AIS2010 Innovative use of AIS data Goal: The main objective is by alternative use of AIS data prevent accidents, reduce impact of oil spill and to provide AIS-data to different end users Øyvind Endresen (Project manager), DNV Research 11. desember 2006 Version 1
Content About the project WP1: Modelling Details Static Risk Modeling, all accident categories Dynamic Risk Modeling (Drift Grounding only) WP 2, 3: In brief WP4: Internet-based GIS-services & Demonstrator (WP1) 11. desember 2006 Version 2
AIS2010-Innovative use of AIS data Schedule Start: January 2005 Duration: 24 months Budget: 7.2 Million NOK Participants: 7 Sponsoring: The Norwegian Research Council Project Owner: DNV Participants: The Norwegian Coastal Administration, CMR, SINTEF, Met.no, SEMEKOR, C-Map Source: The Norwegian Coastal Administration For more information: Det Norske Veritas DNV Research Øyvind Endresen,+4767578558 Per Olaf Brett, +4767578496 Web page: http://www.fargisinfo.com/ais2010/ 11. desember 2006 Version 3
Work Packages WP1 Establish method for estimating real-time and projected dynamic risk for crude oil tankers sailing along the Norwegian coast (focus area: North of Norway) WP 2 Improve oil spill management, combining AIS, ship, contingency, weather information etc. WP 3 Improve safety level on board by integration of AIS, ship, navigation, weather, risk information etc. WP 4 Develop internet-based GIS-services that provide AISdata and other data to different end-users Slide 4
AP1 I AP 1 er metodikk på plass for å angi risiko tonnasje (råolje tankere), med utgangspunkt i statiske risikoberegninger. Videre er det for ulykkeshendelsen Drift Grounding utviklet en dynamisk tilnærming, hvor risiko beregnes for skipet fortløpende En Web-basert demonstrator er på plass, og det er pågående aktiviteter for å teste ut de utviklede metodikkene for råoljetankere (se AP4). Kystverket ønsker å prøve ut metodikken og demonstratoren Slide 5
WP1: Risk Based Ship Prioritisation For the world fleet it is estimated that 25% of the most substandard ships are involved in 50% of the severe accidents* To support intelligent traffic monitoring at a VTS, it is important to be able to differentiate among those vessels which are accident prone and represents high risk compared to less critical ship traffic Risk Based Ship Prioritisation, preventing oil spill accidents by identifying and follow up high risk ships (preventive measure) For a VTS operator, tasked with monitoring hundreds of ships, this is a valuable decision support tool. Studies** indicate that VTS stations may reduce accident risk by 20-80% - this model is believed to enhance this performance *) Soma, T., 2005, Blue-chip or Sub-standard, Doctoral thesis, The Norwegian University of Science and Technology, Trondheim, Norway **) DMA and RDANH, 2002, Risk Analysis of Navigational Safety in Danish Waters, Danish Maritime Authority and Royal Danish Administration of Navigation and Hydrography, Report no. P-054380-2, 2002. Available online at: http://soefart.inforce.dk/graphics/synkron- Slide 6 Library/Sofartsstyrelsen/Publikationer/Summary1.pdf
Our approach: Classic Risk model Global Accident statistics analysis (linking accidents to size, age, flag and Port State inspections) Oil spill probability Oil spill size Frequency Consequence RISK ASSESMENT The model answers the question Which crude oil tankers are likely to produce an oil spill accident, and how much is it likely to spill? This is not the first attempt to single out poor performers Slide 7
Risk Comparison Ship to ship comparison Ship to fleet comparison Total risk estimate 3.0 Loss of manoeuvrab. 12 2.0 8 Fire 1.0 0 Power grounding 2 0 Ship1 Ship2 Ship3 Collision Ship1 Ship2 Ship3 Figure 1: Examples of possible risk presentation setups for overall risk (left) and specified events (right). Note that the indicated attention criteria levels (red, yellow, green) are included purely for illustration purposes. Slide 8
The next step : Dynamic Modelling of Drift Grounding Accidents Preventing oil spill accidents by risk based position of tug(s), and use of other risk reduction measures (e.g. re-routing) Braer, Shetland 1993 (www.itopf.com) Slide 9
Why dynamic? Producing a model which takes into account the circumstances surrounding a potential spill accident gives added value to a VTS operator. The question to be answered is; What is the environmental impact of a potential accident occurring with that ship, at that location under those weather conditions? Risk = Probability x Consequence = P grounding drift x P drift x S (spill size) x I (spill impact) Slide 10
Probability of Drift Grounding Probability of Drift Static Indicators Probability of Grounding given Drift Ship Drift to Shore Tug Response Self Repair Risk = P grounding drift x P drift x S (spill size) x I (spill impact) Slide 11 (Source: Eide et al. 2006)
Calculation of P grounding drift The probability of grounding given a drifting ship is calculated using mathematical integration formulas on the three distributions: Time To Shore (TTS), Tug Response Time (TRT) and Self Repair Time (SRT) Distributions accounts for uncertainty in the estimates! Slide 12 (Source: Eide et al. 2006)
Consequence of Drift Grounding Spill Size (S) Oil spill impact (I) Static Input Data Oil Type Vulnerability Risk = P grounding drift x P drift x S (spill size) x I (spill impact) Slide 13 (Source: Eide et al. 2006)
Exploring the model solution space: Examples of drift grounding scenarios modeled: - 2 weather and currents situations drift time to shore - 2 tug positions - 2 oil types - 2 seasons Start position of drifting ship - Ship drift modelling results provided by met.no - Model developed by DNV and met.no Kirkenes Lødingen Slide 14
Weather and currents: Fast drift to shore Situation 1 3 hours 9 hours 12 hours 15 hours Slide 15 Source: met.no
Great variances in environmental risk, depending on the circumstances Weather & current "Typical summer weather" - slow drift "Typical winter weather" - fast drift Cargo Oil Type Light Heavy Light Heavy Tug Positon Kirkenes Lødingen Kirkenes Lødingen Kirkenes Lødingen Kirkenes Lødingen Case Number 1 2 3 4 5 6 7 8 Risk Estimate 0,13 0,67 0,89 4,47 28,88 39,62 192,18 263,69 Difference between the most favorable situation (case 1) and the most sinister situation (case 8) is a factor 2000 The impact of tug position varies with the weather: in benign weather conditions there is a factor 5 in difference between having a tug in Kirkenes and having a tug in Lødingen. As the weather worsens, this factor is reduced to 1.4 Type of oil carried as cargo has a significant impact on the risks; factor 6.7 The greatest difference in risk is the result of weather and currents. The results show that the risks may differ by a factor between 222 (case 1 vs case 5) and 58 (case 4 vs case 8) Slide 16
WP2: Improve oil spill management Implementation AIS data in existing decisions support system (ActLog og Kystinfo) Find the best ships for oil spill clean up operations (based defined on criteria's) Calculate response time etc. (Manager: Odd Willy Brude DNV) Tugs 11. desember 2006 Source: The Norwegian Coastal Administration 17
AP2: Resultater I AP2 beskrives metodikk for optimale oljevernsoperasjoner, med utgangspunkt i bruk av AIS-data og kobling mot annen informasjon. Nyutviklingen vil inngå som en del av hvor allerede egnede fartøystyper (eks. slepebåter) eksisterende GIS baserte beslutningsstøttesystemer (ActLog), for oljevernaksjoner kan detekteres via AISdata. Det er også blitt foretatt: - Vurdering av bruk av høyoppløselige strømmodeller under en oljevernaksjoner - Datainnsamling og oppdatering av beredskapsdatabaser. - Oversikt over ulike risikoreduserende tiltak. Slide 18
WP3: Improve safety level by integration of AIS, ship, navigation, weather, risk information etc. Improved decision support Alarms, early warnings along planned route Users: Ships and maritime sector Manager: Bjørn Åge Hjøllo ( b.hjollo@c-map.no), C-MAP Marine Forecast Alarm area shows Fast/Very Fast ICING : Temp luft < -3gr.C & Vind< 30knots: 11. desember 2006 19
AP3: Resultater C-MAP har i AP3 lagt til rette for å kunne presentere AIS-data, værinformasjon og andre data på elektroniske sjøkart (om bord i skip) for et område og langs rute. Basert på bla. aktivitet i AIS2010 AP3, bidrag fra de andre partnerne, synergi fra NFR-prosjekt 3DSZ og andre prosjekter og ikke minst gjennom innkomne brukerkrav/-ønsker, har C-MAP Marine Forecast ferdigstilt en kommersiell versjon av WeatherView 2.0 (WV 2.0) høsten 2006. En større del av denne programvaren er utviklet innenfor rammene av AIS2010. Slide 20
WP4: Develop internet-based GIS services that provide AIS-data and other data to different end-users Traffic monitoring, management and planning Environmental assessments etc Users: Maritime sector, maritime authorities etc Manager: Kjell Røang, CMR 11. desember 2006 21
AP4: Resultater I AP4 er det utviklet et Internettbasert system for prosessering og presentasjon av historiske og sanntids AIS-data. Arbeidet er blitt gjennomført med jevnlige brukergruppemøter. Mange ulike typer moduler og funksjoner er blitt implementert i det Internettbaserte systemet, men den modulen som flest har nytte av, er modulen som presenterer AIS-data i sanntid på kartet og som lister. Dette er en modul som loser, skipsinspektører, agenter, rederier, beredskapsavdelinger etc. har stor nytte av og som forbedrer deres effektivitet vesentlig. Muligheten til å prosessere historiske AIS-data er også utviklet. I perioden har det også vært fokus på nyutviklingen av en innovativ Webapplikasjon som demonstrere risikometodikken utviklet under AP1. Slide 22
DEMONSTRATOR Slide 23
Final remarks 4 products developed Web demonstrator developed by CMR Papers: - Eide et al. 2006, Intelligent ship traffic monitoring for oil spill prevention: Risk based decision support building on AIS, accepted by the Marine Pollution Bulletin - Eide et al. 2006, Modelling of Dynamic Risk for Drift grounding, to be submitted Modelling details described in - DNV report no.2006-0435 Risk Based Ship Prioritisation. AMPERA Conference presentation in Spain (J. L. Ervik) Follow-up projects underway: - Analysis of other ship types - Analysis of other accident types - Challenges specific to the Arctic Slide 24
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