Integrated Optimization Software Issues

Curtis H. WhitsonProfessor NTNU

Dept. of Petroleum Engineering & Applied Geoscience

Founder PERA & Petrostreamz

IO09 – Trondheim Sept. 29, 2009

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Integrated Optimization

Holistic …

the value of a composite system is far greater than the sum of the elements making up the system.

the value of a composite system is far greater than the  sum of the elements making up the system.

Schlumberger

To sell oil & gas we have to firstpipe together the operation…

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Integrated Optimization

has a value greater than the sum of the elements  making up the integrated system?

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Integrated Optimization

has a value greater than the sum of the elements  making up the integrated system?

allows ranking of control variables…

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Integrated Optimization

has a value greater than the sum of the elements  making up the integrated system?

allows ranking of control variables…

based on bottomline

economics.

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Software High-Level Issues

• Models• Integration• Optimization• IT• GUI• Costs• Sustainability

Schlumberger

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… Training Olympian

Models … Organs – heart, lungs, muscles…

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… Training Olympian

… Vascular & nervous systemsIntegration

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… Training Olympian

… Training programOptimization

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… Training Olympian

… ArenaIT

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… Training Olympian

… FloJo & suitsGUINASA designed swim

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… Training Olympian

… Coaches, travel & stuffCostsNASA designed swim suit

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… Training Olympian

… Season-OL-Season-OLSustainabilityNASA designed swim suit

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No Integration – Silo Modeling Approach

Holistic …

the value of a composite system is far greater than the sum of the elements making up the system.

the value of a composite system is far greater than the  sum of the elements making up the system.

Schlumberger

Reservoir

Pipeflow

Process Economics

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Software Models

• Use current models & workflow.– Allow continued “silo” modeling.– Allow any app – ecumenical.– Platform independent.

• Allow multiple surrogates & proxies.

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Today’s Modeling

the value of a composite system is far greater than the  sum of the elements making up the system.

Schlumberger

Reservoir

Pipeflow

Process Economics

PipeFlowGAPOLGA

ECLSensorMatbal

HysysUnisysNews

MerakAiresPetec

Linux

Cluster

UnixLaptop

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Model

Make things

as simple as possible, but

not simpler...Albert Einstein

A mathematical representation of a physical process.

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Model Types• Theoretical.

– Based on rigorous physical laws.• Transport.• Conservation.• Equilibrium

• Empirical Proxies.– Based on simplified physical laws.– Correlation of data.– Best-fit to more-rigorous models.

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Modeling Issues

• Cost, availability & license management.

• Computational requirement.– Solution methods.– CPU & memory efficiency.

• Model Parameters.– Estimation.– Tuning to observations.

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• Use current models & workflow.• Proprietary vs Open.• Platform dependent.• System compliant.• Multiple surrogates & proxies.

Software Model Surrogates & Proxies

Compositional – 3 hr

Black-Oil – 30 min

Streamline – 3 min

Material BalanceIPR – 3 sec

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A vision of Integrated Modeling

PipeFlowGAPOLGA

HysysUnisysNews

ECLSensorMatbal

MerakAiresPetec

Schlumberger

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Software Integration

• Automatic model orchestration.• Flexible data integration.• Modularity & reusability.• Multi-level integration.• Global & local integration.• Nesting, branching, looping.

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Integration Issues

• Degree of coupling.• Order dependence.• Parallelization.• Re-execution logic.• Data linking.• Stream translations.

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A vision of Integrated Optimization

PipeFlowGAPOLGA

HysysUnisysNews

ECLSensorMatbal

MerakAiresPetec

Schlumberger

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Software Optimization

• Optimizer-Model interaction.• Defining optimization parameters.

– Target, variables, constraints.• Solver library.• Nested optimizations.• Defining cost functions.

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Optimization Issues• Target Objective (O)

– Minimization, maximization,feasibility– Weight factors.– Scaling.

• Variables (V)– Real and integer.

• Constraints (C)• Derivatives

– dO/dV & dC/dV.

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Optimization Issues

• Solvers– Derivative based.– Non-derivative based.– Mixed integer non-linear (MINL).– Experimental design.– Manual cause-and-effect case matrices.– Nested optimizations, different solvers.– Proprietary vs open.

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Software IT

• Use all available resources.• Flexible data access and transfer.• Handle OS handshaking.• Localization, storage & backup.

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Software GUI

• Platform independent.• Visualization of elements.• Modular visualization.• Multi-level visualization (V & H).• Intuitive and user friendly.• Embedded model GUIs.

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Software Costs

• Licensing & in-house development.• IO Project manager.• IT engineers – data interfacing.• Training.• Cross-disciplinary collaboration.

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Software Sustainability• Local models should be ‘trivial’.• Global and regional – challenging.

– Data linking utilities..– Model application updates.– IO software backward compatability.

• Monitoring and tracking changes.• Reproducibility.• Documentation.• Inheritance and training.