fibre optical distributed sensing -...
TRANSCRIPT
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Fibre Optical Distributed Sensing
STRONGMAR Winter School - Aberdeen 23 March 2017
Jon O. Hellevang – [email protected]
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Outline of Presentation
• About CMR
• Project Examples:– Marin Monitoring
– Optics
– Decision Support
• Distributed Fibre Optical Sensing – ODIMS
– Distributed Acoustic Sensing (DAS)
– Distributed Sensing
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OUR LEGACY
Our legacy can be traced back to 1930, as part of the
Christian Michelsen Institute.
The institute was established to provide free and
independent research, as a personal initiative from former
prime minister Christian Michelsen.
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OUR MISSION AND VALUES
Christian Michelsen Researchcreates value for society andcustomers through innovativeand sustainable solutions
Trustworthy | Innovative | Committed | Competent
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CHRISTIAN MICHELSEN RESEARCH AS
• Owner structureo University of Bergen (50%)
o UNI Research AS (35%)
o Statoil Technology Invest AS (5%)
o Sparebanken Vest (5%)
o CGG Services (NORWAY) AS (5%)
• Non profito CMR is a non profit organization and all
profit is invested into new research
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ORGANISATION
CMR group
CMR
Science and Technology
Prototech Gexcon Tecom
~60 employees
MeasurementscienceComputer science
~ 40 employees
Mechanicaldesign and productionFuel cells
~ 100 employees
Safety and risk managementAdvanced dispersion, explosion and fire modelling
XSENS ASOffshore Sensing ASUPTIME CC ASZegPower ASBioZEG ASSeamless AS
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CMR Science & Technology main market areas
Renewable EnergyOffshore Wind
Geothermal Energy
CO2 Storage
Energy Systems
Marine & EnvironmentCost-effective sensors and
observation platforms
Autonomous and power efficient systems
Distributed sensing
Data management and processing tools
Oil & GasFlow and flow assurance
Fluid characterization
Courses and test facilities
Subsea leak detection
Subsea pipeline inspection
Visualization tools
Advisory services, training courses and testing, test centre
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CMR Science & Technology competence areas
Properties
Temperature
Pressure
Wind
Fluid flow
Etc.
Sensing
Electromagnetics
Optics
Acoustics
Gamma
Processing
Filtering
Optimization
µProcessors
Electronics design
Embedded software
Communication
Mesh
Networking
Cloud
Wireless
Analysis
Analysis
Modelling
Big Data
Decision
Visualization
Automation
Decision support
Machine learning
From sensing to decision
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Acoustic measurement methodsRadiosondeCoastal navigation
1930
High-voltage equipment for treatment of cancerNera AS
1940
Harestua Solar ObservatoryCERN atom colliderNuclear reactors, Norway
1950
1960Andøya Rocket RangeAanderaa Instruments ASModelling assignments
1970Meteorological instrumentation and positioning systemsComputing centre for HydroTemperature system for HansaImproved pacemaker
1980Process safety
Computer technologyMultiphase flow
measurementFluenta AS and Prototech AS
Fuel cells to MjøllnerSpace research
The torch, OL LillehammerGexCon AS
1990
Space flowerpotsFME and SFI centres
Zeg Power AS and CellPower AS
2000
Offshore Sensing ASGreenstat AS
Michelsen Medical ASMF Vågen (on hydrogen)
Sino-AkvaVis (China)
2010 –
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CMR Optics Group
• Jon O. Hellevang (MSc, photonics)
• Peter Thomas (PhD, photonics)
• Erling Kolltveit (PhD, fibre optics)
• Benny Svardal (BSc, optics)
• Dag Roar Hjelme, Scientific Advisor 15% (Professor NTNU)
• Bård Henriksen (MSc, electronics)
• Stian Stavland (MSc, instrumentation)
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Focus area
• Technologies:
• Fibre Optics
• Spectroscopy
• Imaging and robust optics
• Applications:
• Condition monitoring / distributed sensing
• Water monitoring
• Fluid and flow characterisation and measurement
• 3D imaging, structural surveillance
• Marked:
• Main focus on the energy sector
• Marine, industry, transport and medical
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PROJECTS EXAMPLE- MARIN MONITORING
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AALDOG – Active Acoustic
AALDOG – Active Acoustic Leak Detection ofsubsea Oil and Gas • Goal: Develop subsea technology for long
range detection of oil and gas leakage• Partners: Metas, CMR, IMR, UiB, Statoil,
Kongsberg Maritime• Funded by: DEMO 2000 / RFF VestCMR contribution: Acoustic propagation and
scattering – simulations and measurements, measurement data analysis.
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IMR, CMR, FFI, NERSC, UiB, UiT, UNI Research, SINTEF ICT, Statoil, GCE Subsea, Fishermen’s AssosiationFunded by: Research Council of Norway
1
6
7
2
3
4
5
5 km
© Mareano / IMR / NGU
Lofoten Vesterålen (LoVe)Observatory
Node 1 in place:• Hydrophone• Echosounders• Current profilers• Optical cameras• Temperature,
pressure• Chemical sensors• + Auxillary datahttp://love.statoil.com/
Node 2-7 in progress
Node 3 @ 234mNode 5 @ 2490m
• Study the marine ecosystem.
• Real time approach
• Enable safe and sound coexistence of several ocean industries.
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BRIDGES
• Bringing together Research and Industry for the Development of Glider Environmental Services
• ARMINES, ACSA-ALCEN ++
• Gliders for:
– Ecosystem
– Oil and gas
– Deep sea mining
• Sensors: chemical, optical, acoustic
• 2015-2019
Illustration: ACSA
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GLIDER
DEMO 2000 project «GLIDER» (2017-2019)
Akvaplan Niva, NIVA, CMR, NTNU, UiT, NU, MET, industry partners, …
• Mapping environmental data using three unmanned autonomousvehicles
– Seaglider (KM), Sailbuoy (OS) and Waveglider (LR)
• The vehicles will operate and collect data with a wide range of sensors
• In/near LoVe august 2017 (initial deployment), and for an extendedperiod and area winter/spring 2018
– Excact area and mission plans TBD
Illustrations: Kongsberg Maritime Offshore Sensing Liquid Robotics
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Sailbuoy – Technical Data
Unmanned Surface Vehicle
• Length: 2m Width: 0.5 m
• Displacement: 56 kg
• Payload: 10 kg / 60 dm3
• Average speed: 1-2 knots
• Tested navigable wind speed range: 4 – 30 m/s
• On-board autopilot and optional data logger
• Mission design length: 12 months
• Typical operational period: 1 - 3 months
• Global 2 way satellite communication
http://www.sailbuoy.no/
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Acoustic modem
Sailbouy - Sensors and Payload
Water quality
sensors
Antennas
Oceanographic
sensors
Solar panels
Water quality
sensors
Echo sounder
Payload
Wave sensors
Meteorological
sensors
AutopilotBatteries
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Sailbuoy – Application Example
NERSC: Ice edge north/west of Svalbard
Ice waves vs. ocean waves
MET Norway
Wave measurements for improving offorecast models
NORCOWE Fino1 Campaign
Deep-C and MET Norway, Gulf of Mexico
Surface temperature, salinity and oxygen
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Sailbuoy – Video demonstration
• https://www.youtube.com/watch?v=wMbAxejIDAM
• Video gallery: http://www.sailbuoy.no/gallery
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PROJECTS EXAMPLE- OPTICS
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Optical sensors for measuring dissolved CO2
Need for detecting CO2 leakages from geological storage, monitoring ocean acidification and aquaculture applications
• Standard procedures is laboratory analysis of water samples – time consuming and no online monitoring
• Commercially available sensors bulky expensive and consume a lot of power
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Optical CO2 sensors – Project timeline
• 2009: First fluorescence lifetime prototype, development of calibration setup
• 2010-2013: numerous field trials and gradual improvement of performance
• 2014: Available to buy from Aanderaa data Instruments (AADI)
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Optical CO2 sensor - Performance
• Sensitivity between 100-5000 µatm
• ~ 1min response time
• Stable over several months
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Other chemical sensing technologies
• 2017-2019 New RCN funded project
with Aanderaa
• ”A quantum dot on nanostructured
sapphire pH sensor for reliable
long-term monitoring applications”
• Promising results with solid state,
compact technologies for monitoring
NH3 and H2O2 in water
• NH3 sensitivity in the low ppb range
• H2O2 sensitivity in the low ppm range
• Also considering if some of the same
technologies can be used for H2S
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ClearVIEW – Anti fouling optical windows
Fouling of optical windows has hinderedthe wide spread use of optical sensors in oil and gas applications
• Proanalysis OiW probe uses cavitationto periodically clean a sapphire window
• Window has limited lifetime
Develop sapphire windows that resist oildeposition
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ClearView – Project timeline
• 2012: Project startup
• 2013: Development of experimental setups for window characterisation
• 2014-2015: Demonstrate influence of sapphire finish, coating and nanostructuring on resistance to oil deposition
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ClearView – Results
• EU project application: Anti-iceing optical windows
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PROJECTS EXAMPLE - DECISION SUPPORT
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• Data Analysis and Big Data
• Decision Support Systems
– SARA – Search and rescue
– AIS Online
– SHIVR
– Enlighten
– LSSS
Decision support: Putting knowledge to use
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In-house Developed Big Data Technologies
• Interactive Visual Analysis
– Enlighten
• AIS Track Server
– Big Data storage, visualization and statistics
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Video - DEMO
• Video - Visual Analysis of Multivariate Movement Data using Interactive Difference Views
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DISTRIBUTED FIBRE OPTICALSENSING (DXS)
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Fibre optic dowhole communication
More advanced DH logging equipment
=> Higher bandwidth requirements
• Electrical communication systems have limited bandwidth
• Local storage eliminate online monitoring
• Downhole data reduction challenging and limit data quality
• Fibre optical communication enable high data rate
• => Fast and reliable operations with advanced logging tools
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Fibre optic downhole communication - Results
Properties Performance
Temperature 177°C
Data rate 100Mbps
Length 10km
Typical eye-diagram recorded at 177C for 100Mbps
• Successful lab-testing of high temperature, high speed fibre optical communication system
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Distributed Temperature Sensing (DTS)
• CMR have a DTS-unit
• 5km measurement range
• 1.5 meter spatial resolution
• General purpose, wide range of applications
• CMR has used it for geothermal energy and
process monitoring applications
• Linking DTS to other fibre optical distributed
sensing data
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ODIMS – ONLINE DISTRIBUTED INTEGRITY MONITORING SYSTEM
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ODIMS – Project info
• Funded by the Research Council of Norway (RCN) 2015-2018
• Petromaks 2, special «Groundbreaking» call
• Main objective: “Develop a flexible and scalable technology capable of continuously measuring humidity, water, salinity and temperature with a high spatial resolution (< 10cm) over km length scales.”
• Project steering comittee:
• Kari Marvik, Vice President, CMR S&T
• Dr. Arne Ulrik Bindingsbø, Leader, production technology, Statoil
• Geir Harris, Technical director, Senior Vice President Technology and Development, Beerenberg Corperation
• Magne Husebø, CEO, Xsens AS
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The CUI challenge
News article [Statoil website 2012]
Example of CUI
Chevron refinery fire 2012
• Aging infrastructure and lifetime extension
• Corrosion under insulation (CUI) especially challenging
• Current fixed interval strategies for corrosion under insulation (CUI) leads to:
– Unnecessary inspection and maintenance
– Unplanned shutdowns with high costs
– HSE risk with potentially serious HSE consequences
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ODIMS - Idea
• Develop a system capable of early detection of CUI
• Key requirements:
– Truly distributed monitoring covering large structures
– Online continuous monitoring
– Early detection of corrosion indicators
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ODIMS - Architecture
• Distributed fibre optic measurement
• Multi parameter
• Flexible, scalable, modular
• Easy integration of point sensors
• ODIMS will enable targeted and costefficient CUI inspection and maintanance
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ODIMS - Primary sensing mechanism
• Sensing fibre with hygroscopic coating
• Water uptake by coating leads to strainin fibre
• Strain profile along the fibre is measured via backscattered laser light
Water exposed region
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ODIMS - Measurement readout
• PC sized readout instrument
• µ measurement resolution (equal to a few % RH)
• ~ cm spatial resolution
• Low energy laser source (Class 1)
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100 120 140 160 180 200 220
0
10
20
30
40
50
60
70Strain response vs time fibre C06
Time/hours
30% RH
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10
20
30
40
50
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70Strain response vs time fibre B02
Time/hours
30% RH
70% RH
ODIMS - Humidity response
Fibre with
medium
sensitivity
Fibre with low
sensitivity
100 120 140 160 180 200 220-5
0
5
10
15Strain response vs time fibre C06
Time/hours
30% RH
100 120 140 160 180 200 220-5
0
5
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15Strain response vs time fibre C06
Time/hours
30% RH
100 120 140 160 180 200 220
0
10
20
30
40
50
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70Strain response vs time fibre B02
Time/hours
30% RH
70% RH
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ODIMS – Salinity response
• Water salinity
• Increased salinity reduce water ingress in fibre coating
=> Reduced strain in fibre
• Figure shows strain in Fibre Bragg Grating (FBG) when alternating between saline water and fresh water
• Quite repetitive response is observed
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ODIMS – Enhanced sensitivity coatings
• CMR-built coating setup for investigating effect of coating thickness on sensitivity
• Example of one coating type: Sensitivity levels off when coating thickness > 0.5mm
• Identified theoretical model for describing sensitivity vs thickness
• Increasing thickness reduces response time
Coating thickness [µm]
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ODIMS – Sensing range
• Have demonstrated excellent measurement resolution over 2 km fiber with high spatial resolution (<10cm)
Mechanical stretch Complex humidity profile
Short range
Long range
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ODIMS – Multiplexing / Sensing range
• Multiplexing of optical fibres
=> Ability to cover larger infrastructure
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ODIMS - Vibration resistance• System tested in CMR flow loop rig • Found vibration might limit sensing range
when targeting 2km range combined with cm spatial resolution
• Have successfully tested vibration compensation methodology
• Investigating using ODIMS for making vibration measurements
ODIMS interrogator
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ODIMS - Point sensors option
• Demonstrate the possibility to add point sensors along fibre without the need to break/splice the fibre
• Can be used to add points sensors e.g. wall loss or other types of material degradation
• Can integrate 3rd party sensors
PressureVessel
Optical fibrePoint sensors
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ODIMS - Decision support
• Decision support system enabling targeted inspection and condition based maintenance
• Statoil and Beerenberg/Benarx will contribute with expertise
• Flexible and scalable decision support system:
– Automatic calibration
– Easy expansion and integration of new section or fibres and/or point sensors
– Visualisation integrated with infrastructure 3D models
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ODIMS – Pilot testing
• 1500 hour test at Benarx’s CUI test facilities.
• Humidity sensitive and referencecables installed on carbon steelpipes
• Four different insulation types
• On the fly data analysis
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w w w w w
w
w
Temperature switch off
ODIMS - Pilot testing - Results
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Fibre in 6.5 mm tube – Cell glass and air gap - Drying out
ODIMS - Pilot testing - Results
Fan on
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ODIMS - Summary
• Demonstrated distributed measurement of;
• Humidity
• Water
• Salinity
• Temperature
• ODIMS targeting cost efficient CUI inspection and maintenance
• Potential to develop ODIMS solutions for other applications
• We are investigating possibility to develop solutions further to monitor
more parameters
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DISTRIBUTED ACOUSTIC SENSING(DAS)
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DAS – CCS Monitoring
• Distributed seismic monitoring for geological carbon sequestration
• Send a fast puls train into the fibre
• Interpret returning Rayleigh backscattering
Geophones vs. DAS
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DAS – CCS Monitoring - Example
DAS signal recorded after 3 hammer blows
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DAS – CCS Monitoring - Shot location test
Circles = Source
Star = Location identified by DAS
Quite good agreement. Offset only a few meter
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DISTRIBUTED SENSING (DXS)New initiative
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ACT proposal: CO2 Early Warning System «SmartStore»
Seafloor
Topreservoir
Injectionwell
Acoustic signal
Ethernetconnection
CO2 plume
Sea level
Overburdenleakagedetection
Reservoirinjectionmonitoring
Leakage to sea/surfacedetection
Data processing
Fiber optic array(DAS, DCS, DTS)
Ethernetconnection
Digital (cabled) sensor network (seismic, pressure, tilt, EM)
Wireless communication (i.e. acoustics, radio, FSO)
Lander-basedHigh-resolutionsensors
3rd partyHigh-resolutionsensors
Reference/ baseline site
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SmartStore system and sensor sub-systems
SmartStoreEarly Warning System
Sensor system Data processing Communication
Sub-system/ sampling domain
Seismic/EM DxS 3rd ptysensors
Water/Air x x
Seafloor/surface x x
Overburden x x
Reservoir x x Sub-system/ sensors
Seismic/EM
DxS 3rd ptysensors
Acoustic signal
x x
Chemical signal
x x
Pressure x
Temperature x