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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 & Technology Louisiana State University June 23, 2008 CLADE’08 UCoMS

A Grid-enabled Workflow System for Reservoir Uncertainty Analysis · 2014-01-19 · A Grid-enabled Workflow System for Reservoir Uncertainty Analysis ... June 23, 2008 CLADE’08

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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

21

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

Hmm..

This work has been sponsored by:DOE, NSF and LA BoR