Presentazione SABATINO_eni

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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


heavy oil fields

4

Project Scope


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


Case study


Risk Analysis

Conclusions

List of Content


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


Case study

Operating parameters definition

Risk Analysis

Conclusions

List of Content


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.


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


Control Panel

Downhole heater

Producer Well

12

Project Scope

Introduction


Case study


Risk Analysis

Conclusions

List of Content


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


Case study


Risk Analysis

Conclusions

List of Content


heavy oil fields

16


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


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).


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


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


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]


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


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




Electrical heating subsector – Grid refinement


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)


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


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


Case study


Risk Analysis

Conclusions

List of Content


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



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



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


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



2.10E+06 4.29E+06 5.55E+06 3.98E+06 1.32E+06

Stabilization Check

SensitivityAnalysis


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



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]


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


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Cum

ulati

ve P

roba

bilit

y Di

strib

ution


Steamflooding

Cold

39

Risk Analysis

Steamflooding vs. Cold ProductionED&RSM Workflow

@P50 Steamflooding gives

+89% Cumulative Oil Recovery

Monte Carlo Workflow

@P50 Steamflooding gives


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)


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


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


Cold

Electrical Heating

41

Risk Analysis

Electrical Heating vs. Cold ProductionED&RSM Workflow

@P50 Electrical heating gives


Monte Carlo Workflow

@P50 Electrical Heating gives


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


Case study


Risk Analysis

Conclusions

List of Content


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.


www.eni.it


Author: Riccardo Sabatino


Master in Petroleum Engineering 2010-2011

Presentazione SABATINO_eni

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