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8/3/2019 2008 Modeling and optimizing the offshore production of oil and gas under uncertainty: Presentation of phd work (Steinar M. Elgster)
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Modeling and optimizing theoffshore production of oil and gasunder uncertainty
Steinar M. Elgster - 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) andthe 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 timescalesof months and years
fast dynamics on the timescalesof 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 productionoptimization
Production
DisturbancesDecisionVariables(valves)
Measured output(Profits and capacityutilization)
Production constraints(capacities) and object function(profit measure)
Productionoptimization
Production Model
Model parameters:
Watercut,GOR,well potential etc.
current practice: an engineering approach to modelingdetailed physical modelsemprical relations for closurecommerical 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 approachto production modeling andmodel updating
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production datacontainsinformation thatcan be exploitedin optimization
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A data-driven approach to production modelingand model updating
Production
disturbancesdecisionvariables
(valves)
measured output(Profits and capacity
utilization)
Parameter andstate
estimation
fitted
parameters andstates
Productionmodel
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Difference (residual)
model parameters
Production constraints(capacities) and object function(profit measure)
Production optimization
Production Model
A closed loop
modeledoutput
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Challenge
data describing normaloperations are usually not
sufficiently informative,models fitted to data aresubject to parameteruncertainty
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Part II: Methods foruncertainty analysis and
uncertainty handling
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Quantifying
uncertainty bootstrapping
multiple-model
computational
based on data-setresampling
models locally valid
simple performance
curves
motivated by conceptsof system identification
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realizedpotential
Uncertaintydue to lowinformationcontent in data
max
current
?
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Experiments
Optimization
Eliminating uncertainty is not apractical 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 analysis
realizedpotential
uncertainty
due to lowinformationcontent in data
max
current1
Different simulated plausible outcomes
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1
2. Excitationplanning
realizedpotential
uncertainty
due to lowinformationcontent 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
realizedpotential
uncertaintydue to lowinformationcontent 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 beinfeasble when implemented
An opertational strategy isan iterative implementationof setpoint change whilemonitoring profits and
constraints
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4. Operational strategy...
Production
DecisionVariables
Measured
output
Parameter andstate
estimation
Fitted parametersand states
Production
optimization
Operational
strategy
Target
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realizedpotential
uncertaintydue to lowinformationcontent in data
max
current
1
2
3
5.Iterative implementation andmodel updating
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optimize
update modeland re-optimizeupdate model
and re-optimize
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Perform excitation
planning
Perform productionoptimization
Optionally: selectactive decision
variables
Implement setpoint
change suggested
by production
optimizationaccording to
operational strategy
Is the cost/benefittradeoff of any
planned excitationfavorable?
Implement
plannedexcitation
Yes
Update model:
Estimate parametersand parameter
uncertainty
Is result analysisfavorable?
No
Yes
Wait until new databecomes avialable
No
Perform result
analysis
Combined the elements providea framework for optimizing oil
and gas production withuncertain models
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Results
Methods applied to two sets of real-world productiondata 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 (eachiteration 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 optimizingproduction 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 ofoperation, uncertainty and productionoptimization 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 variablesmethod
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