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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

3

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