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Transcript of Presentazione SABATINO_eni
www.eni.it
Uncertainty analyses for thermal development in heavy oil fields
Author: Riccardo Sabatino
San Donato Milanese, 19-20 October 2011
Master in Petroleum Engineering 2010-2011
2San Donato Milanese, 19-20 October 2011
Author
Ph.D. Ing. Riccardo Sabatino
Division Exploration & Production
Dept. TENC/MOGI
Company Tutors
Ing. Filomena M. Contento
Dott. Ivan Maffeis
Dott. Alice Tegami
University Tutor
Prof. Ing. Francesca Verga
Stage Subject Uncertainty analyses for thermal development in
heavy oil fields
Master In Petroleum Engineering 2010-2011
3
Project Scope
Introduction
Thermal EOR techniques
Case study
Operating parameter definition
Risk Analysis
Conclusions
List of Content
Stage Subject Uncertainty analyses for thermal development in
heavy oil fields
4
Project Scope
Uncertainty analyses for thermal development in heavy oil fields
Study the feasibility of thermal EOR techniques for the development of a real
extra-heavy oil Venezuelan field
Select the best operating parameters for steamflooding and electrical heating
Perform a Risk Analysis, highlighting the main uncertainties on reservoir
development
Compare two Risk Analysis workflows: Monte Carlo vs. Experimental Design and
Response Surface Modelling
Study the feasibility of thermal EOR techniques for the development of a real
extra-heavy oil Venezuelan field
Select the best operating parameters for steamflooding and electrical heating
Perform a Risk Analysis, highlighting the main uncertainties on reservoir
development
Compare two Risk Analysis workflows: Monte Carlo vs. Experimental Design and
Response Surface Modelling
5
Project Scope
Introduction
Thermal EOR techniques
Case study
Operating parameter definition
Risk Analysis
Conclusions
List of Content
Stage Subject Uncertainty analyses for thermal development in
heavy oil fields
6
Introduction
Heavy Oil Classification
Heavy Oil
°API 10-20
10-20 cP
Extra-Heavy Oil
°API <10
100-10,000 cP
Tar Sands and Bitumen
°API 7-12
>10,000 cP
Low gravities and high viscosity reduce the mobility within a reservoir.
7
Introduction
Heavy Oil Worldwide
8
Project Scope
Introduction
Thermal EOR techniques
Case study
Operating parameters definition
Risk Analysis
Conclusions
List of Content
Stage Subject Uncertainty analyses for thermal development in
heavy oil fields
9
Thermal EOR Techniques
Thermal techniques are based upon the oil viscosity reduction due to a thermal
power input
Typical thermal EOR techniques
adopted in oil & gas industry:
0.1
1
10
100
1000
10000
100000
100 150 200 250 300 350 400
Visc
osity
[cP]
Temperature [°F]Temperature [°F]Vi
scos
ity [c
P]
CSS (Cyclic Steam Stimulation)
Steamflooding
SAGD (Steam Assisted Gravity Drainage)
In-situ combustion
Electrical Heating
10
Steamflooding
Steam is injected through injection wells. Steam bank spreads away and begins to
condense in hot water. Heat is transferred from steam to oil reducing its viscosity.
Thermal EOR Techniques
11
A heating element is run inside the wellbore; the electric current flowing in the
cable produces heat according to Joule’s law.
Downhole electrical heating
Thermal EOR Techniques
Control Panel
Downhole heater
Producer Well
12
Project Scope
Introduction
Thermal EOR techniques
Case study
Operating parameters definition
Risk Analysis
Conclusions
List of Content
Stage Subject Uncertainty analyses for thermal development in
heavy oil fields
13
Case Study
Approx. 424 km2
About 20 appraisal wells
No production data
Average porosity: 0.29
Average permeability: 4000 mD
Average net-to-gross: 0.64
Oil viscosity: 2000-4500 cP
Reservoir zones thickness: 50 ft
8-10 °API extra-heavy oil
Hydraulically separated units
Depth [ft]
Criticalities
No fluid samples available
Major Issues
High viscosity
14
Case Study
1st phase: Cold Production (RF=6-8%)
2nd phase: EOR techniques to enhance recovery factor
Horizontal 1000 m long wells, 400 m spaced, grouped in clusters
Producers reconverted into injectors (according to pattern)
Development Plan
Cluster configuration
Steamflooding scheme
15
Project Scope
Introduction
Thermal EOR techniques
Case study
Operating parameters definition
Risk Analysis
Conclusions
List of Content
Stage Subject Uncertainty analyses for thermal development in
heavy oil fields
16
Operating parameter definition
Due to runtime, a smaller “thermal” sector has been extracted from full field model for dynamic analyses
Steamflooding screening criteria → Connected Net Pay Map Connected pay thickness > 15-20 ft Permeability > 1000 mD
The sector is completely included in a single unit
Thermal sectorDepth [ft]
Connected Net Pay [ft]
Approx. 2.3 km2 Number of active gridblocks: 14,096 Block dimensions: 81 x 81 x 22 ft3 Average initial pressure: 593 psi Average porosity: 0.319 Average permeability: 5244 mD Average net-to-gross: 0.72 Average oil viscosity: 3279 cP Average reservoir temperature: 117 °F
17
Sensitivity analyses Injection pressure (BHP inj., psi) [650-750 psi] Steam Rate (STW, bbl/d) [1500-2000 bbl/d]
aimed at maximizing and anticipating production,
minimizing the cumulative steam-oil-ratio [CSOR],
which is defined as the ratio between injected
steam (equivalent water volume) and produced oil
(economical threshold 4.0).
CSOR is the main parameter affecting the success
or failure of a steamflooding project.
Steamflooding
Injector 1
Injector 2
Producer 1
Producer 2
Producer 3
Producer 4
Producer 5
849,000 850,000 851,000 852,000 853,000 854,000 855,000 856,000
849,000 850,000 851,000 852,000 853,000 854,000 855,000 856,000
3,05
6,00
03,
057,
000
3,05
8,00
03,
059,
000
3,06
0,00
0
3,056,0003,057,000
3,058,0003,059,000
3,060,0003,061,000
3,062,000
0.00 1015.00 2030.00 feet
0.00 0.25 0.50 0.75 1.00 km
31
2,955
5,879
8,803
11,727
14,651
17,575
20,499
23,423
26,347
29,271
Permeability [mD]
Producer 1
Injector 1
Producer 2
Injector 2
Producer 3
Operating parameter definition
18
SteamfloodingSensitivity on STW (BHP inj. = 700 psi)
Time (Date)
Cum
ulative Oil SC
(bbl)
2015 2020 2025 2030 2035 20400.00e+0
1.00e+6
2.00e+6
3.00e+6
4.00e+6
5.00e+6
6.00e+6
1500 bbl/d1600 bbl/d1700 bbl/d1800 bbl/d1900 bbl/dCold
At the beginning, cold
production is more
convenient than
steamflooding (in fact two
out of five wells are
reconverted into injectors).
Operating parameter definition
X-point
2016start of steamflooding
2012start of simulation
2035end of risk analysis simulation
Steam Rate 1500-2000 bbl/d
+98%
cold
Cum
. Oil
Prod
uctio
n [b
bl]
Time [date]
Sensitivity on STW (BHP inj. = 700 psi)
Time (Date)
Cum
ulative Oil SC
(bbl)
2015 2020 2025 2030 2035 20400.00e+0
1.00e+6
2.00e+6
3.00e+6
4.00e+6
5.00e+6
6.00e+6
1500 bbl/d1600 bbl/d1700 bbl/d1800 bbl/d1900 bbl/dCold
CSOR vs. inj. pressure (STW=1800 bbl/d)
CSOR (inj. pres. 700 psi)CSOR (inj. pres. 750 psi)CSOR (inj. pres. 650 psi)
Time (Date)
Steam O
il Ratio C
um SC
TR (bbl/bbl)
2015 2020 2025 2030 2035 20400.0
1.0
2.0
3.0
4.0
5.0
6.0
CSOR=4
19
Steamflooding
Operating parameter definition
2016start of steamflooding
2012start of simulation
2035end of risk analysis simulation
Injection pressure 650-750 psi
CSO
R [b
bl/b
bl]
Time [date]
20
Steamflooding - Summary
Producers BHP min: 200 psi max surface liquid rate: 3150 bbl/d minimum surface oil rate: 50 bbl/d
Injectors Injection pressure: 700 psi Injection Temperature: 502 °F Steam Rate (STW): 1800 bbl/d Steam Quality: 0.8
Operating parameter definition
Steamflooding sensitivity
Operating parameter Reservoir response
Injection Pressure ↑ CSOR ↑
Injection Pressure ↑ X-point ↓
Steam Rate ↑ Cum. Oil Production slightly ↑
Steam Rate ↑ CSOR ↑
21
After 5 years After 10 years
849,000 850,000 851,000 852,000 853,000 854,000 855,000 856,000
849,000 850,000 851,000 852,000 853,000 854,000 855,000 856,000
3,05
6,00
03,
057,
000
3,05
8,00
03,
059,
000
3,06
0,00
0
3,056,0003,057,000
3,058,0003,059,000
3,060,0003,061,000
3,062,000
0.00 1015.00 2030.00 feet
0.00 0.25 0.50 0.75 1.00 km
116
155
194
232
271
309
348
387
425
464
503
Temperature [F]
Producer 1
Injector 1
Producer 2
Injector 2
Producer 3
849,000 850,000 851,000 852,000 853,000 854,000 855,000 856,000
849,000 850,000 851,000 852,000 853,000 854,000 855,000 856,000
3,05
6,00
03,
057,
000
3,05
8,00
03,
059,
000
3,06
0,00
0
3,056,0003,057,000
3,058,0003,059,000
3,060,0003,061,000
3,062,000
0.00 1015.00 2030.00 feet
0.00 0.25 0.50 0.75 1.00 km
116
155
194
232
271
309
348
387
425
464
503Producer 1
Injector 1
Producer 2
Injector 2
Producer 3Temperature [F]
Operating parameter definition
Steamflooding – Temperature profiles
Grid sensitivity
Time (day)C
umulative O
il SC (bbl)
0 2,000 4,000 6,000 8,000 10,0000.00e+0
2.00e+5
4.00e+5
6.00e+5
8.00e+5
1.00e+6
1.20e+6
k=3 ft, j=3 ft k=3 ft, j=9 ftk=3 ft, j=27 ft k=7 ft, j=3 ftk=7 ft, j=9 ft k=7 ft, j=27 ft
22
Electrical heating subsector – Grid refinement
Injector 1
Injector 2
Producer 1
Producer 2
Producer 3
Producer 4
Producer 5
849,000 850,000 851,000 852,000 853,000 854,000 855,000 856,000
849,000 850,000 851,000 852,000 853,000 854,000 855,000 856,000
3,05
6,00
03,
057,
000
3,05
8,00
03,
059,
000
3,06
0,00
0
3,056,0003,057,000
3,058,0003,059,000
3,060,0003,061,000
3,062,000
0.00 1015.00 2030.00 feet
0.00 0.25 0.50 0.75 1.00 km
31
2,955
5,879
8,803
11,727
14,651
17,575
20,499
23,423
26,347
29,271
Local Grid refinement is a major issue in Electrical Heating simulations
Permeability [mD]
Grid size: 80 x 80 x 22 ft3
Operating parameter definition
Time [days]
Cum
. Oil
Prod
uctio
n [b
bl]
Grid sensitivity
Time (day)
Cum
ulative Oil SC
(bbl)
0 2,000 4,000 6,000 8,000 10,0000.00e+0
2.00e+5
4.00e+5
6.00e+5
8.00e+5
1.00e+6
1.20e+6
k=3 ft, j=3 ft k=3 ft, j=9 ftk=3 ft, j=27 ft k=7 ft, j=3 ftk=7 ft, j=9 ft k=7 ft, j=27 ft
23
R15C04SB10LL-02
R15C04SB10LL-06
851,000 852,000 853,000 854,000
851,000 852,000 853,000 854,000
3,05
9,00
03,
060,
000
3,06
1,00
0
3,059,0003,060,000
3,061,0003,062,000
0.00 470.00 940.00 feet
0.00 145.00 290.00 meters0.00 470.00 940.00 feet
0.00 145.00 290.00 meters
48
2,092
4,136
6,180
8,225
10,269
12,313
14,357
16,402
18,446
20,490
Permeability [mD]
R15C04SB10LL-02
R15C04SB10LL-06
853,100 853,200 853,300 853,400 853,500 853,600 853,700 853,800 853,900
853,100 853,200 853,300 853,400 853,500 853,600 853,700 853,800 853,900
3,06
0,60
03,
060,
700
3,06
0,80
03,
060,
900
3,06
1,00
03,
061,
100
3,060,6003,060,700
3,060,8003,060,900
3,061,0003,061,100
3,061,200
0.00 105.00 210.00 feet
0.00 35.00 70.00 meters
Grid size: 80 x 80 x 22 ft3
Grid size: 80 x 7 x 7 ft3
Grid size: 80 x 27 x 7 ft3
Electrical heating subsector – Grid refinement
Operating parameter definition
Sensitivity on Power Input
Time (day)
Cum
ulative Oil SC
(bbl)
0 2,000 4,000 6,000 8,0000.00e+0
2.00e+5
4.00e+5
6.00e+5
8.00e+5
1.00e+6
1.20e+6
Cold100 W/m150 W/m200 W/m
24
Electrical heating subsector – Power Input
200 W/m: +12.2%
@10 yrs
(39.2 kWh/bbl)
150 W/m: +9.7%
@10 yrs
(30.1 kWh/bbl)
Operating parameter definition
Time [days]
Cum
. Oil
Prod
uctio
n [b
bl]
Sensitivity on Power Input
Time (day)
Cum
ulative Oil SC
(bbl)
0 2,000 4,000 6,000 8,0000.00e+0
2.00e+5
4.00e+5
6.00e+5
8.00e+5
1.00e+6
1.20e+6
Cold100 W/m150 W/m200 W/m
2022end of simulations
Power Input 100-200 W/m
+12%
25
Electrical heating subsector – Summary
Power Input: 150 W/m BHP min: 200 psi max surface liquid rate: 3150 bbl/d minimum surface oil rate: 50 bbl/d
Operating parameter definition
Electrical Heating sensitivity
Operating parameter Reservoir response
Power Input ↑ Cum. Oil Production ↑
Power Input ↓ Efficiency ↑
Block Dimension ↑ Cum. Oil Production Negligible
Block Dimension ↓ Runtime ↑↑
26
Project Scope
Introduction
Thermal EOR techniques
Case study
Operating parameters definition
Risk Analysis
Conclusions
List of Content
Stage Subject Uncertainty analyses for thermal development in
heavy oil fields
27
Monte Carlo Workflow1. Uncertainty identification
2. Stochastic Sampling
3. Run N simulations
4. Stabilization check
5. Sensitivity Analysis
6. Analysis of results
2
4 6
Risk RegisterX1=Contacts
X2=PVTX3=Aquifer size
…
1
5
3
Risk Analysis
28
ED & RSM Workflow
Risk Analysis
1. Uncertainty identification
2. Define N Experiments
3. Run N simulations
4. Build and validate proxy
5. Monte Carlo Sampling
6. Analysis of results
6
2
Risk RegisterX1=Contacts
X2=PVTX3=Aquifer size
…
13
y=a0+a1x1+a2x2+a3x1x2+a4x2x3++a5x1x3+a6x1x2x3+...
4
29
Uncertainty identification
OOIP
Cold ProductionNp @2035
SteamfloodingNp @2035, CSOR @2035
Steamflooding vs. Cold production @2035
Electrical HeatingNp@2022
Electrical Heating vs. Cold Production @2022
List of Content
Risk Analysis Outline
30
Uncertainty Identification
Risk Analysis
ID Uncertainty Distribution Range
VISO Oil viscosity Discrete (equiprobable) [2274-4000-7139] cP
VOLMOD Pore Volume multiplier User assigned (cumulative) [0.8-1.1]
MODPERM Absolute Permeability multiplier Normal =1, =0.13, [0.75-1.25]
THCONR Rock thermal conductivity Uniform [35-85] Btu/day-ft-F
KRWMAX Water relative permeability @ endpoint Uniform [0.2-0.9]
NW Water Exponent in Corey's Equation Uniform [1-5]
31
Risk Analysis – Monte Carlo WorkflowOil in place
Original Oil In Place [bbls]
P10 P50 P90 mean st. dev.
2.00E+07 2.24E+07 2.40E+07 2.21E+07 1.59E+06
Stabilization Check Frequency and Cumulative Distribution
0.00E+00
5.00E+06
1.00E+07
1.50E+07
2.00E+07
2.50E+07
3.00E+07
0 50 100 150 200
OO
IP [b
bls]
number of runs
P10
P50
P90
Mean
St. Dev.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
5
10
15
20
25
30
Cum
ulati
ve P
roba
bilit
y D
istr
ibuti
on
Freq
uenc
y D
istr
ibuti
on
OOIP [bbls]
Frequency
Cumulative %
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
2
4
6
8
10
12
14
16
18
Cum
ulati
ve P
roba
bilit
y D
istr
ibuti
on
Freq
uenc
y D
istr
ibuti
on
Cumulative Oil Production @2035 [bbls]
Frequency
Cumulative %
0.0
0.0
2.3
3.9
4.4
31.6
57.8
0 10 20 30 40 50 60 70
THCONR
KRWMAX
Unexplained
MODPERM
NW
VOLMOD
VISO
% Impact
32
Risk Analysis – Monte Carlo Workflow Cold production: Np @ 2035
Cumulative Oil Production @2035 [bbls]
P10 P50 P90 mean st. dev.
1.81E+06 2.18E+06 2.52E+06 2.17E+06 2.78E+05
0.00E+00
5.00E+05
1.00E+06
1.50E+06
2.00E+06
2.50E+06
3.00E+06
0 20 40 60 80 100
Cum
. Oil
Prod
uctio
n @
203
5 [b
bls]
number of runs
P10
P50
P90
Mean
St. Dev
Stabilization Check
SensitivityAnalysis
Frequency and Cumulative Distribution
33
Risk Analysis – Monte Carlo Workflow
Cold production: Cum. Oil Profiles
Base Case
All profiles 2035
Cum
. Oil
Prod
uctio
n
2035
Cum
. Oil
Prod
uctio
n
Time
Time
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Cum
ulati
ve P
roba
bilit
y D
istr
ibuti
onCumulative Oil Production @2035
Monte CarloRSM
34
Risk Analysis – ED&RSM Workflow
Cold production: Np @ 2035Proxy Validation
Cumulative Oil Production @2035 [bbls]
P10 P50 P90 mean st. dev.
1.78E+06 2.18E+06 2.52E+06 2.17E+06 2.79E+05
√
100 runs
53 runs
VOLMOD
VISO
MODPERM
NW
Proxy terms Monte Carlo vs. ED&RSM
Pred
icte
d [b
bls]
Observed [bbls]
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
5
10
15
20
25
Cum
ulati
ve P
roba
bilit
y D
istr
ibuti
on
Freq
uenc
y D
istr
ibuti
on
Cumulative Oil Production @2035 [bbls]
Frequency
Cumulative %
0.00E+00
1.00E+06
2.00E+06
3.00E+06
4.00E+06
5.00E+06
6.00E+06
7.00E+06
0 50 100 150 200
Cum
. Oil
Prod
uctio
n @
203
5 [b
bls]
number of runs
P10
P50
P90
Mean
St. Dev.
35
Risk Analysis – Monte Carlo Workflow Steamflooding: Np @ 2035
Cumulative Oil Production @2035 [bbls]
P10 P50 P90 mean st. dev.
2.10E+06 4.29E+06 5.55E+06 3.98E+06 1.32E+06
Stabilization Check
SensitivityAnalysis
Frequency and Cumulative Distribution
0.1
0.4
1.5
5.4
6.7
8.6
77.3
0 10 20 30 40 50 60 70 80 90
THCONR
KRWMAX
VOLMOD
MODPERM
Unexplained
NW
VISO
% Impact
36
Risk Analysis – Monte Carlo Workflow
Steamflooding: Cum. Oil Profiles
2035
Cum
. Oil
Prod
uctio
n
Time
2035
Cum
. Oil
Prod
uctio
nTime
Base Case
All profiles
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Cum
ulati
ve P
roba
bilit
y D
istr
ibuti
on
Cumulative Oil Production @2035
Monte Carlo
RSM
37
Risk Analysis – ED&RSM Workflow
Steamflooding: Np @ 2035
√
P50 -3.90% 200 runs
88 runs
Observed [bbls]
MODPERM × VOLMOD
VISO × VOLMOD
NW
MODPERM2
MODPERM × VISO
KRWMAX
KRWMAX2
Proxy terms Monte Carlo vs. ED&RSM
Cumulative Oil Production @2035 [bbls]
P10 P50 P90 mean st. dev.
2.56E+06 4.12E+06 5.26E+06 3.98E+06 1.07E+06
Proxy Validation
Pred
icte
d [b
bls]
38
Risk Analysis – Monte Carlo Workflow Steamflooding: CSOR @ 2035
CSOR @2035 [bbl/bbl]
P10 P50 P90 mean st. dev.
0.757 2.836 4.205 2.703 1.255
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 50 100 150 200
CSO
R @
203
5 [b
bl/b
bl]
number of runs
P10
P50
P90
Mean
St. Dev.
1
3
3
4
10
21
59
0 10 20 30 40 50 60 70
THCONR
KRWMAX
VOLMOD
MODPERM
Unexplained
VISO
NW
% Impact
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
5
10
15
20
25
30
0.58 0.89 1.19 1.49 1.80 2.10 2.40 2.71 3.01 3.31 3.62 3.92 4.22 4.53 More
Cum
ulati
ve P
roba
bilit
y Dis
trib
ution
Freq
uenc
y D
istr
ibuti
on
CSOR @2035 [bbl/bbl]
Frequency
Cumulative %
Stabilization Check
SensitivityAnalysis
Frequency and Cumulative Distribution
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Cum
ulati
ve P
roba
bilit
y Di
strib
ution
Cumulative Oil Production @2035
Steamflooding
Cold
39
Risk Analysis
Steamflooding vs. Cold ProductionED&RSM Workflow
@P50 Steamflooding gives
+89% Cumulative Oil Recovery
Monte Carlo Workflow
@P50 Steamflooding gives
+96% Cumulative Oil Recovery
In both cases, Steamflooding
can be an effective way to
enhance oil recovery
+96%
Oil viscosity is the most impacting unknown
@P10 the economic convenience should be properly evaluated
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Cum
ulati
ve P
roba
bilit
y D
istr
ibuti
onCumulative Oil Production @2022
Monte Carlo
RSM
40
Risk Analysis Electrical Heating: Np @ 2022
100 runs
41 runs
√
Cumulative Oil Production @2022 [bbls] (Monte Carlo)
P10 P50 P90 mean st. dev.
2.11E+06 2.41E+06 2.70E+06 2.41E+06 2.22E+05
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
2
4
6
8
10
12
14
16
18
Cum
ulati
ve P
roba
bilit
y Dis
trib
ution
Freq
uenc
y Dis
trib
ution
Cumulative Oil Production @2022 [bbls]
Frequency
Cumulative %
2.6
7.3
35.6
54.5
0 10 20 30 40 50 60
MODPERM
Unexplained
VISO
VOLMOD
% Impact
SensitivityAnalysis
Monte Carlo vs. ED&RSMFrequency and Cumulative Distribution
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Cum
ulati
ve P
roba
bilit
yDis
trib
ution
Cumulative Oil Production @2022
Cold
Electrical Heating
41
Risk Analysis
Electrical Heating vs. Cold ProductionED&RSM Workflow
@P50 Electrical heating gives
+11% Cumulative Oil Recovery
Monte Carlo Workflow
@P50 Electrical Heating gives
+11% Cumulative Oil Recovery
In both cases, Electrical
Heating can be an effective
way to enhance oil recovery
+11%
Oil viscosity and pore volume are the most impacting unknowns
42
Project Scope
Introduction
Thermal EOR techniques
Case study
Operating parameters definition
Risk Analysis
Conclusions
List of Content
Stage Subject Uncertainty analyses for thermal development in
heavy oil fields
In this work, the feasibility of thermal EOR techniques has been investigated,
within a real extra-heavy oil reservoir
Operating parameters for steamflooding (steam rate, injection pressure) and
electrical heating (power input) have been investigated and best cases have been
selected
ED&RSM Risk Analysis workflow proved to be an effective alternative to Monte
Carlo workflow, although proxy models have to be properly checked
Steamflooding proved to be an effective way to improve oil recovery although for
pessimistic scenarios its convenience should properly be evaluated
Electrical heating can cheaply provide additional oil recovery, also with low power
input, and it is particularly convenient in pessimistic scenarios
43
Conclusions
In this work, the feasibility of thermal EOR techniques has been investigated,
within a real extra-heavy oil reservoir
Operating parameters for steamflooding (steam rate, injection pressure) and
electrical heating (power input) have been investigated and best cases have been
selected
ED&RSM Risk Analysis workflow proved to be an effective alternative to Monte
Carlo workflow, although proxy models have to be properly checked
Steamflooding proved to be an effective way to improve oil recovery although for
pessimistic scenarios its convenience should properly be evaluated
Electrical heating can cheaply provide additional oil recovery, also with low power
input, and it is particularly convenient in pessimistic scenarios
Collect reservoir fluid samples and perform oil viscosity analyses
Extend simulations to a larger thermal sector, in order to get more representative
results (steam flooding)
Introduce economic analyses to assess applicability of thermal recovery methods
44
Recommendations and future activities
Collect reservoir fluid samples and perform oil viscosity analyses
Extend simulations to a larger thermal sector, in order to get more representative
results (steam flooding)
Introduce economic analyses to assess applicability of thermal recovery methods
45
Acknowledgements
I would like to acknowledge
eni e&p division management
for permission to present this work and related results
and TENC/MOGI colleagues
(particularly Alice, Ivan, Michela and Micla)
for the technical support and needed assistance.
San Donato Milanese, 19-20 October 2011
www.eni.it
Uncertainty analyses for thermal development in heavy oil fields
Author: Riccardo Sabatino
San Donato Milanese, 19-20 October 2011
Master in Petroleum Engineering 2010-2011