214 scenarios:- Rock / fluid- Economic- Operational
Red – 214 possible scenariosBlue – best strategy for base caseBlack – representative models
Example 3 - Uncertanties
9 representative models (possible scenarios)
9 different strategies NPV (billions)
E1 E2 E3 E4 E5 E6 E7 E8 E9
RM1 2.24 1.91 0.79 1.33 1.42 0.96 1.48 1.32 1.72RM2 1.64 2.30 0.78 1.84 2.01 0.87 1.54 1.77 1.67RM3 0.80 1.04 1.64 0.97 0.65 1.14 1.06 1.05 0.57RM4 2.00 1.61 1.10 2.50 1.99 1.24 1.18 2.19 2.01RM5 1.36 1.70 0.82 1.68 2.41 0.87 1.31 1.55 1.72RM6 1.10 1.07 0.9.3 0.51 0.39 1.91 0.91 0.71 1.04
RM7 0.62 0.74 0.26 0.62 0.60 0.53 1.48 0.54 0.40
RM8 1.75 1.82 1.25 1.82 1.95 1.23 1.25 2.51 1.69RM9 2.27 2.18 1.08 1.67 2.07 1.56 1.24 2.13 2.97
EMV 1.54 1.56 0.97 1.52 1.53 1.14 1.27 1.47 1.60
Example 3 - Uncertanties
9 representative models (possible scenarios)
9 different strategies NPV (billions)
E1 E2 E3 E4 E5 E6 E7 E8 E9
RM1 2.24 1.91 0.79 1.33 1.42 0.96 1.48 1.32 1.72RM2 1.64 2.30 0.78 1.84 2.01 0.87 1.54 1.77 1.67RM3 0.80 1.04 1.64 0.97 0.65 1.14 1.06 1.05 0.57RM4 2.00 1.61 1.10 2.50 1.99 1.24 1.18 2.19 2.01RM5 1.36 1.70 0.82 1.68 2.41 0.87 1.31 1.55 1.72RM6 1.10 1.07 0.9.3 0.51 0.39 1.91 0.91 0.71 1.04
RM7 0.62 0.74 0.26 0.62 0.60 0.53 1.48 0.54 0.40
RM8 1.75 1.82 1.25 1.82 1.95 1.23 1.25 2.51 1.69RM9 2.27 2.18 1.08 1.67 2.07 1.56 1.24 2.13 2.97
EMV 1.54 1.56 0.97 1.52 1.53 1.14 1.27 1.47 1.60
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Uncertainties
□Need to change production strategy (IOR)
□Control variables (G2)
□Revitalization variables (G3)
□Concept of risk must be considered
Robustness (ex. G3 in advance)
Flexibility (ex. ICV)
Information (ex. 4DS)
MIP (integration with production facilities)
Example 3
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Different objectives: company – agency
Many solutions in-between
□Companies
Additional investments
□ IOR/EOR
□ Flexibilities (ICV, development in stages, ...)
□ Information (4DS, ...)
Additional production time
□ANP
Tax relief / other benefits
Remarks - Part 2
31
Field development and management is a very complex process□People
PRH / research projects
□Technology R&D Innovation
Cooperation Companies/Universities
□ Investments Information (4DS), flexibility (ICV), laboratory experiments,
R&D
Long term projects
Investment in people and research is one way to increase the recovery factor of fields
Part 3 – R&D - Universities
32
UNICAMP
ANP
PETROBRAS
BG/SHELL
FCMG
STATOIL
FAPESP/CAPES/CNPq
Acknowledgments
Uso de modelos de simulação para estimar fator de recuperação
Use of reservoir simulation models to
estimate recovery factor
Denis J. SchiozerMarch – 2017
UNICAMP/CEPETRO/UNISIM
34
Production Sharing (PS) vs Concession (Royaties&Income tax
(R&T)
Reservoir Example 5.4 billion m3
220 different strategies
Production up to zero NCF (net cash flow)
4 economic scenarios (1 more opt. / 4 more pes.)
Reservoir simulation study 3
-2000
-1500
-1000
-500
0
500
1000
1500
0 5 10 15 20 25 30
Mil
lio
n (
US
D)
Production time (years)
NCF
NPV
35
Reservoir simulation study 3
2,7
2,11,8
1,4
2,82,5
2,11,9
0,0
0,5
1,0
1,5
2,0
2,5
3,0
1 2 3 4
Np
(b
ilio
n m
3)
Economic Scenarios
Total Number of
Wells
Scenarios
1 2 3 4
Production Sharing 625 221 221 60
Royalty and Tax 625 255 221 221
RF 52%
RF 26%
36
Reservoir simulation study 3
90
40
21
8
100
52
36
23
0
20
40
60
80
100
120
1 2 3 4
NP
V (b
illi
on
US
D)
Economic Scenarios
PSC R&T
66
33 31
11
73
45
34 34
0
20
40
60
80
1 2 3 4
Investm
ents
(b
illio
n U
SD
)
Economic Scenarios
37
Reservoir simulation study 3
350
205
167
81
361
226
169
135
0
100
200
300
400
1 2 3 4
GR
(b
illio
ns U
SD
)
Economic Scenarios
PSC R&T
GR – govern revenue
38
Reservoir simulation study 3
16%
35%27%
22% 19%
34%29%
19% 19%
34%29%
18% 22%
33%
30%
15%
20%
37%23%
20%22%
39%
22%
16%
20%
34%20%
14%
12%19%
35%22%
20%
5%
24%
43%
22%
11%
22%
39%
20%
10%
8%
26%
45%
21%
9%
14%
25%
12%5%
44%
PSC
R&T
PSC (%Denominator: TR obtained from strategies
optimized for R&T)
1 2 3 4Scenario
Absence of Revenue Generation
Royalties Taxes
Company Take
Special Participation
Government Share
Uso de modelos de simulação para estimar fator de recuperação
Use of reservoir simulation models to
estimate recovery factor
Denis J. SchiozerMarch – 2017
UNICAMP/CEPETRO/UNISIM
40
Estimation of recovery factor is complex
□Reservoir (rock/fluid) properties
□Economic model
□Tax regime
□ Investments / objective
Literature examples: high variability – hard to find strong correlations
Numerical Examples
□1 and 2) Maximum NPV x Maximum RF
□3) influence of tax regime
□4) influence of uncertainties
Final Remarks
