Environmental Risk (ERA) and Oil Spill Contingency Analysis (OSCA) for exploration well 6507/5-9 Shrek in PL838 in the Norwegian Sea

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Environmental Risk (ERA) and Oil Spill Contingency Analysis (OSCA) for exploration well 6507/5-9 Shrek in PL838 in the Norwegian Sea

PGNiG Upstream Norway AS

Report No.: 2019-0216, Rev. 00 Document No.: 278131

Date: 2019-04-12

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Project name: MRABA Exploration well Norwegian Sea DNV GL AS Region Norway 5321,Region Norway P.O. Box 300,

Tel: +47 67 57 99 00 Report title: Environmental Risk (ERA) and Oil Spill Contingency

Analysis (OSCA) for exploration well 6507/5-9 Shrek in PL838 in the Norwegian Sea

Customer: PGNiG Upstream Norway AS, Postboks 344 Forus, 4067, Stavanger, Norway

Customer contact: Anniken Meisler in Well Expertise on behalf of PGNiG upstream Norway AS

Date of issue: 2019-04-12 Project No.: 10143020

Organization unit: Environmental Risk and Preparedness Report No.: 2019-0216, Rev. 00

Document No.: 278131

Objective: Environmental Risk analysis (ERA) and Oil Spill Contingency Analysis (OSCA) for exploration well Shrek in the Norwegian Sea in the proximity of the Skarv field.

Prepared by:

Verified by: Approved by:

Helene Østbøll

Principal Consultant Odd Willy Brude

Senior Principal Consultant Torild R. Nissen-Lie Group Leader

Anders Rudberg

Principal Specialist

Copyright © DNV GL 0001. All rights reserved. Unless otherwise agreed in writing: (i) This publication or parts thereof may not be copied, reproduced or transmitted in any form, or by any means, whether digitally or otherwise; (ii) The content of this publication shall be kept confidential by the customer; (iii) No third party may rely on its contents; and (iv) DNV GL undertakes no duty of care toward any third party. Reference to part of this publication which may lead to misinterpretation is prohibited. DNV GL and the Horizon Graphic are trademarks of DNV GL AS.

DNV GL Distribution: Keywords:

☐ OPEN. Unrestricted distribution, internal and external. Exploration well, Norwegian Sea, Skarv oil, Environmental risk, oil spill contingency, semi-submersible rig, capping stack

☒ INTERNAL use only. Internal DNV GL document.

☐ CONFIDENTIAL. Distribution within DNV GL according to applicable contract.*

☐ SECRET. Authorized access only.

*Specify distribution:

Rev. No. Date Reason for Issue Prepared by Verified by Approved by

00 2019-03-15 Draft issue HELOS BRUDE TRNL

01 2019-03-22 Draft issue including capping HELOS BRUDE TRNL

00 2019-04-12 Final report HELOS BRUDE TRNL

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6.1 Possible consequences given a blowout from exploration well 6507/5-9 Shrek 35 6.2 Environmental Risk 46 6.3 Summary of the environmental risk related to the drilling activity at exploration well 6507/5-9 Shrek 52 7 OIL SPILL CONTINGENCY ANALYSIS FOR EXPLORATION WELL 6507/5-9 SHREK ... 57 7.1 Responsibility for oil spill contingency on the Norwegian Continental Shelf 57

7.2 Method for oil spill contingency analysis 57

7.3 The reference oils properties with regards to mechanical recovery and chemical dispersion 61

7.4 Dimensioning blowout rate 63

7.5 Performance requirements 63

7.6 Oil spill contingency requirements in open sea (Barrier 1 and 2) 63

7.7 Response time 66

7.8 Oil spill contingency modelling for open sea (Barrier 1 & 2) 67 7.9 Oil spill contingency requirements in the coastal areas (Barriers 3-5) 71

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7.10 Summary and conclusion - oil spill contingency 72 8 OPTION FOR USE OF CAPPING STACK FOR WELL 6507/5-9 SHREK ... 74

8.1 Capping stack 74

8.2 Input data 75

8.3 Results – shoreline oiling 76

8.4 Results environmental risk when using capping stack 77

9 REFERENCES ... 79 Appendix A Rate matrix from Add Energy for well 6507/5-9 Shrek

Appendix B Regulatory framework Appendix C Oil drift model OSCAR

Appendix D Population loss for all modelled species

Appendix E Resource description for the Norwegian Sea (in Norwegian)

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EXECUTIVE SUMMARY

PGNiG Upstream Norway AS (PGNiG) is planning the drilling of exploration well 6507/5-9 Shrek in PL838 in the Norwegian Sea. The well is located about 165 km from Vikna in Trøndelag county. The water depth in the area is 359 meters. The drilling is planned with spud in Q3 2019, but to take into

consideration possible changes in the drilling schedule the analysis is performed for the entire year. The drilling will be performed with the semi-submersible drilling rig Deepsea Nordkapp.

As part of the preparation for the upcoming drilling operation, DNV GL have prepared an environmental risk analysis and an oil spill contingency analysis for the activity.

Environmental risk

The environmental risk is performed as a damage-based analysis in accordance to Norwegian Oil and Gas Association (NOG) guideline for performing environmental risk analysis for petroleum activity on the NCS. The environmental risk is measured against PGNiGs operation specific acceptance criteria.

Environmental consequences are calculated for seabirds (both pelagic and coastal), marine mammals, fish and coastal habitats. The analysis is calculated for the whole year and presented seasonally.

Highest probability for population loss is:

✓ 1-5 % population loss: 34 % probability (Razor-billed Auk – Winter, pelagic seabirds, surface blowout).

✓ 5-10 % population loss: 7 % probability (Great Black Cormorant – Spring, coastal seabirds, seabed blowout).

✓ 10-20 % population loss: 12 % probability (Razor-billed Auk – Winter, pelagic seabirds, surface blowout).

✓ 20-30 % population loss: <0.5 % probability (Atlantic Puffin – Autumn, pelagic seabirds surface blowout).

✓ >30 % population loss: No probability for population loss >30 %.

Pelagic seabirds (Razorbilled Auk) is dimensioning for the risk level with 8 % of the acceptance criteria for serious environmental damage (>10 years restitution time) in the winter season (Figure 0-1). The highest risk level for marine mammals is 3 % (Grey Seal) for moderate environmental damage. The highest calculated risk for coastal seabird (national dataset) and coastal habitats is 2 % (Great Black Cormorant – spring and summer, Atlantic Puffin – summer and autumn, King Eider - winter) and 1 % (all seasons) respectively, both in moderate damage category.

The environmental risk calculated for the drilling operation at exploration well 6507/5-9 Shrek is within PGNiG’s operation specific acceptance criteria in the different months and seasons throughout the year for all defined VECs. The conclusion is that the drilling operation is acceptable with regards to

environmental risk.

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Figure 0-1 Calculated environmental risk in the different seasons for all VEC-groups included in the analysis for exploration well 6507/5-9 Shrek. The numbers are given in percentage as part of PGNiG’s operation specific acceptance criteria.

Oil spill contingency

Available amount of emulsion for mechanical recovery on open sea (barrier 1 and 2) is calculated based on the weathering data for Skarv oil (SINTEF, 2004) and weighted blowout rate given a surface blowout from exploration well 6507/5-9 Shrek (Add Energy, 2019).

Skarv oil make emulsions with low viscosity (<1000 cP) at summer conditions and wind speed ≤5 m/s after up to 6 hours on sea, and this may lead to boom leakage in a response scenario. After 6 hours and with higher wind speeds the viscosity is >1000 cP for both summer and winter conditions. Mechanical recovery works optimal with viscosity >1000 cP. At winter conditions and wind speeds ≥10 m/s the oil will make emulsions with viscosity above 20 000 cP after 24-72 hours on sea. Conventional weir skimmers (Transrec) can be used in an oil spill response situation, but at winter conditions and higher wind speeds there may be more efficient to use a Hi-wax/Hi-Visc skimmer. Skarv oil have a reduced and low potential for dispersion.

For the dimensioning scenario (surface blowout with weighted rate 5088 Sm3/day) the calculations give the following requirements for NOFO systems in barrier 1 and 2 in the different seasons:

• Spring: 2 NOFO systems in barrier 1 and 2 NOFO systems in barrier 2, a total of 4 NOFO systems;

• Summer: 3 NOFO systems in barrier 1 and 2 NOFO systems in barrier 2, a total of 5 NOFO systems;

• Autumn and Winter: 3 NOFO systems in barrier 1 and 3 NOFO systems in barrier 2, a total of 6 NOFO systems.

The 6 systems will be operative within 30 hours (ref. Table 0-1). This is well within the requirement for fully functional barrier 1 and 2 in 16.3 days.

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Table 0-1 Calculation of response time for complete response systems given a blowout from exploration well 6507/5-9 Shrek in PL838 for OR and tug vessels.

System Sailing time (h)

Mobilization / release

time (h)

Total response

time NOFO- vessel (h)

1)

Tug vessel

time tug vessel (h)

time for complete response system (h)

Haltenbanken ^2.4 6 10 RS Rørvik 9 10

Aasta Hansteen ^3.8 6 11 RS Kristiansund 11 11

Kristiansund S1 ^11.1 10 23 PS Ballstad 13 23

Gjøa ^20.5 4 26 NOFO pool 24 26

Tampen 21.9 6 29 NOFO pool 24 29

Troll/Oseberg 22.8 6 30 NOFO pool 24 30

For additional decision-making support the effect of mechanical recovery with different system

configurations is modelled in OSCAR. Based on an overall evaluation there is relatively small changes in the effect with more than 6 systems in both seasons for both surface and seabed. The environmental risk is low, and it will not be cost-efficient to base the oil spill contingency on more than 6 systems.

Population loss with effect of different response options is modelled for both a surface and seabed blowout and the results show that the population loss will decrease with increasing number of systems, but that the reduction will stop and stabilize after 5-6 systems.

95-percentile of the shortest drift time and amount of stranded emulsion gives 14 tons of emulsion per day into the coastal area (given weighted duration and effect of oil spill response). One coastal response system in barrier 3 is sufficient to be able to handle this amount of emulsion in each season.

For pre-defined example areas, 95-percentile of the scenarios gives the highest amount of stranded oil emulsion at Røst with 19 tons of emulsion in the autumn season and 95-percentile of the shortest drift time is 19.1 days (Træna in the autumn season) without effect of oil spill response. This gives 1 ton of emulsion per day into the example area (given weighted duration and effect of oil spill response in barrier 1 and 2). One coastal response system in barrier 3 is sufficient to handle this small amount of emulsion in each affected example area.

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DEFINITIONS AND ABBREVIATIONS

Acceptance criteria The criteria define the maximum allowed occurrence of accidents that can cause an environmental damage with a given recovery time. The classification is in line with the NOROG guideline for environmental risk analysis (OLF, 2007).

ALARP As Low As Reasonably Practicable. ALARP expresses that the risk level is reduced (through a documented and systematic process) so far that no further cost-effective measure is identified.

Analysis area

Area that make the basis for environmental risk analyses and that are larger than the influence area (influence area is a result of oil drift modelling). The resource description is carried out in the analyses area to make sure the size of the area is sufficient.

Contingency system

System used in oil spill contingency operations- such as a system for application of chemical dispersants (usually one boat or aircraft) or a system for mechanical recovery (usually includes one OR-ship and a towing boat, including boom and skimmer equipment).

DSHA Defined Situations of Hazard and Accident. DSHA is a selection of hazardous and accidental events that is used for the dimensioning of the emergency

preparedness for the activity and Environmental Risk Analysis.

ERA Environmental Risk Analysis

GOR Gas-Oil Ratio

Hit probability The probability that a given 10  10 km grid is hit by oil from a potential oil spill.

Influence area A defined area with 5 % or more probability for pollution within a 10  10 km grid if an oil discharge has taken place.

MIRA Method for environmental risk analysis (OLF, 2007).

NCS Norwegian Continental Shelf

NOROG The Norwegian Oil and Gas Association (Norsk Olje og Gass) OLF Previous name for The Norwegian Oil and Gas Association.

OR-vessel Oil recovery vessel. The main vessel in a mechanical oil recovery system, containing storage tank and equipment such as skimmer and boom.

OSCA Oil Spill Contingency Analysis

OSCAR Oil Spill Contingency And Response model (SINTEF)

PL Production License

ppb Parts per billion

Recovery system A system for mechanical recovery of oil, which normally includes one OR-ship and a towing boat, including boom and skimmer equipment).

System capacity Anticipated recovery rate in m³/d for a standard NOFO-system, including

discharge time, ineffective time, free water etc. weir (Transec) set to 2400 m³/d, Hi/Wax or HiVisc has 1900 m³/d.

System efficiency Percent of swept area that is recovered.

Restitution/recovery time

Recovery is achieved when the original animal- and plant life in the affected environment is present on the same level as before the oil spill (natural variation considered), and the biological processes works normally. Recovery time is the time from an oil spill occurs until the recovery is achieved.

THC Total Hydrocarbon Concentration

VEC

Valued Ecosystem Component. Recourses with high vulnerability and

conservation value. VECs are chosen as dimensioning resources in the analysis due to high vulnerability to oil pollution and/or high degree of presence in the analytic area. VECs are species that are likely to be affected in the analysis.

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NAMELIST FOR SPECIES

Names for species given in English, Latin and Norwegian for seabirds, marine mammals and fish included in the analysis.

Seabirds

Species (English name) Latin name Norwegian name

Razorbill Alca torda Alke

Little Auk Alle alle Alkekonge

Common Gull Larus canus Fiskemåke

European Herring Gull Podiceps grisegena Gråmåke

Northern Fulmar Fulmarus glacialis Havhest

Northern Gannet Morus bassanus Havsule

Common Loon Gavia immer Islom

Ivory Gull Pagophila eburnean Ismåke

Black-legged Kittiwake Rissa tridactyla Krykkje

Common Guillemot Uria aalge Lomvi

Atlantic Puffin Fratercula arctica Lunde

Common Tern Sterna hirundo Makrellterne

Brünnich's Guillemot Uria lomvia Polarlomvi

Glaucous Gull Larus hyperboreus Polarmåke

King Eider Somateria spectabilis Praktærfugl

Arctic Tern Sterna paradisaea Rødnebbterne

Red-breasted Merganser Mergus serrator Siland

Lesser Black-backed Gull Larus fuscus Sildemåke

Red-throated Loon Gavia stellata Smålom

Steller's Eider Polysticta stelleri Stellerand

Great Skua Stercorarius skua Storjo

Great Black Cormorant Phalacrocorax carbo Storskarv

Great Black-backed Gull Larus marinus Svartbak

Black Guillemot Cepphus grylle Teist

European Shag Phalacrocorax aristotelis Toppskarv

Common Eider Somateria molissima Ærfugl

Marine mammals

Grey Seal Halichoerus grypus Havert

Harbour Seal Phoca vitulina Steinkobbe

Otter Lutra lutra Oter

Fish

Cod Gadhus morhua Torsk

Herring Clupea harrengus Sild

Greenland halibut Reinhardtius hippoglossoides Blåkveite

Pollock Pollachius virens Sei

Haddock Melanogrammus aeglefinus Hyse

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1 INTRODUCTION

1.1 Activity description

PGNiG Upstream Norway AS (from now on PGNiG) is planning the drilling of exploration well 6507/5-9 Shrek in PL 838 in the Norwegian Sea. The well is located about 165 km from Vikna in Trøndelag county (Figure 1-1). The water depth in the area is 359 meters. The drilling is planned with spud in Q3 2019, but to take into consideration possible changes in the drilling schedule the analysis is performed for the entire year. The drilling will be performed with a semi-submersible drilling rig.

As part of the preparation for the upcoming drilling operation, DNV GL have prepared an environmental risk analysis and an oil spill contingency analysis for the activity.

Basis information for the activity is summed up in Table 1-1.

Figure 1-1 Location of exploration well 6507/5-9 Shrek in PL838 in the Norwegian Sea.

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Table 1-1 Basis information for exploration well 6507/5-9 Shrek.

Coordinates for modelled scenario Latitude: 65^° 39’ 35.87” N, Longitude: 07^° 38’ 51.07” E

Water depth 359 meters

Distance to the nearest coastline Ca. 165 km (Vikna)

Oil type Skarv oil (838 kg/m³)

Rig type Semi-submersible drilling rig

Blowout rates

Weighted rate surface: 5088 Sm³/d Weighted rate seabed: 4745 Sm³/d

Vektet rate, sjøbunn: 5593 Sm³/døgn Weighted durations Surface blowout: 9.6 days

Seabed blowout: 10.0 days

GOR (Sm³/Sm³) 107

Time to drill a relief well (longest duration) 50 days

Activity Exploration drilling

Scenario Blowout (surface/seabed)

1.2 Purpose and objective

Environmental risk and oil spill contingency analyses for exploration of and/or production of oil and gas on the Norwegian Continental Shelf (NCS) is required by Norwegian rules and regulations (see Appendix B).

ERA is performed as a damage-based analysis in accordance to NOROG guideline for performing environmental risk analysis for petroleum activity on the NCS (OLF, 2007). A short description of the method is given in Chapter Error! Reference source not found.. For additional and more detailed information it is referred to the guideline. The environmental risk is measured against PGNiG’s operation specific acceptance criteria. In a damage-based analysis the consequences given a spill is linked with the probability (frequency) for the spill to happen, to be able to quantify the environmental risk this spill may have on vulnerable resources in the area. The vulnerable resources are referred to as Valuable Ecosystem Components (VEC) and is a mix of different populations (seabirds, marine mammals and fish) and habitats (coastal areas). Different requirements must be fulfilled to be regarded as a VEC in the analysis (see Chapter 5.1).

When reading an ERA, it is easy to get the impression that the risk is an exact quantitative number that can be used to determine if the planned activity is acceptable or un-acceptable with regards to

environmental impact. But the number contains a lot of parameters with different degrees of uncertainty. Uncertainty in the analysis is addressed in Chapter 4.1.

The OSCA is performed with modelling in OSCAR with different system configurations. The purpose with the modelling is to get an indication of the effect different number of systems of mechanical collection will give. It is also calculated the need for response systems for mechanical collection of oil on open sea, and calculations of need for response in the coastal areas. All calculations are based on the industry guideline «Veiledning for miljørettede beredskapsanalyser» (NOROG, 2013).

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1.3 PGNiGs acceptance criteria for acute pollution

PGNiG has defined acceptance criteria for environmental risk as part of their management system. For exploration well 6507/5-9 Shrek, PGNiG’s operation specific acceptance criteria for environmental risk are used (Table 1-2). The acceptance criteria state the limit for what PGNiG has defined as acceptable risk for the company’s activities (probability for a given consequence). These criteria are formulated as a measure of environmental damage to natural resources, expressed by duration and degree of seriousness.

PGNiG uses the same acceptance criteria in all regions across the NCS. Environmental risk analysis captures differences in environmental vulnerability on a regional level based on the presence and vulnerability of environmental resources in each area and through calculation of restitution time for potentially affected resources. This means that the calculated environmental risk is higher for areas where a larger share of a vulnerable population or habitat is affected.

The acceptance criteria express PGNiG’s attitude to keep nature as far as possible untouched by the company’s activities. The criteria state maximum tolerated incident frequency which can cause harm to the environment.

Table 1-2 PGNiG’s operation specific acceptance criteria for acute pollution (PGNiG, 2018).

Environmental

damage Duration of damage (Recovery/

restitution time) Operation specific acceptance criteria (per operation)

Minor < 1 year < 1 x 10^-3

Moderate 1-3 years < 2.5 x 10^-4

Considerable 3-10 years < 1 x 10^-4

Serious > 10 years < 2.5 x 10^-5

1.4 Regulatory framework

The requirements from the Norwegian authorities include the Pollution Act, the Framework regulation, the Management regulation and the Activities regulation. A brief description of the requirements is given in Appendix B.

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2 DEFINED SITUATIONS OF HAZARD AND ACCIDENT (DSHA)

Most unintentional discharges related to exploration drillings are limited with small amounts of light compounds with no or low potential for damage. Incidents with the greatest potential to harm the surrounding environment are uncontrolled releases from the wells during drilling (blowout). A blowout from Shrek is considered dimensioning for the environmental risk analysis and is further described in the following sections.

2.1 Dimensioning DSHA

Exploration well 6507/5-9 Shrek will be drilled as a vertical well. The purpose of the well is to test for hydrocarbons in the formations. Dimensioning DSHA is a blowout of oil during the drilling operation.

PGNIG have performed a risk evaluation with regards to blowout from the well, and blowout rates and durations with probability distribution is calculated (AddEnergy, 2019).

The drilling of the well is planned with the semi-submersible drilling rig Deepsea Nordkapp (Figure 2-1).

The rig will be anchored during the operation, but because of the size of the rig the thrusters will also be used as support to keep the rig in position.

Figure 2-1 The drilling rig Deepsea Nordkapp is planned used for the drilling of exploration well Shrek (www.oddfjelldrilling.com ).

2.2 Probability for dimensioning DSHA

Well 6507/5-9 Shrek is an exploration well with expectations to find oil. Based on SINTEF offshore blowout database 2017, the total blowout frequency is evaluated to 1,35 x 10^-4 for an average well (Lloyd’s, 2018).

The well is planned to be drilled with a semi-submersible drilling rig with BOP placed on the seabed. This indicate that the highest probability for a blowout will be on the seabed. Probability distribution between blowouts on the seabed versus surface during drilling is calculated to 80 % / 20 % (Lloyd’s, 2018).

2.3 Blowout rates and durations

The longest blowout duration is set to the time it will take to drill a relief well. For exploration well 6507/5-9 Shrek this is calculated to 50 days, including mobilizing a rig, drilling into the reservoir and killing the blowout (AddEnergy, 2019).

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The rate and duration matrix used in the oil drift modelling and environmental risk analysis for

exploration well 6507/5-9 is shown in Table 2-1. The blowout study from Add Energy (2019) is basis for the matrix, but some of the rates are weighted together to compromise the matrix for the modelling.

Complete rate matrix from Add Energy is shown in Appendix A.

Weighted duration for a surface blowout is 9.6 days, and weighted duration for a seabed blowout is 10.0 days. Weighted rate for a surface blowout is 5088 Sm³/d and weighted rate for a seabed blowout is 4745 Sm³/d.

For modelling of a seabed blowout, a release diameter without restrictions (47.63 cm) and with restrictions (2.38 cm) is used. This is in accordance with the Best Practice for use of OSCAR (Acona, Akvaplan-niva og DNV GL, 2016).

Table 2-1 Rate- and duration matrix with associated probabilities for a surface and seabed blowout from exploration well 6507/5-9 Shrek (AddEnergy, 2019).

Blowout location

Distribution surface/

seabed

Rate Sm³/d

Open (O)/

Restricted (R)

Durations (days) and probability

distribution Probability

for rate (%)

2 5 15 35 50

Surface 20 %

3659

NA 52.1 % 18.7 % 17.3 % 6.0 % 6.0 %

65.6 %

6415 27.6 %

8540 3.5 %

14160 1.8 %

24240 1.5 %

Seabed 80 %

3558 R

50.1 % 18.9 % 18.3 % 6.5 % 6.1 %

62.3 %

3992 O 3.3 %

5564 O 23.4 %

7289 R 7.7 %

11560 O 1.8 %

21660 O 1.5 %

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3 OIL SPILL FATE AND TRAJECTORY MODELLING

Well 6507/5-9 Shrek is an exploration well. If hydrocarbons are discovered in the well, it is expected to have similar characteristics as Skarv crude oil. Skarv oil is used as a reference oil in the analysis. In the following chapters, characteristics and properties of the Skarv oil and results from the oil drift modelling are presented.

3.1 Reference oil

Both lifetime on the sea surface and degree of down-mixing and associated potential environmental effects will be dependent of the type of oil. The same apply to suitability and effect of different oil spill recovery techniques (mechanical and chemical recovery).

Skarv crude oil is categorized as a medium paraffinic oil with relative high wax content (6.2 %) and low asphaltene content (0.16 %). The crude has a medium content of light components, and the evaporation loss is moderate (20-30 % after one day on sea). Skarv has a medium water uptake compared with other crude oils on the NCS. At winter conditions (5 °C) the emulsions reach high viscosities and a Hi- Wax skimmer will be more efficient than a traditional weir skimmer in an oil spill operation (SINTEF, 2004).

Key characteristics for Skarv crude oil (SINTEF, 2004) are presented in Table 3-1.

Table 3-1 Key characteristics of Skarv crude oil (SINTEF, 2004) used in the oil drift modelling for exploration well 6507/5-9 Shrek.

3.2 The oil drift model

For the oil drift modelling, SINTEFs OSCAR model (Oil Spill Contingency And Response) version 10.0.1 is applied. The setup of the model is based on Best Practice (Acona, Akvaplan-niva & DNV GL, 2016). The OSCAR model, model limitations and requirements for input data, and processing and generation of statistical parameters are described in Appendix C.

3.3 Description of modelled blowout scenarios

The oil drift simulations are performed for one location in the Norwegian Sea, with coordinates 65° 39' 35.87" N; 07° 38' 51.07" E, and a water depth of 359 meters. Oil drift simulations are performed for a surface release scenario using a 5 × 5 rate and duration matrix, and a seabed release scenario using a 6 x 5 rate and duration matrix (ref. Table 2-1). Number of simulations are distributed evenly throughout the year, to encounter for seasonal current and wind variations. The statistical oil drift modelling results are presented with a horizontal resolution of 10×10 km.

Parameters Skarv oil

Oil density [kg/ m³] 860

Maximum water content at 5/13 °C [volume %] 70/70

Wax content, fresh oil [weight %] 6.2

Asphaltene content, fresh oil [weight %] 0.16

Viscosity at 13 °C [cP] 376

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3.4 Oil drift modelling – Results 3.4.1 Near zone modelling

The near zone subsea release modelling is performed with an annual release area diameter 0.4763 m.

The simulations result from the subsea release show an oil/water plume that will reach the surface after 9 minutes and then spread at the surface with an estimated oil film thickness = 0.012 mm (average thickness in a 3 x 3 km grid cell).

GOR is 107 Sm³/Sm³ and the release depth is 359 m. A weighted release rate of 4745 Sm³/d (subsea release) and weighted duration of 10 days are used as input for the single simulation.

3.4.2 Oil on the sea surface

Oil drift statistics are generated on a grid level (10 x 10 km resolution) and presented seasonally for the different seasons (spring: March-May; summer: June-August; autumn: September-November and winter: December-February). Expected amount of oil (probability for hit x amount of oil given a hit) and influence areas (≥5 % hits of > 1 ton of oil) given a surface blowout is shown in Figure 3-1 and given a seabed blowout in Figure 3-2.

The influence area is based on the probability for hit in a grid cell in the statistic oil drift modelling. For the expected amount of oil (ton) the probability for hit in the grid cell is multiplied with the average of the time averaged amount of oil ≥1 ton in the grid cell (given a hit). The influence area (hit probability) will have a larger extension because it also includes grid cells with more than 1 ton of oil with small hit probabilities.

It is important to keep in mind that expected amounts of oil and hit probability of oil is based on all blowout rates and durations and their individual probabilities. The influence areas do not reflect a single blowout, but the area affected in ≥ 5 % of the single simulations in each season.

The results show that the oil primarily is distributed near the blowout location as well as in central part of the Norwegian Sea, with an extended northward direction in all the seasons because of the Norwegian coastal current. See Figure 3-3 for a snapshot of surface currents in the Norwegian Sea in September.

The results show that the oil is spread and weathered and is mainly probability for hit of oil amounts

<100 ton per 10 x 10 grid cell, but with probability for larger oil amounts up to 499 ton in the area around the well location.

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Figure 3-1 Seasonal expected hit of oil amounts (≥ 5 % hit of more than 1 ton of oil) in 10 x 10 km grid cells given a surface blowout from exploration well 6507/5-9 Shrek. Expected hit of oil is based on all modelled blowout rates and their individual probabilities.

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Figure 3-2 Seasonal expected hit of oil amounts (≥ 5 % hit of more than 1 ton of oil) in 10 x 10 km grid cells given a seabed blowout from exploration well 6507/5-9 Shrek. Expected hit of oil is based on all modelled blowout rates and their individual probabilities.

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Figure 3-3 Snapshot of surface currents in the Norwegian Sea (Meterologisk Institutt: SVIM archive (2015) for current- and ice data: ftp://ftp.met.no/projects/SVIM-public/SVIMresults/). Location for exploration well 6507/5-9 Shrek is marked with a square with a cross inside.

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3.4.3 Stranding of oil in the coastal habitats

Coastal grid cells with ≥5 % hit probability for stranding of more than 1 ton of oil per cell (10 x 10 km) are presented in Figure 3-4 for a surface blowout and in Figure 3-5 for a seabed blowout.

Shortest drift time to shore and amount of stranded emulsion is presented for the different seasons in Table 3-2 (95- and 100-percentile). The amount of emulsion and drift time to shore do not necessarily originate from the same simulation. All simulations for both surface and seabed blowout in the different seasons are basis for the results. 95-percentile of the scenarios gives 294 ton of emulsion along the coastline (autumn season) and 95-percentile of the shortest drift time is 16.3 days (also autumn season).

Table 3-2 Stranded oil emulsion (ton) and drift time (days) to the Norwegian coastline in the different seasons given a blowout from exploration well 6507/5-9 Shrek (95- and 100-percentile).

Percentile Stranded oil emulsion (ton) Drift time (days)

Spring Summer Autumn Winter Spring Summer Autumn Winter

100

56651 54024 40852 59683 3.4 8.7 5.3 4.7

95

278 66 294 221 16.5 23.3 16.3 19.3

Table 3-3 gives the 95-percentile of the shortest drift time and largest amount of emulsion into the pre- defined example areas along the Norwegian shoreline. Relevant example areas are presented in Figure 3-6. 95-percentile of the scenarios gives the highest amount of stranded oil emulsion at the example area Røst with 19 tons of emulsion in the autumn season and 95-percentile of the shortest drift time is 19.1 days (Træna in the autumn season).

Table 3-3 Stranded oil emulsion (ton) and drift time (days) to the pre-defined example areas given a blowout from exploration well 6507/5-9 Shrek (95-percentile) shown for the different seasons.

Example area Stranded oil emulsion (ton) Drift time (days)

Spring Summer Autumn Winter Spring Summer Autumn Winter

Vega

2 - - - 34.5 - - -

Lovunden

3 - 1 - 29.9 - 30.4 -

Træna

18 3 17 18 20.6 34.3 19.1 22.2

Bliksvær

- - 3 - - 64.0 - -

Røst

- - 19 12 - - 25.8 34.1

Moskenesøy &

Flakstadøy

- - 6 2 - - 39.3 53.2

Lofotodden

- - 2 2 - - 45.1 50.2

Bo & Hadseløya

- - 3 3 - - 47.5 48.4

Andøya

1 - 8 8 55.9 - 43.6 34.6

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Figure 3-4 Hit probability of more than 1 ton of oil in 10 x 10 km coastal grid cells given a surface blowout from exploration well 6507/5-9 Shrek shown for the different seasons. The hit probability is based on all modelled blowout rates with their individual probabilities.

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Figure 3-5 Hit probability of more than 1 ton of oil in 10 x 10 km coastal grid cells given a seabed blowout from exploration well 6507/5-9 Shrek shown for the different seasons. The hit probability is based on all modelled blowout rates with their individual probabilities.

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Figure 3-6 Location of the example areas along the coastline in the potential stranding areas for Shrek.

3.4.4 THC in the water column

Total Hydrocarbon Concentrations (THC) represents the sum of dispersed and dissolved oil in the water column. THC concentrations in the water column given a surface blowout from exploration well 6507/5-9 Shrek is shown in Figure 3-7, and a seabed blowout shown in Figure 3-8. The results for a surface blowout show small influence areas in the water column with only one and two grid cells with THC concentrations

<80 ppb. The results for a seabed blowout also show small influence areas, but with THC concentrations up to 390 ppb.

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Figure 3-7 THC concentrations in the water column given a surface blowout from exploration well 6507/5-9 Shrek shown in the different seasons. The influence area is based on all modelled blowout rates and their individual probabilities.

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Figure 3-8 THC concentrations in the water column given a seabed blowout from exploration well 6507/5-9 Shrek shown in the different seasons. The influence area is based on all modelled blowout rates and their individual probabilities.

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3.4.5 Singel simulation

Figure 3-9 shows a single simulation modelled with rate 8540 Sm³/d and duration 15 days and followed for 20 days. The chosen simulation is the 95 percentiles of all simulations for all rates and durations for both surface and seabed blowout (this means that 5 % of the

simulations have a higher amount of stranded oil than the simulation shown). Results show oil drift 2, 5, 10, 15, 20 and 35 days after the release started.

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Figure 3-9 Presentation of a single simulation for 6507/5-9 Shrek. The figures show the oils fate and trajectory per day from 2 to 35 days after the simulation started. The figures also show grid cells with stranding. The categories for oil film thickness is in accordance with the Bonn Agreement Oil Appearance Code.

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4 METHOD FOR ENVIRONMENTAL RISK ANALYIS

Analysis of environmental risks is performed in steps in accordance with the NOROG guideline for environmental risk assessments (OLF, 2007). For exploration well 6507/5-9 Shrek, it has been chosen to perform a damage-based analysis for the predicted most vulnerable environmental resources potentially affected by an oil blowout from Shrek. A summary of the methodology for environmental risk analysis is described below with focus on VEC populations (valued ecosystem components, see section Error! Reference source not found.). For a more detailed description, it is referred to the guideline.

Based on oil drift modelling and the use of effect keys, the population loss for each VEC population is calculated (see Figure 4-1).

Figure 4-1 Overview of the different steps when calculating the population loss and the environmental risk for VEC populations.

Step 1 – Rescaled VEC population data, to match the oil spill cell size, is combined with each oil drift simulation.

An effect key is used, indicating possible population loss in 10 ×10 km grid cell based on the amount of oil entering the area in each simulation (see Table 4-1). Different individual vulnerability to oil gives different effect key, whereas V1 indicates the least vulnerable species, V2 indicates moderate vulnerability and V3 indicates the most vulnerable species.

Step 2 – Population losses per 10 x 10 km grid cell are summarized and give a total population loss for each VEC population for each simulation. Population losses for the different oil drift simulations are categorized in 1-5 %, 5-10 %, 10-20 %, 20-30 % and more than 30 %. Population loss below 1 % is believed to have no significant effect on the population level and is therefore not considered further.

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Table 4-1 Effect key for estimating fraction of birds affected within a 10 x 10 km grid cell, given that a grid is exposed to oil (divided in four mass categories). Values is given for seabirds as an example.

Oil mass in 10 x 10 km grid cell

Effect key – Acute death rate Individual vulnerability for VEC seabirds

V1 V2 V3

1-100 tons 5 % 10 % 20 %

100-500 tons 10 % 20 % 40 %

500-1000 tons 20 % 40 % 60 %

≥1000 tons 40 % 60 % 80 %

Step 3 – It is used a damage key which ties a given population loss for the VEC populations to environmental damage. Environmental damage is expressed as the time it takes for a population to be restored to 99 % of the level before an event occurs (OLF, 2007). As noted above, the vulnerability varies between species (and habitats) and the recovery time will be affected by this. The theoretical recovery time is divided into four categories, see Table 4-2.

• Minor (< 1 years),

• Moderate (1-3 years),

• Considerable (3-10 years) and

• Serious (> 10 years).

The calculations performed for the coastal habitat will differ from the VEC populations, by utilizing a combined effect- and damage key that links the amount of oil in a 10 x10 km habitat directly to the environmental damage and recovery time.

Table 4-2 Damage key for the probability distribution of theoretical recovery time by acute reduction of seabird- and marine mammal stocks with low recovery potential (V3) (OLF, 2007).

Acute reduction of the stocks

Consequence category – Environmental damage Theoretical recovery time in year

Minor

<1 year

Moderate 1-3 years

Considerable 3-10 years

Serious

>10 years

1-5 % 50 % 50 %

5-10 % 25 % 50 % 25 %

10-20 % 25 % 50 % 25 %

20-30 % 50 % 50 %

 30 % 100 %

Step 4 – Environmental risk is then calculated by combining the likelihood of various environmental damages with the frequency of the specific oil leak and can then be measured against the operator’s acceptance criteria for environmental damage.

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4.1 Uncertainty in environmental risk analyses

According to the Norwegian Petroleum Safety Authority’s updated definition of the risk concept (2015), uncertainty should be addressed as part of any risk assessment. The following sections highlights the most significant uncertainties related to input data, models and methods used in an environmental risk analysis.

In an environmental risk analysis, the objective is to reduce the uncertainty as much as possible by using the best available knowledge at any given time. When knowledge is limited, conservative alternatives are selected to ensure that the uncertainty is considered.

The reader may perceive the quantified environmental risk results to be the single factor that unconditionally can determine if a planned activity is acceptable or unacceptable with respect to the calculated effect on the environment. However, behind the risk result, numerous decisions with a range of uncertainties are made, such as:

4.1.1 Methodology

The methodology makes up a large uncertainty factor, due to the limitation of predicting (calculated) the precise effect of a potential future release. To embrace the uncertainty, a set of “effect keys” are prepared for the most common Valuable Ecological Components (VEC) applied in ERA’s along the Norwegian shoreline;

seabirds, marine mammals, coastline and fish (OLF, 2007). The effect keys links potential population losses to predefined oil mass categories (e.g. 1-100 tons of oil per 10 × 10 km grid cell results in 20 % population loss). The calculated population loss is further split into population loss categories (e.g. 1-5 %, 5-10 % and so forth), which is combined with theoretical restitution time (e.g. 10-20 % population loss gives 25 % probability for Moderate environmental damage, 50 % probability for Considerable environmental damage and 25 % probability for Serious environmental damage, respectively) and provides an overall damage frequency for each of the restitution categories. The effect and damage keys are created based on observations of death- rate and damage from former oil spills.

4.1.2 Environmental resources

The determination of exact locations and population sizes throughout large areas, at any given time, is unmanageable. This makes natural resources a highly uncertain and variable parameter. The collected and modelled data for seabirds in the nesting season is generally good. The datasets are based on statistical analysis of counting data and is frequently updated through the SEAPOP-program. However, it is not feasible to predict the exact presence of seabirds due to large variations from year to year, especially for pelagic seabirds. An example of the latter dataset, produced through the SEAPOP-program, is presented in Figure 4-2 (pelagic seabirds). Predicted densities in the maritime zones are given with a 95 % confidence interval and standard deviation. The datasets present an “average” value of the densities of seabirds and not the actual distribution at a given time. This limits the range of consequences compared to actual expectation.

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Figure 4-2 Distribution maps of Common Guillemot in open sea for three seasons (top), in addition to the distribution maps as a 95 % confidence interval (bottom). The maps are processed through SEAPOP (www.seapop.no). ©SEAPOP

4.1.3 Oil type

Reference oil is a parameter that encompasses a varying degree of uncertainty. In some cases, there are good indications with respect to oil properties while in other situations the information is limited. At times, it can be challenging to choose an existing oil type which represents the weathering properties of the expected oil. In addition, there are uncertainties related to the oil’s behavior on the water surface/in the water column during different weather conditions. The weather conditions themselves are uncertain as the prediction is calculated using hindcast data. To consider the uncertainty in the exterior environmental parameters (wind, current, temperature), it is important to model enough simulations. This implies enough simulations distributed evenly throughout the year, to encompass seasonal (monthly) and annual variation. There is used 10 years of data for wind and currents in the model, and this is considered adequate with regards to the best practice for setting up the model (Acona, Akvaplan-niva og DNV GL, 2016).

4.1.4 Frequencies and probabilities

When calculating risk, both consequence (what the consequence is if a blowout occurs) and probability (how probable it is that this event will occur) are estimated. The probability estimates are based on incident numbers from historical data for the North Sea (Norwegian, British and German sector) and the Gulf of Mexico Outer

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Continental Shelf. It is linked a large uncertainty to how good these empirical data are to describe/predict future incidents. When calculating generic blowout frequencies, the latest 20 years of incidents were utilized earlier, but the methodology has been changed to ensure that more recent technology development in the petroleum industry is considered. Current dataset, from 01.01.1980 to 31.12.2015, is utilized with a bigger weight on the latest incidents (Lloyd’s, 2018).

Well specific risk analyses, evaluating well specific parameters with empirical data, is a methodology applied to reduce uncertainties linked to blowout probability. Frequently, the outcome is reduced blowout frequency compared to the empirical value. This is because oil operators, on the NCS, often have improved control and routines compared to historical events. This means that by applying generic blowout frequencies, uncertainty is primarily addressed in a conservative manner.

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5 ENVIRONMENTAL RESOURCES

Exploration well 6507/5-9 Shrek is located in the central part of the Norwegian Sea. A blowout from the well will potentially affect the Norwegian Sea area, and hence vulnerable environmental resources from this area are basis for the analysis. It is referred to Appendix E for a short description of the environmental resources in the area. For a more comprehensive description of the area it is referred to «Føyn, von Quilfeldt, and Olsen (2002)», «Loeng and Drinkwater (2007)», «Helhetlig forvaltningsplan av det marine miljø i

Norskehavet (St.meld., 2009)», «oppdatering av forvaltningsplanen for det marine miljø i Barentshavet og havområdene utenfor Lofoten (St.meld., 2011)» and «kunnskapsinnhentingen rundt petroleumsvirksomhet i nordøstlige Norskehavet (OED, 2012)».

5.1 Valuable Ecosystem Component (VEC)

The potential damage to specific VECs creates the basis for the assessment of the environmental risk level.

These components are used as risk indicators in the environmental risk analysis. According to the Norwegian Oil and Gas Association (OLF, 2007) a VEC is defined as a resource or an environmental characteristic that:

• is important to local human populations, or

• has a national or international interest, and

• if changed from the present state, it will have importance for how the environmental impact is considered, and for which mitigating measures is chosen

The selection of VECs within an influence area is based on the following priority criteria:

• VEC must represent a population, a society or a habitat,

• VEC must be vulnerable to oil contamination in the relevant season,

• VEC population must be represented by a high proportion of the population within the influence area,

• VEC population must be present most of the year, or in the relevant season, and

• VEC habitat must have a high probability for being exposed to oil.

The selection also considers red list species and thus ensures that the ERA is carried out for the type of vulnerable resources with a high probability of being affected by oil pollution.

5.2 Selected VECs for the analysis

Selected VEC’s are based on the criteria described in chapter 5.1 and are further described below.

5.2.1 Seabirds

Table 5-1 shows selected pelagic and coastal seabird species included in the environmental risk analysis for exploration well 6507/5-9 Shrek. Several of the pelagic seabird species is also included in the dataset for coastal seabirds, because there are different datasets for the bird’s whereabouts in the different parts of the year. Breeding populations of these species stays around the breeding colonies along the coast in the spring and summer season before they return to the open ocean after breeding. Swimming migration is not taken

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into consideration in the datasets. The most updated data for seabirds in the Norwegian Sea is used in the analysis.

Dataset for pelagic seabirds is from SEAPOP (2013) and for coastal seabirds from SEAPOP (2017). Data sets for coastal seabirds from the SEAPOP program include both National and regional data (Norwegian Sea for this analysis). Results for the National dataset is shown in the report, and results for the regional dataset is shown in Appendix D.

Table 5-1 Selected VECs of seabirds for the environmental risk analysis for exploration well 6507/5-9 Shrek (SEAPOP, 2013; SEAPOP, 2017; Artsdatabanken (redlist), 2015).

Name Latin name Redlist* Region

Razorbill Alca torda EN

Pelagic seabirds (open sea, Norwegian Sea)

Little Auk Alle alle LC

Common Gull Larus canus NT

European Herring Gull Larus argentatus LC

Northern Fulmar Fulmarus glacialis EN

Northern Gannet Morus bassanus LC

Black-legged Kittiwake Rissa tridactyla EN

Common Guillemot Uria aalge CR

Atlantic Puffin Fratercula arctica VU

Brünnich's Guillemot Uria lomvia EN

Glaucous Gull Larus hyperboreus -

Great Black-backed Gull Larus marinus LC

Razorbill Alca torda EN

Coastal seabirds (National data)

Common Gull Larus canus NT

European Herring Gull Podiceps grisegena LC

Northern Fulmar Fulmarus glacialis EN

Northern Gannet Morus bassanus LC

Common Loon Gavia immer -

Ivory Gull Pagophila eburnean VU

Black-legged Kittiwake Rissa tridactyla EN

Common Guillemot Uria aalge CR

Atlantic Puffin Fratercula arctica VU

Common Tern Sterna hirundo EN

Brünnich's Guillemot Uria lomvia EN

Glaucous Gull Larus hyperboreus -

King Eider Somateria spectabilis -

Arctic Tern Sterna paradisaea LC

Red-breasted Merganser Mergus serrator LC Lesser Black-backed Gull Larus fuscus LC

Red-throated Loon Gavia stellata LC

Steller's Eider Polysticta stelleri VU

Great Skua Stercorarius skua LC

Great Black Cormorant Phalacrocorax carbo LC Great Black-backed Gull Larus marinus LC

Black Guillemot Cepphus grylle VU

European Shag Phalacrocorax aristotelis LC

Common Eider Somateria molissima NT

*NT – Near Threatened, EN – Endangered, CR – Critically Endangered, VU – Vulnerable, LC – Least Concern

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5.2.2 Marine mammals

Grey seal and Harbour seal are most vulnerable during their birth- and moulting periods when they gather in colonies in coastal areas. The Grey seal forms colonies in September-December (birth), with delayed mating with increasing latitude, and in February-March (moulting). The Harbour seal forms colonies in June-July (birth) and in August-September (moulting). The common otter is considered equally vulnerable all year. The influence area for exploration well 6507/5-9 Shrek reach the coastal areas, and Table 5-2 shows the selected marine mammal VECs for the analysis (including their red list status).

Table 5-2 Selected VECs of marine mammals for the environmental risk analysis for exploration well 6507/5-9 Shrek.

Name Latin name Red list*

Grey Seal Halichoerus grypus LC

Harbour Seal Phoca vitulina LC

Otter Lutra lutra VU

*VU – Vulnerable, LC – Least Concern

5.2.3 Fish

The location of the well is in an area with occasionally high concentrations of fish eggs and larvae, and cod and herring are included in the environmental risk analysis (see more information on fish in Appendix E).

There is also performed an overlap analysis between influence areas in the water column (THC

concentrations) and defined spawning areas for selected fish species in the Norwegian Sea and especially valuable areas (SVO) to evaluate potential consequences.

5.2.4 Coastal habitats

A blowout from exploration well 6507/5-9 Shrek results in 5-20 % probability for stranding of oil along the coast from Froan to Nord-Fugløya. It is performed a damage-based analysis for the coast, based on vulnerable habitats along the coastline.

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6 ENVIRONMENTAL RISK ANALYSIS – RESULTS

Possible consequences for seabirds and marine mammals are calculated as the probability for a given loss of the population in the categories <1 %, 1-5 %, 5-10 %, 10-20 %, 20-30 % and > 30 % population loss. The calculations are based on monthly population distributions with maximum value representing a given season (spring: March-May, summer: June-August, autumn: September-November and winter: December-February).

The results are presented for the population with the highest seasonal environmental risk in this chapter, while population loss for all modelled species and population loss for coastal seabirds (regional dataset) is presented in Appendix D.

The population loss is further used to calculate environmental damage. Environmental damage is defined as potential restitution time where 1 month- 1 year restitution time is defined as Minor environmental damage, 1-3 years restitution time as Moderate environmental damage, 3-10 years restitution time as Considerable environmental damage and >10 years restitution time as Serious environmental damage.

The probability for environmental damage in the different damage categories is then combined with the probability (frequency) for the blowout and measured against PGNiG’s operation specific acceptance criteria.

The environmental risk for all VEC groups is presented in Chapter 6.2.

Possible consequences for coastal habitats are estimated as the probability for hit of a given mass of oil (1- 100 tons, 100-500 tons, 500-1000 tons or > 1000 tons) per 10 × 10 km grid cell. The environmental damage for coastal habitats is defined with potential restitution time in the same way as for the other VEC’s. The consequences are presented for the 10 grid cells with the maximum monthly value in each season.

See Chapter Error! Reference source not found. for a summary of applied methodology.

6.1 Possible consequences given a blowout from exploration well 6507/5-9 Shrek

6.1.1 Pelagic seabirds

Probability for population loss and environmental damage – surface blowout - Figure 6-1.

Razor-billed Auk is the species with the highest population loss in the Spring and Winter season, while Atlantic Puffin has the highest population loss in the Summer and Autumn season.

Highest probability for population loss is calculated for:

• 34 % probability for loss of 1-5 % of the population (Razor-billed Auk – Winter).

• 4 % probability for loss of 5-10 % of the population (Atlantic Puffin - Summer).

• <0.5 % probability for loss of 20-30 % of the population (Atlantic Puffin – Autumn).

There is no probability for population loss >30 %.

This gives the following maximum probability for damage with respect to restitution time:

• 17 % probability for Minor environmental damage (Razor-billed Auk – Winter).

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• 20 % probability for Moderate environmental damage (Razor-billed Auk – Winter).

• 6 % probability for Considerable environmental damage (Razor-billed Auk – Winter).

• 3 % probability for Serious environmental damage (Razor-billed Auk – Winter).

Pelagic seabirds – surface blowout

Figure 6-1 Probability for population loss for pelagic seabirds presented seasonally, given a surface blowout from exploration well 6507/5-9 Shrek. The population loss for each VEC is calculated per month, and the maximum value for each season is shown. Population loss is grouped in six categories; <1 %, 1-5 %, 5- 10 %, 10-20 %, 20-30 % and >30 %, and environmental damage/recovery time is defined as follow: No damage, Minor, Moderate, Considerable and Serious.

Probability for population loss and environmental damage – seabed blowout - Figure 6-2.

Razor-billed Auk is the species with the highest population loss in the Spring and Winter season, while Common Guillemot has the highest population loss in the Summer and Autumn season.

• 18 % probability for loss of 1-5 % of the population (Common Guillemot - Summer).

• 4 % probability for loss of 5-10 % of the population (Common Guillemot - Autumn).

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• 10 % probability for Minor environmental damage (Common Guillemot - Summer).

• 11 % probability for Moderate environmental damage (Common Guillemot - Autumn).

• 3 % probability for Considerable environmental damage (Razor-billed Auk – Winter).

• 1 % probability for Serious environmental damage (Razor-billed Auk – Winter).

Pelagic seabirds – subsea blowout

Figure 6-2 Probability for population loss for pelagic seabirds presented seasonally, given a seabed blowout from exploration well 6507/5-9 Shrek. The population loss for each VEC is calculated per month, and the maximum value for each season is shown. Population loss is grouped in six categories; <1 %, 1-5 %, 5- 10 %, 10-20 %, 20-30 % and >30 %, and environmental damage/recovery time is defined as follow: No damage, Minor, Moderate, Considerable and Serious.

6.1.2 Coastal seabirds (National data)

Great Black Cormorant has the highest probability for population loss in the Spring season, Atlantic Puffin in the Summer and Autumn season, while King Eider have the highest probability for population loss during Winter season.

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• 13 % probability for loss of 1-5 % of the population (Atlantic Puffin - Autumn).

• 6 % probability for loss of 5-10 % of the population (Great Black Cormorant - Spring).

• 5 % probability for loss of 10-20 % of the population (Atlantic Puffin – Summer).

• 7 % probability for Minor environmental damage (Great Black Cormorant - Spring).

• 8 % probability for Moderate environmental damage (Great Black Cormorant - Spring).

• 3 % probability for Considerable environmental damage (Atlantic Puffin – Summer).

• 1 % probability for Serious environmental damage (Atlantic Puffin – Summer).

Coastal seabirds (National data)– surface blowout

Figure 6-3 Probability for population loss for coastal seabirds (National dataset) presented seasonally, given a surface blowout from exploration well 6507/5-9 Shrek. The population loss for each VEC is calculated per month, and the maximum value for each season is shown. Population loss is grouped in six categories;

<1 %, 1-5 %, 5-10 %, 10-20 %, 20-30 % and >30 %, and environmental damage/recovery time is defined as follow: No damage, Minor, Moderate, Considerable and Serious.

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Great Black Cormorant has the highest probability for population loss in the Spring season, Atlantic Puffin in the Summer and Autumn season, while King Eider have the highest probability for population loss during Winter season.

• 7 % probability for loss of 1-5 % of the population (Great Black Cormorant - Spring).

• 4 % probability for Minor environmental damage (Great Black Cormorant - Spring).

• 4 % probability for Moderate environmental damage (Great Black Cormorant - Spring).

• 1 % probability for Considerable environmental damage (Atlantic Puffin – Summer).

• <0.5 % probability for Serious environmental damage (Atlantic Puffin – Summer).

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Coastal seabirds (National data)– seabed blowout

Figure 6-4 Probability for population loss for coastal seabirds (National dataset) presented seasonally, given a seabed blowout from exploration well 6507/5-9 Shrek. The population loss for each VEC is calculated per month, and the maximum value for each season is shown. Population loss is grouped in six categories;

<1 %, 1-5 %, 5-10 %, 10-20 %, 20-30 % and >30 %, and environmental damage/recovery time is defined as follow: No damage, Minor, Moderate, Considerable and Serious.

6.1.3 Marine mammals

Grey Seal has the highest probability for population loss in all four seasons.

• 11 % probability for loss of 1-5 % of the population (Grey seal - Spring).

• 4 % probability for loss of 5-10 % of the population (Grey seal – Autumn).

• 5 % probability for Minor environmental damage (Grey seal - Spring).

• 7 % probability for Moderate environmental damage (Grey seal – Autumn).

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• 3 % probability for Considerable environmental damage (Grey seal – Autumn).

• 1 % probability for Serious environmental damage (Grey seal – Autumn).

Marine mammals - surface blowout

Figure 6-5 Probability for population loss for marine mammals presented seasonally, given a surface blowout from exploration well 6507/5-9 Shrek. The population loss for each VEC is calculated per month, and the maximum value for each season is shown. Population loss is grouped in six categories; <1 %, 1-5 %, 5-10 %, 10-20 %, 20-30 %

and >30 %, and environmental damage/recovery time is defined as follow: No damage, Minor, Moderate, Considerable and Serious.

Grey Seal has the highest probability for population loss in the Spring, Autumn and Winter season, while Harbour seal has the highest probability for population loss in the Summer season.

• 1 % probability for loss of 10-20 % of the population (Grey seal – Winter).

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• 4 % probability for Minor environmental damage (Grey seal – Autumn).

• 5 % probability for Moderate environmental damage (Grey seal – Autumn).

• 1 % probability for Considerable environmental damage (Grey seal – Autumn).

• <0.5 % probability for Serious environmental damage (Grey seal – Winter).

Marine mammals - seabed blowout

Figure 6-6 Probability for population loss for marine mammals presented seasonally, given a seabed blowout from exploration well 6507/5-9 Shrek. The population loss for each VEC is calculated per month, and the maximum value for each season is shown. Population loss is grouped in six categories; <1 %, 1- 5 %, 5-10 %, 10-20 %, 20-30 % and >30 %, and environmental damage/recovery time is defined as follow: No damage, Minor, Moderate, Considerable and Serious.

6.1.4 Coastal habitats

Hit probability and environmental damage – surface blowout - Figure 6-7.

Hit probability of oil in 10 x 10 km coastal habitats along the coast is maximum:

• 15 % probability for hit of 1-100 tons of oil per grid cell (Autumn).

• 8 % probability for hit of 100-500 tons of oil per grid cell (Winter).

There is no probability for hit of >500 tons of oil in the coastal habitats.

Environmental Risk (ERA) and Oil Spill Contingency Analysis (OSCA) for exploration well 6507/5-9 Shrek in PL838 in the Norwegian Sea

Environmental Risk (ERA) and Oil Spill Contingency Analysis (OSCA) for exploration well 6507/5-9 Shrek in PL838 in the Norwegian Sea

PGNiG Upstream Norway AS

Table of contents

EXECUTIVE SUMMARY

Environmental risk

Oil spill contingency

DEFINITIONS AND ABBREVIATIONS

NAMELIST FOR SPECIES

Seabirds

Marine mammals

Fish

1 INTRODUCTION

1.1 Activity description

1.2 Purpose and objective

1.3 PGNiGs acceptance criteria for acute pollution

1.4 Regulatory framework

2 DEFINED SITUATIONS OF HAZARD AND ACCIDENT (DSHA)

2.1 Dimensioning DSHA

2.2 Probability for dimensioning DSHA

2.3 Blowout rates and durations

3 OIL SPILL FATE AND TRAJECTORY MODELLING

3.1 Reference oil

3.2 The oil drift model

3.3 Description of modelled blowout scenarios

3.4 Oil drift modelling – Results 3.4.1 Near zone modelling

3.4.2 Oil on the sea surface

3.4.3 Stranding of oil in the coastal habitats

56651 54024 40852 59683 3.4 8.7 5.3 4.7

278 66 294 221 16.5 23.3 16.3 19.3

2 - - - 34.5 - - -

3 - 1 - 29.9 - 30.4 -

18 3 17 18 20.6 34.3 19.1 22.2

- - 3 - - 64.0 - -

- - 19 12 - - 25.8 34.1

- - 6 2 - - 39.3 53.2

- - 2 2 - - 45.1 50.2

- - 3 3 - - 47.5 48.4

1 - 8 8 55.9 - 43.6 34.6

3.4.4 THC in the water column

3.4.5 Singel simulation

4 METHOD FOR ENVIRONMENTAL RISK ANALYIS

4.1 Uncertainty in environmental risk analyses

4.1.1 Methodology

4.1.2 Environmental resources

4.1.3 Oil type

4.1.4 Frequencies and probabilities

5 ENVIRONMENTAL RESOURCES

5.1 Valuable Ecosystem Component (VEC)

5.2 Selected VECs for the analysis

5.2.1 Seabirds

5.2.2 Marine mammals

5.2.3 Fish

5.2.4 Coastal habitats

6 ENVIRONMENTAL RISK ANALYSIS – RESULTS

6.1 Possible consequences given a blowout from exploration well 6507/5-9 Shrek

6.1.1 Pelagic seabirds

6.1.2 Coastal seabirds (National data)

6.1.3 Marine mammals

6.1.4 Coastal habitats