AT LOUISIANA STATE UNIVERSITY A Grid-enabled Workflow System for Reservoir Uncertainty Analysis Emrah Ceyhan, Gabrielle Allen, Chris White, Tevfik Kosar* Center for Computation & Technology Louisiana State University June 23, 2008 CLADE’08 UCoMS
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AT LOUISIANA STATE UNIVERSITY
A Grid-enabled Workflow System for Reservoir Uncertainty Analysis
Emrah Ceyhan, Gabrielle Allen, Chris White, Tevfik Kosar*
Center for Computation & TechnologyLouisiana State University
June 23, 2008CLADE’08
UCoMS
UCoMS Project
Reservoir Modeling & Uncertainty AnalysisComputing and Data Challenges
Data-Aware Workflow Solution
Results and Conclusion
Roadmap
Oil Industry in Louisiana• Major oil producing
state in US:– 5th in production– 8th in reserves– Home to 2 of 4 strategic
petroleum reserves– 17 petroleum refineries
(capacity 2.8M barrels/day)
– Ports receive ultra large oil tankers
– 20,000 oil producing wells, around 4K offshore.
• “Ubiquitous Computing & Monitoring System for Discovery & Management of Energy Resources”
• DOE/Louisiana BOR funded– Petroleum engineering– Wireless sensor networks– Grid technologies
• Applications– Reservoir simulation
• Uncertainty analysis, sensitivity studies, history matching
– Real-time well surveillance– Drilling performance analysis
with high-rate data
UCoMS
Reservoir Simulation• Mathematical model for fluid flow in a reservoir
involves density, permeability (K), mobility, pressure (P), production rate (q), porosity & saturation, where m denotes either oil, water or gas.
• Many geological parameters cannot be measured or modeled and are unknowns.
• We are using UTChem (3D, multiphase, multicomponent, compositional, variable temperature, FD simulator)
Reservoir Uncertainty Analysis
• Understand the effect of uncertainty in reservoir studies to guide development and operational decisions– Uncertainty in different (geological)
parameters (factors)• pressure, permeability, water saturation,
critical gas saturation, gas/water end points
– Factors (parameters) are classified into • Controllable: Can be varied by process implementers,
e.g. Well Location, injection rate, …• Observable: Can be relatively accurately measured
but not controlled, e.g. Depth to a structure, …• Uncertain: Cannot be accurately measured or
controlled, e.g. Permeability far from wells, …
UCoMS ChallengesComputation:• Millions of simulations, each running 16-160
hoursData:• Each simulation processing 40-400 MB of data
– More than 1PB data total• Real-time flow of data from sensors to grid
resources– Sensor control and monitoring– Automated allocation, location, transfer, and archiving \
Workflow:• Coordination of Computation and Data
8
End-to-end ScenarioExperiment
Sensing & Control
Computing
HPC & Grid resources
Data
Storage &Transfer
UTChem/BlackOil
EnKF
Sensor data
Seismic Models
9
Data Evolution
TRUTH
initial POROV =0.18
POROV =0.7
UCoMS Abstract Workflow
UCoMS Concrete Workflow
JOB i
JOB k
JOB j
get
put
Workflow Expansion
Stage-in
Execute job j
Stage-out
JOB i
JOB k
JOB i
JOB k
Individual Jobs
JOB j
get
put
Stage-in
Stage-out
Compute Jobs
Data placement Jobs
Workflow Expansion
Stage-in
Execute job j
Stage-out
Stage-in
Execute job j
Stage-outRelease input space
Release output space
Allocate space for input & output data
JOB i
JOB k
JOB i
JOB i
JOB k
JOB kIndividual Jobs
JOB j
get
put
Stage-in
Stage-out
Stage-in
Stage-outRelease input space
Release output space
Allocate space for input & output data
Compute Jobs
Data placement Jobs
Release input space
Release output space
Allocate space for input & output data
Workflow Expansion
DaP A A.dataDaP B B.dataJob C C.compute…..Parent A child BParent B child CParent C child D, E…..
DAG specification
Workflow Manager
A CBD
E
F
ComputeJob
QueueC
DaPJob
Queue
E
Separation of CPU & I/O
Data-Aware Scheduler Type of a job?
transfer, allocate, release, locate..
Priority, order?Protocol to use?Second vs Third party?Available storage space?Best concurrency level?Reasons for failure?Best network parameters?
tcp buffer sizeI/O block size# of parallel streams
Data-Aware Scheduler Type of a job?
transfer, allocate, release, locate..
Priority, order?Protocol to use?Second vs Third party?Available storage space?Best concurrency level?Reasons for failure?Best network parameters?
tcp buffer sizeI/O block size# of parallel streams
GridFTP
Separation of CPU & IO
Stork Transfer Methods• regular:
– one connection per file, serial transfer
• multi-connection:– one connection per file,
concurrent transfer
• single-connection:– one connection for all
transfers
• data-fusion:– merge small files into
larger chunks
16
A B
A B
A B
a small file
a small file
many small files
A Ba large file
Stork Transfer Results
Monitoring DAGs via WEB
Monitoring DAGs via WEB
Monitoring DAGs via WEB
Summary• Uncertainty Analysis in Reservoir Simulations
– can be both computationally and data intensive– may require complex workflows
• We provide “data-aware workflow management”– computation & I/O separated at lower levels– data related tasks are handled by the data scheduler– data optimizations made easier
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For more informationUCoMS: http://www.ucoms.org
Stork: http://www.storkproject.orgPetaShare:http://www.petashare.org
This work has been sponsored by:DOE, NSF and LA BoR
For more informationUCoMS: http://www.ucoms.org
Stork: http://www.storkproject.orgPetaShare:http://www.petashare.org
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This work has been sponsored by:DOE, NSF and LA BoR
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