production optimization an industry perspective - … optimization – an industry perspective alex...

Production Optimization –

An Industry Perspective

Alex Furtado Teixeira – PETROBRAS/CENPES

Mário César Mello Massa de Campos – PETROBRAS/CENPES

Trondheim, 24 September 2013

9TH International Conference on Integrated Operations in the

Petroleum Industry

Agenda

Decision Making Process

Motivation

Expected Benefits

Adopted Strategy

BR-SiOP (Production Optimization System)

Quantifying Benefits

Main Challenges

Conclusions


Decision Making Process

Data Decisions

Experience

Data and Simulator:

Better understanding of the process;

Indentify opportunities;

Model the process;

Experience:

Knowledge or skills that the engineer gained over time working in the asset;

Decision Support Tools:

Search for an optimal operating point;

Evaluate a huge number of alternatives when operating under severe process constraints;

Enhance process understanding.

Decision

Support

Engineer

Simulator


Mathematical Optimization - Structure

Objective:

Maximize Oil Production;

Maximize NPV (Net Present Value);

Minimize Operational Cost.

Decision Variables:

Production Choke Opening;

Gas Lift Flow rate;

Well Routing;

Water/Gas Injection Flow rate.

Equality and Inequality Constraints:

Water Handling Capacity;

Liquid Handling Capacity;

Compression Capacity;

Amount of Gas Available for Gas Lift;

Pressure Constraints.

Feasible Region!

Degrees of Freedom!

Single or Multi-Objective?

Motivation

The characteristics of the wells are changing over time, which implies in a new optimal operating point.

30.0

40.0

50.0

60.0

70.0

14/1/2004 1/8/2004 17/2/2005 5/9/2005 24/3/2006 10/10/2006 28/4/2007

Test Data

Wate

rcu

t [%

]

50

70

90

110

14/1/2004 1/8/2004 17/2/2005 5/9/2005 24/3/2006 10/10/2006 28/4/2007

Test Data

GO

R [

m3/m

3]

GOR

Watercut


Motivation

The availability of critical process equipments is changing over time, which also implies in a new optimal operating point.

0

500

1000

1500

2000

2500

3000

3500

30/6/2012 5/7/2012 10/7/2012 15/7/2012

Date

Ga

s F

low

rate

[N

m3

/d]

Total Gas

Gas Lift

Produced Gas

Flare

5 days

Unexpected shutdown of a compressor!!!


Expected Benefits

Increases production (1 – 3%);

Reduces operational cost;

Fast response to abnormal situations;

Reduces time of the decision making process;

Increases robustness and reliability of the decision making process;

Increases interaction among disciplines.


Adopted Strategy

OPTIMIZER

New Production

Test Data

Simulator

Recommendations

Decisions

Update

Models!

Run model automatically

covering the entire operational

envelope

Tables

Actions

Valid

Models

Validate

recommendation!


Sec. – Min.

Production Optimization Loop:

Adopted Strategy

Simulators

Optimizer

Decoupled Strategy:

The engineers can use different simulators with the same optimizer.


A B C D

Adopted Strategy

Objective: Maximize Oil Production;

Decision Variables:

Gas Lift Flow rate;

Pressure upstream choke;

Gas Flaring;

Status of the wells (Opened or Closed);

Well routing (subsea manifold).

Constraints:

Compression Capacity;

Liquid Handling Capacity;

Water Treatment Capacity;

Limit allowed to the gas flaring;

Gas lift flow rate and wellhead pressure

lower and upper bound per well.

Characteristics of the scenario: Offshore

platforms with satellite oil wells.

MINLP

MILP

Piecewise

Linearization

(SOS2)

(Gunnerud, V. and Foss, B., 2009)

(Codas, A., Campos, S. R. V., Camponogara, E.,

Gunnerud, V. and Sunjerga, S., 2012)

Adopted Strategy

Production Engineers

Petrobras Network

72 servers HP Dual Xeon quad 3.0 GHz with Microsoft

Windows Server 2003 x64 SP 2.

Asset A Asset B Asset C

Computer Cluster located at Cenpes

Web User Interface

Benefits:

Improved optimization performance with parallel computing;

Easier to maintain and continuously improve the system;

Optimized number of solver licenses.


BR-SiOP – Production Optimization System


Web Graphical Interface:


Graphs available per well:

Oil Flow Rate versus Gas Lift Downhole Pressure versus Gas Lift



Sensitivity Analysis – Process Constraints:



Sensitivity Analysis – Process Constraints:

BR-SiOP is an useful tool to assist engineers in the quantification of losses due to unavailability of

critical process equipments.



Case Study 1 – FPSO with 13 wells:

Total Produced Oil Flow Rate [m3/d]

17,500.00

17,600.00

17,700.00

17,800.00

17,900.00

18,000.00

18,100.00

1 2 3 4

Scenarios

With Optimizer

Without Optimizer

Total Produced Oil Flow Rate [m3/d]

Scenarios - Days

Comparison based in production historical data – normal operating scenario.

Gains ranging from 0.37 to 1.55%


Total Produced Gas Flow Rate [Sm3/d]

2,000,000.00

2,005,000.00

2,010,000.00

2,015,000.00

2,020,000.00

2,025,000.00

2,030,000.00

2,035,000.00

2,040,000.00

2,045,000.00

2,050,000.00

1 2 3 4

Scenarios

With Optimizer

Without Optimizer



Total Produced Gas Flow Rate [Sm3/d]

Scenarios - Days


Gains ranging from 0.24 to 1%


Total Gas Lift [Sm3/d]

1,350,000.00

1,400,000.00

1,450,000.00

1,500,000.00

1,550,000.00

1,600,000.00

1,650,000.00

1,700,000.00

1,750,000.00

1,800,000.00

1 2 3 4

Scenarios

With Optimizer

Without Optimizer



Total Gas Lift [Sm3/d]

Scenarios - Days





Action of the Platform

Operators

Action Based in the

Recommendation of the Optimizer

Comparison based in a real scenario – limited compression capacity.




0 600 1200 1800 2400 3000

Time (sample number)

1,450,000

1,500,000

1,550,000

1,600,000

1,650,000

1,700,000

1,750,000

1,800,000

To

tal G

as L

ift

[Sm

3/d

]

1,737,561

1,603,805

1,564,978

Time (sample number)

To

tal

Gas

Lif

t [S

m3/d

] Comparison based in a real scenario – limited compression capacity.



Total Oil Production [m3/d]

16950

17000

17050

17100

17150

17200

17250

17300

17350

With Optimizer Without Optimizer


Total Oil Produced [m3/d]

With Optimizer Without Optimizer

Comparison based in a real scenario – limited compression capacity.

Production Increased 1.18%


Main Challenges

Instrumentation

Regulatory Control

Advanced Control

Optimization

Planning

S.P.

The production optimization strategy presented assumes that the process is “stable”. It means that dynamic instabilities like severe slugging must be addressed by the regulatory and advanced control layers.

Production Optimization Layer


(OTC-24286)

Reduces variability.

Main Challenges

Simulator

(Model)

Parameters

Variables Results ? Optimizer

Uncertainty

Uncertainty Propagation:

Results

Uncertainty Uncertainty

Watercut

GOR

Reservoir Pressure

Productivity Index


Main Challenges

Histograma

0

5

10

15

20

25

4 7 10 12 15 17 20 23 25M

ais

Bloco

Fre

qü

ên

cia

Histograma

0

5

10

15

20

25

30

3842

4030

4218

4406

4594

4782

4971

5159

5347

Mais

BlocoF

req

üê

nc

ia

Input Parameters

Produced Oil

Histograma

0

5

10

15

20

25

5101

89

5255

73

5409

56

5563

40

5717

24

5871

07

6024

91

6178

75

6332

58M

ais

Bloco

Fre

qü

ên

cia

Gas Lift

Uncertainty Propagation:

Due to the uncertainty the optimum will be a region instead of a point.

This region can be explored by the field operator or by the well control system (closed loop).

Opportunity for the use of Robust Optimization Techniques!


Objective: Maximize Oil and Gas

Production;

Decision Variables:

Gas lift flow rate;

Pressure upstream choke;

Well routing in the subsea manifold;

Status of the wells (opened or closed).

MEG flowrate;

Constraints:

Gas, oil and water treatment capacities;

Pressure in the pipeline network;

CO2 content in the gas;

UTGCA gas and condensate treatment

capacities.

Collaboration:

Integrated Production Optimization System:

Main Challenges

Mathematical

Optimization

Main Challenges

Human Aspects:

Proactive x Reactive approach (continuously search for the opportunities for improvements);

Training (good understanding of what is inside of the optimizer);

Good integration between onshore and offshore team (operator is part of the optimization loop);

Without these items the gain can be 0%.


Main Challenges

Human Aspects: Training and knowledge dissemination.

Production Optimization:

Training courses;

Workshops;

Conferences.

Conference – Rio, May 2013.

Training Course – Rio, January 2012.

Workshop – Rio

January 2012.

Conclusion

A decision support system to assist engineers and operators in the production optimization process was developed and made available for three different assets from Campos Basin (P-54, P-50 and P-53).

The preliminary results obtained with the use of the decision support system are promising.

Two case studies with a quantification of the benefits of the decision support tool for production optimization were presented.

One of these case studies was a real example where the use of the decision support system resulted in an increase of about 1.18% in the total oil flow rate produced by the platform.

The main technological and human challenges related with production optimization were presented.


THANK YOU!

Contact:

Alex Furtado Teixeira

Email: [email protected]

Mobile: +55 21 8162-0774

Phone: +55 21 2162-4397

production optimization an industry perspective - … optimization – an industry perspective alex...

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