2008 Modeling and optimizing the offshore production of oil and gas under uncertainty: Presentation...

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

-

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

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current1

Different simulated plausible outcomes

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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

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2

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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

2008 Modeling and optimizing the offshore production of oil and gas under uncertainty: Presentation...

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