Oil and Gas Spill and PipelineOil and Gas Spill and Pipeline Condition Assessment UsingCondition Assessment Using

Remote SensingRemote Sensing

Using New Tools forUsing New Tools for Situational AwarenessSituational Awareness

By: William E. RoperBy: William E. RoperGeorge Mason UniversityGeorge Mason University

SubijoySubijoy DuttaDuttaS&M Engineering ServicesS&M Engineering Services

Presentation OutlinePresentation Outline

�� IntroductionIntroduction�� Pipeline MonitoringPipeline Monitoring�� HyperspectralHyperspectral Oil Spill CharacterizationOil Spill Characterization�� ConclusionsConclusions

VisualizationVisualization

�� Assists the understanding of dataAssists the understanding of data�� Able to represent temporal changesAble to represent temporal changes�� A more challenging integrationA more challenging integration

requirementrequirement�� New software and hardwareNew software and hardware

developments are in this directiondevelopments are in this direction

Pipeline Monitoring andPipeline Monitoring and Condition AssessmentCondition Assessment

�� Imagery products inImagery products in multiple resolutionsmultiple resolutions and characteristicsand characteristics

�� Integration of dataIntegration of data sourcessources

�� Visualization ProductsVisualization Products�� Tailored products forTailored products for

the decision makerthe decision maker

• Mechanical Damage is #1 Pipeline Hazard

• Mechanical Damage Related to Encroachment – 29% of incidents and 20% of fatalities – Incident Distribution

• 72% Class 1 (rural land use) • 11% Class 2 • 15% Class 3 • 2% Class 4 (high density land use)

Motivation for Advanced Detection of 3rdMotivation for Advanced Detection of 3rd Party EncroachmentParty Encroachment

Satellite Monitoring for Pipeline AssetSatellite Monitoring for Pipeline Asset Safety and Security AssessmentSafety and Security Assessment

�� To develop and deliver a practical,To develop and deliver a practical, reliable, and economical means ofreliable, and economical means of monitoring pipeline assets usingmonitoring pipeline assets using earth observation data in twoearth observation data in two fundamental areas of pipeline safetyfundamental areas of pipeline safety

�� Third party encroachmentThird party encroachment�� Ground motionGround motion

Encroachment Monitoring

Encroachment Monitoring

Encroachment Monitoring

4 metre Multispectral

(colour) image

Encroachment Monitoring

4 metre Multispectral

(colour) image

RADARSAT

Overlay

1 metre

overlay

Targets

Encroachment Monitoring Combined Radar & Optical

8-9metre

panchromatic

Detected

Computerized change detection analysis

encroachment event

Notice Alarm Alert

Geo-referenced encroachment event

Field personnel are notified

Encroachment Monitoring Concept Service

Encroachment Event

Time Sequence Acquisitions

Satellite detected

Satellite Monitoring

88.0%Twice per day

78% to 93%70.0%Once per day

50% to 70%40.0%Twice per week

32% to 55%20.0%Once per week

5.0%Once per month

2.0%Once per 3 months

1.0%Once per 6 months

0.4%Once per year

Probability of Detection (%)

With Satellite Sensor Systems

Probability of Detection (%)

With Aerial Sensor Systems

Frequency of Imagery

Collection

Optical Data: Integration withOptical Data: Integration with GISGIS

Hyperspectral SensingHyperspectral Sensing

Single Pixel

Spectral Bands

Spatial Pixels

Flight Line

Wavelength

Inte

nsity

Pixel Spectrum

Single Sensor Frame Series of Sensor Frames

GREEN

Manmade and NaturalManmade and Natural ReflectancesReflectances50

40

30

20

10

0

0.4 0.6 0.7 0.8 1.3

Artificial turf Asphalt

Fallow field

Sandy loamy Soil

Concrete

Clear water

Wavelength (micrometers)

Grass

Visible 0.5

BLUE GREEN RED

Near IR

R E F L E C T A N C E

High

Low

Detected Large and Small GasDetected Large and Small Gas LeaksLeaks

PutuxentPutuxent River Oil Spill StudyRiver Oil Spill Study AreaArea

Illustration of Data Collected withIllustration of Data Collected with the AISAthe AISA HyperspectralHyperspectral SystemSystem

Supervised Classification of theSupervised Classification of the Image Data using ENVIImage Data using ENVI

ChallengesChallenges

�� Methods and authorities for improvedMethods and authorities for improved data sharingdata sharing

�� Disciplinary differences betweenDisciplinary differences between developers and usersdevelopers and users

�� Multi sensor data integrationMulti sensor data integration�� Interdisciplinary approach to needsInterdisciplinary approach to needs

development and productdevelopment and product requirementsrequirements

Research DirectionsResearch Directions

�� Information Integration and VisualizationInformation Integration and Visualization�� Expand Applications StudiesExpand Applications Studies�� Development of fuzzy classification systemsDevelopment of fuzzy classification systems�� Applications of neural networksApplications of neural networks�� Echelon analysis methodsEchelon analysis methods�� Data fusion and data miningData fusion and data mining �� Integrated Scenario ModelingIntegrated Scenario Modeling

For More InformationFor More InformationContact:Contact:

William Roper George Mason University Ph:703 993-1648 Email: wroper@gmu.edu