unmanned aircraft in the oil & gas...
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UNMANNED AIRCRAFT IN THE OIL & GAS INDUSTRY
Supported by the National Science Foundation
Jamey Jacob
School of Mechanical & Aerospace Engineering
Oklahoma State University
Award #1539070
Unmanned Systems Research Institute
OSU UNMANNED SYSTEMS RESEARCH INSTITUTE
THE UAS HYPE
PEAK OF INFLATED EXPECTATIONS
PLATEAU OF PRODUCTIVITY
SLOPE OF ENLIGHTENMENT
TROUGH OF DISILLUSIONMENTTECHNOLOGY
TRIGGER
(EXPECTATIONS)
WHERE THE PROBLEM BEGINS
When someone says:
“Why don’t we just use a UAV?”
UAS
UGV
THE LAST GENERATION OF DRIVERS
THE UAS BIG DATA DIVIDE
• Most consumer SUAS are good at doing only one thing – taking pretty pictures; this is usually not a big data problem (yet)
• Most high end (i.e., military) UAS do collect Big Data, but they also have Big Data resource support
• Emerging UAS (e.g., precision agriculture, delivery)fall in between – worst of both worlds: some of thecapability with none of the supporting infrastructure
Predator GCS
TYPES OF UAS
• Fixed wing vs. rotary wing
APPLICATIONS TO THE OIL & GAS INDUSTRY
MYRIAD OF USES
• Tank/pipeline/stack inspection, leak/spill detection, facilities surveys
• Visible/thermal cameras
• Gas sensors
• Commercially available
AUTONOMOUS PIPELINE INSPECTION
North American Shale Magazine
CARBON CAPTURE AND STORAGE
Fracture Pavement Near Farnsworth
Oil Unit
PLUME TRACKING
FARNSWORTH FIELD TESTS
MOTIVATION
OPERATIONAL VISION
BHGE RAVEN• Development of BHGE Raven CH4
sensing platform
• Featured at OGTC Grand Opening
RAVEN EVOLUTION
Prototype
Demonstrator
Field Unit
RAVEN MARK 2
RAVEN MARK 3
GSR COMMUNICATIONS
• Worked with Dawson to test aerial communications with seismic recorders
• Found optimum speed and altitude to maximize connectivity
PHOTOGRAMMETRY
Structure from motion image based point-cloud capability
LIDAR
• Requires ground reference station and independent IMU solution, with
high cost & setup time, but provides detailed point clouds in near real-time
Scanner
GNSS Rx
GNSS Tx
LiDaR UAS
PACKAGE DELIVERY
• Regular and emergency supply delivery to remote sites
• Requires DAA capability and ADS-B
TURBOELECTRIC UAS: PIPELINE INSPECTION
➢ Hydrocarbon fuel is 75 times more energy
dense than batteries
➢ Endurance expected to be 5 to 10 times
greater than all electric system
Over 2.7M miles
MODELING EFFORTS
TERRAIN AND FOLIAGE
POLLUTION AND HABS
UTM
• As the NAS opens up to UAS, UTM (unmanned traffic
management) will be one of the biggest drivers of Big Data
needs in the coming years
• Moved to autonomous data, SAA, and flight plan management
• Shifting control paradigms require increased Big Data analysis
• Direct control
• Management by consent
• Management by exception
TAKE AWAY• There is a need for research, training and education
• Short term: Part 107 and device specific flight training
• Long term: the amount and type of training is still unknown
• Data analytics, including image analysis, payload development, and
operation
• In the future, flight training will not be necessary (“self flying”
systems), but data analysis will be required
• Developments in autonomy, vehicle systems and payloads will open new
opportunities in environmental monitoring
• The future depends on both technical and regulatory developments in the
industry
BACKUP
METEOROLOGY T E T H E R E D
U A S F O R
A T M O S P H E R I C
P R O F I L I N G
A U T O N O M O U S &
R E M O T E S A M P L I N G :
- P R O G R A M M A B L E
S A M P L I N G I N T E R VA L S
A N D A L T I T U D E S
- 15 m i n u t e b a t t e r y
e n d u r a n c e
- O P T I O N F O R P O W E R
O V E R T E T H E R O R
I N D U C T I V E C H A R G I N G
- P R O T E C T E D B Y
E N C L O S U R E W H E N
N O T I N U S E
3 D R I R I S + P L A T F O R M
C O M P A C T S E N S O R S O L U T I O N :
- T E M P E R A T U R E
- P R E S S U R E
- H U M I D I T Y
- W I N D S P E E D
( T o S C A L E )
“The vertical component of U.S. mesoscale
observations is inadequate. Assets required to
profile the lower troposphere above the near-
surface layer (first 10m) are too limited in what
they measure, too sparsely or unevenly
distributed, sometimes too coarse in vertical
resolution, sometimes limited to regional areal
coverage, and clearly do not qualify as a
mesoscale network of national dimensions.
Likewise, vertical profiles above the Earth’s
surface are inadequately measured in both
space and time. The solutions to these particular
deficiencies require leadership and infrastructure
investments from each of the pivotal federal
agencies.” – 2009 NRC Report
“THERE ARE MORE REGISTERED DRONE OWNERS (325,000) THAN
THERE ARE LICENSED PILOTS (320,000) OF MANNED AIRCRAFT.” - FAA
ADMINISTRATOR HUERTA, FEB. 2016
2,500,000
UTM VISION
Amazon
NASA
UAS NAS INTEGRATION
• How do we utilize UAS and Big
Data to solve current traffic
management problems?
• Every UAS becomes a data feeder
into the ATM “BDS” – e.g., CAT,
evolving weather, SAA, etc.
• What is perceived as a problem
(UAS NAS integration) becomes
an advantage
PRECISION AGRICULTURE
• To date high resolution generated by UAS has been a drawback, not an asset
Manned/Satellite UAS
UA
PRECISION AGRICULTURE
Bird view
A B
Looking from Pt. B towards A
Cross section between mark A and mark B (depth 5m)
Uni
vers
ity o
f Fre
ibur
g
PRECISION AGRICULTURE
Data acquisition
Image preprocessing
Stitch images
Extract indiv. plant data
WEATHER & ENVIRONMENTAL MONITORING
OSU CO2 EOR-CCUS Monitoring Network
Scale
- Base
CLOUD-MAP
• June 2016 flight campaign: 250 flights of 12 instrumented systems over 3
flight days; each vehicle with >1 meteorological sensor (some with 10+)
• Over 107 data points – still analyzing
SUAS Operations w/ Ground Network
FW
RW
Eddy
Covariance
Mesonet
Profiling
Volumetric
SamplingLayered
Sampling
Transects
APPLICATIONS TO FORECASTING
“Nature”Simulation of
ObservationsData Assimilation
Forecasts with
and without
Observations
Verification
Calibration
Boundary layer variability
observed using UAS
Forecast
Observation
OTHER EXAMPLES
OSBI Fish & Wildlife Service
YOU GET A DRONE! YOU GET A DRONE!
EVERYONE GETS A DRONE!
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