1 Modeling and optimizing the offshore production of oil and gas under uncertainty Steinar M. Elgsæter - October 14, 2008
Jun 22, 2015
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Modeling and optimizing the offshore production of oil and gas under uncertainty
Steinar M. Elgsæter - October 14, 2008
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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.
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”slow” dynamics on the timescales of months and years
”fast” dynamics on the timescales of hours and days
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production
disturbance
decision variables
measured output:profits and capacities
production optimization timescale: hours and days
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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
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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)
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Part I: A data-driven approach to production modeling and model updating
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production data contains information that can be exploited in optimization
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A data-driven approach to production modeling and model updating
Production
disturbances decision variables(valves)
measured output (Profits and capacity utilization)
Parameter andstate
estimation
fitted parameters and states
Production model
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Difference (residual)
model parameters
Production constraints(capacities) and object function(profit measure)
Production optimization
Production Model
A ”closed loop”
modeled output
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Challenge
• data describing normal operations are usually not sufficiently informative, models fitted to data are subject to parameter uncertainty
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Part II: Methods for uncertainty analysis and uncertainty handling
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Quantifying uncertainty
• bootstrapping– multiple-model
– computational
– based on data-set resampling
• models– locally valid
– simple ”performance curves”
– motivated by concepts of system identification
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realized potential
Uncertaintydue to low information content in data
max
current
?
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Experiments
Optimization
Eliminating uncertainty is not a practical option
Cost
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An approach for structured uncertainty handlingmy 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
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1.Result analysisrealizedpotential
uncertaintydue to low information content in data
max
current1
Different simulated plausible outcomes
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2. Excitation planning
realizedpotential
uncertaintydue to low information content in data
current2Experiment
Cost
Simulated plausible outcomesof optimization without exictation
Simulated outcome of excitation
Simulated plausible outcomesof optimization with exictation
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3. Active decision variables
realized potential
uncertaintydue to low information content in data
current1
Simulated change in all decision variablesSimulated change in active decision variables
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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 andstate
estimation
Fitted parameters and states
Production optimization
Operational strategy
Target
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realized potential
uncertaintydue to low information content in data
max
current1
2
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5.Iterative implementation and model updating
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optimize
update model and re-optimizeupdate model
and re-optimize
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Perf o rm ex c ita tion p lanning
Perf o rm produc tion optimiz a tion
O ptiona lly : s e lec t ac tiv e dec is ion
v ariables
Implement s e tpo int c hange s ugges ted
by produc tion optimiz a tion ac c ording to
opera tiona l s tra tegy
Is the c os t/benef it tradeo f f o f any
planned ex c ita tion f av orable?
Implement p lanned
ex c ita tion
Y es
Update mode l: Es timate parameters
and parameter unc erta inty
Is res ult ana ly s is f av orable?
No
Y es
W ait until new data bec omes av ia lable
No
Perf o rm res ult ana ly s is
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
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Results: Active decision variables(1)
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Results: Active decision variables(2)
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Discussion and Conclusions
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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
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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