the instrumented oil field -...
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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/– [email protected]