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

Placement

M. Parashar et alThe Applied Software Systems Laboratory

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

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

Knowledge-based Data-driven Management of Subsurface Geosystems: The Instrumented Oil Field (ITR/DDDAS)

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.

Joel SaltzUmit Catalyurek

Tahsin KurcBiomedical Informatics Dept.

Ohio State University

Manish ParasharViraj Bhat

Sumir ChandraZhen LiHua Liu

Vincent MatossianElectrical and Computer

Eng. Dept.Rutgers University

Mary WheelerHector KlieClint Dawson

Todd ArbogastWolfgang Bangerth

Shuyu SunBurak AksoyluRaul Tempone

Xiuli GaiRuijie Liu

Jareen RungamornortCSM, UT

Paul StoffaMrinal Sen

IG, UT

The Research Team

LandfillsLandfills OilfieldsOilfields

ModelsModels SimulationSimulation

DataDataControlControlUndergroundPollution

UndergroundPollution

UnderseaReservoirsUnderseaReservoirs

Vision: Diverse Geosystems – Similar Solutions

Effective Oil Reservoir Management: Well Placement/Configuration

• 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: Well Placement/Configuration

• 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

A new generation of integrated and seamless simulations

Objectivefunction

AutonomicMonitoring

ManagementControl

Data mgmt../assimilation

Dynamic data

Static data

Visualization

Collaboration

Clients

Optimizing Oil Production on the Grid

Multiphysics

Black-

oil

Thermal

Compositional1-phase

Chemistry

Fully

Implicit

IMPES

IMPES

IMPESIMPES

MultinumericsMultiscale

Multiblock Approach

Integrated Parallel Accurate Reservoir Simulation SystemIntegrated Parallel Accurate Reservoir Simulation SystemState of the art solvers

Highly scalable

Couplings with geomechanics and chemistry

Multiblock approach (subdomain can treat unstructured grids, DG or MPFMFE)

IPARSIPARS

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.

Data Virtualization Support: STORM

• Support efficient selection of the data of interest from distributed scientific datasets and transfer of data from storage clusters to compute clusters

• Data stored in distributed collections of files. • Data Virtualization and Subsetting Model: Grid virtualized

object relational database – Virtual Tables– Select Queries– Distributed Arrays

SELECT <DataElements>FROM Dataset-1, Dataset-2,…, Dataset-n

WHERE <Expression> AND <Filter(<DataElement>)>GROUP-BY-PROCESSOR ComputeAttribute(<DataElement>)

Data Service DataVirtualization

Virtual Tables

Scientific Datasets

Optimal Well Placement/Configuration

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

AutoMate Programming System/Grid Middleware

History/ Archived

Data

SensorData

Pervasive Portals for Collaborative Monitoring and Steering

AutonomicAutonomic OilOil Well Placement/Configuration

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/Configuration (VFSA)

“An Reservoir Framework for the Stochastic Optimization of Well Placement,” V. Matossian, M. Parashar, W. Bangerth, H. Klie, M.F. Wheeler, Cluster 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.

Solution for 7 different initial guesses

Convergence history

AutonomicAutonomic OilOil Well Placement/Configuration (SPSA)

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 Well PlacementOptimal Well Placement

Comparison of optimization approachesComparison of optimization approaches

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

Stochastic realization

2

Stochastic realization

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/– parashar@caip.rutgers.edu