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www.eni.it
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
Integrated Asset Modelling (IAM):Advanced Techniques
Network Modelling and Calibrations
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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
Integrated Asset Modelling (IAM):Advanced Techniques
Network Modelling and Calibrations
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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
Integrated Asset Modelling (IAM):Advanced Techniques
Network Modelling and Calibrations
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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
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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
Linea1_3 PetroleumExperts5 1 1 YES
Linea2_1 PetroleumExperts5 1 1
Linea2_2 PetroleumExperts5 1 1 YES
Linea2_3 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
Linea1_1 PetroleumExperts5 1 1
Linea1_2 PetroleumExperts5 1 1
Linea1_3 PetroleumExperts5 1 1
Linea2_1 PetroleumExperts5 1 1
Linea2_2 PetroleumExperts5 1 1
Linea2_3 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
GAP.MOD[{PROD}].JOINT[{WH_2}].
SolverResults[0].PresWH_2 61 6.09E+01 1.63E-02 1.28E-01
YES
GAP.MOD[{PROD}].JOINT[{WH_3}].
SolverResults[0].PresWH_3 60 6.02E+01 4.00E-02 2.00E-02
YES
GAP.MOD[{PROD}].JOINT[{WH_7}].
SolverResults[0].PresWH_7 67 6.70E+01 2.39E-04 1.54E-02
YES
GAP.MOD[{PROD}].JOINT[{WH_8}].
SolverResults[0].PresWH_8 63 6.30E+01 9.74E-06 3.12E-03
YES
GAP.MOD[{PROD}].JOINT[{WH_10}].
SolverResults[0].PresWH_10 66 6.59E+01 4.50E-03 6.71E-02
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[{WH_2-3}].SolverResults[0].Pres WH_2-3 56 52.78 54.27 59.33 63.69 45.57 48.57 49.95 54.73 52.07
GAP.MOD[{PROD}].JOINT[{WH_2-4}].SolverResults[0].Pres WH_2-4 55 51.54 52.84 57.41 61.60 44.90 47.53 48.86 52.80 50.74
GAP.MOD[{PROD}].JOINT[{WH_2-5}].SolverResults[0].Pres WH_2-5 55 51.36 52.64 57.04 61.18 44.66 47.51 48.82 52.48 50.44
GAP.MOD[{PROD}].JOINT[{WH_2-7}].SolverResults[0].Pres WH_2-7 51 54.92 53.68 58.84 63.43 46.62 49.63 51.43 52.64 52.08
GAP.MOD[{PROD}].JOINT[{WH_2-9}].SolverResults[0].Pres WH_2-9 47 45.41 46.22 48.56 51.15 41.12 43.46 43.70 45.52 44.68
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[{IFM3}].SolverResults[0].Pres IFM3 54 51.12 52.45 56.79 60.94 44.51 47.27 48.56 52.35 50.30
GAP.MOD[{PROD}].JOINT[{IFM2}].SolverResults[0].Pres IFM2 46 45.38 46.08 48.32 50.89 41.14 43.49 43.71 45.38 44.55
GAP.MOD[{PROD}].JOINT[{IFM1}].SolverResults[0].Pres IFM1 51 46.18 47.13 50.28 53.02 41.54 44.30 44.45 49.38 45.82
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
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Project Scope
Background
Workflow
Applications
Conclusions
List of Content
Stage Subject
Integrated Asset Modelling (IAM):Advanced Techniques
Network Modelling and Calibrations
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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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Applications: Case 1
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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Applications: Case 1
Only one calibration timestep, but different fluid
conditions
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Applications: Case 1
Best Overall Target Functionat default values
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Applications: Case 1
Optimized parameters defined to match pressure on Marchthe 8th2021
Optimized parameters give a good solution over timedespite changed conditions
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Applications: Case 1
Check the matching of oil, gasand water production rate withthe previous forecast scenario
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Applications: Case 2
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Applications: Case 2
Only friction
Friction & gravity
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Applications: Case 2
Effect of the boundary
0.5
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Applications: Case 2
59
66
64
63
61
67
60
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Applications: Case 2
59
66
64
63
61
67
60
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Applications: Case 2
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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Applications: Case 3
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Applications: Case 3
Only friction
Friction & gravity
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Applications: Case 3
56.5
56
49
55
51
47
57
58
50
54
45
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Applications: Case 3
56
49
55
51
47
57
58
50
54
45
56.5
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Applications: Case 3
Increased number of iterations
Best OTF
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Applications: Case 3
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.