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Integrated Asset Modelling (IAM):

Advanced TechniquesNetwork Modelling and Calibrations

Author: Giuseppe Sabetta

San Donato Milanese 19-20 October 2011

Master in Petroleum Engineering 2010-2011


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Stage Subject

Integrated Asset Modelling (IAM):Advanced Techniques

Network Modelling and Calibrations

San Donato Milanese 19-20 October 2011

Author

Ing. Giuseppe Sabetta

Division Exploration & Production

Dept. RESM

Company Tutors

Dott. Roberto Rossi

Ing. Stefano Giliberti

UniversityTutor

Prof. Ing.Francesca Verga

Master in Petroleum Engineering 2010-2011


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Project Scope

Background

Workflow

Applications

Conclusions

List of Content

Stage Subject




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Project Scope

Network models are used in the oilindustry to optimize production

Calibration of models based on

current production/pressuredata is a fundamental step

Develop a tool to facilitate andautomate the calibration processaccording to eni workflow


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Project Scope

Background

Workflow

Applications

Conclusions

List of Content

Stage Subject




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Background

Petroleum Experts GAP (General

Allocation Package) is a multiphaseflow simulator that is able to modeland optimize production andinjection networks. The concept ofnetwork is here intended as general,therefore both surface and downhole

The fluid phase behavior can bemodeled using black oil formulationor Equation of State compositionalmodelling

GAP allows to model both surfaceand downhole network elements:wells, tubing, compressors, pumps


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Background

Joints

Pipelines

Injection Wells

Production Wells

Separator


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Background

Conventional approach for pressure losses:

use of empirical correlations (22 correlationsavailable in GAP)

Sum of three terms:GravityFriction

Acceleration

1.00

0.10

0.01

10.0

75.0

0.1 1.0 10.0 900.0100.0

Intermittent

Annular

StratifiedWavy

StratifiedSmooth

Bubbly

UsL

(ft/s)

UsG (ft/s)

AL

AG


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GAP has limitations in calibration phase

Automatic calibration of one pipeline at a time

Multiple simulations are difficult to be managed

Simulated pressures are not returned in calibration output

Background


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Project Scope

Background

Workflow

Applications

Conclusions

List of Content

Stage Subject




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Workflow

CalibrationParameters

?

Measuredvalues of

pressure

Givenfluid rates


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Workflow

Calibration Variables

Changed manually line by line to match available data


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Workflow

Open server is the software package of PE that allows external

programs to access the suite of IPM (Integrated ProductionModelling) and transfer data

All programs that act as automation clients (VBA macros, VBprograms, C++ programs) can call the public functions of OS

VBprograms

Cprograms

OSOS

OS

OS
http://tomasella.altervista.org/it/matlab/immagini/matlab.jpghttp://icdn.pro/images/en/m/i/microsoft-office-excel-2007-icone-6007-128.png


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Workflow

Define an index of overall goodness of simulated

pressures. This index is the Overall Target Function

(OTF):

OTF is the function to minimize

Define an index of distance from default values of

calibration parameters:


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Workflow

Step 1: Parameters Determination

1. Read current situation from GAP: pipeline labels

correlations

friction and gravity coefficients

2. Decide which pipelines must be calibrated

PIPELINE CORRELATIONFRICTION

COEFFICIENT

GRAVITY

COEFFICIENT

LINE TO

CALIBRATE

Riser PetroleumExperts5 1 1 YES

Linea1_1 PetroleumExperts5 1 1

Linea1_2 PetroleumExperts5 1 1 YES





GetParameters


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Workflow

Step 2: Parameters Setting

1. Import line to be calibrated from the previous step

2. Set correlation, friction and gravity coefficient for each pipeline

3. Set the solver (with/without optimization)

PIPELINE CORRELATION FRICTIONCOEFFICIENT GRAVITYCOEFFICIENT

Riser PetroleumExperts5 1 1







Import Status

from Output

SetParameters


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Workflow

Step 3: Read GAP results

1. Read desired values from GAP simulation

2. Calculate TARGET Function and pressure errors

CA

LCULATE

GAP COMMAND STRING COMMENTMEASURED

VALUE

SIMULATED

VALUE

NODAL

TARGET

FUNCTION

OVERALL

TARGET

FUNCTION

ERRORMEDIUM

ERROR

ERROR

STANDARD

DEVIATION

YES

GAP.MOD[{PROD}].JOINT[{WH_1}].

SolverResults[0].PresWH_1 64 6.40E+01 5.67E-04 9.66E-01 2.38E-02 1.73E-01 4.81E-01

YES


SolverResults[0].PresWH_2 61 6.09E+01 1.63E-02 1.28E-01

YES



YES



YES



YES



YES

GAP.MOD[{PROD}].JOINT[{11}].

SolverResults[0].PresManifold 59 5.80E+01 9.04E-01 9.51E-01

GAP.MOD[{PROD}].JOINT[{12}].

SolverResults[0].Pres

Monte

collettore46 4.51E+01

GAP.MOD[{PROD}].PIPE[{Collettore}].SolverResults[0].Qliq

Liquido totale

collettore 3322 3.40E+03

Extract Values


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Workflow

Step 4: Test correlations

1. Solve network for all selected correlations (only physically compatible with the problem)

2. Read values from GAP simulations & calculate OTF

3. Indicate the best overall correlation matching measured pressures

GAP COMMAND STRING COMMENT

MEASURED

VALUE

SIMULATED VALUE

Beggsand

Brill

Beggsand

BrillGasHead

Duk

lerEaton

Fl

annigan

Dukler

Fl

annigan

Muk

erjeeBrill

Pe

troleum

E

xperts2

Pe

troleum

E

xperts3

Pe

troleum

E

xperts4

Pe

troleum

E

xperts5

GAP.MOD[{PROD}].JOINT[{WH_2-1}].SolverResults[0].Pres WH_2-1 54 48.78 50.34 54.33 57.25 43.34 46.18 46.42 50.97 49.04

GAP.MOD[{PROD}].JOINT[{WH_2-2}].SolverResults[0].Pres WH_2-2 56.5 50.74 53.23 57.62 60.84 44.72 47.53 47.88 54.50 51.69






GAP.MOD[{PROD}].JOINT[{IFM4}].SolverResults[0].Pres IFM4 50 45.43 47.51 50.28 52.81 41.41 43.42 43.51 48.06 46.39




GAP.MOD[{PROD}].JOINT[{FGS-1}].SolverResults[0].Pres FGS-1 45 43.41 44.05 45.85 47.96 39.97 42.05 42.07 43.49 42.74

292.13 178.46 143.22 507.20 1378.56 712.57 601.00 102.21 330.41

Test Correlations


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Workflow

Step 5: Optimization settings

1. Set the maximum and minimum values of frictionand gravity allowed for each pipe to be calibrated

2. Decide whether to launch an experimental designor Matlab optimizer

PIPELINEMINIMUM

FRICTION

MAXIMUM

FRICTION

FRICTION

POINTS

MINIMUM

GRAVITY

MAXIMUM

GRAVITY

GRAVITY

POINTSSFN1-5 0.5 5 2 1 1 1

SFN2-5 0.5 5 2 1 1 1

SFN3-5 0.5 5 2 1 1 1

SFN7-5 0.5 5 2 1 1 1

SFN8-5 0.5 5 2 1 1 1

SFN10-5 0.5 5 2 1 1 1

IFM-FGS 0.5 5 2 1 1 1

Collettore 0.5 5 2 1 1 1

Data Risk

Import LinesOrder Runs


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Workflow

Step 6: Launch Matlab Optimizator

1. Excel produces txt files that Matlab uses as INPUT

2. Excel runs Matlab

3. Matlab executes optimization program (based on Nelder-Mead simplex direct search) thatcontrols GAP

4. Matlab produces txt files as OUTPUT

5. Excel reads results

OSOS

OS

OS

txt files

txt files
http://tomasella.altervista.org/it/matlab/immagini/matlab.jpghttp://icdn.pro/images/en/m/i/microsoft-office-excel-2007-icone-6007-128.png


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Project Scope

Background

Workflow

Applications

Conclusions

List of Content

Stage Subject




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Applications

Case 1

Case 2

Case 3


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Applications: Case 1

Production Network

Gas InjectionNetwork

Water InjectionNetwork

Riser Base 1

Riser Base 2


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Calibration Point:March 8th2021

Check Points:May 19th2024May 14th2026July 1st 2030

GAP/OLGA mismatch inpressure forecast

OLGA is a transient tool forflow assurance study

Pressure data are availablefrom OLGA simulation


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Only one calibration timestep, but different fluid

conditions


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Best Overall Target Functionat default values


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Optimized parameters defined to match pressure on Marchthe 8th2021

Optimized parameters give a good solution over timedespite changed conditions


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Check the matching of oil, gasand water production rate withthe previous forecast scenario


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Only friction

Friction & gravity


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Effect of the boundary

0.5


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59

66

64

63

61

67

60


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59

66

64

63

61

67

60


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M&B gives the best optimization result but the worst indicator

Iterations increase with the number of parameters without

significant improvements in OTF

A good initial OTF is not a sufficient condition for convergence(see PE4)


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Only friction

Friction & gravity


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56.5

56

49

55

51

47

57

58

50

54

45


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56

49

55

51

47

57

58

50

54

45

56.5


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Increased number of iterations

Best OTF


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JointMeasured

Value

Previous

Calibration

B&B

Optimized

DEF

Optimized

O2P

Optimized

O3P

Optimized

PE3

Optimized

PE4

Optimized

B&B (36p)

Optimized

Joint 1 54 -0.86 -0.01 0.05 -0.04 -0.30 0.15 0.01 0.01

Joint 2 56.5 -1.35 0.00 0.03 -0.05 -0.17 -0.04 0.04 -0.01

Joint 3 56 0.28 -0.01 0.03 0.03 -0.14 -0.04 0.40 0.01

Joint 4 55 -0.58 -0.08 0.09 -0.04 -0.18 -0.22 -0.19 -0.34

Joint 5 55 -0.47 0.00 -0.08 -0.47 -0.33 -0.24 -0.48 0.08

Joint 6 51 1.54 0.35 1.13 0.00 0.07 0.13 0.02 0.23

Joint 7 49 -0.04 0.00 -0.01 0.01 -0.02 0.59 0.00 0.00

Joint 8 47 -0.36 -0.60 -0.46 -0.54 -0.07 -0.82 -0.68 -0.36

Joint 9 47 0.08 -0.05 0.33 0.31 1.36 -0.13 0.83 -0.04

Joint 10 57 -2.86 0.01 0.01 -0.47 -0.21 -1.60 -0.53 0.00

Joint 11 58 -2.18 0.01 0.02 0.06 -0.22 -0.06 0.00 -0.01

Joint 12 50 -1.17 0.03 0.02 0.06 -0.41 0.38 -0.09 -0.04

Joint 13 54 -0.62 0.09 0.01 0.45 0.47 0.54 0.29 0.26

Joint 14 46 0.23 0.31 -0.10 0.18 0.56 0.08 0.19 0.21

Joint 15 51 -1.62 -0.04 0.00 0.37 0.58 1.54 0.54 0.01

Joint 16 45 -0.24 -0.02 -0.73 -0.08 0.00 -0.18 -0.33 0.00


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ProjectScope

Background

Workflow

Applications

Conclusions

List of Content

Stage Subject

Integrated Asset Modelling (IAM):Advanced TechniquesNetwork Modelling and Calibrations


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Conclusions

Developed tool is useful and effective in network calibration

Optimization algorithm gives good results but has somelimitations when different variables with the same effect onpressure are used together

Best fitting if selected correlation at default valuesunderestimates pressure on all joints

Further improvements:

Automatic saving and summarizing of the manualcalibration attempts

Automatic testing of the optimized parameters for different

time steps

Test other optimization algorithms


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Acknowledgements

I would thank eni E&P Division Management for

permission to present this work and related results

and RESM colleagues for the technical support and

needed assistance.

13 Sabetta Peaf-ipet

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