the instrumented oil field - dddas.org gai ruijie liu jareen rungamornort ... mrinal sen ig, ut. ......

The Instrumented Oil FieldTowards Dynamic Data-Driven Optimization of Oil Well

Placement

Manish ParasharThe Applied Software Systems Laboratory

ECE/CAIP, Rutgers Universityhttp://www.caip.rutgers.edu/TASSL

(NSF ITR-DDDAS 04 (UT-CSM, UT-IG, RU, OSU)

http://www.caip.rutgers.edu/TASSL

The Research Team

Mary WheelerHector KlieClint Dawson

Todd ArbogastWolfgang Bangerth

Shuyu SunBurak AksoyluRaul Tempone

Xiuli GaiRuijie Liu

Jareen RungamornortCSM, UT

Manish ParasharViraj Bhat

Sumir ChandraZhen LiHua Liu

Vincent MatossianElectrical and Computer

Eng. Dept.Rutgers University

Joel SaltzUmit Catalyurek

Tahsin KurcBiomedical Informatics Dept.

Ohio State UniversityPaul StoffaMrinal Sen

IG, UT

Emerging Computational Science & Engineering

• Realistic, physically accurate computational modeling– Large computation requirements

• millions of elements, hundreds of functions, thousands of time steps, etc. – Dynamic and complex couplings

• multi-physics, multi-model, multi-resolution, ….– Dynamic and complex (ad hoc, opportunistic) interactions

• application ↔ application, application ↔ resource, application ↔ data, application ↔ user, …

– Very large design/parameter spaces• exhaustive searches not feasible

– Software/systems engineering/programmability• volume and complexity of code, community of developers, …

• Knowledge-based, data-driven, adaptive and interactive applications can enable accurate solutions to complex applications – symbiotically and opportunistically combine computations, experiments,

observations, and real-time data to model, manage, control, adapt, optimize, …

• scale (models/codes, data/information, systems) complexity, dynamism, heterogeneity, uncertainty (signal/noise, non-uniqueness)

• requirements, objectives and strategies (algorithms, behaviors, interactions, etc.) are dynamic and not known a priori

• solutions depend on state, execution context, and content/information

Knowledge-based Data-driven Global Scientific Investigation

Models dynamically composed.

“WebServices”discovered &

invoked.

Resources discovered, negotiated, co-allocated

on-the-fly. Model/Simulation

deployed

Experts query, configure resources

Experts interact and collaborate using ubiquitous and

pervasive portals

Applications& Services

Model A

Model BLaptop

PDA

ComputerScientist

Scientist

Resources

Computers, Storage,Instruments, ...

Data Archive &Sensors

DataArchives

Sensors, Non-Traditional Data

Sources

Experts mine archive, match

real-time data with history

Real-time data injection (sensors, laboratory

investigations, instruments, data

archives),

Automated mining & matching

Models write into

the archive

Experts monitor/interact with/interrogate/steer models (“what if”

scenarios,…). Application notifies experts of interesting phenomenon.

– Oil Reservoir Design• Applications/Services:

– Reservoir models, economic models, data analysis services, visualization services,…

• Data Sources:– History data archives, real-time market data, real-time

oil field data, …. • Resources:

– Instrumented oilfield (sensors/actuators), thin clients, compute servers, data servers, …

• Experts: – Field engineer, Petroleum engineer, Scientist,

Mathematician, Economist, etc.

HK1

The Instrumented Oil Field

““Closing the loopClosing the loop”” with optimizationwith optimization

Production forecast

Well placementSurface facility

planning

Gassman’sequations

Biot’s theory & Geomechanics

Seismic modeling

Reservoir characterization

Elastic Elastic parametersparameters

Seismic Seismic attributes/ attributes/ geologygeology

Pore fluid properties

Pressure/ Pressure/ saturation/saturation/pore pore volumevolume

IPARS

HK1 So. To summarize, we are closing the loop between production and seimic data. This is a interoperability framework that seek to reduce uncertainty. Traditionally, geophysics and simulation have been well separated compartments in science. The current technology opens the possibility to make both world much closer to each other for the sake of several crucial problems sucha as production forecast, well placement and surface explotaition plans. Hector Klie, 10/18/2003

DDDAS: The Instrumented Oil Field (UT-CSM, UT-IG, RU, OSU, UMD, ANL) (NSF ITR 01, 04)

Detect and track changes in data during production.Invert data for reservoir properties.Detect and track reservoir changes.

Assimilate data & reservoir properties intothe evolving reservoir model.

Use simulation and optimization to guide future production.

Vision: Diverse Geosystems – Similar SolutionsLandfillsLandfills OilfieldsOilfields

ModelsModels SimulationSimulation

DataDataControlControlUndergroundPollution

UndergroundPollution

UnderseaReservoirsUnderseaReservoirs

The Instrumented Oil Field of the Future (UT-CSM, UT-IG, RU, OSU, UMD, ANL) (NSF ITR 01, 04)

Detect and track changes in data during productionInvert data for reservoir propertiesDetect and track reservoir changes

Assimilate data & reservoir properties intothe evolving reservoir model

Use simulation and optimization to guide future production, future data acquisition strategy

• Production of oil and gas can take advantage of permanently installed sensors that will monitor the reservoir’s state as fluids are extracted

• Knowledge of the reservoir’s state during production can result in better engineering decisions

– Economical evaluation– Physical characteristics (bypassed oil, high pressure zones)– Productions techniques for safe operating conditions in complex and difficult areas

“Application of Grid-Enabled Technologies for Solving Optimization Problems in Data-Driven Reservoir Studies,” M. Parashar, H. Klie, U. Catalyurek, T. Kurc, V. Matossian, J. Saltz and M Wheeler, FGCS. The International Journal of Grid Computing: Theory, Methods and Applications (FGCS), Elsevier Science Publishers, Vol. 21, Issue 1, pp 19-26, 2005.

Knowledge-based Data-driven Management of Subsurface Geosystems

FactoryFactoryContaminantContaminantSensorsSensors

BiologyBiology ChemistryChemistryGeoGeo--

mechanicsmechanicsFluid Fluid

PhysicsPhysics

Management of Subsurface/Near Surface Geosystems

The Computational and Information Grid

• Emerging paradigm for distributed computational science– seamless aggregation– seamless (opportunistic) interactions/couplings

• Unprecedented complexity, challenges– Very large scales– Ad hoc (amorphous) structures/behaviors of virtual organizations

• p2p/hierarchical architecture– Dynamic

• entities join, leave, move, change behavior– Heterogeneous

• capability, connectivity, reliability, guarantees, QoS– Unreliable, lack of guarantees

• components, communication– Lack of common/complete knowledge

• number, type, location, availability, connectivity, protocols, semantics, etc.

Effective Oil Reservoir Management

• Why is it important – Better utilization of existing reservoirs– Discovering new reservoirs– Minimizing adverse effects to the environment

Better Management

Less Bypassed Oil

Bad Management

Much Bypassed Oil

Effective Oil Reservoir Management

• What needs to be done– Exploration of possible well placements for optimized production

strategies– Understanding field properties and interactions between and across

subdomains– Tracking and understanding long term changes in field characteristics

• Challenges – Geologic uncertainty: Key engineering properties unattainable– Large search space: Infinitely many production strategies possible– Complex physical properties and interactions: Complex numerical

models

Challenges: Optimization Process

• Numerical simulations – Simulations with millions of grid points are needed to realize highly

accurate models.– Compute intensive– Simulations are executed at multiple sites

• Function evaluation is very expensive. – Large models take too long to execute. – We need many evaluations:

• To completely characterize the solution surface, we may have to do thousands of computations

• Need to sample probability space, increasing the number of computations drastically.

• On-the-fly steering of optimization processes are desired.

A new generation of integrated and seamless simulations

Optimizing Oil Production on the Grid

Data mgmt../assimilation

AutonomicMonitoring

ManagementControl

Objectivefunction

Dynamic data

Static data

Visualization

Collaboration

Clients

Project AutoMate: Enabling Autonomic Applications(http://automate.rutgers.edu)

• Conceptual models and implementation architectures – programming models, frameworks and middleware services

• autonomic elements, dynamic and opportunistic composition/interactions• policy, content and context driven execution and management

Rudder

Coordination

Middlew

are

Sesam

e/DA

IS P

rotection Service

Autonomic Grid Applications

Programming SystemAutonomic Components, Dynamic Composition,

Opportunistic Interactions, Collaborative Monitoring/Control

Decentralized Coordination EngineAgent Framework,

Decentralized Reactive Tuple Space

Semantic Middleware ServicesContent-based Discovery, Associative Messaging

Content OverlayContent-based Routing Engine,

Self-Organizing Overlay

Ont

olog

y, T

axon

omy

Met

eor/S

quid

Con

tent

-bas

edM

iddl

ewar

e

Acc

ord

Prog

ram

min

gFr

amew

ork

“AutoMate: Enabling Autonomic Grid Applications,” M. Parashar, H. Liu*, Z. Li*, V. Matossian*, C. Schmidt*, G. Zhang* and S. Hariri, Cluster Computing: The Journal of Networks, Software Tools, and Applications, Special Issue on Autonomic Computing, Kluwer Academic Publishers.

Optimize

• Economic revenue

• Environmental hazard

• …

Based on the present subsurface knowledge and numerical model

Improve numerical model

Plan optimal data acquisition

Acquire remote sensing data

Improve knowledge of subsurface to

reduce uncertainty

Update knowledge of modelM

anag

emen

t dec

isio

n

START

Dynamic Decision Dynamic Decision SystemSystem

Dynamic DataDynamic Data--Driven Assimilation Driven Assimilation

Data assimilation

Subsurface characterization

Experimental design

Autonomic Autonomic Grid Grid

MiddlewareMiddleware

Grid Data ManagementGrid Data ManagementProcessing MiddlewareProcessing Middleware

A New Approach: Dynamic, Data Driven Reservoir A New Approach: Dynamic, Data Driven Reservoir ManagementManagement

Client Client. . . .

Grid-based Data Management and ManipulationTools

Grid-based Data Management and ManipulationTools

Visualization ToolsVisualization Tools

Oil ReservoirSimulation ToolsOil Reservoir

Simulation Tools

Reservoir MonitoringField Measurements

Data AnalysisData Analysis

ProductionForecasting

ReservoirCharacteristics

Datasets from Simulations and Field Measurements


Seismic DataSimulation Tools

Seismic DataSimulation Tools

ReservoirPerformance


Web Portal, Steering,Collaboration Tools

Web Portal, Steering,Collaboration Tools

Grid-enabled Production Optimization and Reservoir Management




Optimizing Oil Production

•Max: economic value of production (well position, rates)•Min: bypassed oil (infill drilling)•History matching (find reservoir parameters so that

simulation data matches production data)...........

AutonomicAutonomic OilOil Well Placement (UT-CSM, UT-IG)

• Optimization algorithm: use VFSA (Very Fast Simulated Annealing) – requires function evaluation only, no gradients

• IPARS delivers – fast-forward model (guess->objective function value) – post-processing

• Formulate a parameter space – well position and pressure (y,z,P)

• Formulate an objective function: – maximize economic value Eval(y,z,P)(T)

• Normalize the objective function NEval(y,z,P) so that:

( ) ( )min max Neval y,z,P Eval y,z,P⇔

Optimal Well Placement

If guess not in DBinstantiate IPARS

with guess asparameter

Send guesses

MySQLDatabase

If guess in DB:send response to Clientsand get new guess fromOptimizer

OptimizationService

IPARSFactory

SPSA

VFSA

ExhaustiveSearch

DISCOVERclient

client

Generate Guesses Send GuessesStart Parallel

IPARS InstancesInstance connects to

DISCOVER

DISCOVERNotifies ClientsClients interact

with IPARS

The Autonomic Grid FrameworkThe Autonomic Grid Framework

AutoMate Programming System/Grid Middleware

Knowledge/Data Driven Optimization of Oil Production

Components of the AORO Application

• IPARS : Integrated Parallel Accurate Reservoir Simulator– Parallel reservoir simulation framework

• IPARS Factory– Configures instances of IPARS simulations– Deploys them on resources on the Grid– Manages their execution

• VFSA : Very Fast Simulated Annealing– Optimizes the placement of wells and the inputs (pressure, temperature)

to IPARS simulations. • Economic Modeling Service

– Uses IPARS simulations outputs and current market parameters (oil prices, costs, etc.) to compute estimated revenues for a particular reservoir configuration.

• DISCOVER Computational Collaboratory– Interaction & Collaboration – Distributed Interactive Object Substrate (DIOS)– Collaborative Portals

Pervasive Portals for Collaborative Monitoring and Steering

AutonomicAutonomic OilOil Well Placement

permeability Pressure contours3 wells, 2D profile

Contours of NEval(y,z,500)(10)

Requires NYxNZ (450)evaluations. Minimumappears here.

VFSA solution: “walk”: found after 20 (81) evaluations

AutonomicAutonomic OilOil Well Placement (VFSA)

“An Autonomic Reservoir Framework for the StochasticM. Parashar, W. Bangerth, H. Klie, M.F. Wheeler, Cl

Optimization of Well Placement,” V. Matossian, uster Computing: The Journal of Networks, Software

Tools, and Applications, Kluwer Academic Publishers (to appear).“Autonomic Oil Reservoir Optimization on the Grid,” V. Matossian, V. Bhat, M. Parashar, M. Peszynska, M. Sen, P. Stoffa and M. F. Wheeler, Concurrency and Computation: Practice and Experience, John Wiley and Sons, Volume 17, Issue 1, pp 1 – 26, 2005.

AutonomicAutonomic OilOil Well Placement (SPSA)

Permeability field showing the positioning of current wells. The symbols “*” and “+” indicate injection and producer wells, respectively.

Search space response surface: Expected revenue - f(p) for all possible well locations p. White marks indicate optimal well locations found by SPSA for 7 different starting points of the algorithm.

AutonomicAutonomic OilOil Well Operation (SC ‘04)


Solution for 7 different initial guesses

Convergence history

Optimal Well PlacementOptimal Well Placement3 injectors and 4 producers

PressurePressure

Oil SaturationOil Saturation

Optimal Well PlacementOptimal Well Placement

Comparison of optimization approachesComparison of optimization approaches

MethodMetric

Nelder-Mead GA VFSA FDSA SPSA

Best solution -1.018e8 -1.073e8 -1.083e8 -1.062e8 -1.075e8

Average number of function evaluations 99.95 104.02 75.5 57.0 37.8

Optimal solution: F* = -1.098E8

Learned lessons:Learned lessons:

• Robust stochastic algorithms increases the chances to find (near) optimal solutions (VFSA)

• Several trials of a fast algorithm pay off against sophisticated algorithms (SPSA)

• Need to develop hybrid strategies

Parallel Optimization at Large ScaleParallel Optimization at Large Scale

Optimizer

Stochastic realization

1

Initial guess1

Initial guess2


2


n

Initial guessm

Initial guess1

Initial guess2

Initial guessm

Initial guess1

Initial guess2

Initial guessm

IPARS1

IPARS2

IPARSm

IPARSm+1

IPARSm+2

IPARS2m

IPARS(n-1)m+1

IPARS(n-1)m+2

IPARSnm

n*m parallel independent runs of IPARS n*m parallel independent runs of IPARS

A 3-level parallelism on the Grid:

… … ……

Conclusion

• DDDAS: Enabling the next generation knowledge-based, data-driven, dynamically adaptive applications on the Grid– can enable accurate solutions to complex applications; provide

dramatic insights into complex phenomena

• The Instrumented Oil Field: DDDAS for the management and control of subsurface geosystems– Models, algorithms, numerics – IPARS– Programming system, middleware – AutoMate– Data management, assimilation – DataCutter/Storm– Collaborative monitoring, interaction, control - Discover

• Autonomic oil reservoir optimization on the Grid• More Information, publications, software

– www.caip.rutgers.edu/~parashar/– [email protected]

Opportunities

FIELD & LABORATORY

SITES

DYNAMIC DATACOLLECTION

COMPUTATIONALSCIENCE

CS tools

Numerical algorithms

Remote sensing

Monitoring

Data assimilation

Reduction of uncertainty

the instrumented oil field - dddas.org gai ruijie liu jareen rungamornort ... mrinal sen ig, ut. ......

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