report - Miljødirektoratet

Transcription

REPORT
Stochastic oil drift simulation, environmental risk
analysis, and oil spill contingency analysis for
drilling of exploration well 6306/5-2 at the oil
prospect Hagar (PL 642)
An analysis for Repsol Exploration Norge AS
ACONA AS Rådhusgata 17, NO-0158 Oslo Norway, T:(+47) 22 42 35 00, www.acona.com, Org. nr. NO 984 113 005 MVA
Approval form
Report title:
Stochastic oil drift simulation, environmental risk analysis, and oil spill
contingency analysis for drilling of exploration well 6306/5-2 at the oil prospect
Hagar (PL 642). An analysis for Repsol Exploration Norge AS
Customer:
Customers contact person:
Repsol Exploration Norge AS
Laurits Hosar
Conducted by:
Signature:
Anders Bjørgesæter
Julie Damsgaard Jensen
Katrine Selsø
Controlled by:
Signature:
Anders Bjørgesæter
Astrid Pedersen
Approved by:
Signature:
Astrid Pedersen
Version:
Date:
Version-02
2014-12-16
Acona's project number:
820025
Refer to this report as:
Acona AS 2014. Stochastic oil drift simulation, environmental risk analysis, and oil spill contingency
analysis for drilling of exploration well 6306/5-2 at the oil prospect Hagar (PL 642). An analysis for
Repsol Exploration Norge AS. Version date: 2014-12-16. Acona's project number: 820025, www.acona.
com.
2
Report version history
Version / Date
Description of change:
VERSION-01
First draft to customer
2014-11-17
VERSION-02
Net Environment Benet Analysis included in Chapter 4.3
2014-12-16
3
Executive summary
Acona AS has performed oil drift simulations, environmental risk analysis and oil spill contingency analysis
for the exploration well 6306/5-2 at the Hagar prospect. Given a blowout at Hagar, there may be large
inuence areas on the sea surface and shoreline, with high probabilities of stranding in a relatively large
geographic area of the coast without oil spill contingency. The probability of environmental damage given
a blowout is relatively high for the Moderate and Minor damage categories. The environmental risk is
moderate, and below the operation specic acceptance criteria for all investigate valuable ecosystem
components in all damage categories. Areas along the coast of Møre og Romsdal and Sør Trøndelag have
high probabilities of being hit by oil concentrations dened as harmful in MIRA. Smøla, Frøya and Froan
have high probabilities of being aected. These areas are important living and breeding areas for coastal
sea birds and seals and should be prioritized during a potential spill situation.
Spatial distribution of oil
The inuence areas for oil on the sea surface follow the coastline towards
the North-East from the release point. The area is consistently larger for topside releases than for seabed
releases, with the largest inuence area during Summer (218 500 km2 ). The largest inuence areas for
shoreline (6 200 km2 ) and water column (1 300 km2 ) are also during Summer, for topside and seabed
releases, respectively. The stranding probabilities are high (62.7 - 68.9 %), with small variation between
seasons or release point. The stranding times are short (3.7 days in Autumn to 6.6 days in Spring) with
signicantly higher stranding amounts for topside spills than subsea spills. Nine IUA regions and seven
NOFO example areas have stranding probabilities above 5 %.
Environmental consequences
The Norwegian Sea population of Great cormorant shows the highest
environmental risk of all investigated VEC-populations and habitats, at 38 % of the operation-specic
acceptance criteria for Serious damage. The highest environmental risk for the remaining VEC groups is
25 % in Moderate damage for marine mammals (mid-Norwegian population of grey seal), 27 % in Serious
and Moderate damage for pelagic seabirds (Atlantic pun and common guillemot, respectively) , < 0.5 %
for sh (Cod) and 8 % in Moderate damage for shoreline habitats. Note that the conditional probabilities
for Moderate damage are high for all investigated VEC
Preparedness requirements
populations (> 40 %).
It is recommended to establish an oil spill response of three NOFO sys-
tems in barrier 1A and two NOFO systems in barrier 1B in the drilling period. Both systems should have
equipment for chemical dispersion. In barrier 2 it is recommended to establish a coastal oil spill response
for eight IUA regions. The number of coastal systems appropriate to handle the dimensioning emulsion
amounts should be evaluated in collaboration with NOFO. It is recommended to verify that beach cleaning units are available for eight IUA regions along the coast from Sunnmøre to Troms. The resource
requirement, calculated as days work, is high in IUA region Sør-Trøndelag and personnel availability
during the drilling period must be claried with NOFO prior to drilling.
4
Denitions used
Accenptance criteria
The operators accepted maximal probability of environmental damage in the
damage categories. Used to determine whether the environmental risk is acceptable.
ALARP
As low as reasonably practical: principle to evaluate risk reducing measures. The probability
of environmental damage is dened as being in an ALARP area when that probability exceed 50 %
of an acceptance criteria.
Damage categories
Restitution time-based categorization of environmental damage into the categories
Minor, Moderate, Consid. and Serious.
DSHA Dened situation of hazard and accident.
ERA Environmental Risk Analysis - estimates the enviornmental consequences of an oil spill.
Field A collection of installations drilling/producing from one or serveral resevoirs or within a naturally
conned geological area.
IMR/HI Institute of Marine Research/Havforskningsinstituttet.
NEA/MDir Norwegian Environmental Agency/Miljødirektoratet
MIRA Method for environmental risk analysis.
NOFO example areas 50 example areas dened by NOFO.
NORSOK (Norsk sokkels konkurranseposisjon) - standards developed
by the Norwegian petroleum
industry.
ODS Oil drift simulation
OLF The Norwegian Oil and Gas Association, formerly Oljeindustriens Landsforening.
OSCA Oil Spill Contingency Analysis
OSCAR Oil spill contingency and response. Module for oil drift simulations in the MEMW 6.2 program
package.
OSCP Oil Spill Contingency Plan
Pycnocline A horizontal boundary in the ocean separating two layers of dierent densities, a function
of salinity and temperature.
Restitution time
The amount of time before restitution is achieved after an oil spill. Restitution is
achieved when the population or habitat has returned to approcximately the same level as prior to
the oil spill. Restitution times exceeding 1 month are dened as environmental damage.
THC
Total Hydrocarbon Concentration. Total mengde hydrokarbon - inkluderer både dispergert olje
og løste komponenter.
Weighted rate/duration The weighted average of the probability distribution of blowout rates/durations.
VEC Valued Ecosystem Components. A population and/or habitat fullling a specic set of criteria
and denitions.
5
Table of contents
Approval form
2
Report version history
3
Executive summary
4
Denitions used
5
Table of contents
6
1 Introduction
8
1.1
1.2
Report scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
1.1.1
Planned activity and dened situations of hazard and accidents (DSHA) . . . . . .
9
1.1.2
The weathering properties of the oil . . . . . . . . . . . . . . . . . . . . . . . . . .
9
1.1.3
Simulation periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
1.1.4
Valued ecosystem components (VEC) used in ERA . . . . . . . . . . . . . . . . . .
10
1.1.5
Accepted maximum probability for environmental damage . . . . . . . . . . . . . .
10
1.1.6
Dimensioning of oil spill response . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11
1.1.7
Requirements to oil spill response . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11
Report structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12
2 Description of the analysis region
13
3 Methods
17
3.1
Method for simulating oil drift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17
3.2
Method for analyzing environmental risk . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19
3.3
Method for analysing oil spill contingency . . . . . . . . . . . . . . . . . . . . . . . . . . .
22
3.3.1
OSCA premises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22
3.3.2
Local weather data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
24
3.3.3
Estimating response times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
24
3.3.4
Calculation of system requirements . . . . . . . . . . . . . . . . . . . . . . . . . . .
25
3.3.5
Net Environmental Benet Analysis (NEBA) . . . . . . . . . . . . . . . . . . . . .
27
4 Results
4.1
4.2
30
ODS results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
30
4.1.1
Inuence areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
30
4.1.2
Stranding statistics for oil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
31
Environmental risk analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
43
4.2.1
44
Results for pelagic birds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
4.3
4.2.2
Results for coastal birds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
46
4.2.3
Results for seals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
48
4.2.4
Results for sh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
50
4.2.5
Results for shoreline habitats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
52
Oil spill contingency analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
54
4.3.1
Dimensioning rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
54
4.3.2
Response times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
54
4.3.3
Location of barriers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
57
4.3.4
Reduction factors and system eciency . . . . . . . . . . . . . . . . . . . . . . . .
57
4.3.5
Oil spill requirements in barrier 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . .
58
4.3.6
Oil spill requirements in barrier 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . .
59
4.3.7
Oil spill response in barrier 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60
4.3.8
Chemical dispersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
63
4.3.9
Recommended oil spill contingency . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
5 Discussion
70
6 Conclusion
71
7 List of references
72
A Appendix: results for drilling of exploration well 6306/5-2
74
A.1 Stranding statistics for prioritised areas . . . . . . . . . . . . . . . . . . . . . . . . . . . .
74
A.2 ERA results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
84
A.3 ODS results topside- and seabed seperate . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
B Appendix: input data
106
B.1 Valued ecosystem components (VEC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
B.2 Overview geographic populations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
B.3 The ecosystem components vulnerability to oil . . . . . . . . . . . . . . . . . . . . . . . . 109
B.4 Overview of analysed NEBA grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
C Appendix: methods
114
C.1 Denition of inuence areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
C.2 Calculation of percentiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
C.3 Conversion tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
C.4 Damaging oil concentration for sh larvae . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
7
1 Introduction
This report is prepared by Acona AS on behalf of Repsol Exploration Norge AS in connection with
planned activities. RENAS is planning to drill the exploration well 6306/5-2 at the prospect Hagar. This
report contains three analyses for the well, which have the purpose to: (1) simulate the spatial distribution
of oil following accidental oil spills during the planned activities, (2) estimate any subsequent environmental consequences of these spills and (3) estimate the oil spill contingency requirements in the given
situation. The analyses are performed according to the standards of the Management regulations (paragraph 17), Metode for miljørettet risikoanalyse (MIRA) and the oil spill contingency guideline (veiledning
for miljørettede beredskapsanalyser ) [NOFO & OLF, 2007, OLF, 2007, Norsk olje og gass, 2013].
Figure 1: The geographical location of the exploration well 6306/5-2 Hagar and
surrounding elds.
The Hagar prospect, belonging to production license (PL) 642, is located in the southern part of
the Norwegian Sea, west of the border between the counties of Møre & Romsdal and Sør-Trøndelag. The
distance to the closest shore is approximately 65 km, to the NOFO example area Smøla. The location
depth is 224 m, and the expected hydrocarbon of the prospect is oil.
8
1.1
Report scope
1.1 Report scope
The work with this report is determined and delimited by the following input data:
(1) planned
activities, with accompanying dened situation of hazard and accident (DSHA), (2) the weathering
properties of the oil, (3) simulation periods, (4) the valued ecosystem components (VEC) for which
environmental risk is calculated, (5) accepted maximum values for environmental risk, (6) dimensioning
of oil spill response, and (7) requirements to oil spill response. These input data are described below.
1.1.1 Planned activity and dened situations of hazard and accidents (DSHA)
Repsol Exploration Norge AS plans to drill an exploration well, with well number 6306/5-2, in the oil
prospect Hagar. The dened situation of hazard and accidents for Hagar is a blowout. The DSHA is
dependent on three statistics: (1) the probability of release, (2) the probability distribution (ow path)
between subsea and topside releases and (3) the probability distribution of release rates and durations
(rate and duration matrix).
The probability for oil release is 1.43e-04 and given a release the probability for subsea and topside
releases are0.82 and 0.18, respectively. The owpath statistics are based on SINTEF Oshore's blowout
database [Scandpower, 2012]. The probability distributions for release depth, release rates, and release
durations are given in Table 1. The statistcs are aggregated from the original rate matrix in the blowout
and wellkill study by Acona Flow Technology [Acona Flow Technology, 2014]. The probability weighted
averages for subsea and topside releases are 4967 S m3 /d and 16.5 days for subsea releases, and 4898 S m3 /d
and 12.5 days for topside releases.
Table 1: Predicted probability distributions for oil release rate and duration for
the exploration well 6306/5-2 Hagar (PL 642) .
Release point
Rates
Probability for duration
Depth
Prob. (%)
S m3 /day
Prob. (%)
2 days
15 days
25 days
75 days
Topside
18
2342
49
59
29
4
8
...
4867
41
...
13399
8
...
29415
2
2541
49
40
38
13
9
...
4893
41
...
13079
8
...
28876
2
Seabed
82
1.1.2 The weathering properties of the oil
The oil expected to be found at the Hagar prospect is anticipated to have similar weathering characteristics
as Draugen, described by SINTEF [2008], and the properties of this oil are used in the oil drift simulations
9
1.1
Report scope
conducted for the prospect. In the oil database used by OSCAR this oil is named Draugen 2007 13C.
Draugen is a biodegraded paranic crude with a medium wax content (2.4 %) and a low content of
asphaltenes (0.13 %). High evaporation after an oil release at sea will cause rapid increase in wax and
asphaltene contents.
The Draugen crude forms a stable water/oil-emulsion after some time. The maximun water content
is high, 91 % during winter conditions (5 ◦ C) and 79 % during summer conditions (13 ◦ C). The emulsion
viscosity, though high, is still within the limits of mechanical recovery. Chemical dispersion is an eective
combat strategy in the initial phase for the Draugen oil for both summer and winter terperatures even
with low dosage (ratio of 1:50) of the dispersion chemical. The eect of chemical dispersion may be
reduced after several days weathering at sea, especially at winter conditions, when the viscosity increases
faster than at summer temperatures.
1.1.3 Simulation periods
The results from the oil drift simulation, environmental risk analysis and oil spill contingency are presented
for the following periods of the year: Winter (December - February), Spring (March - May), Summer
(June - August) and Autumn (September - November).
1.1.4 Valued ecosystem components (VEC) used in ERA
The VECs used in the environmental risk analysis consists of population and habitat data. The population
data include two species of sea mammals (seals, www.mrdb.no), 29 species of birds (17 coastal and 12
pelagic, www.seapop.no), and six species of sh (www.imr.no). The habitat data cover the shoreline (12
dierent shoreline habitats). Planktonic organisms are not included due to their low sensitivity to oil
exposure, resulting from a large geographical distribution and short restitution time.
The VEC-populations used in the ERA are presented in Appendix B.1, while their geographical
regions are described in Appendix B.2. Note that dierent species of seabirds and marine mammals have
dierent genetic populations for the various geographic regions.
1.1.5 Accepted maximum probability for environmental damage
RENAS' accepted maximum probabilities for environmental damage used in the ERA are given in Table
2, and follow the NORSOK example [NTS, 1998] The values are operation specic and state the highest
probability accepted by the operator for environmental damage of dierent durations. The acceptance
criteria are used in the calculation of relative risk to determine whether a risk is acceptable or unacceptable. if the relative risk is below 100 % the risk is lower than the maximum accepted risk value,
and the risk is dened as acceptable. According to The Framework Regulations (cf. Section 11, "Risk
reduction principles", with guidelines at www.ptil.no), probability of environmental damage should also
be evaluated in relation to the ALARP principle.
10
1.1
Report scope
Table 2: RENAS` accepted maximum probability for environmental damage in dierent categories.
Damage class
Restitution time (yr)
Max. prob.
0.11
1.00E-03
Moderate
13
2.50E-04
Considerable
310
1.00E-04
Serious
>10
2.50E-05
Less
1.1.6 Dimensioning of oil spill response
RENAS has dened the dimensioning situation for oil spill contingency as a topside release. The probability of a topside release given a blowout is 0.18 (Table 1). The dimensioning rate in barrier 1 is the
weighted blowout rate for topside release, at 4898 S m3 /d. The dimensioning rates for barriers 2 and
3 are calculated from the 95-percentiles for stranded amount of oil emulsion in the IUA regions. The
dimensioning duration is the weighted release duration of 12.5 days. The duration is used in estimating
the stranding period (explained in Section 3.3).
1.1.7 Requirements to oil spill response
The requirement for the oil spill contingency for Hagar is based on the Norwegian Oil and Gass Assosiation
guidelines [Norsk olje og gass, 2013]. The following system requirements have been established to combat
acute pollution:
Barriers at open sea (1A and B) shall each have sucient capacity to handle the available emulsion
given the dimensioning rate. The response time for a fully developed barrier shall be shorter than
the 5-percentile of the stranding time.
The coastal barrier (barrier 2) shall have sucient daily capacity to handle the 95-percentile of
the emulsion (from the oil drift statistics) into the barrier, with the eect of barrier 1 taken into
account. The daily capacity is the amount of emulsion divided by the calculated stranding period.
The response sime for a fully developed barrier shall be shorter than the 5-percentile of stranding
time.
The barrier in the beach zone (barrier 3) shall in function A (mobile oil) have sucient capacity
to combat the incoming emulsion to the barrier, with the eect of the previous barriers taken into
account. Function B (non-mobile oil) shall also have the capacity to combat the calculated stranded
amount of oil in the IUA-regions within the NCA regions in the inuence area. The response time
shall be shorter than the 5-percentile of stranding time.
11
1.2
Report structure
1.2 Report structure
Results from oil drift simulations, environmental risk analysis and oil spill contingency analysis are
presented in the Chapters 4.1, 4.2 and 4.3, respectively. A discussion of all results is given in chapter 5
and a conclusion is given in Chapter 6. The results of the oil drift simulations are presented as inuence
areas of oil (sea surface, water column and shoreline) and stranding statistics. From the environmental
risk analysis results are presented as environmental damage and environmental risk for coastal and pelagic
sea birds, marine mammals, sh and shoreline habitats. The results of the oil spill contingency analysis
are presented as "oil spill response requirements" and recommended oil spill contingency on the open sea
and in the coastal barriers.
12
2 Description of the analysis region
The
analysis region is dened as the inuence area on the sea surface (dened in section C.1) and any
area located between the inuence area and land. The following paragraph gives a short description of the
VEC areas within the analysis region for the Hagar prospekt, illustrated in Figure 2. A short description
of the vulnerability of each VEC to oil spills are given in Appendix B.3.
Figure 2: Important areas for valued ecosystem components (VEC) within the analysis
region for the exploration well 6306/5-2 at the prospect Hagar (PL 642) as dened in
this report: 1) Eggakanten 2) Runde and the Møre coast 3) Smøla 4) Frøya and
Froan 5) Halten and Sklinna banks 6) Helgeland coast 7) Vestfjorden/Vesterålen
and surrounding bird areas 8) Sør- and Nordfugløya.
Eggakanten
The Eggakanten area is a front zone between coastal and pelagic waters where mixing of
the water masses contribute to large biological production and a high biodiversity. The area is a transport
area for spawning products and is an important grazing area for pelagic birds (auks, Northern fulmar
and kittiwakes). Large amounts of zooplankton in the area make Eggakanten an important grazing area
13
for baleen whales. Deep sea sh species, like deepwater redsh, rosesh, Greenland halibut and Atlantic
argentine spawn along Eggakanten. The area is also rich in coral reefs and sponge communities and is
dened as a particularly valuable and vulnerable (SVO) area in the management plan for the Barents
Sea [St.meld.nr. 10, 2008-2009].
Runde and the Møre coast
Runde is a very important bird cli. Atlantic pun is the most numerous
species, but the area is also important for the common guillemot, kittiwakes, razorbill, northern fulmar,
northern gannet and European shag. During breeding season the pelagic species use a sea area up to
100 km outside the colony as a foraging area [NINA, 2008]. It is also an important moulting site, and
in autumn there is a high density of moulting birds and juveniles (both incapable of ight) at sea o
Runde [NINA, 2007]. Mørebankene is an important spawning area for cod, saithe and norwegian spring
spawning herring, with high densities of larvae and fry during spring. The bank area also has a diverse
bird community foraging on pelagic sh species. Mørekysten, and the area from Stadtlandet to Sandøy
in particular, is an important breeding- and living area for harbour seal [Henriksen, G. og Røv, N., 2004.].
Mørebankene is regarded as a particularly valuable and vulnerable area (SVO) in the management plan
for the Norwegian Sea.
Smøla
The area around Smøla in the Møre region holds important breeding and overwintering sites
for coastal seabirds like European shag, great cormorant, common eider, and lesser black-backed gull.
The area is of particular importance during winter and spring [NINA, 2007]. The Smøla region contain
several important breeding sites for harbour seal [HI & DN, 2007].
Frøya and Froan
Frøya is an important breeding area for great cormorant, but also European schag
and black guillemot. The area is dened as a nature reserve to preserve the important bird habitat and
protect red-listed species. The Froan archipelago is a very important breeding- and wintering- area for
coastal sea bird species, holding several large breeding colonies of the great cormorant, European shag,
greylag goose, common eider, lesser black-backed gull, great black-backed gull, common tern, Arctic
tern, and black guillemot. During breeding season the coastal sea bird species use a sea area up to 60 km
outside of the colony as a foraging area [NINA, 2008]. The harbour seal and grey seal also have important
breeding sites at the archipelago, and more than half of the Norwegian grey seal population breed here.
Froan is considered to be a particularly valuable and vulnerable area (SVO) in the management plan for
the Norwegian Sea and is also a candidate area for the National Marine Protection Plan [DN, 2004].
Halten and Sklinna banks
The area west of Vikna i Nord-Trøndelag (Haltenbanken) are particularly
important areas for spawning and early life stage development for Norwegian spring spawning herring
and saithe. The area is also a highly productive retention area for pelagic sh eggs and -larvae. The bank
area is also the basis for a diverse bird community that grazes on pelagic sh species. Haltenbanken is
regarded as a particularly valuable and sensitive area (SVO) in the management plan for the Norwegian
Sea. Sklinnabanken outside the coast of Helgeland is a particularly important breeding area for norwegian
spring spawining herring and saithe. The area is also a highly productive retention area for pelagic sh
14
eggs and larvae. The bank area forms the basis for a diverse bird community that graze on pelagic sh
species. Sklinnabanken is regarded as a particularly valuable and sensitive area (SVO) in the management
plan for the Norwegian Sea.
Helgeland coast
The Helgeland coastline has a number of important bird areas. Vikna, Sklinna
and Vega are important breeding and wintering areas for coastal seabirds. The areas have breeding
populations of European shag, great cormant, common eider and seagulls. In the breeding periods the
coastal species use the sea area up to 60 km from the coast as grazing areas, and the area within this
radius is therefore especially important and vulnerable [NINA, 2008]. The Vikna archipelago and Sklinna,
together with Sømna north of Sklinna, are also important for coastal species during autumn (moulting)
and spring season (migration to breeding areas) [NINA, 2007]. The Vega area is dened as particularly
vulnerable and valuable for coastal seabirds [NINA, 2007] and is particularly important during the spring
and winter. Vega is a UNESCO World Heritage site due to its rich breeding populations of common
eider. In addition there are also several known breeding and moulting areas for grey seals on Vega [HI
& DN, 2007]. Lovunden and Fugleøya are important bird clis for pelagic diving seabirds, especially the
Atlantic pun and auk specices.
Vestfjorden/Vesterålen and surrounding bird areas
Vestfjorden is important as a spawning area
for cod. The area has traditionally been very important as a wintering area for herring and grazing area
for whale and seabirds. There are several important retention areas for zooplankton in this area. This
constitutes important nourishment for juvenile herring in the autumn and cod eggs- and larvae in the
spring (January-March).
Røst is one of the most important bird clis along the coast of the Norwegian Sea, with more than
500 000 breeding pairs of Atlantic pun [NINA, 2007]. The area is also important for coastal species
such as the black guillemot, common eider, great cormorant and European shag. During breeding season
the pelagic species use the sea area up to 100 km outside the colony as a foraging area [NINA, 2008].
Røst is also an important wintering- and moulting- area with high densities of razorbills in the period
August-October. The Røst area also have several breeding sites for harbour seal. Fuglenyken, Anda
and Bleiksøya in Vesterålen are large bird clis along the coast of Vesterålen. The area is particularly
important for Atlantic pun and other pelagic diving species like common guilemot, but the area is also
important for coastal species. During the breeding season a sea area up to 100 km out from the bird cli
is used as a foraging area. Vestfjorden, together with Vesterålen, is dened as a particularly valuable and
vulnerable area (SVO) in the management plan for the Norwegian sea [St.meld. nr. 37 (2008-2009)].
Sør- and Nordfugløya
The bird clis Sør- and Nord-Fugløya in Troms are both very important
for colonies of pelagic diving and surface feeding species of sea birds in the spring and summer. The
areas house signicant breeding colonies of common guillemot, razorbill, Atlantic pun, kittiwake and
gannet. The breeding population of razorbill at Nord-Fugløya is the highest in the Southern Barents Sea
with approximately 10 000 breeding pairs [Alpha Miljørådgivning og NINA, 2003]. In spring hundred
thousands of alcoid species may be present in the shallow waters of the coast when prey of capelin and
15
other species drift in the area [HI & NP, 2003]. The pelagic species utilize the area up to 100 km outside
the colonies for feeding in the breeding period [NINA, 2008]. These sea areas are also important in the
autumn when the birds are on the sea unable to y during the molting period. Of the coastal species,
the lesser black-backed gull have historically been important at Nord-Fugløya, while herring gull and the
great black-backed gull breed in great numbers all along the coast of Troms and Finnmark.
16
3 Methods
The spatial distribution of released oil is estimated by means of stochastic oil drift simulations, conducted with the software OSCAR (Oil Spill Contingency And Response, SINTEF), the environmental
consequences of the oil spill are estimated according to MIRA (Metode for miljørettet risikoanalyse, OLF
2007), and the oil spill contingency analysis is conducted according to the Norwegian Oil and Gass Association guideline [NOFO & OLF, 2007]. The following chapters, 3.1 (ODS), 3.2 (ERA) and 3.3 (OSCA),
gives a thorough explanation of the methods in each analysis. Readers familiar with the methods used
may continue to chapter 4 for results from the analyses.
3.1 Method for simulating oil drift
Stochastic oil drift simulations are done by means of the module Oil Spill Contingency And Response
(OSCAR), a part of the software package MEMW 6.2 from SINTEF. Based on relevant input data
(elaborated below) this software is able to simulate the spread and spatial distribution of oil on the sea
surface, in the water column and on the shoreline due to an oil spill or blowout. The following section
describes the input data and the use of OSCAR. A more advanced description is available in the user
manual for the software [SINTEF, 2012].
Table 3: Input data for the stochastic oil drift simulations for blowouts
during drilling of the exploration well 6306/5-2 in the prospect Hagar.
Parameter
Value/Reference
Wind data
Norwegian Meteorological Institute 1970-2007
Sea current data
Institute of Marine Research 1997-2005
Reference oil name
Draugen 2007 13C
Water depth (m)
224
Latitude ( N)
63.730850
Longitude(◦ E)
6.572492
Geodetic system
WGS 84
◦
Oil density (kg/m3 )
823
Gas density (kg/m )
0.80
Gas-to-oil ratio
107
3
Input data
The oil drift simulations are based on input data, or variables, of two dierent kinds: (1)
xed variables and (2) stochastic variables (Table 3). Variables of the rst category can be predicted
to a fair degree of accuracy for a potential oil spill situation. These variables include the properties
of the released oil, the geographic position of the release site, water depth, as well as the temperature
and salinity proles of the water column at the release site at dierent periods of the year. Variables
of the second category are dicult to predict accurately for future oil spills, and their values must be
17
3.1
Method for simulating oil drift
represented with probability distributions. These distributions are based on other types of simulations or
on historical data. This category includes oil release rate, oil release duration, oil release depth (seabed
or topside), as well as the speed and direction of wind and sea currents.
The monthly sea water temperature and salinity (above and below the pycnocline) and the depth of
the pycnocline is a function of the release point's geographic position and time of year [SINTEF, 2012].
SINTEF have adapted the wind- and current data in Table 3 for modelling in OSCAR. The wind
data have a horizontal and temporal resolution of 21 km and 3 hours. The current data have a horizontal
and temporal resolution of 4 km and 1 day.
Stochastic simulations
The stochastic simulations are performed in
batch value mode, performing a
stochastic simulation for all combinations of release depth, rate and duration. Every stochastic simulation
contains a number of single simulations performed consecutively through the year. The number of single
simulations in a stochastic simulation is determined by the release rate and the number of years with
available wind- and current data. The purpose is to have a sucient number of simulations to transplant
the variability in wind- and current data to variation in the output, giving a measure of the uncertainty
of the data. All output data from this method are stochastic variables because some of the input data
are stochastic variables.
Scope of oil drift simulations
All oil drift simulations are done within a three dimensional (3D)
modelling grid (habitat grid in OSCAR) with 2×2 km horizontal resolution and 5 m vertical resolution
down to 50 m. The ODS included 32 scenarios for each combination of release- depth, rate and duration
(2 × 4 × 4), a total of 9 984 single simulations. The periods for presenting the results are given in
Section1.1.3.
Output data
The results from each stochastic oil drift simulation are exported from OSCAR to text
les. The data are post-processed using an Acona-developed program code in MatLab®. The processed
data are used to calculate two types of data: (1) inuence
areas, a statistical description of the spatial
distribution of oil on the sea surface, in the water column, and on the shoreline, and (2) stranding
statistics, including the probability of stranding and the probability distributions of shortest stranding
time and stranded mass of oil and oil-water emulsion. The probability distributions are presented by
their percentile values (dened in Appendix C.2). The stranding statistics are calculated for the entire
coastal shoreline (all aected coastline), for IUA regions, and for the example areas described by NOFO,
i.e. 50 coastal areas chosen due to their special value or vulnerability with respect to oil pollution. Since
the entire coastline includes these example areas, the stranding statistics of the example areas will be
included in the stranding statistics for the entire coastline.
18
3.2
Method for analyzing environmental risk
3.2 Method for analyzing environmental risk
In this report environmental risk, due to oil pollution, is calculated by means of the damage-based part
of MIRA (Metode for miljørettet risikoanalyse, OLF 2007, p. 34).
Input data The damage-based part of MIRA is based on four sets of input data as shown in Table 4:
(1) Stochastically simulated oil drift data : the geographical extent of oil pollution made by means of
stochastic oil drift simulation (2) Blowout probability based on the operators activity level, (3) Ecosystem data : the geographical distribution of valued ecosystem components and their vulnerability to oil
pollution, and (4) Acceptance criteria : the operator's chosen maximum values for environmental risk.
Table 4: Input data used for environmental risk analysis for blowouts from
exploration drilling of well 6306/5-2 in the prospect Hagar.
Parameter
Reference
Stochastically simulated oil drift data
from this report
Blowout probability
1.43e-04
Ecosystem data
Coastal and pelagic sea birds
www.seapop.no
Seals
www.mrdb.no
Fish
www.imr.no
Shoreline habitat
www.mrdb.no
Accepted max. prob. damage
Table 2
Ecosystem data The ecosystem components are divided into two groups, populations (birds, sh, sea
mammals) and habitats (shoreline). Spatial data for each of these components exist in a format adapted
to the geographical grid ContAct© [Alpha Miljørådgivning AS, 2003], consisting of 10×10 km grid cells
covering coast and open sea in Norwegian waters (shoreline grid
cells and oshore grid cells respectively).
However, the spatial data dier for the two groups of ecosystem components.
The spatial data for habitats state to which degree a shoreline grid cell recovers from oil pollution,
i.e. at which rate stranded oil is removed by natural degradation processes (on-site). This rate depends
on the wave and wind exposure of the shoreline. The restitutional ability is given qualitatively through
the
restitutional classes R1, R2 or R3, where R3 designates the lowest restitutional ability (longest
restitutional time for a given amount of stranded oil). For each shoreline grid cell, the data state the
percentage of the total length of the shoreline that belongs to each of the three restitutional classes. The
shoreline habitat of one grid cell can e.g. have a vulnerability R1 in 30 % of its length, vulnerability R2
in 60 % of its length and vulnerability R3 in 10 % of its length.
On the other hand, the spatial data for each of the populations state the number of individuals in each
cell in the ContAct grid. In addition to these numbers, each population have been given a categorization of
1) the individual's direct vulnerability for oil pollution and 2) the restitutional ability of the populations
19
3.2
after a population loss. The individual's direct vulnerability for oil pollution, that is how easily they are
damaged if oil is present in a grid cell, is reported qualitatively by the use of the
vulnerability categories
S1, S2 and S3, where S3 designates the highest vulnerability. Fulmar for instance, a sea bird spending a
minor part of its time on the sea surface, is given vulnerability category S1. Common Guillemot, on the
other hand, spending much time on the surface, is given vulnerability category S3 1 . The restitutional
ability of the populations is reported with the same restitutional categories as for shoreline habitats (R1,
R2, R3), although the interpretation of these categories will dier for shoreline grid cell vs. a population.
The restitution category states at which rate a population can return to its original size after a certain
part of the population have been killed due to oil damage.
The population data used in this report include two species of sea mammals (seal, www.mrdb.no), 29
species of seabirds (17 coastal and 12 pelagic, www.seapop.no), and six species of sh (www.imr.no). The
habitat data cover shoreline (12 dierent shoreline habitats, www.mrdb.no). Planctonic organisms are not
included due to their low sensitivity to oil exposure, resulting from the large geographical distribution of
each species and from the short restitution time of these organisms.
For species of seabirds and marine mammals there are dierent genetic populations for various
geographical regions (Table 33). In our analysis for each region only species that breed there are included,
hereby excluding species present in other periods of the year. When it comes to coastal birds, only the
species that normally spend time on the water surface are included.
Calculation of relative population loss
For each simulated oil drift, all grid cells which have been
hit by oil are marked, and will in the following be referred to as
oil cells. For each of these oil cells,
the share of present individuals which dies is calculated for each population. This is done by usage of
Table 35 (oil-to-loss-Table 2 ), which gives the share of individuals which dies in a cell as a function of
two variables: (1) oil amount in the cell and (2) the vulnerability category for the population that the
individuals belong to. This can be exemplied by the seabird species Common Guillemot: If the simulated
amount of oil in a grid cell is within the interval 1-100 metric tonnes and the vulnerability category for
Common Guillemot is S3, then 20 % of the individuals of this species in this grid cell will die due to
oil damage (relative
individual loss per oil cell ).
This percentage is then multiplied with the number
of individuals of Common Guillemot present in the cell to calculate the absolute number of individuals
dying (absolute
individual loss per oil cell ). This process is repeated for all other populations which are
present in the grid cell. By summarizing the absolute individual loss per oil cell for all oil cells in a single
oil drift simulation, the total number of individuals dying in every population as a consequence of this
single oil drift is found (absolute
individual loss per oil drift ).
Then the relative population loss per oil drift can be calculated for each population by dividing the
absolute individual loss per oil drift with the total number of individuals in the population (population
size ). The size of each population is found by summarizing individuals from this population over all cells
1 It is not necessary to map vulnerability categories to individual coastal grid cells, i.e. categorize how easily they are
damaged if oil is present, because the oil drift analyses reports directly how much oil that accumulates in, and thereby,
damages a shoreline grid cell.
2 This Table is called "Damage key" in MIRA.
20
3.2
in the ContAct-grid.
Since a stochastic oil drift simulation consists of n single simulations, all dierent with respect to the
amount and spatial distribution of the oil, n dierent values for relative population loss can be calculated
for each population. These n relative population-loss values can be sorted into ve dierent
relative
population loss intervals, 1 - 5 %, 5 - 10 %, 10 - 20 %, 20 - 30 % and >30 %. Fish populations are sorted into
the intervals 1 - 2 %, 2 - 5 %, 5 - 10 %, 10 - 20 %, 20 - 30 %, 30 - 50 % and >50 %. The number of relative
population loss values that t into each of these intervals are then divided by n, the total number of
simulations. This gives the share of the simulations having loss values in each of these intervals. For
instance, 45 % of the simulations may give relative population loss values in the interval 15 %, 22 % of
the simulations may give relative population loss values in the interval 510 %, and so on. These shares
are our best estimates for the probability of relative loss values in the dierent intervals,
if a blowout
takes place from the release site in the future. These conditional probabilities for dierent population
loss intervals will be symbolized with PP Lx |Oil where P Lx is the population loss in interval x.
Calculation of restitution time for populations
For each of the n values of relative loss for a
population, the corresponding restitution time for the population can be calculated by means of the
conversion table 37 (loss-to-restitution-time-table ). This table gives the probability for a set of restitution
time intervals as a function of relative population loss value and the aforementioned restitutional class of
the population. Calculation of restitution time for sh populations are rst estimated through Table 38,
giving the probability for dierent losses in yearly recruitment as a function of sh egg and sh larvae
loss ratios. The sh population's restitution time values are then calculated from these probabilities by
means of the conversion tables 39 and 40. The resulting n restitution time values are sorted into four
dierent
restitution time intervals, 0.11 yr, 13 yr, 310 yr, and >10 yr. The number of restitution time
values that end in each interval are then divided by n, the total number of simulations. This gives the
share of the simulations with restitution time values in each of the intervals. These shares are our best
estimates for the probability of the dierent restitution time intervals
if a blowout takes place from the
release site in the future. These conditional probabilities for for dierent restitution time intervals will
be symbolized with PRTy |Oil , where RTy is the restitution time in interval y .
Calculation of restitution time for shoreline habitats
The restitution times of shoreline habitats
are calculated for each individual shoreline grid cell using Table 41, based on the amount of oil stranded
in each cell.
Calculation of environmental risk
By multiplying the conditional probability PRTy |Oil , i.e. the
probability of a restitution time in interval y if a blowout takes place, with POil , the probability for a
blowout at the release site, one gets the absolute probability PRTy for restitution time in interval y .
PRTx = PRTx |Oil × POil
(1)
In order to calculate relative environmental risk, PRTx for each restitution time interval is divided
21
3.3
Method for analysing oil spill contingency
Acc
by the maximum accepted probability for this interval, PRT
x
Acc
RiskRTx = PRIx /PRI
x
Estimation of loss potential for sh egg and larvae
(2)
To include population loss of important sh
species not covered by the MIRA method, Acona performed a simple overlap analysis between the total
area of spawning products of important sh species and the inuence area for oil in the water column
(the area of all map grid cells that have oil concentrations in the water column above 375 ppb in more
than 5 % of the simulations).
3.3 Method for analysing oil spill contingency
The oil spill contingency analysis (OSCA) is performed according to the Norwegian Oil and Gas Association guideline
Veiledning for miljørettet beredskapsanalyser [NOFO & OLF, 2007, Norsk olje og gass,
2013] and NOFO's framework for oil spill contingency [NOFO, 2014b]. The purpose of the OSCA is to
identify system- and resource requirements to combat the dimensioning oil spill scenario (DSHA) and to
prepare recommendations for the oil spill response strategy. The OSCA form the decision basis for the
operator's choice of contractual oil spill contingency solution.
3.3.1 OSCA premises
Barriers
The main objective for an oil spill response strategy is to minimize the consequences of an
oil spill on natural resources. The primary strategy for combating acute oil spills on the Norwegian
Continental Shelf is mechanical recovery close to the source. Chemical dispersion shall be used when this
method provides an equally good or better impact-reducing eect on the environment than mechanical
recovery. Barriers are technical, operational and organizational elements that work in succession to
conne or prevent damage and/or loss given an acute oil spill. The system- and resource requirements
are estimated for the following barriers:
Barrier 1: Combating at open sea (function A) or along the oil trajectory (function B) using NOFO
systems
Barrier 2: Combating in the coastal and inshore zone by use of coastal systems
Barrier 3: Combating of mobile oil in the littoral zone (function A) and and recovery of immobile
oil on shore (function B - beach cleaning)
The minimal performance requirements of the oil spill contingency at Hagar are presented in Section
1.1.7.
Available oil spill contingency resources
Primary resources (NOFO OR-vessel, equipment, per-
sonnel and coastal systems) are available from NOFO bases at Stavanger, Mongstad, Kristiansund,
Sandnessjøen and Hammerfest (Figure 3). Another ten standby-vessels are available from dedicated
22
3.3
areas around Ekosk, Ula/Gyda/Tambar, Sleipner/Volve, Balder, Troll/Oseberg (2), Gjøa, Tampen,
Haltenbanken and Barentshavet as part of the area-specic oil spill emergency contingency plans. Each
NOFO-base may mobilize ten coastal recovery systems for oil spill contingency in barrier 2. NOFO has
contracts with the IUAs (Interkommunale utvalg mot akutt forurensing) for access to a specialized team
of 63 people for beach cleaning operations. Approximately another 850 people competent in beach cleaning operations are available through contracts and agreements between NOFO and dierent organizations
and parties.
Oil spill response systems and nominal system capacity
A
NOFO-system consists of an oil
recovery vessel (OR-vessel) that satisfy the current NOFO-standard [NOFO, 2011] and a towing vessel.
A NOFO OR-vessel is equipped with oil spill response equipment (including NO-1200-R oil and spray
boom, TransRec skimmer and a storage tank for recovered oil of minimum 1500 m3 ) and advanced
remote sensing systems including oil radar with automatic oil detection. A
coastal system consists of an
oil recovery vessel with Current Buster 4 (recovery system), an accompanying vessel dedicated for uptake
(with skimmer and tank capacity), and a command and support system. This is referred to as a 10 vessels
collection system. A command- and support system can lead and support up to six recovery systems and
two recovery vessels. In the acute phase of barrier 3 (function A), stranding and re-mobilization of
oil is prevented using pumps, sludge suction, deection, protection and containment booms and other
measures. In the beach clean-up phase (function B), oil is removed from the shore using both mechanical
and non-mechanical techniques.
The nominal
system capacity of the oil spill response systems- and resources is presented in Table 5.
The values are based on experience, trials and exercises, including NOFO's "oil-on-water-exercises" and
represent the maximal capacity of the systems or resources during optimal operative conditions. The
values for NOFO and coastal systems include a "down-time" of 12 hours per day due to correction of various errors ("hassle and annoyance"), connections of equipment, emptying and transit to deliver collected
oil, retrieval/waiting for chemical dispersion agents, personnel replacements, rest and re-positioning to
locate oil slicks. The capacity given for barrier 3 (function A) do not include IGSA (innsatsgruppe strand
akutt), a team with considerably higher nominal capacity.
Table 5: System capacity for the dierent oil recovery systems used in the analysis. The
vessel's travel speed is used to estimate the response times [Norsk olje og gass, 2013].
Typical operation area
Barrier 1 and 2
System
System capacity
Travel speed
(m3 /day)
(nautical mile/h)
2400
14
Coast guard system
1200
20
Coastal system
120
10
Fjord system
17
10
NOFO system,
Transrec 350 skimmer
Barrier 3
23
3.3
Figure 3: Overview of
NOFO-resources
[NOFO, 2014a].
3.3.2 Local weather data
Wave and wind dataset for the Hagar prospect is downloaded from the Norwegian Meteorological Institute
[Norwegian Meteorological Institute]. Oshore data is from weather station Point 1269, 24 km from the
release point, at position 63.7 ◦ N 6.1 ◦ E, and coastal data from weather stations along the coast in the
inuence area for oil on the shoreline. Temperature data is downloaded from the LEVITUS online
database [Levitus]. The absence of daylight, dened as a solar angle of 6 degrees or less below the
horizon, is calculated for the release point and the chosen coastal weather stations. The visibility during
a potential oil spill is not known, and the method uses 50 % "good visibility" and 50 % "poor visibility"
to mitigate this uncertainty.
3.3.3 Estimating response times
All oil spill response systems are generally required to mobilize according to best attainable response
times, dened as the sum of release times for OR-vessels (incl. any mobilization time from NOFO-bases),
sailing time, deployment of booms and skimmers and a buer of three hours. The buer has been
added because the exact position of the standby-vessels may dier from the position given by NOFO.
The distance coastal systems must travel to reach the polluted area is dened as the distance from a
NOFO-base to the center-point of all IUA-regions with stranding probabilities above 5 %. The average
24
3.3
sailing (travelling) speed is 14 knots for NOFO OR-vessels and 7 knots for coastal systems.
3.3.4 Calculation of system requirements
The system requirements for barriers 1 and 2 are calculated as the number of oil response systems giving
sucient
nominal uptake capacity to combat the dimensioning emulsion rate at each barrier, i.e.:
System requirement = dimensioning emulsion rate/nominal uptake capacity
(3)
System requirements are given as the number of NOFO systems in barrier 1 and as the number of coastal
systems in barrier 2. The system requirements in barrier 3 are given as the number of day's work needed
to complete the beach clean-up operation and the number of beach-cleaning units.
Dimensioning emulsion rate
The dimensioning emulsion rate in barrier 1 is dened as the inux
(m3 /day) of emulsion at the chosen position of barriers 1A and B. The calculation incorporates the
weathering properties of the reference oil (evaporation, natural dispersion of oil into the water column and
water-in-oil emulsication at specic wind speeds and temperatures) and historic wind- and temperature
data from the area. As a result, the inux estimates for barriers 1A and 1B are based on location-specic
expected values for the area in the dierent periods of the year. The dimensioning emulsion rate in barrier
2 is dened as the 95-percentile of stranded amount of emulsion in the IUA-regions (estimated in the ODS)
divided by the estimated stranding period (weighted duration of the DSHA). The dimensioning emulsion
amount in barrier 3 is dened as the 95-percentile of stranded amount of emulsion in the IUA-regions
multiplied by an experienced-based reduction factor of 0.60 [Norsk olje og gass, 2013]. The reduction
factor is included because national clean-up operations have shown that only 20 - 40 % of the stranded
emulsion is available for clean-up. Each barrier incorporates the eect of uptake in the preceding barrier.
Expected capacity
Table 5 presents the
nominal capacity, i.e., capacity during optimal operational
conditions for the dierent oil response systems. The values for oil response systems in barriers 1 and
2 include a "down-time" of 12 hours (0.5 days), and the capacity given is in reality half of their nominal capacity. Norsk olje og gass [2013] denes this value as nominal capacity during operational-time
(oppe-tid) in the
Veiledning for miljørettede beredskapsanalyser. These nominal values are corrected by
reduction factors for wind speed, wave height, absence of daylight and visibility to calculate the expected
capacity using the following equation:
Expected daily capacity = (1 day − down-time) × nominal daily capacity × reduction factors)
(4)
Reduction factors as a function of wind speed and wave height is presented in Table 6. Wave and wind
statistics from the weather stations presented in Section 3.3.2 are input data for calculating the reduction
factors for sea state for NOFO and coastal systems. Reduction factors for absence of daylight is presented
in Table 7. A reduction factor of 1 indicates that the expected capacity equals the nominal capacity while
a reduction factor of 0 indicates that the expected capacity is zero.
25
3.3
Table 6: Reduction factors as a function of signicant wave height (Hs) and wind speed.
Wave height
Reduction factor
Wind speed
(m)
Reduction factor
(m/s)
0-1 m
0.80
0 - 1 m/s
0.72
1-2 m
0.75
1 - 2 m/s
0.72
2-3 m
0.65
2 - 3 m/s
0.72
3-4 m
0.55
3 - 4 m/s
0.72
>4 m
0.00
4 - 5 m/s
0.71
5 - 6 m/s
0.68
6 - 7 m/s
0.58
7 - 8 m/s
0.33
> 8 m/s
0.00
Table 7: Reduction factors for absence of daylight for dierent remote sensory devices and
visibility [Norsk olje og gass, 2013]. Reduced visibilty is dened as 2000 meters or less
measured horizontally.
Remote sensory device
Visibility
IR and oil radar
Good
0.9
Reduced
0.8
Good
0.7
Reduced
0.5
Good
0.7
Reduced
0.6
Good
0.3
Reduced
0.3
Good
0.0
Reduced
0.0
IR
Oil radar
Buouys and simple IR
No remote sensing
26
Reduction factor
3.3
As an example, according to equation 4, the expected daily capacity for a NOFO system is 1382 m3 /day
given a reduction factor of 0.60 for sea state and 0.95 for loss of visibility. Reduced capacity due to sea
state is usually caused by emulsion passing below the booms, and the emulsion must be collected by the
succeeding barrier. Reduced capacity due to absence of daylight are presumed possible to compensate
for by increasing the number of recovery systems in the barrier.
Then reduction factors in barrier 3, function A and B are generic and dierent for each given season
(Table 8). These include the presumption that combat and clean-up are reduced at low temperatures
and bad weather conditions, i.e., the reduction factors are lower in winter [Norsk olje og gass, 2013].
Table 8: Generic seasonal reduction factors for barrier 3, function A and B.
Period
Reduction factor
Function A
Function B
Winter
0.50
0.50
Spring
0.75
0.75
Summer
1.00
1.00
Autumn
0.75
0.75
3.3.5 Net Environmental Benet Analysis (NEBA)
A NEBA identies geographic areas and periods in which chemical dispersions is a benecial combat
strategy against oil spills. The results from the NEBA are useful tools for evaluation of the suitability
of chemical dispersion during an oil spill response, and to facilitate the decision-making process. The
analysis will prove especially valuable during the preliminary phase of the oil spill response, in the period
before organised monitoring and mapping of emissions and natural resources is operational. The results
of the NEBA are based on statistical data. Field observations of seabird presences during an oil spill shall
overrule presumptions from the statistical data of this analysis and contribute to the decision regarding
chemical dispersion.
Method The analysis method are based on the principles described in the report Netto Miljøgevinst
Analyse: Akutt Oljeforurensning; Mekanisk Bekjempelse - Kjemisk Dispergering" [Østby, C., Brude,
O.W. & Moe, K.A., 2002]. This simple and direct approach recognize the premise that biological eects
will only occur in those instances when natural resources and contaminants coincide in both time and
space. Chemical dispersion removes oil from the sea surface and increases the natural dispersion of oil
and dispersion chemicals into the water column. In result, chemical dispersion is considered an advantageous strategy for vulnerable natural resources on the surface, but detrimental to organisms in the water
column. The NEBA method is a three-step model:
Stage 1:
Map an area where the oil either is chemically dispersible or has reduced chemical dispersibility.
This area is dened by the weathering properties of the reference oil, i.e., the time window when the oil is
27
3.3
chemically dispersible, and the standard current velocities in the area. The resulting area is then assigned
to the (ContAct©) grid (10×10 km grid cells) [Alpha Miljørådgivning AS, 2003].
Stage 2:
Map vulnerable areas in the area dened in Stage 1. The following are areas where chem-
ical dispersion is considered
benecial : (1) Areas near shore with high concentrations of seabirds, (2)
Areas in the open sea with particularly high concentrations of pelagic seabirds, (3) Areas with particular physical conditions for aggregations of high concentrations of foraging seabirds in the open sea (i.e.,
frontal and upwelling areas), and (4) important breeding areas for harbour seal and grey seal. The following are areas where chemical dispersion is considered
detrimental : (1) Shallow areas (with depths <
20m occurring) near shore, including fjords, (2) areas with high concentrations of sh egg and larvae, i.e.,
spawning grounds, (3) protected areas of valuable marine systems with focus on resources in the water
column.
Stage 3:
An overlap analysis of the two regions in Stage 1 and 2. The overlap analysis results in
the net environmental benet for each grid cell in the area and is given in four categories: (1)
net en-
vironmental gain in grid cells where chemical dispersion is benecial, (2) net environmental loss in grid
cells where chemical dispersion is detrimental, (3) conict in grid cells where chemical dispersion may
be both benecial and detrimental (i.e., a cell where high presence of seabirds overlap with a spawning
area), and (4)
neutral environmental benet in grid cells where no areas of for instance sh or seabirds
occur statistically, and chemical dispersion shall be decided by eld observations of natural resources and
the spread and drift of the oil slick.
Input data, criteria and thresholds
The data for coastal seals and seabird have been implemented
by the Institute for Marine Research (IMR) and the Norwegian Institute for Nature Research (NINA)
from the MRDB and SEAPOP data sets (www.mrdb.no and www.seapop.no). The mapping of sh
spawning areas is based on qualitative data of spawning grounds and/or habitats dened by the IMR
(www.imr.no).
The selection criteria for the three categories (1) low, (2) medium, and (3) high concentration of a
natural resource is based on methods from
Miljøverdig og sjøfugl by Systad Systad, G. H [2011] and is
the same used by Havmiljø (www.havmiljo.no), where a grid cell containing a certain proportion of the
total population (relative count) in the region is assigned to the dierent categories.
Criteria for pelagic seabirds are:
(1) Low concentration: 0.05 - 0.10 % of the total population,
(2) Medium concentration: 0.10 - 0.20 % of the total population and
(3) High concentration: > 0.20 % of the total population.
Criteria for coastal seabirds and seals are:
(1) Low concentration: 1.00 - 2.50 % of the total population,
28
3.3
(2) Medium concentration: 2.50 - 5.00 % of the total population and
(3) High concentration: > 5.00 % of the total population.
There are no quantitative data available to estimate the proportions for natural resources in the
water column, and any grid cell within a spawning area for VEC-populations of sh or MPA areas for
resources in the water column are dened as "high concentration (3)".
29
4 Results
4.1 ODS results
Presentation of results
Results from the stochastic oil drift simulations are presented as inuence
areas and stranding statistics for a blowout for the exploration well 6306/5-2. Results for the weighted
combination of surface and subsea blowouts are presented in Appendix A.3. The results are presented
for the following periods of the year: Winter (December - February), Spring (March - May), Summer
(June - August) and Autumn (September - November).
4.1.1 Inuence areas
The inuence areas for oil on the sea surface, in the water column and accumulated on the shoreline
consist of all 10×10 km grid cells having more oil than a certain threshold value in more than 5 % of
the simulations. The threshold values are 0.01 tonne/km2 for surface, 375 ppb THC (Total Hydrocarbon
Concentration, dissolved and as droplets) for the water column, and 0.01 tonne/km for shoreline (Appendix C.1). Note that an inuence area is not the same as the area of any single oil drift (oil slicks) in
a stochastic simulation. The dierent oil drifts represent dierent time intervals, with various wind- and
current conditions, and will dier greatly in area and spatial extent. Simulations where the individual
oil drifts have large areas and are spatially close can give inuence areas much larger than the individual
oil drifts.
Inuence areas for oil on the sea surface
The inuence areas have an oval shape along the coast
to the North-East from the release point. The extent of the inuence area vary with season and release
point, where topside spills (Figure 5) give consistently larger inuence areas than subsea releases (Figure
4). The inuence areas reach the coastline from Møre & Romsdal county to Nordland county. The largest
inuence area for oil on the sea surface is during Summer (218 500 km2 ) for topside spills, while the
smallest area is during the period Winter (102 800 km2 ) for subsea spills. The extent of the remaining
inuence areas are presented in Table 9.
Inuence areas for oil in the water column
The inuence areas for oil in the water column range
from 800 to 1 300 km given a subsea release, with the largest area during Summer (Table 9 and Figure
2
6). Note that Figure 7 is blank because the inuence areas for water column are zero given a topside
release. This indicates that there are no map grid cells with a larger than 5 % probability of exceeding
the threshold value of 375 ppb.
Inuence areas for oil on the shoreline
The inuence area for oil on the shoreline stretch from
Møre & Romsdal to Nordland, with inuence areas for topside releases (Figure 9) that are consistently
larger than inuence areas for subsea releases (Figure 8). The inuence area with the largest extent
(6 200 km2 ) is given a topside release during Summer, while the smallest inuence area (2 000 km2 ) is
during Winter, given a subsea release. The extent of the remaining inuence areas are presented in Table
9.
30
4.1
ODS results
4.1.2 Stranding statistics for oil
The stranding statistics for oil is based on the use of percentiles, see Appendix C.2 for an explanation of
this concept. These statistics is presented for all shoreline, IUA regions and NOFO example areas. For the
latter two, only areas with higher than 5 % probability for stranding is shown for the dimensioning release
point for the oil spill contingency analysis. Complete statistics for IUA regions and NOFO example areas
are presented in Appendix A.1.
All shoreline
Stranding statistics for all aected shoreline is presented in Table 10. The probability for
stranding shows little variation between periods of the year and release point, with a probability ranging
from 62.7 % during Winter to 68.9 % during Autumn. The stranding time is relatively short, from 3.7
days in Autumn to 6.6 days in Spring, represented by the 5-percentile. Topside releases have slightly
shorter stranding times than seabed releases for all periods.
Shoreline in IUA regions
There are nine IUA regions stranding probabilities higher than 5 % given a
topside release, the dimensioning release point for the oil spill contingency analysis in Chapter 4.3. Table
11 presents the 5-percentile for stranding time and 95-percentile for stranding amounts in the nine IUA
regions, while Table 23 in Appendix A give the complete stranding statistics for all IUA regions. There
is large variation in stranding probabilities, stranding time and stranding amounts in the nine regions.
Shoreline in example areas dened by NOFO
There are eight NOFO example areas with stranding
probabilities higher than 5 % given a topside release, the dimensioning release point for the oil spill contingency analysis in Chapter 4.3. Table 12 presents the 5-percentile for stranding time and 95-percentile
for stranding amounts in the seven regions, while Table 24 in Appendix A give the complete stranding
statistics for all NOFO example areas.
31
4.1
ODS results
Table 9: The size of the inuence areas for oil on the sea surface, in the water column, and
accumulated on the shoreline, calculated from the stochastic oil drift simulations for releases
caused by drilling of the exploration well 6306/5-2 at the prospect Hagar. See the denition
of these areas in Appendix C.1.
Release
Area (km2 )
Period
Depth
Watercolumn
Surface
Shore line
Winter
Topside
0
151 500
2 600
...
Seabed
800
102 800
2 000
Spring
Topside
0
192 100
4 300
...
Seabed
900
134 400
2 900
Summer
Topside
0
218 500
6 200
...
Seabed
1 300
151 600
3 200
Autumn
Topside
0
176 200
3 200
...
Seabed
1 000
120 300
2 400
Table 10: Stranding statistics for all aected shoreline, calculated from the stochastic oil drift
simulations for oil spills caused by drilling of the exploration well 6306/5-2 at the prospect
Hagar. The columns cover probability for stranding, stranding time, as well as stranded
amount of oil emulsion. The stranding time and amount of oil emulsion is tabulated as three
dierent percentiles from their respective probability distributions. See an explanation of
percentiles in Appendix C.2.
Release
Period
Depth
Winter
Topside
...
Prob. (%)
Time (days)
Amount (tonne)
P0
P5
P50
P50
P95
P100
68.2
2.4
4.7
13.1
30
5 840
199 543
Seabed
66.5
3.1
6.0
17.2
11
1 466
80 744
Spring
Topside
65.6
2.1
5.1
15.8
32
33 380
820 486
...
Seabed
65.6
3.3
6.6
21.6
17
8 449
563 684
Summer
Topside
67.3
2.1
4.8
14.6
92
41 825
969 861
...
Seabed
62.7
3.0
6.5
22.3
19
8 777
370 797
Autumn
Topside
68.7
2.1
3.7
12.8
35
8 938
297 484
...
Seabed
68.9
2.2
5.1
16.9
16
2 071
76 300
32
4.1
ODS results
Table 11: Stranding statistics for IUA regions, calculated from the stochastic oil drift simulations
for topside spills caused by drilling of the exploration well 6306/5-2 at the prospect Hagar. Only
regions with a standing probability above 5 % are presented. The columns cover the probability of stranding, shortest stranding time, as well as amount of stranded oil emulsion. Shortest
stranding time is represented by the 5-percentile and the amount of stranded oil emulsion by the
95-percentile. See an explanation of percentiles in Appendix C.2.
IUA region
Prob. (%)
Time (days)
Amount (tonne)
Helgeland
18.9
20
79
Winter
...
12.5
27
565
Spring
...
21.5
19
1 131
Summer
...
28.4
14
245
Autumn
Lofoten og Vesterålen
5.6
79
9
Winter
...
8
59
142
Summer
...
12.3
31
118
Autumn
Midt- og Nord-Troms
5.7
85
18
Autumn
Namdal
40.6
8
373
Winter
...
41.2
11
4 162
Spring
...
40.1
11
8 782
Summer
...
43
8
812
Autumn
Nordmøre
30
6
1 564
Winter
...
25.3
7
2 756
Spring
...
27.4
7
7 693
Summer
...
25.8
5
2 705
Autumn
Romsdal
6.4
31
18
Winter
...
7.1
31
35
Spring
...
10.2
23
263
Summer
...
8.1
22
179
Autumn
Salten
7.7
42
18
Winter
...
7
66
53
Spring
...
11
32
184
Summer
...
18.4
22
153
Autumn
Sunnmøre
5.1
90
11
Summer
Sør-Trøndelag
61.5
5
3 483
Winter
...
61.9
5
18 453
Spring
...
63.2
5
20 929
Summer
...
56.1
4
4 287
Autumn
33
Period
4.1
ODS results
Table 12: Stranding statistics for NOFO example areas, calculated from the stochastic oil
drift simulations for topside spills caused by drilling of the exploration well 6306/5-2 at
the prospect Hagar. Only areas with a standing probability above 5 % are presented. The
columns cover the probability of stranding, shortest stranding time, as well as amount of
stranded oil emulsion. Shortest stranding time is represented by the 5-percentile and the
amount of stranded oil emulsion by the 95-percentile. See an explanation of percentiles in
Appendix C.2
Area
Prob. (%)
Time (days)
Amount (tonne)
Bliksvær
5.3
71
9
Summer
...
7.1
40
9
Autumn
Frøya og Froan
42.7
6
1 831
Winter
...
51.1
6
9 916
Spring
...
51.2
5
10 197
Summer
...
41.5
5
2 328
Autumn
Moskenesøy og Flakstadøy
6.1
75
37
Summer
...
8.6
40
37
Autumn
Røst
5.2
88
7
Summer
...
10.6
29
52
Autumn
Smøla
29.2
6
1 476
Winter
...
24.3
6
2 613
Spring
...
27.7
8
5 928
Summer
...
25.5
6
1 974
Autumn
Træna
17.9
21
69
Winter
...
8.9
40
53
Spring
...
17.4
23
274
Summer
...
27.2
14
211
Autumn
Vega
7.2
64
119
Spring
...
10.3
25
253
Summer
Vikna vest
15.1
15
52
Winter
...
18.4
16
557
Spring
...
24.7
15
1 141
Summer
...
17.5
14
87
Autumn
34
Period
4.1
ODS results
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . This page is intentionally left blank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
35
4.1
ODS results
Figure 4: The inuence areas for oil on the surface for subsea spills caused by drilling of the
exploration well 6306/5-2 at the prospect Hagar. The areas are calculated from stochastic oil
drift simulations. Each area consists of all 10×10 km map cells containing more oil on the surface than 0.01 tonne/km2 , in more than 5% of the single simulations. The maps cover the simulation periods Winter (December - February), Spring (March - May), Summer (June - August) and
Autumn (September - November).
36
4.1
ODS results
Figure 5: The inuence areas for oil on the surface for topside spills caused by drilling of the
exploration well 6306/5-2 at the prospect Hagar. The areas are calculated from stochastic oil
drift simulations. Each area consists of all 10×10 km map cells containing more oil on the surface than 0.01 tonne/km2 , in more than 5% of the single simulations. The maps cover the simulation periods Winter (December - February), Spring (March - May), Summer (June - August) and
Autumn (September - November).
37
4.1
ODS results
Figure 6: The inuence areas for oil in the water column for subsea spills caused by drilling of
the exploration well 6306/5-2 at the prospect Hagar. The areas are calculated from stochastic oil
drift simulations. Each area consists of all 10×10 km map cells with higher oil concentration in
the water column than 375 ppb, in more than 5% of the single simulations. The maps cover the
simulation periods Winter (December - February), Spring (March - May), Summer (June - August)
and Autumn (September - November).
38
4.1
ODS results
Figure 7: The inuence areas for oil in the water column for topside spills caused by drilling of
the exploration well 6306/5-2 at the prospect Hagar. The areas are calculated from stochastic oil
drift simulations. Each area consists of all 10×10 km map cells with higher oil concentration in
the water column than 375 ppb, in more than 5% of the single simulations. The maps cover the
simulation periods Winter (December - February), Spring (March - May), Summer (June - August)
and Autumn (September - November).
39
4.1
ODS results
Figure 8: Inuence areas for oil accumulated on the shoreline for subsea spills caused by drilling
of the exploration well 6306/5-2 at the prospect Hagar. The areas are calculated from stochastic
oil drift simulations. Each area consists of all 10×10 km map cell containing shore line and with
more accumulated oil on the shore line than 0.01 tonne/km, in more than 5% of the single simulations. The maps cover the simulation periods Winter (December - February), Spring (March - May),
Summer (June - August) and Autumn (September - November).
40
4.1
ODS results
Figure 9: Inuence areas for oil accumulated on the shoreline for topside spills caused by drilling
of the exploration well 6306/5-2 at the prospect Hagar. The areas are calculated from stochastic
oil drift simulations. Each area consists of all 10×10 km map cell containing shore line and with
more accumulated oil on the shore line than 0.01 tonne/km, in more than 5% of the single simulations. The maps cover the simulation periods Winter (December - February), Spring (March - May),
Summer (June - August) and Autumn (September - November).
41
4.1
ODS results
42
4.2
Environmental risk analysis
4.2 Environmental risk analysis
Presentation of results
Results for environmental damage and environmental risk are presented for
(1) coastal seabirds, (2) pelagic seabirds, (3) seals, (4) sh and (5) shoreline habitats. The main results
of the environmental risk analysis are summarized and presented graphically in bar plots for each group
(Figures 11, 12, 13, 14, and 16).
The bar plots show the VEC population or habitat in each group with the highest conditional
probability for environmental damage and relative environmental risk in each period and damage category.
The damage categories are (ref. Table 2):
Minor (restitution time 1 month - 1 years)
Moderate (restitution time 1 - 3 years)
Considerable (restitution time 3 - 10 years)
Serious (restitution time > 10 years)
The results are presented for the following periods of the year: Winter (December - February), Spring
(March - May), Summer (June - August) and Autumn (September - November).
Figure 10 gives an example illustrating the interpretation of the bar plot gures. The bar plot has
two columns with four bars each, one for each damage category (rest. time). The left column shows the
conditional probability (cond. prob) of environmental damage, i.e., the probability of damage
given an
oil spill. The right column shows relative environmental risk (rel. env. risk), i.e., the absolute probability
of damage divided by the operator's acceptance criteria for environmental risk in each damage category.
The complete results from the ERA are shown in Tables 25, 26, 27, 28 and 29 in Appendix A.
Period
Rest. time
Cond. prob. (%)
0
Winter
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
50
Rel. env. risk (%)
100
0
50
10
14
50
50
29
7
Population
100
No population
Atlantic pun (NH)
Atlantic pun (NH)
Common guillemot (NH)
Figure 10: The highest conditional probability in the period Winter for Minor damage (on any
of the investigated pelagic seabird populations) is 50 % in this example. Similarly, the conditional
probabilities for the damage categories Moderate, Considerable and Serious are 50 %, 10 % and 0 %
(no probability of damage), respectively. The highest environmental risk in the period Winter is
29 % in the damage category Moderate and represents the Norwegian Sea population of the Atlantic
pun. Similarly, the environmental risk for the damage categories Moderate, Considerable and
Serious are 7 %, 14 % and 0 %.
43
4.2
4.2.1 Results for pelagic birds
For pelagic seabirds the largest environmental risk, for each of the four damage classes, is shown in
Figure 11, while the complete results are shown in Table 26 in Appendix A.2. The maximal conditional
probability for the damage categories Serious, Considerable, Moderate and Minor are 5 % , 16 %, 48 %
and 43 %, respectively, regardless of period and population (Figure 11, left column). The conditional
probability for all damage categories is highest in the simulation period Winter.The relative environmental
risk is highest in the Winter and constitutes 27 % of the operation specic acceptance criteria for damage
categories Serious and Moderate. The highest relative environmental risk for each damage category is:
27 % in the damage category Serious for the Norwegian Sea population of Atlantic pun (Winter)
23 % in the damage category Considerable for the Norwegian Sea populations of Atlantic pun
(Winter)
27 % in the damage category Moderate for the Norwegian Sea population of common guillemot
(Winter)
6 % in the damage category Minor for the Norwegian Sea population of herring gull (Winter)
The environmental risk within each period is highest for damage category Moderate for all periods
(between 20 and 27 %), except for Winter, when both Serious and Moderate damage categories have the
same environmental risk. The populations with the highest relative environmental risk are the Norwegian
Sea populations of Atlantic pun, razorbill and common guillemot (see Figure 12, right column).
44
4.2
Period
Rest. time
Cond. prob. (%)
0
50
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
5
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
3
Summer
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
2
6
Autumn
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
2
7
Winter
Spring
Rel. env. risk (%)
100
0
50
27
23
27
16
48
43
6
Population
100
Atlantic pun (NH)
Atlantic pun (NH)
Common guillemot (NH)
Herring gull (NH)
5
Razorbill
Razorbill
Razorbill
Razorbill
(NH)
(NH)
(NH)
(NH)
36
31
13
9
20
4
Razorbill
Razorbill
Razorbill
Razorbill
(NH)
(NH)
(NH)
(NH)
38
33
10
10
22
5
Razorbill
Razorbill
Common
Common
(NH)
(NH)
guillemot (NH)
guillemot (NH)
17
13
24
9
42
36
Figure 11: The main results for damage on pelagic seabirds calculated from the stochastic oil
drifts simulations for spills caused by drilling of exploration well 6306/5-2 at the prospect Hagar.
The two columns with horizontal bar graphs show: (1) the conditional probability for restitution
time in the dierent intervals, i.e. the probability if (given) a blowout takes place, and (2) the
relative risk for each of the restitution time intervals, i.e. absolute risk for each of the restitution
time intervals divided by the accepted maximum risk for the same intervals. In the column of
population names ("Population") the codes in brackets designate the geographical region that the
populations belongs to, see section B.2.
45
4.2
4.2.2 Results for coastal birds
For coastal seabirds the largest environmental risk, for each of the four damage classes, is shown in
Figure 12, while the entire results are shown in Table 25 in Appendix A.2.
The maximum conditional probability for the damage categories Serious, Considerable, Moderate
and Minor are 7 %, 15 %, 41 % and 37 %, respectively, regardless of period and population (Figure 12,
left column). The conditional probability for all damage categories is highest in the simulation period
Winter. The relative environmental risk is highest in the Summer and constitutes 38 % of the operation
specic acceptance criteria for Serious damage. The highest relative environmental risk for each damage
category is:
38 % in the damage category Serious for the Norwegian Sea population of great cormorant (Summer)
21 % in the damage category Considerable for the Norwegian Sea population of black guillemot
(Summer)
23 % in the damage category Moderate for the Norwegian Sea population of European shag (Winter)
5 % in the damage category Minor for the Norwegian Sea populations of great cormorant (Winter)
The environmental risk within each season is highest for damage category Moderate during Winter
and Autumn represented by the European shag, and for damage category Serious during Spring and
Summer represented by the black guillemot and great cormorant, respectively (see Figure 12, right
column).
46
4.2
Period
Rest. time
Cond. prob. (%)
0
Winter
Spring
Summer
Autumn
50
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
2
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
4
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
7
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
1
2
7
6
Rel. env. risk (%)
100
0
50
12
41
37
25
20
European Shag (NH)
European Shag (NH)
European Shag (NH)
Great Cormorant (NH)
23
15
14
Black Guillemot (NH)
European Shag (NH)
European Shag (NH)
3
3
Great Cormorant (NH)
European Shag (NH)
Red-breasted Merganser (NH)
3
3
4
1
European Shag (NH)
European Shag (NH)
European Shag (NH)
38
15
21
15
27
22
100
13
17
23
5
10
Population
Figure 12: The main results for damage on coastal seabirds calculated from the stochastic oil drifts
simulations for spills caused by drilling of the exploration well 6306/5-2 at the prospect Hagar.
The two columns with horizontal bar graphs show: (1) the conditional probability for restitution
time in the dierent intervals, i.e. the probability if (given) a blowout takes place, and (2) the
relative risk for each of the restitution time intervals, i.e. absolute risk for each of the restitution
time intervals divided by the accepted maximum risk for the same intervals. In the column of
population names ("Population") the codes in brackets designate the geographical region that the
populations belongs to, see section B.2.
47
4.2
4.2.3 Results for seals
For seals the largest environmental risk, for each of the four damage classes, is shown in Figure 13, while
the entire results are shown in Table 27 in Appendix A.2.
The maximal conditional probability for the damage categories Serious, Considerable, Moderate
and Minor are 4 %, 13 %, 43 % and 36 %, respectively, regardless of period and population (Figure 13,
left column). The relative environmental risk is highest in Winter and constitutes 25 % of the operation
specic acceptance criteria for damage category Moderate. The highest relative environmental risk for
each damage category is:
21 % in the damage category Serious for the mid-Norwegian population of grey seal (Winter)
19 % in the damage category Considerable for the mid-Norwegian population of grey seal (Summer)
25 % in the damage category Moderate for the mid-Norwegian population of grey seal (Winter)
5 % in the damage category Minor for the mid-Norwegian population of grey seal (Winter, Spring
and Autumn)
The environmental risk within each season is highest for damage category Moderate for all periods
(from 21 to 25 %), except for Summer, when both Serious and Moderate damage categories have the
same environmental risk. It is the mid-Norwegian population of grey seal that have the highest relative
risk in all periods. (see Figure 13, right column).
48
4.2
Period
Rest. time
Cond. prob. (%)
0
Winter
Spring
Summer
Autumn
50
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
1
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
3
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
4
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
1
Rel. env. risk (%)
100
0
50
4
8
11
43
36
25
5
16
14
23
10
40
33
5
21
19
21
13
37
30
4
8
10
14
23
40
32
5
Population
100
Grey
Grey
Grey
Grey
seal
seal
seal
seal
(MI)
(MI)
(MI)
(MI)
Grey
Grey
Grey
Grey
seal
seal
seal
seal
(MI)
(MI)
(MI)
(MI)
Grey seal (MI)
Grey seal (MI)
Grey seal (MI)
Harbour seal (MI)
Grey
Grey
Grey
Grey
seal
seal
seal
seal
(MI)
(MI)
(MI)
(MI)
Figure 13: The main results for damage on seals calculated from the stochastic oil drifts simulations
for spills caused by drilling of the exploration well 6306/5-2 at the prospect Hagar. The two columns
with horizontal bar graphs show: (1) the conditional probability for restitution time in the dierent
intervals, i.e. the probability if (given) a blowout takes place, and (2) the relative risk for each of the
restitution time intervals, i.e. absolute risk for each of the restitution time intervals divided by the
accepted maximum risk for the same intervals. In the column of population names ("Population")
the codes in brackets designate the geographical region that the populations belongs to, see section
B.2.
49
4.2
4.2.4 Results for sh
The results for the MIRA for sh are shown in Figure 14. Both the conditional probability for environmental damage and the relative environmental risk is negligible for Norwegian spring-spawning herring
and north-east arctic cod (Figure 14). There is no overlap between inuence area for water column and
spawning areas of other sh populations.
Period
Rest. time
Cond. prob. (%)
0
50
100
Rel. env. risk (%)
0
50
Population
100
Winter
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
Spring
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
Summer
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
No
No
No
No
population
population
population
population
Autumn
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
No
No
No
No
population
population
population
population
No
No
No
No
value below 0.5 %
value below 0.5 %
value below 0.5 %
value below 0.5 %
value below 0.5 %
value below 0.5 %
population
population
population
population
No population
Cod (NH)
Cod (NH)
Cod (NH)
Figure 14: The main results for damage on sh species listed in shNorwegianSea.xlsx calculated
from the stochastic oil drifts simulations for possible oil spills caused drilling of exploration well
6306/5-2 at the prospect Hagar. The two columns with horizontal bar graphs show: (1) the
conditional probability for restitution time in the dierent intervals, i.e. the probability if (given)
a blowout takes place, and (2) the relative risk for each of the restitution time intervals, i.e. absolute
risk for each of the restitution time intervals divided by the accepted maximum risk for the same
intervals. In the column of population names ("Population") the codes in brackets designate the
geographical region that the populations belongs to, see section B.2.
50
4.2
51
4.2
4.2.5 Results for shoreline habitats
For the shoreline the largest environmental risk, for each of the four damage classes, is shown in Figure 16,
while the entire results are shown in Table 29 in Appendix A.2.The probabilities for environmental damage
and the relative environmental risk for shoreline habitats are low for all damage categories and simulation
periods. The maximal conditional probability for the damage categories Serious, Considerable, Moderate
and Minor are < 0.5 %, 2 %, 14 % and 31 %, respectively, regardless of period and population (Figure 16,
left column). The highest relative environmental risk is the same for all seasons and constitutes 8 % of the
operation specic acceptance criteria for damage category Moderate. The highest relative environmental
risk for each damage category is:
1 % in the damage category Serious for ID 22252 (Spring) and ID 22464 (Summer)
2 % in the damage category Considerable for ID 22252 (all periods)
8 % in the damage category Moderate for ID 22252 (all periods)
4 % in the damage category Minor for ID 20975 (all periods)
The grid cell with ID 22252 has the highest environmental risk in all periods, and corresponds to Osen
municipality in Sør-Trøndelag county.
Figure 15: The geographical location of all grid cells presented in Figure 16
and marked position of the exploration well 6306/5-2.
52
4.2
Period
Rest. time
Cond. prob. (%)
0
50
Rel. env. risk (%)
100
0
50
Square ID
100
Winter
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
value below 0.5 %
1
14
30
value below 0.5 %
2
8
4
22252 (Osen)
22252 (Osen)
22252 (Osen)
20975 (Frøya)
Spring
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
value below 0.5 %
2
15
31
1
2
22252 (Osen)
22252 (Osen)
22252 (Osen)
20975 (Frøya)
Summer
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
value below 0.5 %
1
14
25
1
2
Autumn
>10 yr
3-10 yr
1-3 yr
0.1-1 yr
value below 0.5 %
1
14
29
value below 0.5 %
2
8
4
8
4
8
4
22464 (Flatanger)
22252 (Osen)
22252 (Osen)
20975 (Frøya)
22252 (Osen)
22252 (Osen)
22252 (Osen)
20975 (Frøya)
Figure 16: The main results for damage on the shoreline calculated from stochastic oildrift simulations of spills caused by drilling of the exploration well 6306/5-2 at the prospect Hagar. The two
columns with horizontal bar graphs show: (1) the conditional probability for restitution time in
the dierent intervals, i.e. the probability if (given) a blowout takes place, and (2) the relative risk
for each of the restitution time intervals, i.e. absolute risk for each of the restitution time intervals
divided by the operator's accepted maximum risk for the same intervals. The column "Square ID"
contains identication numbers for each of the cells (10×10 km) in the ContAct map grid [Alpha
Miljørådgivning AS, 2003], with the municipality in parenthesis.
©
53
4.3
Oil spill contingency analysis
4.3 Oil spill contingency analysis
The recommendations of number of NOFO-systems and suggestion of OR-vessels in this analysis are
based on best attainable response times. A NOFO-system consists of one NOFO classied vessel with
equipment and towing vessel. Note that the response time for towing vessels in the NOFO pool is 24
hours. Based on the results from this analysis RENAS must establish an oil spill response for Hagar. An
oil spill contingency plan (OSCP) shall be prepared based on RENAS's proposed oil spill response and
requirements set in the permit by the Norwegian Environment Agency (MD).
4.3.1 Dimensioning rates
Topside blowouts have considerably larger stranding amounts than subsea blowouts (Table 10, Table 11,
and Table 12). Therefore, the dimensioning scenario for the oil spill contingency analysis is a topside
blowout. The dimensioning blowout rate for barrier 1 (function A and B) is 4898 S m3 /d. The maximum
stranded amount of emulsion (represented by the 95-percentiles) to IUA regions are used as basis to
calculate the dimensioning emulsion rates for barrier 2 and the dimensioning amounts for barrier 3
(function A and B). These are given in Table 13.
Table 13: The stranded amount of emulsion used as basis to calculate
the dimensioning rates and amounts for barriers 2 and 3. The values are
95-percentiles of stranded amount of emulsion for topside release without
any oil spill response present.
IUA-region
Stranding amounts (tonne emulsion)
Winter
Spring
Summer
Autumn
Sør-Trøndelag
3 483
18 453
20 929
4 287
Nordmøre
1 564
2 756
7 693
2 705
373
4 162
8 782
812
Helgeland
79
565
1 131
245
Romsdal
18
35
263
179
Salten
18
53
184
153
9
0
142
118
Midt- og Nord-Troms
0
0
0
18
Sunnmøre
0
0
11
0
Namdal
4.3.2 Response times
The calculated response times for NOFO OR-vessels for Hagar are presented in Table 14. At each NOFO
depot two systems are available with mobilization time of 10 and 30 hours for the rst and second system,
respectively. Due to uncertainty in the action radius of the oshore OR-vessels a 3 hour buer has been
added to the sailing time. The calculations use a a standard transit speed of 14 knots. The response time
54
4.3
for towing vessels in the NOFO pool is 24 hours. Thus, a special agreement with NOFO may be necessary
to obtain response times shorter than 24 hours for complete NOFO-systems. Shorter response times may
also be obtained using Norwegian Sea Rescue vessels ("Redningskøyter") from Måløy, Kristiansund-N,
Rørvik (Vikna), Ballstad (Lofoten) and Sørvær (Sørøya) (cf. NOFO [2014b]). Their average sailing
(travelling) speed is 20 knots.
The response times for barrier 2 depend on the location of the vessels at time of mobilisation and
location of oil in the coastal zone. According to NOFO's framework the mobilisation time of a fully
developed
10 vessels collection system (with ten collection, four recovery and two command systems) is
within 120 hours. Earliest initiation of the barrier is 48 hours. The minimum requirement for response
time for the aected IUA regions are as follows:
Sør-Trøndelag: 4 days
Nordmøre: 5 days
Namdal: 8 days
Helgeland: 14 days
Romsdal: 22 days
Salten: 22 days
Lofoten og Vesterålen: 31 days
Midt- og Nord-Troms: 85 days
Sunnmøre: 90 days
55
4.3
Table 14: Best attainable response times for NOFO OR-vessels. Best attainable response time
is the sum of mobilisation/release time, sailing time (transit), buer and deployment of boom,
rounded up to the nearest whole hour.
Resource
Depot and
area
Distance
Mobilisation
Transit
Buer
Boom
Response
km
h
h
h
h
h
NOFO
Hammerfest 01
1 144
10
44
3
1
59
depot
Hammerfest 02
1 144
30
44
3
1
79
Sandnessjøen 01
395
10
15
3
1
30
Sandnessjøen 02
395
30
15
3
1
50
Kristiansund 01
90
10
3
3
1
18
Kristiansund 02
90
30
3
3
1
38
Mongstad 01
369
10
14
3
1
29
Mongstad 02
369
30
14
3
1
49
Stavanger 01
610
10
24
3
1
38
Stavanger 02
610
30
24
3
1
58
1 094
4
42
3
1
51
Standby
Goliat (Barentshavet)
vessels
Haltenbanken
161
1
6
3
1
12
Tampen
372
1
14
3
1
20
Gjøa
300
4
12
3
1
20
Troll/Oseberg 01
360
1
14
3
1
19
Troll/Oseberg 02
399
1
15
3
1
21
Balder
539
6
21
3
1
31
Sleipner/Volve
645
3
25
3
1
32
Ula/Gyda/Tambar
780
6
30
3
1
41
Ekosk
854
6
33
3
1
43
56
4.3
4.3.3 Location of barriers
The barriers at open sea are located at 6 and 12 hours drift time from the release point for barrier 1A
and 1B, respectively. The expected weathering properties of the oil spill at the location of barrier 1A
and barrier 1 B is presented in Table 15. The proposed locations are based on considerations of safety
(dangerous gas, explosion hazard), viscosity of the emulsion and maneuverability of the vessels. Barrier
1A is located at six hours drift time since the weathered Draugen oil has a relatively low ash point
[SINTEF, 2008]. At 5 m/s wind speed a ash point limit for liquids to be stored on board (60◦ C) will be
reached after 3 hours in summer conditions and after 6 hours in winter conditions. The ash point limit
will be reached later at lower wind speeds and faster at higher wind speeds. Barrier 1B is located at 12
hours drift time. The lower limit of optimal mechanical recovery (1 000 cP) will typically be reached after
2 to 3 hours at a wind speed of 10 m/s.
Table 15: Expected weathering properties of the oil spill at the location
of barrier 1A and barrier 1B.
Period
Barrier 1A (6 hours)
Barrier 1B (12 hours)
Water content
Evaporation
Dispersed
Water content
Evaporation
Dispersed
Winter
0.58
0.35
0.21
0.64
0.37
0.27
Spring
0.51
0.34
0.13
0.59
0.36
0.17
Summer
0.44
0.36
0.07
0.55
0.38
0.10
Autumn
0.54
0.37
0.16
0.62
0.39
0.21
4.3.4 Reduction factors and system eciency
Reduction factors for wind speed, signicant wave height (Hs), absence of daylight (darkness) and visibility, and the expected capacities are presented in Table 16. The expected capacity is highest during
Summer and lowest during Winter. This applies to both NOFO systems and coastal systems.
Table 16: Reduction factors for wind speed, wave height, absence of daylight (darkness)
and visibility and expected capacity in barrier 1 (NOFO-system with function A and B)
and barrier 2 (coastal system). The product of all reduction factors is presented in a
separate column.
Period
Wind and waves
Darkness and visibility
Prod. red. factor
Capacity (S m3 /d)
Barrier
1
2
1
2
1
2
1
2
Winter
0.41
0.22
0.90
0.90
0.37
0.20
889
48
Spring
0.58
0.36
0.96
0.96
0.56
0.35
1 342
83
Summer
0.71
0.47
0.99
0.99
0.70
0.46
1 670
111
Autumn
0.53
0.31
0.92
0.92
0.48
0.28
1 164
68
57
4.3
4.3.5 Oil spill requirements in barrier 1
The calculated inux emulsion rate (dimensioning emulsion rate) and the resource requirement for barrier
1A and 1B are presented in Table 17. The inux rate for barrier 1A is approximately the same for all
seasons, while the inux rate for barrier 1B is highest in Winter and lowest in Summer. The resource
requirement is three NOFO systems in barrier 1A for all seasons. The resource requirement in barrier
1B is two NOFO systems in Winter, Spring and Autumn and one NOFO system in Summer. The inux
rate as a function of time, with and without eect of oil spill response is illustrated in Figure 17.
Table 17: Expected inux of emulsion (dimensioning emulsion rate) and system requirements for barrier 1 (function A and B). The system requirement is rounded up the
nearest whole number.
Barrier
Inux emulsion (S m3 /d)
System requirement (number of systems)
Winter
Spring
Summer
Autumn
Winter
Spring
Summer
Autumn
1 (A)
5 167
5 273
4 990
4 924
3
3
3
3
1 (B)
2 929
2 358
1 675
2 384
2
2
1
2
58
4.3
Figure 17: The inux rate as a function of time without (blue) and with (brown) the eect of
mechanical oil spill response. The proposed locations of barrier 1 (B1A) and 2 (B1B) are marked.
4.3.6 Oil spill requirements in barrier 2
The calculated inux emulsion rate and the resource requirement for barrier 2 is presented in Table
18. Resource requirement has been calculated for the nine IUA regions with more than 5 % probability
of stranding, given a topside blowout. In total, for all aected IUA regions, the calculated resource
requirement is seven to eight coastal systems with the following distribution over the seasons:
Winter and Spring: seven systems
Summer and Autumn: eight systems
59
Table 18: Expected inux of emulsion (dimensioning emulsion rate) and system requirements for barrier 2. The system requirement is rounded up to the nearest whole number.
NCA-region IUA-region
Midt-Norge Sør - Trøn-
Inux emulsion (S m3 /d)
System requirement
Winter Spring Summer Autumn
Winter Spring Summer Autumn
117.8 311.3
177.0
93.7
1
2
1
1
delag
Midt-Norge Nordmøre
52.9
46.5
65.1
59.1
1
1
1
1
Midt-Norge Namdal
12.6
70.2
74.3
17.7
1
1
1
1
2.7
9.5
9.6
5.4
1
1
1
1
Midt-Norge Romsdal
0.6
0.6
2.2
3.9
1
1
1
1
Nordland
Salten
0.6
0.9
1.6
3.3
1
1
1
1
Nordland
Lofoten og
0.3
0.0
1.2
2.6
1
0
1
1
0.0
0.0
0.0
0.4
0
0
0
1
0.0
0.0
0.1
0.0
0
0
1
0
Nordland
Helgeland
Vesterålen
Troms
og
Finnmark
Midt-
og
Nord-Troms
Midt-Norge Sunnmøre
4.3.7 Oil spill response in barrier 3
Stranded emulsion amounts and calculated resource requirement for barrier 3 is presented in Table 19.
The oil spill response requirement is calculated based on dimensioning stranding amounts in IUA regions.
The total resource requirement for beach cleaning operations, given stranding in all areas, is as follows:
43 176 working days in Winter
58 144 working days in Spring
22 141 working days in Summer
22 365 working days in Autumn
The number of beach cleaning units of ten persons required to fulll the requirement of completing the
beach cleaning operations within 100 work days is also given.
In addition to IUA regions resource requirement for barrier 3 has also been calculated for dimensioning emulsion amounts in NOFO example areas. This is presented in Table 20. The resource requirement
is lower than that in Table 19 since the NOFO areas cover a smaller portion of the coast than the IUA
regions. In total nine NOFO example areas have more than 5 % probability of stranding, given a topside
blowout.
60
Table 19: Estimated stranded amount emulsion and resource requirement per IUA-region in barrier 3. Each beach cleaning unit consists of
10 persons and it is presupposed that the clean-up operations shall be nished within 100 days. P1 = Winter, P2 = Spring, P3 = Summer,
P4 = Autumn.
NCA-region
IUA-region
Stranded amount emulsion (tonne)
Resource requirement (day's work)
Beach cleaning units
P1
P2
P3
P4
P1
P2
P3
P4
P1
P2
P3
P4
Sør-Trøndelag
512.9
1 153.7
477.6
323.4
30 173
45 245
14 047
12 684
31
46
15
13
Midt-Norge
Nordmøre
202.0
106.7
110.5
175.6
11 882
4 184
3 250
6 887
12
5
4
7
Midt-Norge
Namdal
9.0
200.5
140.7
37.9
530
7 861
4 138
1 488
1
8
5
2
Nordland
Helgeland
6.4
18.8
15.7
11.4
377
739
461
449
1
1
1
1
Midt-Norge
Romsdal
1.5
1.2
3.6
8.4
86
46
107
328
1
1
1
1
Nordland
Salten
1.5
1.8
2.6
7.1
86
69
75
280
1
1
1
1
Nordland
0.7
0.0
2.0
5.5
43
0
58
216
1
0
1
1
Troms og Finnmark
Midt- og Nord-Troms
0.0
0.0
0.0
0.8
0
0
0
33
0
0
0
1
Midt-Norge
Sunnmøre
0.0
0.0
0.2
0.0
0
0
4
0
0
0
1
0
Midt-Norge
61
Table 20: Estimated stranded amount emulsion and resource requirement per NOFO example area in barrier 3. Each beach cleaning unit
consists of 10 persons and it is presupposed that the clean-up operation shall be nished within 100 days. P1 = Winter, P2 = Spring,
P3 = Summer, P4 = Autumn.
IUA-region
NOFO example area
Stranded emulsion (tonne)
Resource requirment (day's work)
Beach cleaning-units
P1
P2
P3
P4
P1
P2
P3
P4
P1
P2
P3
P4
Frøya og Froan
245.3
584.3
179.9
140.4
14 427
34 370
10 585
8 258
15
35
11
9
Nordmøre
Smøla
187.7
97.1
61.5
107.3
11 043
5 714
3 620
6 312
12
6
4
7
Namdal
Vikna vest
4.2
18.6
15.8
4.1
248
1 093
931
239
1
2
1
1
Helgeland
Træna
5.6
1.8
3.8
9.9
329
104
224
580
1
1
1
1
Helgeland
Vega
0.0
4.0
3.5
0.0
0
233
206
0
0
1
1
0
Salten
Røst
0.0
0.0
0.1
2.4
0
0
6
143
0
0
1
1
0.0
0.0
0.5
1.7
0
0
30
102
0
0
1
1
Salten
Bliksvær
0.0
0.0
0.1
0.4
0
0
7
25
0
0
1
1
Sør-Trøndelag
62
4.3.8 Chemical dispersion
Chemical dispersion should be considered when this combat strategy is equally or more benecial to
the environment than mechanical recovery of emulsion. In an acute oil spill situation the presence of
vulnerable resources should be evaluated before chemical dispersion is initiated. A chemical dispersibility
study of the reference oil Draugen has been performed by SINTEF [2008].
Draugen oil is expected to have a good eect of chemical dispersion during the rst hours after a
release. In summer and autumn the eect of chemical dipsersants is good or reduced for up to 120 hours
at low wind speed (2m/s) (Table 21). A Net Environment Benet Analysis (NEBA) has been conducted
to identify geographical areas and periods where chemical dispersion is a suitable combat method. The
results from the analysis are illustrated in Figure 19, 20 and 21. The primary use of the NEBA results
will be in the start phase of an oil spill action, where the maps together with eld observations, will
form the basis for decisions regarding chemical dispersion. The method and geographical coordinates of
the grid cells are given in Appendix 3.3.5 and Appendix B.4. The analysis area consists of all grid cells
within a diameter of 50 km from the release site. Based on the presence of biological resources on the sea
surface (sea birds) and in the water column (sh spawning areas) every 10×10 km grid cell is grouped
into one of the following categories:
Gain:
High occurrence of seabirds and low occurrence of sh eggs and/or -larvae
Loss:
Low occurrence of seabirds and high occurrence of sh eggs and/or -larvae
Conict:
High occurrence of seabirds and high occurrence of sh eggs and/or -larvae
Neutral:
Low occurrence of seabirds and low occurrence of sh eggs and/or -larvae
Most of the cells in the analysis area fall into the Gain category in November - February (Figure
19 and 20). This is due to high presence of sea birds, in particular little auk, fulmar and herring gull.
In March there is Conict in most of the analysis area, but also Gain in a large number of the grid
cells. The mentioned conict is caused by overlap between high presence of sea birds and spawning areas
of Norwegian spring spawning herring and northeast Arctic haddock. Overlap with the same spawning
areas is the cause of Loss in the vicinity of the well, as well as northeast and southwest of the well in
April, May and June. In these months there is Conict in the area landwards of the well, whereas the
open sea area west and northwest of the well is Neutral. In the period August - September the whole
area is Neutral.
63
Figure 18: Analysis area for the net environment benet analysis. The area consists of a grid with
a diameter of 50 km from the release location. The geographic grid (ContAct ) has a resolution
of 10×10 km [Alpha Miljørådgivning AS, 2003]. The gure to the right illustrate the grid with a
graticule (A to K at the top and bottom and 1-11 at the left and right) for identication of cell
number and corresponding position. Example colour codes for results are shown to the right. In
cells C4 and D3 there is a net envionmental gain (green colour code) of chemical dispersion, in cell
D7 there is a net environmental loss (red colour code). The geographical coordinates of the grid
cells are given in Table 34 in Appendix B.4.
©
Table 21: Time window for chemical dispersibility of the reference oil for dierent periods and
wind speeds. G = chemically dispersible, R = reduced dispersibility, P = poor dispersibility.
Period
Wind speed
Time (h)
(m/s)
1
2
3
6
9
12
24
48
72
96
120
Winter
2
G
G
G
G
G
G
R
R
R
P
P
Spring
5
G
G
G
G
R
R
R
P
P
P
P
10
G
G
R
R
R
P
P
P
P
P
P
15
G
R
R
P
P
P
P
P
P
P
P
Summer
2
G
G
G
G
G
G
G
R
R
R
R
Autumn
5
G
G
G
G
R
R
R
P
P
P
P
10
G
G
R
R
R
R
P
P
P
P
P
15
G
R
R
R
P
P
P
P
P
P
P
64
Figure 19: Results from the net environment benet analysis (NEBA) of biological resources on
the sea surface (sea birds) and in the water column (sh spawning areas) for the months December
to March. The maps show areas that are suitable (gain) and not suitable (loss) for chemical
dispersion. In addition areas with high (conict) and low (neutral) densities of biological resources
on the sea surface and in the water column are shown. The geographical coordinates of the grid
cells are given in Table 34 in Appendix B.4. Hagar is marked with a black square.
65
the sea surface (sea birds) and in the water column (sh spawning areas) for the months April to
July. The maps show areas that are suitable (gain) and not suitable (loss) for chemical dispersion.
In addition areas with high (conict) and low (neutral) densities of biological resources on the sea
surface and in the water column are shown. The geographical coordinates of the grid cells are
given in Table 34 in Appendix B.4. Hagar is marked with a black square.
66
the sea surface (sea birds) and in the water column (sh spawning areas) for the months August
to November. The maps show areas that are suitable (gain) and not suitable (loss) for chemical
dispersion. In addition areas with high (conict) and low (neutral) densities of biological resources
on the sea surface and in the water column are shown. The geographical coordinates of the grid
cells are given in Table 34 in Appendix B.4. Hagar is marked with a black square.
67
4.3.9 Recommended oil spill contingency
It is recommended to establish an oil spill response of three NOFO systems in barrier 1A and two NOFO
systems in barrier 1B in the drilling period. Both systems should have equipment for chemical dispersion.
In barrier 2 it is recommended to establish a coastal oil spill response for eight IUA regions. The number
of coastal systems appropriate to handle the dimensioning emulsion amounts should be evaluated in
collaboration with NOFO. Allocation of coastal systems within the barrier will depend on the geographic
distribution of the oil. It is recommended to verify that beach cleaning units are available for eight IUA
regions along the coast from Sunnmøre to Troms. The resource requirement, calculated as days work, is
high in IUA region Sør-Trøndelag and personnel availability during the drilling period must be claried
with NOFO prior to drilling. It should also be veried that the calculated requirement of days' work
can be covered by the other relevant IUA regions. Plans to include additional resources should be made.
Utilization of WWF and NOFO special teams may be necessary.
Response times for barrier 2 must be veried to be within the following drift times to the closest
IUA regions: Sør-Trøndelag 4 days, Nordmøre 5 days, Namdal 8 days and Helgeland 14 days. Response
times and number of coastal systems must be established in deliberation with NOFO.
Early detection of oil spills is crucial. Oil spill detection radar should be evaluated for the standby
vessel. The IUA regions Sør-Trøndelag and Nordmøre should be notied immediately in case of an oil
spill and mobilized at least 24 hours prior to stranding if oil is expected to drift towards the coast. IUAs
should be involved in rehearsals prior to the drilling operation.
Oil drift simulations have shown that the coast of Møre og Romsdal and Sør-Trøndelag have high
probabilities of being aected by an oil spill. It may be necessary to set requirements for completion of
beach cleaning actions in vulnerable areas. Harbor seals have pups during the drilling period and grey
seals give birth at Froan in October to September. The grey seal pups are born with foster fur and are
vulnerable to oil. In the event of an oil spill during summer beach cleaning at the Froan archipelago
should have as a target to be nished by the start of October. Experts from the Institute of Marine
Research and NINA should be consulted regarding cleanup strategies and protection of seals and seabirds
before cleaning units enters vulnerable areas.
Chemical dispersion shall be evaluated as a supplement to or substitute to mechanical combat.
Chemical dispersion has been shown to somewhat reduce stranding amounts and environmental risk in
OSCAR simulations of oil spill response [Acona, 2014]. Chemical dispersion by NOFO systems or plane
shall be evaluated in areas with the category Gain or Neutral in the net environmental benet analysis.
All cells in the analysis area (50 km radius from Hagar well location) are in these categories in the period
July to November. When the environmental monitoring is established, sightings of seabirds shall be the
basis for decisions regarding chemical dispersion.
The recommended oil spill response is summarized in Table 22.
68
Table 22: Recommended oil spill contingency with suggested response times for the drilling period for the well 6306/5-2 Hagar (PL 642).
The suggested response times are based on best attainable response time.
Period
Barrier
System requirement
1A
Summer
3
Systems
Resource
3
Response time (h)
Haltenbanken
12
Kristiansund 01
18
Troll/Oseberg 01
19
Tampen
20
1B
1
1
2
One coastal system
At least one ten vessel coastal system with oil recovery and command systems.
in eight IUA re-
The number of coastal systems as well as response times to various IUA regions
gions.
should be evaluated in cooperation with NOFO.
22 141 days work/
At least 15 beach cleaning units in Sør-Trøndelag IUA, 4 units in Nordmøre
Sør-Trøndelag, Nordmøre, Namdal,
29 beach cleaning
and 5 in Namdal. Numbers of beach cleaning units and prioritised locations
Helgeland, Romsdal, Salten, Lo-
units
for beach cleaning operations is decided by the operation management.
foten and Vesterålen, Sunnmøre.
3
69
1A
Autumn
Recommended contingency
3
3
Kristiansund, Sandnessjøen
Haltenbanken
12
Kristiansund 01
18
Troll/Oseberg 01
19
Tampen
20
Gjøa
20
1B
2
2
2
One coastal system
At least one ten vessel coastal system with oil recovery and command systems.
in eight IUA re-
The number of coastal systems as well as response times to various IUA regions
gions.
should be evaluated in cooperation with NOFO.
22 365 days work/
At least 13 beach cleaning units in Sør-Trøndelag IUA, 7 units in Nordmøre
Sør-Trøndelag, Nordmøre, Namdal,
27 beach cleaning
and 2 in Namdal. Numbers of beach cleaning units and prioritised locations
Helgeland, Romsdal, Salten, Lo-
units
for beach cleaning operations is decided by the operation management.
foten og Vesterålen, Midt- and
3
Kristiansund, Sandnessjøen
Nord-Troms
5 Discussion
The inuence areas for sea surface show that areas along the coast of Møre & Romsdal and Sør-Trøndelag
have high probabilities (50-75 %) of being hit by oil concentrations dened as harmful in MIRA. Smøla,
Frøya and Froan have high probabilities of being aected. These areas are important living and breeding
areas for coastal sea birds and seals [Røv, N, 2006]. Among diving seabirds (species that are particular
vulnerable towards oil because they spend a large time on the sea surface), the Froan area is considered
to be of particular importance for breeding populations of the great cormorant, European shag, black
guillemot and common eider. The breeding sites of grey and harbour seals within the archipelago are
considered to be of national importance, and the area also has a dense population of European Otter.
Grey seals come ashore to give birth in September to October and a signicant number of common eiders,
long-tailed ducks and red-breasted mergansers spend the winter in the area. These areas should have
high priority during oil spill response operations.
The highest environmental risk in the summer period (June, July and August) is 38 % of the operation specic acceptance criteria for serious damage. The highest environmental risk was calculated for
coastal seabirds (great cormorant).
Monthly variation in environmental risk
In order to investigate in more detail how the environ-
mental risk varies during drilling in oil bearing layers (i.e. during the summer and autumn months) a
MIRA has been performed for period intervals with one month displacement of the period interval, i.e.
for the periods (A) July, August and September, (B) August, September and October, (C) September,
October and November, (D) October, November and December. The analysis showed that the environmental risk for coastal seabirds was reduced with increasing displacement. This may be attributed to
the following reasons: (1) the coastal seabirds have a more scattered distribution during the autumn and
winter months than during the spring and summer months, and (2) considerable less amount of oil reaches
the coastal areas during the autumn and winter months than during the spring and summer months (e.g.
see Table 10, 11 and 12). Thus the probability that a large proportion of the population will be aected
by the oil slicks is highest during the spring and summer months because the birds have a more clumped
distribution (with high density around the breeding sites) and because the weather conditions increases
the probability that large amounts of oil reaches the coastal areas.
The environmental risk during drilling in oil bearing layers is thus lower than that in the summer
period described in the results section. The highest environmental risk regardless of VEC groups for the
periods A, B, C and D are: A: 23 % of the operation specic acceptance criteria for Considerable damage
(common guillemot), B: 23 % of the operation specic acceptance criteria for Considerable damage (grey
seal), C: 23 % of the operation specic acceptance criteria for Considerable damage (grey seal), and
D: 25 % of the operation specic acceptance criteria for Considerable damage (grey seal). Thus the
relative environmental risk is reduced from a maximum of 38 % to a maximum of 23-25 %, depending on
displacement A, B, C or D.
70
6 Conclusion
Given a blowout at Hagar, there may be large inuence areas on the sea surface and shoreline, with high
probabilities of stranding in a relatively large geographic area of the coast without oil spill contingency.
The probability of environmental damage given a blowout is relatively high for the Moderate and Minor
damage categories. The environmental risk is moderate, and below the operation specic acceptance
criteria for all investigate valuable ecosystem components in all damage categories. Areas along the coast
of Møre og Romsdal and Sør Trøndelag have high probabilities of being hit by oil concentrations dened
as harmful in MIRA. Smøla, Frøya and Froan have high probabilities of being aected. These areas are
important living and breeding areas for coastal sea birds and seals and should be prioritized during a
potential spill situation.
71
7 List of references
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Acona Flow Technology. Blowout and dynamic wellkill simulations - Exploration well Hagar (PL642)
rev.2. 2014.
Alpha Miljørådgivning AS. ContAct GIS. Tilgjengelig fra: http://www.beredskapsportalen.no/
Contact/contact_gis.htm, 2003. Nedlastet: 2010-03-28.
Alpha Miljørådgivning og NINA. Ulb delutredning - studie 7b. uhellsutslipp til sjø. miljøkonsekvenser
på sjøfugl, sjøpattedyr, strand, iskant mv. 2003.
DN. (Direktoratet for naturforvaltning) Endelig tilrådning med forslag til referanseområder. Råd til
utforming av marin verneplan for marine beskyttede områder i Norge. Rådgivende utvalg for marine
verneplan 30. juni 2004. 2004.
DNV & SINTEF. (Det norske veritas og SINTEF) Petroleumsvirksomhet. Oppdatering av faglig grunnlag
for forvaltningsplanen for Barentshavet og områdene utenfor Lofoten (HFB). Konsekvenser av akutt
utslipp for sk. 2010.
DNV og NINA. (Det norske veritas og Norsk institutt for naturforskning) Grunnlagsrapport. Oppdatering
av faglig grunnlag for forvaltningsplanen for Barentshavet og havområdene utenfor Lofoten (HFB).
Konsekvenser av akuttutslipp for sjøfugl, sjøpattedyr og strand. Rapportnr.: 2010-0539. 2010.
Henriksen, G. og Røv, N.
Kystsel, havert og steinkobbe. ISBN 82-519-1853-7. Tapir akademiske forlag,
Trondheim., 2004.
HI. (Havforskningsinstituttet) Ressurser, miljø og akvakultur på kysten og i havet. Havforskningsrapporten.
Fisken og havet, særnummer, 1, 2011.
HI & DN. (Havforskningsinstituttet og Direktoratet for naturforvaltning) Helheltlig forvaltningsplan for
Norskehavet: Arealrapport med natur-og ressursbeskrivelser.
Fisken og Havet nr.6, 2007.
HI & NP. (Havforskningsinstituttet og Norsk Polarinstititutt) Identisering av særlig verdifulle områder
i Lofoten-Barentshavet. 2003.
J.A. Kålås, Å. Viken, S. Henriksen, and S. (red.) Skjelseth. Rødliste for arter 2010. Artsdatabanken,
Norge. isbn-13: 978-82-92838-26-6. 2010.
Levitus. URL http://iridl.ldeo.columbia.edu/SOURCES/.LEVITUS/.
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NINA.
(Norsk institutt for naturforskning) Tverrsektoriell vurdering av konsekvenser for sjøfugl.
Grunnlagsrapport til en helhetlig forvaltningsplan for Norskehavet. 2008.
NOFO. Nofo-standard: Krav til oljevernfartøy på norsk sokkel. 2011. URL http://www.nofo.no/
NOFO-standard/437C8132-FDCB-4E88-92D8-96915803D9A8/1. Visited 11.03.2013.
NOFO. (Norsk oljevernforening for operatørselskap) Utstyrsplassering. Tilgjengelig fra: http:/http:
//www.nofo.no/Documents/Beredskap/NOFO%20utstyrsoversikt%20pr%20Jan%202013.pdf, 2014a.
Nedlastet: 2014-01-19.
NOFO. Norsk Oljevernforening For Operatørselskap Plangrunnlag. Sist oppdatert 28.08.2014, 2014b.
URL http://www.nofo.no/Plangrunnlag/.
NOFO & OLF. Veileder for Miljørettet Beredskapsanalyse. DNV rapport til NOFO - Norsk Oljevernforening for Operatørselskaper og OLF - Oljeindustriens Landsforening. Rapport nr. 2007-0934. Revisjon
nr. 1. 2007.
Norsk olje og gass. Veiledning for miljørettede beredskapsanalyser. Rev. dato: 16.08. 2013. 2013.
Norwegian Meteorological Institute. URL www.met.no.
NTS. (Norwegian Technology Standards Institution) NORSOK STANDARD. Risk and emergency preparedness analysis. Z-013. Rev.1. 1998.
OLF. (Oljeindustriens landsforbund) Metode for miljørettet risikoanalyse (MIRA). Revisjon. 2007.
Røv, N. Kartlegging og overvåking av sjøfugl og sjøpattedyr i Froan.
NINA Rapport 202: 36 pp., 202,
2006.
Scandpower. Blowout and well release frequencies - Based on SINTEF oshore blowout database 2011.
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SEAPOP. Sjøfugl i Norge 2011. Resultater fra SEAPOP programmet. www.seapop. 2011.
SINTEF. Draugen - egenskaper og forvitring på sjøen relatert til beredskap. SINTEF A5637, 2008.
SINTEF. MEMW (Marine environmental modeling workbench) OSCAR and Dream Models. User manual
version 6.2. 2012.
Østby, C., Brude, O.W. & Moe, K.A. Netto Miljøgevinst Analyse Akutt Oljeforurensning; Mekanisk
Bekjempelse - Kjemisk Dispergering. Rapportnr. 1113-01. 2002.
St.meld. nr. 37 (2008-2009). Helhetlig forvaltning av det marine miljø i Norskehavet (forvaltningsplan).
St.meld.nr. 10. Oppdatering av forvaltningsplanen for det marine miljø i Barentshavet og havområdet
utenfor Lofoten. 2008-2009.
Systad, G. H. Miljøverdi og sjøfugl - Metodebeskrivelse.
73
NINA, 2011.
A Appendix: results for drilling of exploration well 6306/5-2
A.1 Stranding statistics for prioritised areas
Table 23: Stranding statistics for oil in areas listed in NO_IUA.xls. The statistic is calculated
from the stochastic oil drift simulations for spills caused by exploration drilling of well 6306/5-2
at the prospect Hagar.
Release
Area
Period
Depth
Winter
Topside
Helgeland
...
Topside
...
Prob. (%)
Time (days)
Amount (tonne)
P0
P5
P50
P50
P95
P100
18.9
9
20
Inf
0
79
11976
5.6
26
79
Inf
0
9
5685
Topside
Midt- og Nord-Troms
3.7
29
Inf
Inf
0
0
3729
...
Topside
Midt-Finnmark
0.0
65
Inf
Inf
0
0
99
...
Topside
Namdal
40.6
4
8
Inf
0
373
46132
...
Topside
Nordfjord
0.0
12
Inf
Inf
0
0
11
...
Topside
Nordmøre
30.0
2
6
Inf
0
1564
92259
...
Topside
Ofoten
0.0
63
Inf
Inf
0
0
9
...
Topside
Romsdal
6.4
4
31
Inf
0
18
18361
...
Topside
Salten
7.7
14
42
Inf
0
18
5546
...
Topside
Sør-Troms
0.0
76
Inf
Inf
0
0
8
...
Topside
Sør-Trøndelag
61.5
3
5
16
13
3483
121308
...
Topside
Sunnmøre
0.3
8
Inf
Inf
0
0
656
...
Topside
Vest-Finnmark
1.0
53
Inf
Inf
0
0
981
...
Seabed
Helgeland
8.2
10
30
Inf
0
21
5619
...
Seabed
4.6
26
Inf
Inf
0
0
4520
...
Seabed
Midt- og Nord-Troms
2.8
31
Inf
Inf
0
0
4418
...
Seabed
Midt-Finnmark
0.0
79
Inf
Inf
0
0
2
...
Seabed
Namdal
38.3
5
10
Inf
0
187
30153
...
Seabed
Nordmøre
22.1
3
11
Inf
0
187
19092
...
Seabed
Ofoten
0.0
39
Inf
Inf
0
0
19
...
Seabed
Romsdal
1.7
5
Inf
Inf
0
0
2373
...
Seabed
Salten
5.9
13
73
Inf
0
7
7198
...
Seabed
Sør-Troms
0.0
47
Inf
Inf
0
0
168
...
Seabed
Sør-Trøndelag
59.5
3
7
21
5
698
48700
...
Seabed
Sunnmøre
0.0
8
Inf
Inf
0
0
150
...
Seabed
Vest-Finnmark
0.8
54
Inf
Inf
0
0
302
Spring
Topside
Bergen
0.0
86
Inf
Inf
0
0
8815
...
Topside
Helgeland
12.5
11
27
Inf
0
565
165122
Continued on next page
74
Table 23:
Release
Periode
Continued from previous page
Area
Prob. (%)
Dyp
Time (days)
Amount (tonne)
P0
P5
P50
P50
P95
P100
...
Topside
4.9
26
Inf
Inf
0
0
19796
...
Topside
Midt- og Nord-Troms
2.9
28
Inf
Inf
0
0
7244
...
Topside
Midt-Finnmark
0.0
82
Inf
Inf
0
0
1103
...
Topside
Namdal
41.2
5
11
Inf
0
4162
245953
...
Topside
Nordfjord
0.9
38
Inf
Inf
0
0
19284
...
Topside
Nordmøre
25.3
2
7
Inf
0
2756
154557
...
Topside
Ofoten
0.1
32
Inf
Inf
0
0
13131
...
Topside
Øst-Finnmark
0.0
88
Inf
Inf
0
0
266
...
Topside
Romsdal
7.1
7
31
Inf
0
35
38858
...
Topside
Salten
7.0
16
66
Inf
0
53
82007
...
Topside
Sogn og Sunnfjord
0.3
63
Inf
Inf
0
0
480
...
Topside
Sør-Troms
0.2
37
Inf
Inf
0
0
180
...
Topside
Sør-Trøndelag
61.9
2
5
17
19
18453
324529
...
Topside
Sunnmøre
1.7
8
Inf
Inf
0
0
49811
...
Topside
Vest-Finnmark
0.6
72
Inf
Inf
0
0
1863
...
Seabed
Helgeland
10.9
12
34
Inf
0
112
98243
...
Seabed
3.3
31
Inf
Inf
0
0
11067
...
Seabed
Midt- og Nord-Troms
2.0
35
Inf
Inf
0
0
3418
...
Seabed
Midt-Finnmark
0.0
89
Inf
Inf
0
0
95
...
Seabed
Namdal
34.2
6
14
Inf
0
1777
143293
...
Seabed
Nordfjord
0.0
64
Inf
Inf
0
0
1214
...
Seabed
Nordmøre
24.7
4
11
Inf
0
390
105586
...
Seabed
Ofoten
0.4
79
Inf
Inf
0
0
2699
...
Seabed
Romsdal
4.6
8
Inf
Inf
0
0
12576
...
Seabed
Salten
5.8
22
85
Inf
0
7
34633
...
Seabed
Sør-Troms
0.4
36
Inf
Inf
0
0
667
...
Seabed
Sør-Trøndelag
63.0
3
7
23
11
4427
249380
...
Seabed
Sunnmøre
0.0
58
Inf
Inf
0
0
7058
...
Seabed
Vest-Finnmark
0.2
73
Inf
Inf
0
0
293
Summer
Topside
Bergen
0.0
53
Inf
Inf
0
0
1437
...
Topside
Helgeland
21.5
9
19
Inf
0
1131
217489
...
Topside
8.0
24
59
Inf
0
142
113608
...
Topside
Midt- og Nord-Troms
4.1
37
Inf
Inf
0
0
16406
...
Topside
Midt-Finnmark
0.7
57
Inf
Inf
0
0
689
75
Table 23:
Release
Periode
Area
Prob. (%)
Dyp
Time (days)
Amount (tonne)
P0
P5
P50
P50
P95
P100
...
Topside
Namdal
40.1
5
11
Inf
0
8782
202052
...
Topside
Nordfjord
3.2
12
Inf
Inf
0
0
8985
...
Topside
Nordmøre
27.4
2
7
Inf
0
7693
116015
...
Topside
Ofoten
0.5
35
Inf
Inf
0
0
8161
...
Topside
Øst-Finnmark
0.3
75
Inf
Inf
0
0
118
...
Topside
Romsdal
10.2
6
23
Inf
0
263
62372
...
Topside
Salten
11.0
18
32
Inf
0
184
113246
...
Topside
Sogn og Sunnfjord
0.1
26
Inf
Inf
0
0
3963
...
Topside
Sør-Troms
0.7
50
Inf
Inf
0
0
1084
...
Topside
Sør-Trøndelag
63.2
2
5
16
48
20929
295391
...
Topside
Sunnmøre
5.1
9
90
Inf
0
11
26075
...
Topside
Vest-Finnmark
1.6
50
Inf
Inf
0
0
3580
...
Seabed
Helgeland
16.2
11
26
Inf
0
230
88081
...
Seabed
7.6
25
67
Inf
0
34
15183
...
Seabed
Midt- og Nord-Troms
2.7
39
Inf
Inf
0
0
14107
...
Seabed
Midt-Finnmark
0.0
81
Inf
Inf
0
0
589
...
Seabed
Namdal
34.0
7
14
Inf
0
1198
111095
...
Seabed
Nordfjord
0.4
14
Inf
Inf
0
0
1305
...
Seabed
Nordmøre
22.8
4
13
Inf
0
608
49096
...
Seabed
Ofoten
0.6
56
Inf
Inf
0
0
2481
...
Seabed
Romsdal
4.6
6
Inf
Inf
0
0
18551
...
Seabed
Salten
8.9
23
51
Inf
0
60
22714
...
Seabed
Sogn og Sunnfjord
0.0
15
Inf
Inf
0
0
71
...
Seabed
Sør-Troms
0.2
76
Inf
Inf
0
0
659
...
Seabed
Sør-Trøndelag
58.1
3
7
26
10
5432
170027
...
Seabed
Sunnmøre
0.4
10
Inf
Inf
0
0
24852
...
Seabed
Vest-Finnmark
0.5
64
Inf
Inf
0
0
2257
Autumn
Topside
Helgeland
28.4
7
14
Inf
0
245
55101
...
Topside
12.3
20
31
Inf
0
118
18938
...
Topside
Midt- og Nord-Troms
5.7
29
85
Inf
0
18
4682
...
Topside
Midt-Finnmark
0.2
56
Inf
Inf
0
0
857
...
Topside
Namdal
43.0
4
8
Inf
0
812
74143
...
Topside
Nordmøre
25.8
2
5
Inf
0
2705
45720
...
Topside
Ofoten
0.7
30
Inf
Inf
0
0
1510
76
Table 23:
Release
Periode
Area
Prob. (%)
Dyp
Time (days)
Amount (tonne)
P0
P5
P50
P50
P95
P100
...
Topside
Øst-Finnmark
0.0
86
Inf
Inf
0
0
167
...
Topside
Romsdal
8.1
3
22
Inf
0
179
32561
...
Topside
Salten
18.4
10
22
Inf
0
153
16541
...
Topside
Sør-Troms
0.8
36
Inf
Inf
0
0
123
...
Topside
Sør-Trøndelag
56.1
2
4
17
9
4287
113876
...
Topside
Sunnmøre
1.6
8
Inf
Inf
0
0
7297
...
Topside
Vest-Finnmark
2.1
38
Inf
Inf
0
0
2977
...
Seabed
Helgeland
21.3
8
19
Inf
0
119
13862
...
Seabed
12.3
22
36
Inf
0
65
15935
...
Seabed
Midt- og Nord-Troms
4.4
24
Inf
Inf
0
0
5113
...
Seabed
Midt-Finnmark
0.4
55
Inf
Inf
0
0
930
...
Seabed
Namdal
40.1
4
9
Inf
0
341
39258
...
Seabed
Nordmøre
20.4
2
10
Inf
0
119
28155
...
Seabed
Ofoten
0.9
31
Inf
Inf
0
0
513
...
Seabed
Øst-Finnmark
0.2
64
Inf
Inf
0
0
523
...
Seabed
Romsdal
1.3
4
Inf
Inf
0
0
10995
...
Seabed
Salten
14.9
10
26
Inf
0
40
10103
...
Seabed
Sør-Troms
0.2
38
Inf
Inf
0
0
389
...
Seabed
Sør-Trøndelag
56.9
3
5
24
4
686
40782
...
Seabed
Sunnmøre
0.4
25
Inf
Inf
0
0
403
...
Seabed
Vest-Finnmark
0.7
53
Inf
Inf
0
0
1828
Table 24: Stranding statistics for oil in areas listed in NO_NOFO.xls. The statistic is calculated
from the stochastic oil drift simulations for spills caused by exploration drilling of well 6306/5-2
at the prospect Hagar.
Release
Area
Period
Depth
Winter
Topside
Andøya
...
Topside
...
Prob. (%)
Time (days)
Amount (tonne)
P0
P5
P50
P50
P95
P100
1.8
33
Inf
Inf
0
0
887
Bliksvær
2.5
16
Inf
Inf
0
0
1497
Topside
Bø og Hadseløya
1.6
28
Inf
Inf
0
0
604
...
Topside
Frøya og Froan
42.7
3
6
Inf
0
1831
41851
...
Topside
Hjelmsøy
0.2
77
Inf
Inf
0
0
54
77
Table 24:
Release
Periode
Area
Prob. (%)
Dyp
Time (days)
Amount (tonne)
P0
P5
P50
P50
P95
P100
...
Topside
Hjelmsøystauren
0.2
77
Inf
Inf
0
0
54
...
Topside
Ingøya
0.1
62
Inf
Inf
0
0
135
...
Topside
Karlsøy
1.5
40
Inf
Inf
0
0
1431
...
Topside
Karlsøyvær
0.0
38
Inf
Inf
0
0
82
...
Topside
Lofotodden
1.6
27
Inf
Inf
0
0
3724
...
Topside
Lovunden
1.1
12
Inf
Inf
0
0
3259
...
Topside
3.5
27
Inf
Inf
0
0
3948
...
Topside
Røst
2.2
14
Inf
Inf
0
0
2359
...
Topside
Runde
0.3
10
Inf
Inf
0
0
271
...
Topside
Skogsøy
1.0
26
Inf
Inf
0
0
815
...
Topside
Skogvoll
1.8
33
Inf
Inf
0
0
887
...
Topside
Smøla
29.2
2
6
Inf
0
1476
38669
...
Topside
Sørøya nordvest
0.0
87
Inf
Inf
0
0
9
...
Topside
Stadtlandet
0.0
12
Inf
Inf
0
0
11
...
Topside
Steigen
0.3
35
Inf
Inf
0
0
2151
...
Topside
Træna
17.9
10
21
Inf
0
69
2635
...
Topside
Værøy
0.9
28
Inf
Inf
0
0
1567
...
Topside
Vega
1.3
9
Inf
Inf
0
0
2707
...
Topside
Vigra - Godøya
0.0
11
Inf
Inf
0
0
35
...
Topside
Vikna vest
15.1
6
15
Inf
0
52
12200
...
Seabed
Andøya
1.0
34
Inf
Inf
0
0
1983
...
Seabed
Bliksvær
1.8
19
Inf
Inf
0
0
1058
...
Seabed
Bø og Hadseløya
0.6
34
Inf
Inf
0
0
756
...
Seabed
Frøya og Froan
42.8
4
8
Inf
0
436
22117
...
Seabed
Hjelmsøy
0.0
77
Inf
Inf
0
0
50
...
Seabed
Hjelmsøystauren
0.0
77
Inf
Inf
0
0
50
...
Seabed
Karlsøy
0.9
39
Inf
Inf
0
0
1986
...
Seabed
Karlsøyvær
0.2
40
Inf
Inf
0
0
65
...
Seabed
Lofotodden
1.0
26
Inf
Inf
0
0
1758
...
Seabed
Lovunden
0.3
13
Inf
Inf
0
0
783
...
Seabed
3.3
26
Inf
Inf
0
0
1774
...
Seabed
Røst
1.1
17
Inf
Inf
0
0
2264
...
Seabed
Runde
0.0
11
Inf
Inf
0
0
20
...
Seabed
Skogsøy
0.6
44
Inf
Inf
0
0
840
78
Table 24:
Release
Periode
Area
Prob. (%)
Dyp
Time (days)
Amount (tonne)
P0
P5
P50
P50
P95
P100
...
Seabed
Skogvoll
1.0
34
Inf
Inf
0
0
1983
...
Seabed
Smøla
21.1
3
11
Inf
0
166
19059
...
Seabed
Steigen
0.4
34
Inf
Inf
0
0
1667
...
Seabed
Træna
7.8
11
34
Inf
0
18
2670
...
Seabed
Værøy
0.9
25
Inf
Inf
0
0
2055
...
Seabed
Vega
1.2
10
Inf
Inf
0
0
3917
...
Seabed
Vigra - Godøya
0.0
9
Inf
Inf
0
0
9
...
Seabed
Vikna vest
9.5
7
24
Inf
0
27
7472
Spring
Topside
Andøya
2.4
26
Inf
Inf
0
0
2325
...
Topside
Bliksvær
3.4
16
Inf
Inf
0
0
2533
...
Topside
Bø og Hadseløya
0.7
43
Inf
Inf
0
0
1278
...
Topside
Frøya og Froan
51.1
2
6
33
6
9916
50385
...
Topside
Hjelmsøy
0.0
87
Inf
Inf
0
0
27
...
Topside
Hjelmsøystauren
0.0
87
Inf
Inf
0
0
27
...
Topside
Ingøya
0.0
72
Inf
Inf
0
0
9
...
Topside
Karlsøy
1.2
33
Inf
Inf
0
0
1689
...
Topside
Karlsøyvær
0.7
42
Inf
Inf
0
0
21900
...
Topside
Lofotodden
1.6
38
Inf
Inf
0
0
9758
...
Topside
Lovunden
4.9
12
Inf
Inf
0
0
5347
...
Topside
2.5
36
Inf
Inf
0
0
10767
...
Topside
Røst
1.2
29
Inf
Inf
0
0
2468
...
Topside
Runde
1.1
26
Inf
Inf
0
0
24286
...
Topside
Skogsøy
0.6
26
Inf
Inf
0
0
2327
...
Topside
Skogvoll
2.4
26
Inf
Inf
0
0
2325
...
Topside
Smøla
24.3
2
6
Inf
0
2613
62314
...
Topside
Sørøya nordvest
0.0
84
Inf
Inf
0
0
1595
...
Topside
Stadtlandet
0.3
38
Inf
Inf
0
0
12096
...
Topside
Steigen
0.7
45
Inf
Inf
0
0
10345
...
Topside
Træna
8.9
14
40
Inf
0
53
2734
...
Topside
Værøy
1.2
34
Inf
Inf
0
0
2652
...
Topside
Vega
7.2
11
64
Inf
0
119
26961
...
Topside
Vigra - Godøya
1.2
8
Inf
Inf
0
0
21931
...
Topside
Vikna vest
18.4
9
16
Inf
0
557
21737
...
Topside
Ytre Sula
0.0
89
Inf
Inf
0
0
570
79
Table 24:
Release
Periode
Area
Prob. (%)
Dyp
Time (days)
Amount (tonne)
P0
P5
P50
P50
P95
P100
...
Seabed
Andøya
1.1
31
Inf
Inf
0
0
875
...
Seabed
Bliksvær
2.4
27
Inf
Inf
0
0
2626
...
Seabed
Bø og Hadseløya
0.2
39
Inf
Inf
0
0
1546
...
Seabed
Frøya og Froan
51.3
3
7
38
1
1790
45096
...
Seabed
Gjesværstappan-01
0.0
89
Inf
Inf
0
0
95
...
Seabed
Ingøya
0.0
87
Inf
Inf
0
0
53
...
Seabed
Karlsøy
0.4
49
Inf
Inf
0
0
2164
...
Seabed
Karlsøyvær
0.8
39
Inf
Inf
0
0
3944
...
Seabed
Lofotodden
1.1
36
Inf
Inf
0
0
5035
...
Seabed
Lovunden
2.4
22
Inf
Inf
0
0
5231
...
Seabed
1.7
33
Inf
Inf
0
0
8057
...
Seabed
Røst
1.7
29
Inf
Inf
0
0
2219
...
Seabed
Skogsøy
0.1
43
Inf
Inf
0
0
427
...
Seabed
Skogvoll
1.1
31
Inf
Inf
0
0
875
...
Seabed
Smøla
23.3
4
12
Inf
0
279
46003
...
Seabed
Sørøya nordvest
0.0
89
Inf
Inf
0
0
18
...
Seabed
Stadtlandet
0.0
64
Inf
Inf
0
0
1214
...
Seabed
Steigen
0.7
29
Inf
Inf
0
0
6016
...
Seabed
Træna
7.8
14
54
Inf
0
30
2662
...
Seabed
Værøy
0.6
36
Inf
Inf
0
0
1218
...
Seabed
Vega
5.3
14
86
Inf
0
5
16416
...
Seabed
Vigra - Godøya
0.0
62
Inf
Inf
0
0
3935
...
Seabed
Vikna vest
12.0
9
29
Inf
0
229
19909
Summer
Topside
Andøya
2.5
37
Inf
Inf
0
0
11892
...
Topside
Atløy-Værlandet
0.0
54
Inf
Inf
0
0
1998
...
Topside
Bliksvær
5.3
19
71
Inf
0
9
2706
...
Topside
Bø og Hadseløya
2.1
30
Inf
Inf
0
0
4095
...
Topside
Frøya og Froan
51.2
2
5
30
5
10197
48144
...
Topside
Gjesværstappan-01
0.1
70
Inf
Inf
0
0
62
...
Topside
Gjesværstappan-02
0.1
86
Inf
Inf
0
0
9
...
Topside
Hjelmsøy
0.2
62
Inf
Inf
0
0
57
...
Topside
Hjelmsøystauren
0.2
62
Inf
Inf
0
0
57
...
Topside
Ingøya
0.2
83
Inf
Inf
0
0
284
...
Topside
Karlsøy
1.5
38
Inf
Inf
0
0
7213
80
Table 24:
Release
Periode
Area
Prob. (%)
Dyp
Time (days)
Amount (tonne)
P0
P5
P50
P50
P95
P100
...
Topside
Karlsøyvær
1.9
30
Inf
Inf
0
0
9544
...
Topside
Lofotodden
4.5
24
Inf
Inf
0
0
24167
...
Topside
Lovunden
4.2
12
Inf
Inf
0
0
5351
...
Topside
6.1
24
75
Inf
0
37
62886
...
Topside
Nordkinn
0.2
57
Inf
Inf
0
0
261
...
Topside
Nordkinnhalvøya nordøst
0.0
73
Inf
Inf
0
0
44
...
Topside
Røst
5.2
22
88
Inf
0
7
2675
...
Topside
Runde
4.1
9
Inf
Inf
0
0
15167
...
Topside
Skogsøy
1.3
38
Inf
Inf
0
0
2210
...
Topside
Skogvoll
2.5
37
Inf
Inf
0
0
11892
...
Topside
Smøla
27.7
2
8
Inf
0
5928
54589
...
Topside
Sørøya nordvest
0.5
59
Inf
Inf
0
0
1206
...
Topside
Stadtlandet
2.5
12
Inf
Inf
0
0
5745
...
Topside
Steigen
2.0
29
Inf
Inf
0
0
3835
...
Topside
Sværholtklubben
0.1
61
Inf
Inf
0
0
18
...
Topside
Sverslingsosen - Skorpa
0.0
54
Inf
Inf
0
0
1965
...
Topside
Træna
17.4
10
23
Inf
0
274
2868
...
Topside
Værøy
4.1
21
Inf
Inf
0
0
2676
...
Topside
Vega
10.3
10
25
Inf
0
253
31361
...
Topside
Vigra - Godøya
3.2
9
Inf
Inf
0
0
13201
...
Topside
Vikna vest
24.7
6
15
Inf
0
1141
21430
...
Seabed
Andøya
2.0
44
Inf
Inf
0
0
2760
...
Seabed
Bliksvær
2.9
25
Inf
Inf
0
0
2578
...
Seabed
Bø og Hadseløya
1.7
35
Inf
Inf
0
0
3119
...
Seabed
Frøya og Froan
42.9
3
7
Inf
0
2438
44506
...
Seabed
Gjesværstappan-01
0.0
89
Inf
Inf
0
0
26
...
Seabed
Hjelmsøy
0.0
90
Inf
Inf
0
0
8
...
Seabed
Ingøya
0.1
71
Inf
Inf
0
0
2257
...
Seabed
Karlsøy
0.2
54
Inf
Inf
0
0
2253
...
Seabed
Karlsøyvær
0.7
31
Inf
Inf
0
0
4737
...
Seabed
Lofotodden
3.8
25
Inf
Inf
0
0
4873
...
Seabed
Lovunden
2.3
14
Inf
Inf
0
0
2978
...
Seabed
5.4
25
83
Inf
0
8
8389
...
Seabed
Nordkinnhalvøya nordøst
0.0
82
Inf
Inf
0
0
326
81
Table 24:
Release
Periode
Area
Prob. (%)
Dyp
Time (days)
Amount (tonne)
P0
P5
P50
P50
P95
P100
...
Seabed
Røst
6.1
24
78
Inf
0
10
2570
...
Seabed
Runde
0.3
11
Inf
Inf
0
0
4831
...
Seabed
Skogsøy
1.2
44
Inf
Inf
0
0
578
...
Seabed
Skogvoll
2.0
44
Inf
Inf
0
0
2760
...
Seabed
Smøla
22.3
4
13
Inf
0
685
41265
...
Seabed
Stadtlandet
0.2
14
Inf
Inf
0
0
1193
...
Seabed
Steigen
1.3
33
Inf
Inf
0
0
3173
...
Seabed
Træna
12.8
14
28
Inf
0
134
2739
...
Seabed
Værøy
3.1
26
Inf
Inf
0
0
2508
...
Seabed
Vega
4.9
11
Inf
Inf
0
0
22454
...
Seabed
Vigra - Godøya
0.2
10
Inf
Inf
0
0
15516
...
Seabed
Vikna vest
11.9
8
28
Inf
0
100
20181
Autumn
Topside
Andøya
4.0
24
Inf
Inf
0
0
3131
...
Topside
Bliksvær
7.1
10
40
Inf
0
9
2330
...
Topside
Bø og Hadseløya
3.4
23
Inf
Inf
0
0
3550
...
Topside
Frøya og Froan
41.5
2
5
Inf
0
2328
39276
...
Topside
Gjesværstappan-01
0.0
76
Inf
Inf
0
0
9
...
Topside
Gjesværstappan-02
0.0
76
Inf
Inf
0
0
9
...
Topside
Hjelmsøy
0.2
79
Inf
Inf
0
0
77
...
Topside
Hjelmsøystauren
0.2
79
Inf
Inf
0
0
77
...
Topside
Ingøya
0.1
61
Inf
Inf
0
0
411
...
Topside
Karlsøy
1.7
38
Inf
Inf
0
0
914
...
Topside
Karlsøyvær
1.0
22
Inf
Inf
0
0
1253
...
Topside
Kongsfjord
0.0
86
Inf
Inf
0
0
167
...
Topside
Lofotodden
4.8
20
Inf
Inf
0
0
7009
...
Topside
Lovunden
1.1
13
Inf
Inf
0
0
2775
...
Topside
8.6
20
40
Inf
0
37
13571
...
Topside
Nordkinn
0.0
84
Inf
Inf
0
0
8
...
Topside
Røst
10.6
13
29
Inf
0
52
2591
...
Topside
Runde
1.0
9
Inf
Inf
0
0
1000
...
Topside
Skogsøy
1.8
23
Inf
Inf
0
0
951
...
Topside
Skogvoll
4.0
24
Inf
Inf
0
0
3131
...
Topside
Smøla
25.5
2
6
Inf
0
1974
36313
...
Topside
Sørøya nordvest
0.2
69
Inf
Inf
0
0
1709
82
Table 24:
Release
Periode
Area
Prob. (%)
Dyp
Time (days)
Amount (tonne)
P0
P5
P50
P50
P95
P100
...
Topside
Steigen
2.2
20
Inf
Inf
0
0
1560
...
Topside
Træna
27.2
7
14
Inf
0
211
2819
...
Topside
Værøy
3.6
19
Inf
Inf
0
0
2273
...
Topside
Vega
3.4
9
Inf
Inf
0
0
12300
...
Topside
Vigra - Godøya
1.1
8
Inf
Inf
0
0
2373
...
Topside
Vikna vest
17.5
7
14
Inf
0
87
12151
...
Seabed
Andøya
2.6
24
Inf
Inf
0
0
1336
...
Seabed
Bliksvær
4.2
10
Inf
Inf
0
0
2370
...
Seabed
Bø og Hadseløya
3.5
23
Inf
Inf
0
0
1244
...
Seabed
Frøya og Froan
41.3
3
6
Inf
0
285
17433
...
Seabed
Gjesværstappan-01
0.3
68
Inf
Inf
0
0
930
...
Seabed
Gjesværstappan-02
0.3
76
Inf
Inf
0
0
12
...
Seabed
Hjelmsøy
0.0
76
Inf
Inf
0
0
447
...
Seabed
Hjelmsøystauren
0.0
76
Inf
Inf
0
0
447
...
Seabed
Ingøya
0.2
61
Inf
Inf
0
0
1012
...
Seabed
Karlsøy
0.5
35
Inf
Inf
0
0
969
...
Seabed
Karlsøyvær
0.8
21
Inf
Inf
0
0
694
...
Seabed
Kongsfjord
0.2
84
Inf
Inf
0
0
17
...
Seabed
Lofotodden
4.5
23
Inf
Inf
0
0
4346
...
Seabed
Lovunden
0.9
12
Inf
Inf
0
0
2073
...
Seabed
8.6
23
44
Inf
0
27
7876
...
Seabed
Røst
8.2
12
33
Inf
0
10
2512
...
Seabed
Runde
0.0
27
Inf
Inf
0
0
403
...
Seabed
Skogsøy
1.3
23
Inf
Inf
0
0
1017
...
Seabed
Skogvoll
2.6
24
Inf
Inf
0
0
1336
...
Seabed
Smøla
20.3
2
10
Inf
0
119
23787
...
Seabed
Sørøya nordvest
0.3
60
Inf
Inf
0
0
254
...
Seabed
Steigen
1.7
22
Inf
Inf
0
0
1969
...
Seabed
Træna
19.8
8
19
Inf
0
88
2752
...
Seabed
Værøy
1.9
17
Inf
Inf
0
0
2547
...
Seabed
Vega
3.1
9
Inf
Inf
0
0
4601
...
Seabed
Vigra - Godøya
0.0
33
Inf
Inf
0
0
81
...
Seabed
Vikna vest
12.2
7
19
Inf
0
33
7310
83
A.2 ERA results
84
Table 25: Environmental risk for coastal seabirds calculated from the stochastic oil drifts simulations given an oil spill caused by activity in
(DSHA-1) for the prospect Hagar. PP Lx |Oil is the probability for a relative population loss (PL) in interval x if an oil spill has taken place, i.e. a
Acc
, where
conditional probability. Likewise, PRTy |Oil is the conditional probability for a restitution time (RT) in the interval y. The ratio PRTy /PRT
y
the calculated probability for a restitution time in the interval y is divided with the highest probability accepted by the operator, is identical to the
relative environmental risk, as explained in section 3.2, equations 1 and 2. See Table 33 for an explanation of regional codes.
Period
Winter
Region
Species
PP Lx |Oil
PRTy |Oil
Acc
PRTy /PRT
y
% simul. w. relative population loss:
% siml. w. restitution time interval:
Relative risk for restitution time interval:
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
15%
510%
1020%
2030%
30100%
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
85
BH
Red-breasted Merganser
0.95
0.01
0.00
0.00
0.00
0.48
0.48
0.00
0.00
0.07
0.28
0.01
0.00
...
BH
Great Cormorant
0.48
0.00
0.00
0.00
0.00
0.24
0.24
0.00
0.00
0.03
0.14
0.00
0.00
...
BH
European Shag
0.66
0.01
0.00
0.00
0.00
0.33
0.34
0.00
0.00
0.05
0.19
0.01
0.00
...
BH
Common Eider
0.32
0.00
0.00
0.00
0.00
0.16
0.16
0.00
0.00
0.02
0.09
0.00
0.00
...
NH
44.00
2.05
0.34
0.01
0.00
22.51
23.11
0.68
0.09
3.22
13.22
0.98
0.50
...
NH
Red-throated Loon
65.23
12.06
1.91
0.13
0.01
35.63
39.12
4.04
0.55
5.10
22.38
5.77
3.14
...
NH
Great Cormorant
68.43
9.73
1.38
0.10
0.00
36.65
39.42
3.17
0.40
5.24
22.55
4.54
2.27
...
NH
Black Guillemot
60.61
17.59
3.37
0.26
0.06
34.70
39.94
6.21
1.04
4.96
22.85
8.89
5.94
...
NH
European Shag
47.67
30.63
7.61
0.50
0.13
31.49
41.05
11.71
2.28
4.50
23.48
16.75
13.06
...
NH
Common Eider
51.06
4.33
0.58
0.01
0.00
26.62
27.84
1.38
0.15
3.81
15.93
1.97
0.86
Spring
BH
Greylag Goose
0.17
0.05
0.00
0.00
0.00
0.10
0.11
0.01
0.00
0.01
0.06
0.02
0.00
...
BH
0.08
0.02
0.00
0.00
0.00
0.05
0.05
0.01
0.00
0.01
0.03
0.01
0.00
...
BH
Great Cormorant
0.21
0.00
0.00
0.00
0.00
0.11
0.11
0.00
0.00
0.02
0.06
0.00
0.00
...
BH
Black Guillemot
0.07
0.00
0.00
0.00
0.00
0.04
0.04
0.00
0.00
0.01
0.02
0.00
0.00
...
BH
European Shag
0.27
0.00
0.00
0.00
0.00
0.14
0.14
0.00
0.00
0.02
0.08
0.00
0.00
...
BH
Common Eider
0.06
0.00
0.00
0.00
0.00
0.03
0.03
0.00
0.00
0.00
0.02
0.00
0.00
Continued on the next page
Table 25:
Period
Region
Results for coastal seabirds in activity scenario-1. . . continued
Species
PP Lx |Oil
PRTy |Oil
Acc
PRTy /PRT
y
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
15%
510%
1020%
2030%
30100%
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
86
...
NH
Greylag Goose
20.99
4.25
2.35
0.70
0.34
11.56
13.21
2.59
1.28
1.65
7.56
3.70
7.31
...
NH
34.31
7.58
2.92
0.69
0.28
19.05
21.67
3.70
1.35
2.72
12.40
5.29
7.74
...
NH
Red-throated Loon
33.72
6.42
4.00
0.62
0.34
18.47
21.07
3.91
1.65
2.64
12.05
5.60
9.45
...
NH
Great Cormorant
31.51
13.77
6.91
1.93
0.87
19.20
24.36
7.86
3.56
2.74
13.94
11.24
20.36
...
NH
Black Guillemot
22.04
21.62
7.64
2.35
0.99
16.43
23.74
10.40
4.07
2.35
13.58
14.88
23.30
...
NH
European Shag
32.94
12.84
7.40
1.76
0.70
19.68
24.74
7.79
3.43
2.81
14.15
11.14
19.62
...
NH
Common Eider
34.23
6.43
3.18
0.43
0.22
18.72
21.12
3.41
1.23
2.68
12.08
4.88
7.02
...
NS
Greylag Goose
0.00
0.03
0.00
0.00
0.00
0.01
0.02
0.01
0.00
0.00
0.01
0.01
0.01
...
NS
0.08
0.00
0.00
0.00
0.00
0.04
0.04
0.00
0.00
0.01
0.02
0.00
0.00
...
NS
Red-throated Loon
0.10
0.03
0.00
0.00
0.00
0.06
0.07
0.01
0.00
0.01
0.04
0.01
0.01
...
NS
Great Cormorant
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
...
NS
Black Guillemot
0.06
0.00
0.00
0.00
0.00
0.03
0.03
0.00
0.00
0.00
0.02
0.00
0.00
...
NS
European Shag
0.03
0.00
0.00
0.00
0.00
0.02
0.02
0.00
0.00
0.00
0.01
0.00
0.00
...
NS
Common Eider
0.05
0.00
0.00
0.00
0.00
0.03
0.03
0.00
0.00
0.00
0.02
0.00
0.00
Summer
BH
Greylag Goose
0.67
0.00
0.01
0.00
0.00
0.34
0.34
0.01
0.00
0.05
0.19
0.01
0.02
...
BH
0.07
0.00
0.00
0.00
0.00
0.04
0.04
0.00
0.00
0.01
0.02
0.00
0.00
...
BH
Great Cormorant
0.27
0.01
0.00
0.00
0.00
0.14
0.14
0.00
0.00
0.02
0.08
0.00
0.00
...
BH
Black Guillemot
0.07
0.01
0.00
0.00
0.00
0.03
0.04
0.00
0.00
0.01
0.02
0.00
0.00
...
BH
European Shag
0.09
0.00
0.00
0.00
0.00
0.05
0.05
0.00
0.00
0.01
0.03
0.00
0.00
...
BH
Common Eider
0.02
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.00
0.01
0.00
0.00
Table 25:
Period
Region
Species
PP Lx |Oil
PRTy |Oil
Acc
PRTy /PRT
y
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
15%
510%
1020%
2030%
30100%
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
87
...
NH
Greylag Goose
30.44
9.39
6.78
1.64
0.84
17.57
21.61
6.56
3.36
2.51
12.36
9.38
19.20
...
NH
38.63
9.60
5.93
1.05
0.53
21.72
25.60
5.89
2.54
3.11
14.64
8.42
14.50
...
NH
Red-throated Loon
34.39
9.05
5.58
1.05
0.54
19.46
23.11
5.58
2.46
2.78
13.22
7.97
14.08
...
NH
Great Cormorant
27.81
18.39
11.98
4.07
1.69
18.50
26.10
12.62
6.72
2.65
14.93
18.05
38.41
...
NH
Black Guillemot
18.04
27.41
11.59
4.07
1.61
15.87
25.62
14.69
6.55
2.27
14.66
21.00
37.45
...
NH
European Shag
32.04
16.38
11.00
2.61
1.09
20.11
26.96
10.90
5.14
2.88
15.42
15.58
29.43
...
NH
Common Eider
36.89
10.49
6.09
0.98
0.37
21.07
25.21
6.16
2.38
3.01
14.42
8.80
13.63
...
NS
Greylag Goose
0.03
0.09
0.01
0.00
0.00
0.04
0.06
0.02
0.00
0.01
0.03
0.04
0.01
...
NS
0.13
0.01
0.00
0.00
0.00
0.07
0.07
0.00
0.00
0.01
0.04
0.00
0.00
...
NS
Red-throated Loon
0.23
0.11
0.01
0.00
0.00
0.14
0.18
0.03
0.00
0.02
0.10
0.05
0.01
...
NS
Black Guillemot
0.07
0.00
0.00
0.00
0.00
0.04
0.04
0.00
0.00
0.01
0.02
0.00
0.00
...
NS
European Shag
0.01
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
...
NS
Common Eider
0.08
0.01
0.00
0.00
0.00
0.04
0.04
0.00
0.00
0.01
0.03
0.00
0.00
Autumn
BH
0.27
0.00
0.00
0.00
0.00
0.13
0.13
0.00
0.00
0.02
0.08
0.00
0.00
...
BH
Great Cormorant
0.08
0.00
0.00
0.00
0.00
0.04
0.04
0.00
0.00
0.01
0.02
0.00
0.00
...
BH
European Shag
0.14
0.00
0.00
0.00
0.00
0.07
0.07
0.00
0.00
0.01
0.04
0.00
0.00
...
BH
Common Eider
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
...
NH
8.42
0.47
0.12
0.00
0.00
4.33
4.47
0.18
0.03
0.62
2.56
0.26
0.18
...
NH
Red-throated Loon
10.87
2.54
0.49
0.05
0.00
6.07
6.83
0.91
0.15
0.87
3.90
1.30
0.87
...
NH
Great Cormorant
10.76
2.62
0.34
0.02
0.00
6.04
6.78
0.84
0.10
0.86
3.88
1.20
0.55
Table 25:
Period
Region
Species
PP Lx |Oil
PRTy |Oil
Acc
PRTy /PRT
y
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
15%
510%
1020%
2030%
30100%
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
...
NH
Black Guillemot
10.51
3.72
0.78
0.08
0.01
6.18
7.31
1.36
0.24
0.88
4.18
1.95
1.40
...
NH
European Shag
8.19
5.57
1.83
0.15
0.02
5.49
7.34
2.38
0.55
0.78
4.20
3.41
3.17
...
NH
Common Eider
9.80
1.33
0.24
0.00
0.00
5.23
5.62
0.45
0.06
0.75
3.22
0.65
0.35
88
Table 26: Environmetnal risk for pelagic seabirds calculated from the stochastic oil drifts simulations given an oil spill caused by activity in
(DSHA-1) for the prospect Hagar. PP Lx |Oil is the probability for a relative population loss (PL) in interval x if an oil spill has taken place, i.e. a
Acc
, where
conditional probability. Likewise, PRTy |Oil is the conditional probability for a restitution time (RT) in the interval y. The ratio PRTy /PRT
y
the calculated probability for a restitution time in the interval y is divided with the highest probability accepted by the operator, is identical to the
relative environmental risk, as explained in section 3.2, equations 1 and 2. See Table 33 for an explanation of regional codes.
Period
Winter
Region
Species
PP Lx |Oil
PRTy |Oil
Acc
PRTy /PRT
y
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
1-5%
5-10%
10-20%
20-30%
30-100%
0.1-1 yr
1-3 yr
3-10 yr
>10 yr
0.1-1 yr
1-3 yr
3-10 yr
>10 yr
89
BH
Little auk
1.46
0.00
0.00
0.00
0.00
0.73
0.73
0.00
0.00
0.10
0.42
0.00
0.00
...
BH
Razorbill
1.87
0.05
0.00
0.00
0.00
0.95
0.96
0.01
0.00
0.14
0.55
0.02
0.00
...
BH
Atlantic pun
0.05
0.00
0.00
0.00
0.00
0.03
0.03
0.00
0.00
0.00
0.02
0.00
0.00
...
BH
Black-backed gull
0.47
0.00
0.00
0.00
0.00
0.24
0.24
0.00
0.00
0.03
0.14
0.00
0.00
...
BH
Herring gull
0.88
0.00
0.00
0.00
0.00
0.44
0.44
0.00
0.00
0.06
0.25
0.00
0.00
...
BH
Northern gannet
0.06
0.00
0.00
0.00
0.00
0.03
0.03
0.00
0.00
0.00
0.02
0.00
0.00
...
NH
Razorbill
49.65
34.83
12.48
1.29
0.28
33.53
45.36
15.59
4.04
4.80
25.95
22.30
23.11
...
NH
Atlantic pun
49.22
34.50
13.62
1.91
0.45
33.23
45.26
16.39
4.81
4.75
25.89
23.43
27.49
...
NH
Northern fulmar
14.36
0.13
0.00
0.00
0.00
7.21
7.25
0.03
0.00
1.03
4.14
0.05
0.00
...
NH
Black-backed gull
67.67
3.15
0.54
0.02
0.00
34.63
35.55
1.07
0.14
4.95
20.33
1.53
0.82
...
NH
Herring gull
81.36
8.14
1.61
0.12
0.00
42.72
45.15
2.90
0.46
6.11
25.83
4.15
2.65
...
NH
Black-legged kittiwake
33.88
1.74
0.11
0.00
0.00
17.38
17.84
0.49
0.03
2.48
10.20
0.70
0.16
...
NH
Northern gannet
18.58
0.32
0.05
0.00
0.00
9.37
9.47
0.11
0.01
1.34
5.41
0.15
0.08
...
NH
Common guillemot
68.27
23.27
7.19
0.69
0.24
39.95
47.57
9.76
2.39
5.71
27.21
13.96
13.65
Spring
BH
Little auk
0.17
0.00
0.00
0.00
0.00
0.09
0.09
0.00
0.00
0.01
0.05
0.00
0.00
...
BH
Razorbill
0.37
0.00
0.00
0.00
0.00
0.19
0.19
0.00
0.00
0.03
0.11
0.00
0.00
Table 26:
Period
Region
Results for pelagic seabirds in activity scenario 1. . . continued
Species
PP Lx |Oil
PRTy |Oil
Acc
PRTy /PRT
y
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
15%
510%
1020%
2030%
30100%
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
90
...
BH
Atlantic pun
0.94
0.01
0.00
0.00
0.00
0.47
0.47
0.00
0.00
0.07
0.27
0.00
0.00
...
BH
Northern fulmar
0.27
0.00
0.00
0.00
0.00
0.14
0.14
0.00
0.00
0.02
0.08
0.00
0.00
...
BH
Black-backed gull
0.09
0.00
0.00
0.00
0.00
0.05
0.05
0.00
0.00
0.01
0.03
0.00
0.00
...
BH
Herring gull
0.19
0.00
0.00
0.00
0.00
0.09
0.09
0.00
0.00
0.01
0.05
0.00
0.00
...
BH
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
...
BH
Northern gannet
0.07
0.00
0.00
0.00
0.00
0.04
0.04
0.00
0.00
0.01
0.02
0.00
0.00
...
NH
Razorbill
63.29
16.65
8.68
1.05
0.34
35.81
42.14
9.03
3.04
5.12
24.10
12.91
17.39
...
NH
Atlantic pun
31.42
16.94
5.92
0.93
0.25
19.94
25.66
7.66
2.20
2.85
14.68
10.95
12.56
...
NH
Northern fulmar
15.76
0.40
0.03
0.00
0.00
7.98
8.09
0.12
0.01
1.14
4.63
0.17
0.05
...
NH
Common gull
25.35
1.31
0.19
0.00
0.00
13.00
13.37
0.42
0.05
1.86
7.65
0.60
0.28
...
NH
Black-backed gull
56.22
3.60
0.81
0.03
0.00
29.01
30.11
1.32
0.22
4.15
17.22
1.88
1.24
...
NH
Herring gull
49.48
4.10
0.75
0.08
0.00
25.77
26.98
1.44
0.23
3.68
15.43
2.06
1.30
...
NH
26.84
1.66
0.22
0.00
0.00
13.84
14.30
0.52
0.06
1.98
8.18
0.75
0.32
...
NH
Northern gannet
18.33
0.83
0.11
0.01
0.00
9.37
9.60
0.27
0.03
1.34
5.49
0.38
0.19
...
NH
Common guillemot
54.84
13.00
4.14
0.79
0.20
30.67
34.95
5.71
1.62
4.39
19.99
8.17
9.29
...
NS
Atlantic pun
0.06
0.00
0.00
0.00
0.00
0.03
0.03
0.00
0.00
0.00
0.02
0.00
0.00
...
NS
Northern fulmar
0.04
0.00
0.00
0.00
0.00
0.02
0.02
0.00
0.00
0.00
0.01
0.00
0.00
...
NS
Black-backed gull
0.05
0.00
0.00
0.00
0.00
0.03
0.03
0.00
0.00
0.00
0.01
0.00
0.00
...
NS
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Summer
BH
Razorbill
1.14
0.03
0.00
0.00
0.00
0.58
0.59
0.01
0.00
0.08
0.34
0.01
0.01
Table 26:
Period
Region
Species
PP Lx |Oil
PRTy |Oil
Acc
PRTy /PRT
y
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
15%
510%
1020%
2030%
30100%
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
91
...
BH
Atlantic pun
1.98
0.16
0.02
0.00
0.00
1.03
1.07
0.05
0.01
0.15
0.61
0.07
0.04
...
BH
Northern fulmar
0.91
0.03
0.00
0.00
0.00
0.46
0.47
0.01
0.00
0.07
0.27
0.01
0.00
...
BH
Glaucous gull
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
...
BH
Black-backed gull
0.59
0.01
0.00
0.00
0.00
0.30
0.30
0.00
0.00
0.04
0.17
0.00
0.00
...
BH
Herring gull
1.03
0.01
0.00
0.00
0.00
0.52
0.52
0.00
0.00
0.07
0.30
0.01
0.00
...
BH
0.18
0.00
0.00
0.00
0.00
0.09
0.09
0.00
0.00
0.01
0.05
0.00
0.00
...
BH
Northern gannet
0.91
0.01
0.00
0.00
0.00
0.46
0.46
0.00
0.00
0.07
0.26
0.00
0.00
...
BH
Common guillemot
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
...
NH
Razorbill
56.72
11.37
6.14
0.81
0.30
31.20
35.58
6.32
2.24
4.46
20.35
9.04
12.81
...
NH
Atlantic pun
26.85
5.02
3.14
0.58
0.18
14.68
16.72
3.12
1.25
2.10
9.57
4.46
7.17
...
NH
Northern fulmar
25.17
1.99
0.30
0.02
0.00
13.08
13.65
0.66
0.09
1.87
7.81
0.94
0.49
...
NH
Common gull
28.27
1.70
0.34
0.00
0.00
14.56
15.08
0.60
0.09
2.08
8.62
0.85
0.49
...
NH
Black-backed gull
49.75
5.02
1.06
0.08
0.00
26.13
27.65
1.82
0.31
3.74
15.81
2.61
1.77
...
NH
Herring gull
33.40
1.96
0.31
0.01
0.00
17.19
17.76
0.65
0.08
2.46
10.16
0.93
0.48
...
NH
17.81
1.21
0.14
0.01
0.00
9.21
9.54
0.37
0.04
1.32
5.46
0.53
0.21
...
NH
Northern gannet
27.94
2.51
0.36
0.02
0.00
14.60
15.32
0.82
0.10
2.09
8.76
1.17
0.58
...
NH
Common guillemot
56.69
9.49
2.95
0.42
0.13
30.72
33.83
4.06
1.08
4.39
19.35
5.81
6.20
...
NS
Atlantic pun
0.05
0.00
0.00
0.00
0.00
0.03
0.03
0.00
0.00
0.00
0.01
0.00
0.00
...
NS
Northern fulmar
0.04
0.00
0.00
0.00
0.00
0.02
0.02
0.00
0.00
0.00
0.01
0.00
0.00
...
NS
Black-backed gull
0.01
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
Table 26:
Period
Region
Species
PP Lx |Oil
PRTy |Oil
Acc
PRTy /PRT
y
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
15%
510%
1020%
2030%
30100%
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
92
...
NS
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Autumn
BH
Little auk
0.41
0.00
0.00
0.00
0.00
0.21
0.21
0.00
0.00
0.03
0.12
0.00
0.00
...
BH
Razorbill
0.78
0.01
0.00
0.00
0.00
0.39
0.39
0.00
0.00
0.06
0.23
0.00
0.00
...
BH
Atlantic pun
0.05
0.00
0.00
0.00
0.00
0.03
0.03
0.00
0.00
0.00
0.02
0.00
0.00
...
BH
Black-backed gull
0.12
0.00
0.00
0.00
0.00
0.06
0.06
0.00
0.00
0.01
0.03
0.00
0.00
...
BH
Herring gull
0.60
0.00
0.00
0.00
0.00
0.30
0.30
0.00
0.00
0.04
0.17
0.00
0.00
...
BH
Northern gannet
0.36
0.00
0.00
0.00
0.00
0.18
0.18
0.00
0.00
0.03
0.10
0.00
0.00
...
NH
Razorbill
50.90
16.82
5.57
0.62
0.08
29.66
35.26
7.30
1.78
4.24
20.17
10.44
10.20
...
NH
Atlantic pun
35.87
13.39
4.70
0.50
0.11
21.28
25.80
5.94
1.53
3.04
14.76
8.50
8.75
...
NH
Northern fulmar
28.43
2.96
0.47
0.01
0.00
14.96
15.82
0.98
0.12
2.14
9.05
1.40
0.70
...
NH
Black-backed gull
58.47
2.60
0.41
0.00
0.00
29.88
30.64
0.86
0.10
4.27
17.53
1.23
0.59
...
NH
Herring gull
46.13
3.12
0.60
0.01
0.00
23.84
24.77
1.09
0.16
3.41
14.17
1.55
0.90
...
NH
19.66
0.96
0.04
0.00
0.00
10.07
10.32
0.26
0.01
1.44
5.90
0.37
0.05
...
NH
Northern gannet
31.84
3.18
0.48
0.01
0.00
16.71
17.63
1.04
0.12
2.39
10.08
1.49
0.70
...
NH
Common guillemot
59.61
14.32
4.71
0.34
0.03
33.38
38.14
6.10
1.38
4.77
21.82
8.73
7.89
Table 27: Environmental risk for seals calculated from the stochastic oil drifts simulations given an oil spill caused by activity in (DSHA-1) for the
prospect Hagar. PP Lx |Oil is the probability for a relative population loss (PL) in interval x if an oil spill has taken place, i.e. a conditional probability.
Acc
, where the calculated probability
Likewise, PRTy |Oil is the conditional probability for a restitution time (RT) in the interval y. The ratio PRTy /PRT
y
for a restitution time in the interval y is divided with the highest probability accepted by the operator, is identical to the relative environmental
risk, as explained in section 3.2, equations 1 and 2. See Table 33 for an explanation of regional codes.
Period
Winter
Region
Species
PP Lx |Oil
PRTy |Oil
Acc
PRTy /PRT
y
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
15%
510%
1020%
2030%
30100%
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
93
MI
Grey seal
60.29
24.83
2.89
0.05
0.00
36.35
43.28
7.68
0.75
5.20
24.76
10.98
4.28
...
NO
Grey seal
0.89
0.00
0.00
0.00
0.00
0.45
0.45
0.00
0.00
0.06
0.26
0.00
0.00
...
MI
Harbour seal
62.91
2.64
0.16
0.00
0.00
32.11
32.81
0.74
0.04
4.59
18.77
1.06
0.23
Spring
MI
Grey seal
57.40
18.41
9.22
0.64
0.17
33.30
40.21
9.53
2.79
4.76
23.00
13.63
15.98
...
NO
Grey seal
0.26
0.00
0.00
0.00
0.00
0.13
0.13
0.00
0.00
0.02
0.07
0.00
0.00
...
MI
Harbour seal
60.54
10.07
0.97
0.00
0.00
32.79
35.55
3.01
0.25
4.69
20.33
4.30
1.41
Summer
MI
Grey seal
42.33
26.52
11.96
1.05
0.14
27.79
37.42
13.14
3.66
3.97
21.40
18.79
20.93
...
NO
Grey seal
0.88
0.00
0.00
0.00
0.00
0.44
0.44
0.00
0.00
0.06
0.25
0.00
0.00
...
MI
Harbour seal
53.36
12.34
1.26
0.01
0.00
29.77
33.17
3.72
0.32
4.26
18.97
5.32
1.83
...
NO
Harbour seal
0.03
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.00
0.01
0.00
0.00
Autumn
MI
Grey seal
49.49
28.44
5.64
0.12
0.00
31.85
40.37
9.99
1.47
4.56
23.09
14.28
8.40
...
NO
Grey seal
0.57
0.00
0.00
0.00
0.00
0.28
0.28
0.00
0.00
0.04
0.16
0.00
0.00
...
MI
Harbour seal
52.91
4.25
0.12
0.00
0.00
27.52
28.61
1.12
0.03
3.94
16.37
1.60
0.17
Table 28: Risk for damage on sh species listed in shNorwegianSea.xlsx. The risk is calculated from the stochastic oil drifts simulations for spills caused by
exploration drilling of the exploration well 6306/5-2 Hagar. PP Lx |Oil is the probability for a relative population loss (PL) in interval x if an oil spill has taken
Acc
, where
place, i.e. a conditional probability. Likewise, PRTy |Oil is the conditional probability for a restitution time (RT) in the interval y. The ratio PRTy /PRT
y
the calculated probability for a restitution time in the interval y is divided with the highest probability accepted by the operator, is identical to the relative
environmental risk, as explained in section 3.2, equations 1 and 2. See Table 33 for an explanation of regional codes.
Period
Species
15%
Winter
PP Lx |Oil
PRTy |Oil
Acc
PRTy /PRT
y
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
510%
1020%
2030%
30100%
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
94
No population
...
...
...
...
...
...
...
...
...
...
...
...
...
Spring
Cod (NH)
0.02
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.00
0.00
0.01
0.00
Summer
No population
...
...
...
...
...
...
...
...
...
...
...
...
...
Autumn
No population
...
...
...
...
...
...
...
...
...
...
...
...
...
Table 29: Risk for damage on shoreline grid cells calculated from the stochastic oil drifts simulations given an oil spill
caused by activity in (DFU-1) for the prospect Hagar. PRTy |Oil is the probability for a restitution time (RT) in the interval
Acc
, where the calculated probability for a restitution time in the interval y
y if an oilspill has taken place. The ratio PRTy /PRT
y
is divided with the highest probability accepted by the operator, is identical to the relative environmental risk, as explained
in section 3.2, equations 1 and 2. The column "Grid ID" contains identication numbers for each cell (10×10 km) in the
ContAct map grid [Alpha Miljørådgivning AS, 2003].
©
Period
Grid ID
PRTy |Oil
Acc
PRTy /PRT
y
% simul. w. restitution time interval:
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
22252
21.85
14.20
1.08
0.03
3.12
8.12
1.54
0.15
Winter
22464
13.30
9.47
0.67
0.01
1.90
5.42
0.95
0.05
Winter
20975
30.40
9.27
0.05
0.00
4.35
5.30
0.07
0.00
Winter
20553
22.40
6.37
0.08
0.00
3.20
3.64
0.11
0.00
Winter
21828
6.38
4.68
0.33
0.01
0.91
2.68
0.47
0.05
Winter
22463
16.70
4.61
0.02
0.00
2.39
2.64
0.02
0.00
Winter
20340
5.97
4.46
0.17
0.01
0.85
2.55
0.24
0.03
Winter
20339
14.32
4.26
0.06
0.00
2.05
2.44
0.09
0.00
Winter
20128
3.92
3.17
0.22
0.01
0.56
1.81
0.31
0.06
Winter
21187
11.39
3.16
0.01
0.00
1.63
1.81
0.01
0.00
Winter
22887
11.85
3.09
0.00
0.00
1.69
1.77
0.00
0.00
Winter
20342
3.48
2.58
0.09
0.00
0.50
1.48
0.14
0.02
Winter
20764
8.76
2.57
0.06
0.00
1.25
1.47
0.09
0.00
Winter
20976
7.93
2.27
0.06
0.00
1.13
1.30
0.09
0.00
Winter
22040
3.11
2.23
0.15
0.00
0.44
1.28
0.22
0.02
Winter
95
Table 29:
Period
Results for shoreline grid cells in activity scenario 1. . . continued
Grid ID
PRTy |Oil
Acc
PRTy /PRT
y
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
96
Winter
27324
7.52
2.08
0.00
0.00
1.08
1.19
0.01
0.00
Winter
23732
7.78
2.05
0.01
0.00
1.11
1.18
0.01
0.00
Winter
22676
2.62
1.99
0.13
0.00
0.38
1.14
0.18
0.01
Winter
23308
6.63
1.76
0.00
0.00
0.95
1.01
0.00
0.00
Spring
22252
19.52
14.57
1.70
0.12
2.79
8.33
2.43
0.68
Spring
20975
30.89
10.27
0.12
0.00
4.42
5.87
0.17
0.00
Spring
21828
10.53
9.01
1.12
0.10
1.51
5.15
1.60
0.55
Spring
22464
10.96
8.90
1.07
0.09
1.57
5.09
1.53
0.53
Spring
20553
23.69
7.61
0.36
0.00
3.39
4.35
0.51
0.00
Spring
21187
20.50
6.60
0.06
0.00
2.93
3.77
0.09
0.00
Spring
20340
7.99
6.53
0.54
0.04
1.14
3.73
0.77
0.20
Spring
22676
7.04
6.20
0.77
0.07
1.01
3.55
1.10
0.41
Spring
20976
15.40
5.46
0.65
0.00
2.20
3.13
0.93
0.00
Spring
20980
13.72
4.95
0.06
0.00
1.96
2.83
0.08
0.00
Spring
22463
15.28
4.93
0.12
0.00
2.19
2.82
0.17
0.00
Spring
20767
5.24
4.70
0.44
0.02
0.75
2.69
0.63
0.09
Spring
20339
14.00
4.59
0.27
0.00
2.00
2.63
0.38
0.00
Spring
22887
13.99
4.15
0.02
0.00
2.00
2.37
0.03
0.00
Spring
23732
10.96
3.92
0.25
0.00
1.57
2.24
0.35
0.00
Spring
20764
11.94
3.85
0.28
0.00
1.71
2.20
0.40
0.00
Table 29:
Period
Grid ID
PRTy |Oil
Acc
PRTy /PRT
y
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
97
Spring
20342
4.96
3.79
0.17
0.00
0.71
2.17
0.25
0.02
Spring
20128
4.08
3.78
0.71
0.09
0.58
2.16
1.01
0.53
Spring
20977
9.90
3.77
0.36
0.00
1.42
2.15
0.52
0.02
Spring
22040
4.54
3.68
0.46
0.04
0.65
2.11
0.65
0.24
Spring
23520
4.07
3.66
0.39
0.03
0.58
2.09
0.55
0.17
Spring
21188
10.30
3.51
0.04
0.00
1.47
2.01
0.05
0.00
Spring
20769
9.40
3.35
0.03
0.00
1.34
1.91
0.05
0.00
Spring
21616
7.34
2.65
0.18
0.00
1.05
1.52
0.25
0.03
Spring
22888
7.41
2.52
0.16
0.01
1.06
1.44
0.22
0.03
Spring
20981
1.98
2.26
0.60
0.10
0.28
1.29
0.86
0.60
Spring
23308
6.66
2.10
0.01
0.00
0.95
1.20
0.02
0.00
Spring
25213
2.38
2.08
0.31
0.03
0.34
1.19
0.44
0.19
Spring
19706
2.67
2.07
0.13
0.01
0.38
1.18
0.18
0.03
Spring
27324
6.11
1.85
0.01
0.00
0.87
1.06
0.01
0.00
Spring
19492
4.62
1.75
0.04
0.00
0.66
1.00
0.06
0.00
Summer
22252
18.51
13.59
1.43
0.08
2.65
7.77
2.04
0.47
Summer
20975
25.50
9.57
0.15
0.00
3.65
5.47
0.22
0.00
Summer
21828
10.64
8.97
0.99
0.07
1.52
5.13
1.42
0.37
Summer
22464
9.95
8.10
1.02
0.10
1.42
4.63
1.46
0.57
Summer
20340
8.82
7.90
0.93
0.07
1.26
4.52
1.33
0.39
Table 29:
Period
Grid ID
PRTy |Oil
Acc
PRTy /PRT
y
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
98
Summer
20553
20.20
6.95
0.45
0.00
2.89
3.98
0.64
0.00
Summer
21187
20.98
6.82
0.08
0.00
3.00
3.90
0.11
0.00
Summer
20767
7.42
6.53
0.59
0.01
1.06
3.74
0.84
0.06
Summer
20980
17.83
5.88
0.05
0.00
2.55
3.36
0.07
0.00
Summer
20976
15.88
5.31
0.42
0.00
2.27
3.04
0.60
0.00
Summer
20128
5.38
4.92
0.71
0.07
0.77
2.82
1.02
0.40
Summer
20339
12.48
4.64
0.27
0.00
1.79
2.65
0.39
0.00
Summer
20977
11.33
4.55
0.42
0.00
1.62
2.60
0.60
0.02
Summer
22676
4.92
4.45
0.57
0.05
0.70
2.55
0.81
0.28
Summer
23732
12.71
4.40
0.38
0.00
1.82
2.52
0.54
0.00
Summer
22463
12.40
4.11
0.11
0.00
1.77
2.35
0.16
0.00
Summer
22887
13.50
4.00
0.02
0.00
1.93
2.29
0.02
0.00
Summer
20764
11.00
3.72
0.30
0.00
1.57
2.13
0.42
0.00
Summer
20769
10.94
3.61
0.03
0.00
1.56
2.06
0.04
0.00
Summer
23520
3.93
3.58
0.47
0.04
0.56
2.05
0.68
0.24
Summer
20766
3.54
3.48
0.47
0.01
0.51
1.99
0.67
0.06
Summer
21188
10.02
3.42
0.03
0.00
1.43
1.96
0.04
0.00
Summer
27324
10.26
3.32
0.01
0.00
1.47
1.90
0.02
0.00
Summer
22040
3.42
3.11
0.39
0.03
0.49
1.78
0.55
0.17
Summer
20342
3.61
3.04
0.30
0.01
0.52
1.74
0.42
0.07
Table 29:
Period
Grid ID
PRTy |Oil
Acc
PRTy /PRT
y
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
99
Summer
20981
2.86
2.96
0.70
0.09
0.41
1.70
1.01
0.54
Summer
21616
7.81
2.79
0.13
0.00
1.12
1.60
0.18
0.02
Summer
23308
7.87
2.50
0.01
0.00
1.13
1.43
0.02
0.00
Summer
22888
6.92
2.43
0.18
0.01
0.99
1.39
0.26
0.04
Summer
20768
6.68
2.29
0.04
0.00
0.96
1.31
0.06
0.02
Summer
25213
2.05
2.01
0.37
0.05
0.29
1.15
0.52
0.29
Summer
19280
1.72
1.84
0.44
0.05
0.25
1.05
0.62
0.29
Summer
21405
1.85
1.79
0.35
0.04
0.26
1.03
0.50
0.22
Autumn
22252
20.90
13.74
1.07
0.03
2.99
7.86
1.54
0.18
Autumn
22464
13.80
9.87
0.72
0.01
1.97
5.65
1.03
0.08
Autumn
20975
28.61
8.57
0.04
0.00
4.09
4.90
0.06
0.00
Autumn
21828
9.00
6.71
0.51
0.01
1.29
3.84
0.72
0.09
Autumn
20553
23.33
6.42
0.07
0.00
3.34
3.67
0.10
0.00
Autumn
20340
7.40
5.53
0.27
0.02
1.06
3.16
0.39
0.11
Autumn
22463
17.60
4.86
0.01
0.00
2.52
2.78
0.02
0.00
Autumn
27324
16.43
4.67
0.01
0.00
2.35
2.67
0.02
0.00
Autumn
22676
5.70
4.23
0.26
0.01
0.82
2.42
0.38
0.04
Autumn
21187
15.40
4.22
0.00
0.00
2.20
2.41
0.00
0.00
Autumn
22887
15.69
4.12
0.00
0.00
2.24
2.35
0.00
0.00
Autumn
20339
11.71
3.70
0.11
0.00
1.67
2.11
0.16
0.00
Table 29:
Period
Grid ID
PRTy |Oil
Acc
PRTy /PRT
y
Minor
Moderate
Consid.
Serious
Minor
Moderate
Consid.
Serious
0.11 yr
13 yr
310 yr
>10 yr
0.11 yr
13 yr
310 yr
>10 yr
100
Autumn
22040
5.01
3.57
0.23
0.00
0.72
2.04
0.33
0.03
Autumn
20128
3.88
3.30
0.34
0.03
0.56
1.89
0.48
0.15
Autumn
20976
11.05
3.17
0.06
0.00
1.58
1.81
0.09
0.00
Autumn
20342
4.03
2.86
0.07
0.00
0.58
1.64
0.10
0.00
Autumn
22888
9.02
2.62
0.07
0.00
1.29
1.50
0.10
0.01
Autumn
20764
9.15
2.57
0.03
0.00
1.31
1.47
0.04
0.00
Autumn
23732
9.42
2.48
0.01
0.00
1.35
1.42
0.01
0.00
Autumn
23520
3.13
2.22
0.06
0.00
0.45
1.27
0.08
0.00
Autumn
23308
7.93
2.15
0.00
0.00
1.13
1.23
0.00
0.00
Autumn
29646
6.78
1.86
0.00
0.00
0.97
1.07
0.01
0.00
Autumn
20980
6.77
1.81
0.00
0.00
0.97
1.04
0.00
0.00
A.3 ODS results (combination of topside- and seabed releases)
101
Figure
22:
The
inuence
areas
for
oil
on
the
surface
for
the combination of seabed and topside spills caused by drilling of the exploration well 6306/5-2 at
the prospect Hagar. The areas are calculated from stochastic oil drift simulations. Each area consists of all 10×10 km map cells containing more oil on the surface than 0.01 tonne/km2 , in more than
5% of the single simulations. The maps cover the simulation periods Winter (December - February),
Spring (March - May), Summer (June - August) and Autumn (September - November).
102
Figure 23:
The inuence areas for oil in the water column for
the combination of seabed and topside spills caused by drilling of the exploration well 6306/5-2
at the prospect Hagar. The areas are calculated from stochastic oil drift simulations. Each
area consists of all 10×10 km map cells with higher oil concentration in the water column
than 375 ppb, in more than 5% of the single simulations. The maps cover the simulation
periods Winter (December - February), Spring (March - May), Summer (June - August) and Autumn (September - November).
103
Figure 24: Inuence areas for oil accumulated on the shoreline for the combination of subsea and
topside spills caused by drilling of the exploration well 6306/5-2 at the prospect Hagar. The areas
are calculated from stochastic oil drift simulations. Each area consists of all 10×10 km map cell containing shore line and with more accumulated oil on the shore line than 0.01 tonne/km, in more than
5% of the single simulations. The maps cover the simulation periods Winter (December - February),
Spring (March - May), Summer (June - August) and Autumn (September - November).
104
Table 30: The size of the inuence areas for oil on the sea surface, in the water column, and accumulated on the shoreline, calculated from the stochastic oil drift simulations given a combination
of subsea- and surface release from the exploration well 6306/5-2at the prospect Hagar. See the
denition of these areas in the Appendix ??.
Period
Area (km2 )
Watercolumn
Surface
Shore line
Winter
300
112200
2100
Spring
800
146100
3000
Summer
1100
164000
3300
Autumn
800
131600
2600
Table 31: Stranding statistics for all aected shoreline, calculated from the
stochastic oil drift simulations given a combination of subsea and surface
release in (DSHA-1) for the prospect Hagar. The columns cover probability for stranding, stranding time, as well as stranded amount of oil
emulsion. The stranding time and amount of oil emulsion is tabulated as
three dierent percentiles from their respective probability distributions.
See an explanation of percentiles in Appendix ??.
Period
Prob. (%)
Period
Time (days)
Amount (tonne)
P0
P5
P50
P50
P95
P100
Winter
66.8
2.4
5.7
16.9
13
2059
199543
Spring
65.6
2.1
6.3
20.4
18
11587
820486
Summer
63.5
2.1
6.0
21.1
23
14398
969861
Autumn
68.8
2.1
4.7
16.6
17
2737
297484
105
B Appendix: input data
B.1 Valued ecosystem components (VEC)
106
Table 32: Valued Ecosystem Components (VEC's ) in dierent geographical regions. The red list
status of the VEC is shown by use of the following codes: CR = Critically Endagered, EN = Endangered, VU = Vulnerable, NT = Nearly Threatened, LC = Least Concern, NA = not applicable.
* designates their red list status for Svalbard
Group
Species
Red liststatus
Pelagic seabird
107
Coastal seabirds
Little auk
LC*
Razorbill
VU
Atlantic Pun
VU
Northern fulmar
NT
Common gull
NT
Glaucous gull
NT*
Black-backed gull
LC
Herring gull
LC
EN
Northern gannet
LC
Brï¿½nnich's guillemot
VU
Common guillemot
CR
Greylag Goose
LC
Long-tailed Duck
LC
Common Merganser
LC
Black Scoter
NT
LC
White-winged Scoter
NT
Red-throated Loon
LC
Table 32:
Group
VEC list continued
Species
Red liststatus
Sea mammals
Fish
108
Shoreline habitats
Great Cormorant
LC
Black Guillemot
VU
European Shag
LC
Common Eider
LC
King Eider
LC
Grey seal
LC
Harbor seal
VU
Norwegian spring-spawning herring
LC
Northeast Arctic Saithe
LC
Northeast Arctic Haddock
LC
Northeast Arctic Cod
LC
North Sea Mackerel
LC
North Sea Cod
LC
North Sea Herring
LC
North Sea Saithe
LC
North Sea Haddock
LC
Sandeels
LC
Deep-sea redsh
VU
Capelin
LC
Greenland halibut
LC
-
-
B.2 Overview geographic populations
Table 33: Genetic populations, and the geographical regions to which they
belong, for species or groups of species. The population codes are used in
the result tables in the Appendix.
Specie(s)
Geographical code
Coastal sea birds
Pelagic birds
.
Grey Seal
Harbor Seal
Geographical region
BH
Barents Sea
NH
Norwegian Sea
NS
North Sea
SK
Skagerrak
SV
Coastal waters around Svalbard
BH
Barents Sea
NH
Norwegian Sea
NS
North Sea
SO
Southern Norwegian waters
MI
Central
NO
Northern
SO
Southern
MI
Central
NO
Northern
B.3 The ecosystem components vulnerability to oil
A description of the ecosystem components vulnerability for oil is given below. See Table B.2 for the red
list status of all the VEC's evaluated in the environmental risk analysis.
Water column resources; plankton
The phytoplankton bloom in coastal areas of the North Sea and
the Norwegian Sea generally start in March/April while the bloom in the Barents Sea generally starts
in mid April. However, there are variations between years, and also large geographical variations within
years in the onset of the bloom.
The phytoplankton production is grazed by a variety of dierent zooplankton species, and generally
zooplankton abundance peaks about two weeks after the phytoplankton bloom; in the Barents Sea the
Calanus nmarchicus
and Calanus helgolandicus are the dominating species. In the Barents Sea Calanus nmarchicus dominates the Atlantic water masses while in Arctic water masses Calanus glacialis is the the dominating
maximum peak is in June. In the North Sea and the Norwegian Sea the copopods
species.
109
In the North Sea and the Norwegian Sea there ususally is a second phytoplankton bloom in August.
Because of wide and varied distribution, short generation time and rapid immigration from unaected
areas, plankton is generally not considered vulnerable for oil pollution.
Fish resources
Fish are vulnerable to toxic eects from oil. Species attached to geographically limited
areas through whole or parts of their life cycles are most vulnerable to eects from the petroleum industry.
Accumulations of juvenile sh are especially vulnerable to oil spills.
Seabirds
Seabirds are generally vulnerable to oil spills as feathers loose their isolating eect when
contaminated with oil. Hence, seabirds have high mortality during large oil spills [NINA, 2008]. Birds
are also subject to toxic eects through grooming of soiled feathers and through eating oil polluted prey.
Pelagic seabirds and coastal diving species that spend much time on the surface are most vulnerable,
while pelagic surface grazing species spend more time in the air and will avoid the oil to a larger extent.
Seabirds on land, by the coast (pelagic and coastal species) and at open sea (pelagic species) are
generally vulnerable to oil during the breeding season (April-August). Pelagic seabirds overwinter at
open sea and are vulnerable in this period, while coastal species gather in large densities along the coast
during the winter. Auk species are especially vulnerable to oil in the autumn (August-October), during
the moulting period, when they are unable to y for a period of up to 50 days [HI & DN, 2007].
Results from the national monitoring programme for seabirds show that many of the populations
along the Norwegian coastline have a negative population trend [SEAPOP, 2011]. Pelagic species generally face most problems, and collapse in prey populations is believed to be one of the main reasons for
the decline. Several of the species in the analysis area are listed on the Norwegian Red List [Kålås et al.,
2010].
Sea mammals
Individual sea mammals are vulnerable for inhalation and digestion of oil. Seal popula-
tions are vulnerable during the breeding- (whelping and mating) and moulting seasons when individuals
congerate in small areas. Seal pups are vulnerable to oil pollution, and grey seal pups are particulary
vulnerable as they are dependent on their foster fur for isolation after birth. On a population level whales
are not vulnerable to oil spills, chemical discharges or operational discharges.
Harbour seals and grey seals are common species along the Norwegian coast. Grey seals in the area
between Froan and Lofoten have their breeding- and whelping seasons from mid-September to the end of
October, while grey seals in Troms, Finnmark and Rogaland whelp from mid-November to mid-December.
The harbour seal whelping period is JuneJuly, with overlapping and successive breeding and moulting
periods in August-September, while grey seal moult in FebruaryApril [HI, 2011].
Otter
The mid-Norwegian otter populations are large, and the species is assumed to have a continuous
distribution from the coastline of Sør-Trøndelag and northwards [DNV og NINA, 2010]. The highest
occurences are along the coast of Finnmark. Otters lack the insulating blubber of whales and seals
and are subject to hypothermia and death if they are soiled with oil. The European otter is listed as
Vulnerable (VU) on the Norwegian Red List [Kålås et al., 2010].
110
The data on the otter population in the analysis region is not sucient to be used in statistical
modelling to estimate potential population loss. Therefore otters are not evaluated in the environmetal
risk assessment.
B.4 Overview of analysed NEBA grid
Table 34: Position of grid cells in the grid in Figure 19 and Figure 20. Latitude and longtitude
(UTM coordinates) are given as the center-point of each 10×10 km grid cell (WGS1984 UTM Zone
33N). To get the position of the edges of each grid cell, add (eastern or southern edge) and subtract
(western or northern edge) 5 km from the coordinate in the table.
Letter code
Numeric code
Latitude
Longtitude
A
1
35000
7145000
A
2
35000
7135000
A
3
35000
7125000
A
4
35000
7115000
A
5
35000
7105000
A
6
35000
7095000
A
7
35000
7085000
A
8
35000
7075000
A
9
35000
7065000
A
10
35000
7055000
A
11
35000
7045000
B
1
45000
7145000
B
2
45000
7135000
B
3
45000
7125000
B
4
45000
7115000
B
5
45000
7105000
B
6
45000
7095000
B
7
45000
7085000
B
8
45000
7075000
B
9
45000
7065000
B
10
45000
7055000
B
11
45000
7045000
C
1
55000
7145000
C
2
55000
7135000
C
3
55000
7125000
C
4
55000
7115000
C
5
55000
7105000
C
6
55000
7095000
C
7
55000
7085000
cont. on next page
111
Table 34:
NEBA grid cells continued
Letter code
Numeric code
Latitude
Longtitude
C
8
55000
7075000
C
9
55000
7065000
C
10
55000
7055000
C
11
55000
7045000
D
1
65000
7145000
D
2
65000
7135000
D
3
65000
7125000
D
4
65000
7115000
D
5
65000
7105000
D
6
65000
7095000
D
7
65000
7085000
D
8
65000
7075000
D
9
65000
7065000
D
10
65000
7055000
D
11
65000
7045000
E
1
75000
7145000
E
2
75000
7135000
E
3
75000
7125000
E
4
75000
7115000
E
5
75000
7105000
E
6
75000
7095000
E
7
75000
7085000
E
8
75000
7075000
E
9
75000
7065000
E
10
75000
7055000
E
11
75000
7045000
F
1
85000
7145000
F
2
85000
7135000
F
3
85000
7125000
F
4
85000
7115000
F
5
85000
7105000
F
6
85000
7095000
F
7
85000
7085000
F
8
85000
7075000
F
9
85000
7065000
F
10
85000
7055000
F
11
85000
7045000
G
1
95000
7145000
cont. on next page
112
Table 34:
Letter code
Numeric code
Latitude
Longtitude
G
2
95000
7135000
G
3
95000
7125000
G
4
95000
7115000
G
5
95000
7105000
G
6
95000
7095000
G
7
95000
7085000
G
8
95000
7075000
G
9
95000
7065000
G
10
95000
7055000
G
11
95000
7045000
H
1
105000
7145000
H
2
105000
7135000
H
3
105000
7125000
H
4
105000
7115000
H
5
105000
7105000
H
6
105000
7095000
H
7
105000
7085000
H
8
105000
7075000
H
9
105000
7065000
H
10
105000
7055000
H
11
105000
7045000
I
1
115000
7145000
I
2
115000
7135000
I
3
115000
7125000
I
4
115000
7115000
I
5
115000
7105000
I
6
115000
7095000
I
7
115000
7085000
I
8
115000
7075000
I
9
115000
7065000
I
10
115000
7055000
I
11
115000
7045000
J
1
125000
7145000
J
2
125000
7135000
J
3
125000
7125000
J
4
125000
7115000
J
5
125000
7105000
J
6
125000
7095000
cont. on next page
113
Table 34:
Letter code
Numeric code
Latitude
Longtitude
J
7
125000
7085000
J
8
125000
7075000
J
9
125000
7065000
J
10
125000
7055000
J
11
125000
7045000
K
1
135000
7145000
K
2
135000
7135000
K
3
135000
7125000
K
4
135000
7115000
K
5
135000
7105000
K
6
135000
7095000
K
7
135000
7085000
K
8
135000
7075000
K
9
135000
7065000
K
10
135000
7055000
K
11
135000
7045000
C Appendix: methods
C.1 Denition of inuence areas
Based on stochastic oil drift simulation one can dene dierent types of inuence areas. These are
geographic maps showing the probability of dierent map grid cells to be "hit" by oil from the single
simulations within a stochastic simulation. The size of these map grid cells are normally 2 × 2 km or
10 × 10 km. The following three denitions are used to calculate/visualize the inuence areas for oil on
the surface, for oil in the water column, and for oil on the shoreline.
Inuence area for oil on the sea surface
The inuence area for oil on the sea surface consists of
all map cells containing more oil on the surface than 0.01 ton per km2, in more than 5 % of the single
simulations within a stochastic simulation. This threshold value is the assumed minimum amount of oil
per unit area being able to give measurable losses of sea birds [OLF, 2007].
Inuence area for oil in the water column
The inuence area for oil in the water column consists
of all map cells with an oil concentration in the water column which is higher than 375 ppm, in more
than 5 % of the single simulations within a stochastic simulation. This threshold value represents the
assumed lowest concentration of oil that is lethal to sh eggs and larvae [OLF, 2007].
114
Inuence area for oil accumulated on the shoreline
The inuence area for oil accumulated on
the shore line consists of map cells with more accumulated oil on the shore line than 0.01 ton per km,
in more than 5 % of the single simulations within a stochastic simulation. This threshold value is the
assumed lower mass of oil per unit length that can give measurable damage on the shore line [OLF, 2007].
Inuence areas in relation to the area of individual oil drifts
It is important to be aware of
that an inuence area is not the same as the area of any of the single oil drifts (oil slicks) i a stochastic
simulation. Since the dierent oil drifts represents dierent time intervals, with various wind- and current
conditions, they will dier much in area and spatial extent. The inuence area may therefore depart from
the areas of the single oil drifts in the simulation. Two extremes are worth commenting on. Simulations
where the individual oil drifts have large areas and are spatially close can give inuence areas much larger
than the individual oil drifts. Simulations where the single oil drifts have small areas and large spatial
resolution will on the other hand give inuence areas that are smaller than any of the single oil drifts.
C.2 Calculation of percentiles
The oil drift simulations are based on input data, or variables, of two dierent kinds: (1) Fixed variables,
and (2) stochastic variables. To the rst category belongs variables that we can predict the value of,
to a fair degree of accuracy, for a future oil spill situation.These variables includes the properties of the
released oil, the geographic position of the release site, water depth, as well as the temperature and
salinity proles of the water column at the release site at dierent periods of the year. To the second
category belongs variables that are dicult to predict the value of for future oil spills. Therefore, the
values of these variables must be represented with probability distributions. These distributions are
based on other types of simulations or on historical data. For each of these single simulations we can
calculate a suite of descriptive variables, each of which captures some relevant property of the oil spill.
Examples of such descriptive variables, among many others, are sea surface area covered by oil, stranding
time of emulsion and stranded mass of emulsion. Hence, from a single stochastic simulation it can be
produced a probability distribution with n dierent values for each of the descriptive variables, making
them stochastic variables also. The probability distributions of values, one for each of the descriptive
variables, informs us on how oil drifts from the same release point may dier when combining all the
variation in the input data being used. Therefore, these distributions are valuable when interpreting the
stochastic oil drift simulations, and this information are therefore used in our oil drift reports. However,
since
n is a large number (typically n = 5001000), it is impractical to tabulate all the values from
these distributions. Some sort of data reduction is thus needed to reveal the major properties of the
distributions in a compact form. The concept of percentiles is normally used for this purpose within oil
drift analyses, based on guidelines from the Climate and Pollution Agency.
A percentile (PX ) is the value of a stochastic variable that is larger than a certain percentage X of
the remaining values in the distribution. So the 5th percentile for the stranded mass of emulsion is the
single value, among all the n calculated values, which is larger than 5 % of the other calculated values.
In practical terms we calculate the percentiles as follows. Make a sorted list of the n values, from the
115
smallest to largest value. The 5th percentile of the distribution is the smallest value in the list that has
position number larger than n × 5/100. Hence, if
n = 100, the sorted list will contain 100 values, and
the 5th percentile will be value number 6 in the list, if you cont from the smallest to the largest value.
Similarly the 50th percentile will be the value number 51 in the list, and the 95th percentile will be value
number 96 in the list.
The percentiles used in this report are P0 , P5 , P50 , P95 , and P99 . The 100-percentile (P100 ) does
not exist given the denition we use for percentiles. This is because P100 should be the lowest number in
a distribution that is larger than 100 % of all the values in the same distribution. But since P100 is one of
the values in this distribution, this would imply that P100 should be larger than itself, which is logically
impossible.
C.3 Conversion tables
Table 35: Table for converting from (1) the amount of oil in a 10 × 10 km
grid cell (metric tonne) to (2) the percentage of seabird individuals in the
cell that will die from oil exposure, for species of vulnerability class S1,
S2 og S3. The table originates from OLF, 2007 (Table 3.7, p. 40), where
it is called "Eekt-nøkkel for akutt dødelighet for sjøfugl".
Amount
Vulnerability
tonne
S1
S2
S3
1100
5%
10%
20%
100500
10%
20%
40%
5001000
20%
40%
60%
>1000
40%
60%
80%
Table 36: Table for converting from (1) the amout of oil in a 10 × 10 km
grid cell (metric tonne) to (2) the percentage of sea mammal individuals
in the cell that will die from oil exposure, for species of vulnerability class
S1, S2 og S3. The table originates from OLF, 2007 (Table 3.8, p. 40),
where it is called "Eektnøkkel for akutt dødelighet for sjøpattedyr".
Amount
Vulnerability
S1
S2
S3
1100
5%
15%
20%
100500
10%
20%
35%
5001000
15%
30%
50%
>1000
20%
40%
65%
116
Table 37: Table for converting from (1) the percentage reduction of a
population, due to oil damage, to (2) the restitution time (years) for the
same population. The table orignates from OLF, 2007 (Table 3-10, p. 42)
where it is called "Skadenøkkelen for sjøfugl/sjøpattedyr bestander med
høy sårbarhet (S3)".
Restitution class
Reduction (%)
R3
Restitution time (year)
0.11
13
310
>10
1-5
50%
50%
...
...
5-10
25%
50%
25%
...
10-20
...
25%
50%
25%
20-30
...
...
50%
50%
> 30
...
...
...
100%
Table 38: Table for converting from (1) the percentage reduction of yearly
recruitment in the spawning population of herring due to oil damage into
(2) the restitution time (years) for the same spawning population. The
table orignates from ? (Table 8-5) where it is called "Skadenøkkelen for
gytebestand av sild basert på restitusjonstidsfordelinger ved ulike årklassetap (Based on Table 8-4)".
Percentage reduction
Reduction in yearly recruitment
eggs/larvae (%)
0%
1%
2%
5%
10 %
20 %
30 %
50 %
100 %
1%
50 %
30 %
15 %
5%
...
...
...
...
...
2%
10 %
20 %
40 %
20 %
10 %
...
...
...
...
5%
...
10 %
20 %
40 %
20 %
10 %
...
...
...
10 %
...
...
10 %
20 %
40 %
15 %
10 %
5%
...
20 %
...
...
...
10 %
20 %
40 %
15 %
10 %
5%
30 %
...
...
...
5%
10 %
15 %
40 %
20 %
10 %
50 %
...
...
...
...
5%
10 %
15 %
40 %
30 %
117
recruitment in the spawning population of cod due to oil damage into (2)
the restitution time (years) for the same spawning population. The table
orignates from ? (Table 8-6) where it is called "Skadenøkkelen for gytebestand av torsk basert på restitusjonstidsfordelinger ved ulike årklassetap".
Percentage reduction (%)
Restitution time (years)
0.11
13
310
>10
1-5
45 %
35 %
20 %
...
5-10
15 %
30 %
55 %
...
10-20
5%
15 %
80 %
...
20-30
...
5%
90 %
5%
> 30
...
...
90 %
10 %
recruitment in the spawning population of herring due to oil damage into
(2) the restitution time (years) for the same spawning population. The
table orignates from [?] (Table 8-6) where it is called "Skadenøkkelen
for gytebestand av torsk basert på restitusjonstidsfordelinger ved ulike
årklassetap".
Percentage reduction (%)
Restitution time (years)
0.11
13
310
>10
1-5
60 %
40 %
...
...
5-10
35 %
40 %
25 %
...
10-20
20 %
25 %
50 %
5%
20-30
10 %
25 %
40 %
25 %
> 30
...
10 %
30 %
60 %
118
Table 41: Table for converting from (1) the amount of oil stranding in a shoreline grid cell (metric
tonne), to (2) the restitution time (year) for the same cell. Each interval for amount of oil is
converted into a percentage distribution of restitution times. The table originates from OLF, 2007
(Table 3-9, p. 41) where it is called "Skadenøkkel for kysthabitater med sårbarhet 1-3".
Vulnerability class
Amount (metric tonne)
Less
Moderate
Considerable
Serious
0.11 yr
13 yr
310 yr
> 10 yr
1100
20 %
50 %
30 %
...
100500
10 %
60 %
20 %
10 %
5001000
...
20 %
50 %
30 %
>1000
...
...
40 %
60 %
1100
60 %
40 %
...
...
100500
30 %
60 %
10 %
...
5001000
10 %
60 %
30 %
...
>1000
...
40 %
50 %
10 %
1100
80 %
20 %
...
...
100500
60 %
40 %
...
...
5001000
40 %
50 %
10 %
...
>1000
20 %
40 %
40 %
...
pr 100 km
2
S3
S2
S1
Table 42: Vulnerability index for shoreline map squares of dierent wave
exposure, where "1" denotes lowest vulnerability. The table originates
from [OLF, 2007](table on p. C-3). In this study a vulnerability index of
"3" is used for shoreline grid cells where the vulnerability is unknown. In
MIRA the vulnerability index for such cells is suggested set to "1".
Shoreline type
Degree of vulnerability
Wave exposure
High
Low
Block beach
1
3
Cli
1
1
Clay/Beach meadow
3
3
Man-made
1
1
Sand dune
2
2
Sand beach
1
2
Stone beach
2
3
Bear rock-face
1
2
119
C.4 Damaging oil concentration for sh larvae
The lowest concentration of oil in seawater that may kill sh egg and larva (eect
concentration ) is not
established for all sh species in Norwegian waters. However, based on laboratory experiments, a total
hydrocarbon concentration (THC: dissolved and droplets) of 375 ppb has been estimated to be the eect
concentration for cod and herring [DNV & SINTEF, 2010]. This value is lower than published elsewhere,
possibly due to several conservative assumptions in the study, and will therefore be used for all sh species
analyzed in this report. Further, since dierent crude oils do not dier substantially in their content of
toxic oil components, it is considered justiable to use a single eect concentration for all types of crude
oils.
120

report - Miljødirektoratet

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