Steinar Elgsæter presentation held at PhD dissertation on 2008-10-14. The dissertation was about using time-series analysis to find modeling of offshore oil and gas production that could be used to automatically optimize production within constraints. Uncertainty was treated explicitly in models by estimation using a bootstrapping technique. Optimization was then run multiple times by a form of monte carlo analysis.

1. 1
Modeling and optimizing the
offshore production of oil and gas
under uncertainty
Steinar M. Elgsæter - October 14, 2008

2. 2
Thesis introduction
• supervised by Professor Tor Arne Johansen (NTNU)
and Dr.Ing Olav Slupphaug (ABB),
• funded by ABB, Norsk Hydro (later StatoilHydro) and
the Norwegian Research Council,
• work conducted in the period 2005-2008,
• three conference papers presented,
• two journal papers submitted,
• one patent application submitted.

3. 3
”slow” dynamics on the timescales
of months and years
”fast” dynamics on the timescales
of hours and days

4. 4
production
disturbance
decision variables
measured output:
profits and capacities
production optimization timescale:
hours and days

5. 5
Model-based production
optimization
Production
Disturbances
Decision
Variables
(valves)
Measured output
(Profits and capacity
utilization)
Production constraints
(capacities) and object function
(profit measure)
Production optimization
Production Model
Model parameters:
Watercut,GOR,well potential etc.
current practice: an ”engineering” approach to modeling
•detailed physical models
•emprical relations for closure
•commerical simulators

6. 6
Challenges of current practice
1. challenging production modeling
– complexity of systems considered
– multiphase flow
– measurement difficulties (such as multiphase flow meters)
– disturbances (reservoir depletion)
2. model updating (high update frequency, laborious)
3. numerical and optimization issuses (numerical
stability,identifiability,convexity,run-time)

7. 7
Part I: A data-driven approach
to production modeling and
model updating

8. 8
production data
contains
information that
can be exploited
in optimization

9. 9
A data-driven approach to production modeling
and model updating
Production
disturbances
decision
variables
(valves)
measured output
(Profits and capacity
utilization)
Parameter and
state
estimation
fitted
parameters and
states
Production
model
-
Difference (residual)
model parameters
Production constraints
(capacities) and object function
(profit measure)
Production optimization
Production Model
A ”closed loop”
modeled
output

10. 10
Challenge
• data describing normal
operations are usually not
sufficiently informative,
models fitted to data are
subject to parameter
uncertainty

11. 11
Part II: Methods for
uncertainty analysis and
uncertainty handling

12. 12
Quantifying
uncertainty
• bootstrapping
– multiple-model
– computational
– based on data-set
resampling
• models
– locally valid
– simple ”performance
curves”
– motivated by concepts
of system identification

13. 13
realized
potential
Uncertainty
due to low
information
content in data
max
current
?
1
2
3
Experiments
Optimization
Eliminating uncertainty is not a
practical option
Cost

14. 14
An approach for structured
uncertainty handling
my thesis proposes a five-element strategy for
optimization with uncertain models
1. result analysis
2. excitation planning
3. active decision variables
4. operational strategy
5. iterative implementation and model updating

15. 15
1.Result analysis
realized
potential
uncertainty
due to low
information
content in data
max
current
1
Different simulated plausible outcomes

16. 16
1
2. Excitation
planning
realized
potential
uncertainty
due to low
information
content in data
current
2
Experiment
Cost
Simulated plausible outcomes
of optimization without exictation
Simulated outcome of excitation
Simulated plausible outcomes
of optimization with exictation

17. 17
3. Active decision variables
realized
potential
uncertainty
due to low
information
content in data
current
1
Simulated change in all decision variables
Simulated change in active decision variables

18. 18
4. Operational strategy
When models are uncertain,
a target setpoint can be
infeasble when implemented
An opertational strategy is
an iterative implementation
of setpoint change while
monitoring profits and
constraints

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4. Operational strategy...
Production
Decision
Variables
Measured
output
Parameter and
state
estimation
Fitted parameters
and states
Production
optimization
Operational
strategy
Target

20. 20
realized
potential
uncertainty
due to low
information
content in data
max
current
1
2
3
5.Iterative implementation and
model updating
4
optimize
update model
and re-optimize
update model
and re-optimize

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Perform excitation
planning
Perform production
optimization
Optionally: select
active decision
variables
Implement setpoint
change suggested
by production
optimization
according to
operational strategy
Is the cost/benefit
tradeoff of any
planned excitation
favorable?
Implement
planned
excitation
Yes
Update model:
Estimate parameters
and parameter
uncertainty
Is result analysis
favorable?
No
Yes
Wait until new data
becomes avialable
No
Perform result
analysis
Combined the elements provide
a framework for optimizing oil
and gas production with
uncertain models

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Results
• Methods applied to two sets of real-world production
data from North Sea oil fields
• Simulations indicate:
– promising active decision variable candidates found
– in simulations 30-80% of potential profits were realized using
uncertain models in combination with the suggested framework

23. 23
Results: Active decision
variables(1)

24. 24
Results: Active decision
variables(2)

25. 25
Discussion and Conclusions

26. 26
I. Data-driven modeling and
model updating
• adresses weaknesses of current practice:
– models easy to design
– models updated with less effort
• this may increase frequency at which production optmization can run
– models are less prone to issues of convexity, numerical stability,
identifiability and computational effort.
– models especially well suited for iterative optimization (each
iteration reveals information)
• challenge
– requires measurement maintenance and may be prone to issues of
low information content in data

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II. Framework for optimizing
production with uncertain
models
• a method that can exploit current real-world data
as a starting point
• iterative approach ideal for combination with low-
maintenace data-driven models
• analog to the current approach
– but: decision support based on objective analysis at every
step of decision-making process
• relationship between current manner of
operation, uncertainty and production
optimization is made explicit

28. 28
Further work

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A ”low-hanging fruit” for
practicioners
• perform a ”proof of concept” experiment
– implement setpoint change according to active decision variables
method
• an experiment that
– will be profitable with high confidence
– validates the ”control” approach of this thesis

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Thank you for your attention