Annex A Cover - Naalakkersuisutnaalakkersuisut.gl/~/media/Nanoq/Files/Hearings/2010/Offentlig... · Annex D Drilling and Contingency Chemicals . CAIRN Greenland M-I SWACO PONS XVb

Annex C

Fish Species List

ENVIRONMENTAL RESOURCES MANAGEMENT CAPRICORN GREENLAND EXPLORATION-1

1

1 ANNEX C – FISH SPECIES LIST

Table 1.1 contains a list of all 208 species fish found in offshore and around the

coast of Greenland (1).

Table 1.1 Fish species in Greenland

Family Species Common Name

Alepisauridae Alepisaurus brevirostris Shortnose lancetfish

Alepisauridae Alepisaurus ferox Longnose lancetfish

Alepocephalidae Alepocephalus agassizii Agassiz' slickhead

Alepocephalidae Alepocephalus bairdii Baird's smooth-head

Rajidae Amblyraja hyperborea Arctic skate

Rajidae Amblyraja radiata Thorny skate

Ammodytidae Ammodytes dubius Northern sand lance

Ammodytidae Ammodytes marinus Lesser sand-eel

Anarhichadidae Anarhichas denticulatus Northern wolffish

Anarhichadidae Anarhichas lupus Wolf-fish

Anarhichadidae Anarhichas minor Spotted wolffish

Anguillidae Anguilla rostrata American eel

Stichaeidae Anisarchus medius Stout eelblenny

Anoplogastridae Anoplogaster cornuta Common fangtooth

Anotopteridae Anotopterus pharao Daggertooth

Moridae Antimora rostrata Blue antimora

Trichiuridae Aphanopus carbo Black scabbardfish

Gadidae Arctogadus borisovi East Siberian cod

Gadidae Arctogadus glacialis Arctic cod

Paralepididae Arctozenus risso Ribbon barracudina

Argentinidae Argentina silus Greater argentine

Sternoptychidae Argyropelecus hemigymnus Half-naked hatchetfish

Sternoptychidae Argyropelecus olfersii Hatchetfish

Cottidae Artediellus atlanticus Atlantic hookear sculpin

Cottidae Artediellus uncinatus Arctic hookear sculpin

Agonidae Aspidophoroides monopterygius Alligatorfish

Nemichthyidae Avocettina infans Avocet snipe-eel

Alepocephalidae Bajacalifornia megalops Bigeye smooth-head

Barbourisiidae Barbourisia rufa Velvet whalefish

Bathylagidae Bathylagus euryops Goiter blacksmelt

Rajidae Bathyraja spinicauda Spinetail ray

Bathysauridae Bathysaurus ferox Deepsea lizardfish

Myctophidae Benthosema glaciale Glacier lanternfish

Berycidae Beryx decadactylus Alfonsino

Gadidae Boreogadus saida Polar cod

Stomiidae Borostomias antarcticus Large-eye snaggletooth

Lotidae Brosme brosme Tusk

Bythitidae Bythites fuscus Arctic brotula

Liparidae Careproctus kidoi Kido's snailfish

Liparidae Careproctus micropus

Liparidae Careproctus reinhardti Sea tadpole

Caristiidae Caristius groenlandicus

Caulophrynidae Caulophryne jordani Fanfin angler

Labridae Centrolabrus exoletus Rock cook

(1) Table information source: Fishbase.

http://www.fishbase.org/Country/CountryChecklist.php?c_code=304&vhabitat=saltwater&csub_code=

Accessed on 23/12/2009


2


Etmopteridae Centroscyllium fabricii Black dogfish

Ceratiidae Ceratias holboelli Krøyer's deep sea angler fish

Cetorhinidae Cetorhinus maximus Basking shark

Stomiidae Chauliodus sloani Sloane's viperfish

Chiasmodontidae Chiasmodon bolangeri

Chiasmodontidae Chiasmodon niger Black swallower

Clupeidae Clupea harengus Atlantic herring

Macrouridae Coryphaenoides armatus Abyssal grenadier

Macrouridae Coryphaenoides rupestris Roundnose grenadier

Psychrolutidae Cottunculus microps Polar sculpin

Psychrolutidae Cottunculus sadko

Psychrolutidae Cottunculus thomsonii Pallid sculpin

Ceratiidae Cryptopsaras couesii Triplewart seadevil

Cyclopteridae Cyclopteropsis mcalpini Arctic lumpsucker

Cyclopteridae Cyclopterus lumpus Lumpsucker

Gonostomatidae Cyclothone braueri Garrick

Gonostomatidae Cyclothone microdon Veiled anglemouth

Cyematidae Cyema atrum Bobtail eel

Rajidae Dipturus linteus Sailray

Diretmidae Diretmoides pauciradiatus Longwing spinyfin

Oneirodidae Dolopichthys longicornis

Lotidae Enchelyopus cimbrius Fourbeard rockling

Stichaeidae Eumesogrammus praecisus Fourline snakeblenny

Cyclopteridae Eumicrotremus derjugini Leatherfin lumpsucker

Cyclopteridae Eumicrotremus spinosus Atlantic spiny lumpsucker

Eurypharyngidae Eurypharynx pelecanoides Pelican eel

Gadidae Gadus morhua Atlantic cod

Gadidae Gadus ogac Greenland cod

Lotidae Gaidropsarus argentatus Arctic rockling

Lotidae Gaidropsarus ensis Threadfin rockling

Gasterosteidae Gasterosteus aculeatus aculeatus Three-spined stickleback

Gigantactinidae Gigantactis vanhoeffeni

Pleuronectidae Glyptocephalus cynoglossus Witch

Zoarcidae Gymnelus retrodorsalis Aurora unernak

Zoarcidae Gymnelus viridis Fish doctor

Cottidae Gymnocanthus tricuspis Arctic staghorn sculpin

Himantolophidae Himantolophus groenlandicus Atlantic footballfish

Pleuronectidae Hippoglossoides platessoides American plaice

Pleuronectidae Hippoglossus hippoglossus Atlantic halibut

Synaphobranchidae Histiobranchus bathybius Deepwater arrowtooth eel

Platytroctidae Holtbyrnia anomala Bighead searsid

Platytroctidae Holtbyrnia macrops Bigeye searsid

Trachichthyidae Hoplostethus atlanticus Orange roughy

Chimaeridae Hydrolagus affinis Smalleyed rabbitfish

Cottidae Icelus bicornis Twohorn sculpin

Cottidae Icelus spatula Spatulate sculpin

Lamnidae Lamna nasus Porbeagle

Myctophidae Lampanyctus crocodilus Jewel lanternfish

Myctophidae Lampanyctus intricarius Diamondcheek lanternfish

Myctophidae Lampanyctus macdonaldi Rakery beaconlamp

Lampridae Lampris guttatus Opah

Moridae Lepidion eques North Atlantic codling

Agonidae Leptagonus decagonus Atlantic poacher

Stichaeidae Leptoclinus maculatus Daubed shanny

Linophrynidae Linophryne coronata

Linophrynidae Linophryne lucifer

Liparidae Liparis atlanticus Atlantic seasnail

Liparidae Liparis fabricii Gelatinous snailfish

Liparidae Liparis gibbus Variegated snailfish


3


Liparidae Liparis liparis liparis Striped seasnail

Liparidae Liparis tunicatus Kelp snailfish

Oneirodidae Lophodolos acanthognathus Whalehead dreamer

Stichaeidae Lumpenella longirostris Longsnout prickleback

Stichaeidae Lumpenus fabricii Slender eelblenny

Stichaeidae Lumpenus lampretaeformis Snakeblenny

Zoarcidae Lycenchelys alba

Zoarcidae Lycenchelys kolthoffi Checkered wolf eel

Zoarcidae Lycenchelys muraena Moray wolf eel

Zoarcidae Lycenchelys paxillus Common wolf eel

Zoarcidae Lycenchelys sarsii Sar's wolf eel

Zoarcidae Lycodes adolfi Adolf's eelpout

Zoarcidae Lycodes esmarkii Greater eelpout

Zoarcidae Lycodes eudipleurostictus Doubleline eelpout

Zoarcidae Lycodes frigidus Glacial eelpout

Zoarcidae Lycodes luetkenii Lütken's eelpout

Zoarcidae Lycodes pallidus Pale eelpout

Zoarcidae Lycodes polaris Canadian eelpout

Zoarcidae Lycodes reticulatus Arctic eelpout

Zoarcidae Lycodes rossi Threespot eelpout

Zoarcidae Lycodes seminudus Longear eelpout

Zoarcidae Lycodes squamiventer Scalebelly eelpout

Zoarcidae Lycodes turneri Polar eelpout

Zoarcidae Lycodes vahlii Vahl's eelpout

Macrouridae Macrourus berglax Onion-eye grenadier

Paralepididae Magnisudis atlantica Duckbill barracudina

Stomiidae Malacosteus niger Northern stoplight loosejaw

Osmeridae Mallotus villosus Capelin

Platytroctidae Maulisia mauli Maul's searsid

Platytroctidae Maulisia microlepis Smallscale searsid

Melamphaidae Melamphaes microps

Gadidae Melanogrammus aeglefinus Haddock

Gadidae Micromesistius poutassou Blue whiting

Lotidae Molva dypterygia Blue ling

Lotidae Molva molva Ling

Myctophidae Myctophum punctatum Spotted lanternfish

Cottidae Myoxocephalus scorpioides Arctic sculpin

Cottidae Myoxocephalus scorpius Shorthorn sculpin

Myxinidae Myxine glutinosa Hagfish

Microstomatidae Nansenia groenlandica Greenland argentine

Nemichthyidae Nemichthys scolopaceus Slender snipe eel

Macrouridae Nezumia aequalis Common Atlantic grenadier

Platytroctidae Normichthys operosus Multipore searsid

Notacanthidae Notacanthus bonaparte Shortfin spiny eel

Notacanthidae Notacanthus chemnitzii Spiny eel

Myctophidae Notoscopelus kroyeri Lancet fish

Salmonidae Oncorhynchus gorbuscha Pink salmon

Oneirodidae Oneirodes eschrichtii Bulbous dreamer

Paralepididae Paralepis coregonoides Sharpchin barracudina

Liparidae Paraliparis bathybius Black seasnail

Liparidae Paraliparis copei copei Blacksnout seasnail

Liparidae Paraliparis garmani Pouty seasnail

Liparidae Paraliparis hystrix

Petromyzontidae Petromyzon marinus Sea lamprey

Pholidae Pholis fasciata Banded gunnel

Pholidae Pholis gunnellus Rock gunnel

Alepocephalidae Photostylus pycnopterus Starry smooth-head

Oneirodidae Phyllorhinichthys micractis

Platytroctidae Platytroctes apus Legless searsid


4


Platytroctidae Platytroctes mirus Leaf searsid

Pleuronectidae Pleuronectes platessa European plaice

Gadidae Pollachius virens Saithe

Melamphaidae Poromitra capito

Myctophidae Protomyctophum arcticum Arctic telescope

Liparidae Psednos gelatinosus Gelatinous dwarf snailfish

Gasterosteidae Pungitius pungitius Ninespine stickleback

Rajidae Rajella bathyphila Deepwater ray

Rajidae Rajella fyllae Round ray

Pleuronectidae Reinhardtius hippoglossoides Greenland halibut

Stomiidae Rhadinesthes decimus Slender snaggletooth

Rondeletiidae Rondeletia loricata Redmouth whalefish

Alepocephalidae Rouleina maderensis Madeiran smooth-head

Saccopharyngidae Saccopharynx ampullaceus Gulper eel

Platytroctidae Sagamichthys schnakenbecki Schnakenbeck's searsid

Salmonidae Salmo salar Atlantic salmon

Salmonidae Salvelinus alpinus alpinus Charr

Melamphaidae Scopelogadus beanii Bean's bigscale

Notosudidae Scopelosaurus lepidus Blackfin waryfish

Platytroctidae Searsia koefoedi Koefoed's searsid

Sebastidae Sebastes fasciatus Acadian redfish

Sebastidae Sebastes marinus Ocean perch

Sebastidae Sebastes mentella Deepwater redfish

Sebastidae Sebastes viviparus Norway redfish

Serrivomeridae Serrivomer beanii Bean's sawtoothed eel

Gonostomatidae Sigmops bathyphilus Spark anglemouth

Somniosidae Somniosus microcephalus Greenland shark

Oneirodidae Spiniphryne gladisfenae Prickly dreamer

Squalidae Squalus acanthias Piked dogfish

Sternoptychidae Sternoptyx pseudobscura Highlight hatchetfish

Stichaeidae Stichaeus punctatus punctatus Arctic shanny

Stomiidae Stomias boa boa Scaly dragonfish

Stomiidae Stomias boa ferox Boa dragonfish

Synaphobranchidae Synaphobranchus kaupii Kaup's arrowtooth eel

Bythitidae Thalassobathia pelagica

Trachipteridae Trachipterus arcticus Deal fish

Cottidae Triglops murrayi Moustache sculpin

Cottidae Triglops nybelini Bigeye sculpin

Cottidae Triglops pingelii Ribbed sculpin

Cottidae Triglopsis quadricornis Fourhorn sculpin

Stomiidae Trigonolampa miriceps Threelight dragonfish

Gadidae Trisopterus esmarkii Norway pout

Agonidae Ulcina olrikii Arctic alligatorfish

Phycidae Urophycis tenuis White hake

Sternoptychidae Valenciennellus tripunctulatus Constellationfish

Alepocephalidae Xenodermichthys copei Bluntsnout smooth-head

Annex D

Drilling and Contingency

Chemicals

CAIRN Greenland M-I SWACO PONS XVb CHEMICALS - T8 TERTIARY Well

36" SECTION

WBM OPF

Mud / fluid name Spud Mud

Mud / fluid supplier M-I SWACO

Mud / fluid density (g/cm3) 1.20

Drilling time (days) 1

basis (m3) 281.00

Continuous discharge rate (m3/hr) 11.7 1000 gpmbasis (m3) 81.70 23.80952381 bpm

Batch discharge rate (m3/hr) 272.55 227.1428571 m3/hrDilution factor for batch discharge

Formulations and Chemicals Chemical Function Group Chemical Label Code Estimated Use

(tonnes)

Estimated

Discharge

(tonnes)

Dosage

(lb/bbl)

HQ* RQ CHARM

Algorithm

Code

Discharge

Code

Danish Product Register #

Caustic Soda Water based Drilling Fluid Additive INORGANIC 1.00 1.00 - E - CNA BAT/CTN 336808

M-I BAR (All Grades) Weighting Chemical PLO 54.00 54.00 - E - CNA BAT/CTN 1154758M-I GEL Viscosifier PLO 24.00 24.00 - E - CNA BAT/CTN 1130203Soda Ash Other PLO 1.00 1.00 - E - CNA BAT/CTN 336795Contingency Chemicals

Caustic Soda Water based Drilling Fluid Additive INORGANIC 1.00 1.00 - E - CNA BAT/CTN 336808Citric Acid Water based Drilling Fluid Additive PLO 1.00 1.00 - E - CNA BAT/CTN 701692DUO-VIS Viscosifier - 1.00 2.00 2.00 GOLD DR BAT/CTN 2180317LIME OPF Additive PLO 1.00 1.00 - E - CNA BAT/CTN 342757

M-I BAR (All Grades) Weighting Chemical PLO 108.00 108.00 - E - CNA BAT/CTN 1154758M-I GEL Viscosifier PLO 48.00 48.00 - E - CNA BAT/CTN 1130203Mica Lost Circulation Material PLO 2.00 2.00 - E - CNA BAT/CTN 336920Soda Ash Other PLO 2.00 2.00 - E - CNA BAT/CTN 336795Sodium Bicarbonate Cement or Cement Additive PLO 2.00 2.00 - E - CNA BAT/CTN 336787

26" SECTION

WBM OPFMud / fluid name Spud Mud




basis (m3) 379.00




(tonnes)

Estimated

Discharge

(tonnes)

Dosage

(lb/bbl)

HQ* RQ CHARM

Algorithm

Code

Discharge

Code






17.5" SECTION

WBM OPF

Mud / fluid name ULTRADRILL




basis (m3) 535.00




(tonnes)

Estimated

Discharge

(tonnes)

Dosage

(lb/bbl)

HQ* RQ CHARM

Algorithm

Code

Discharge

Code


POTASSIUM CHLORIDE Water Based Drilling Fluid Additive PLO 69.00 69.00 - E - CNA BAT/CTN 336939

Potassium Chloride Brine Water based Drilling Fluid Additive PLO 349.50 349.50 - E - CNA BAT/CTN 1164884

M-I BAR (All Grades) Weighting Chemical PLO 390.00 390.00 - E - CNA BAT/CTN 1154758SAFE-CIDE Biocide - 1.00 1.00 0.20 GOLD - DR BAT/CTN 1282469

MAGNESIUM OXIDE Acidity Control Chemical PLO 1.00 1.00 - E - CNA BAT/CTN 1038693

DEFOAM NS Defoamer (Drilling) - 1.00 1.00 0.20 GOLD - DR BAT/CTN 1297038

ULTRAHIB Shale Inhibitor / Encapsulator SUB 22.50 22.50 11.00 GOLD - DR BAT/CTN 1601618

ULTRACAP Shale Inhibitor / Encapsulator - 4.50 4.50 2.00 GOLD - DR BAT/CTN 1601597

ULTRAFREE NS Drilling Lubricant - 25.50 25.50 9.00 GOLD - DR BAT/CTN 1607278FLO-TROL Fluid Loss Control Chemical PLO 4.50 4.50 - E - CNA BAT/CTN 1154790POLYPAC - All Grades Viscosifier PLO 4.50 4.50 - E - CNA BAT/CTN 920684DUO-VIS Viscosifier - 4.50 4.50 1.20 GOLD - DR BAT/CTN 2180317Soltex® Additive Shale Inhibitor / Encapsulator SUB 7.50 7.50 3.00 GOLD - DR BAT/CTN 1524501Contingency Chemicals







Greenland T8 TERTIARY PON15B CHARM Proposal + DPR submitted 11dec.xlsx



ULTRAFREE NS Drilling Lubricant - 51.00 51.00 9.00 GOLD - DR BAT/CTN 1607278FLO-TROL Fluid Loss Control Chemical PLO 9.00 9.00 - E - CNA BAT/CTN 1154790POLYPAC - All Grades Viscosifier PLO 9.00 9.00 - E - CNA BAT/CTN 920684DUO-VIS Viscosifier - 9.00 9.00 1.20 GOLD - DR BAT/CTN 2180317

Soltex® Additive Shale Inhibitor / Encapsulator SUB 15.00 15.00 3.00 GOLD - DR BAT/CTN 1524501Caustic Soda Water based Drilling Fluid Additive INORGANIC 1.00 1.00 - E - CNA BAT/CTN 336808Citric Acid Water based Drilling Fluid Additive PLO 2.00 2.00 - E - CNA BAT/CTN 701692

FORM-A-SQUEEZE Fluid Loss Control Chemical PLO 2.00 2.00 - E - CNA BAT/CTN 1871407G-Seal Lost Circulation Material PLO 2.00 2.00 - E - CNA BAT/CTN 1203433

Ironite Sponge Hydrogen Sulphide Scavenger PLO 2.00 2.00 - E - CNA BAT/CTN 701684LIME OPF Additive PLO 1.00 1.00 - E - CNA BAT/CTN 342757M-I GEL Viscosifier PLO 50.00 50.00 - E - CNA BAT/CTN 1130203Mica Lost Circulation Material PLO 5.00 5.00 - E - CNA BAT/CTN 336920SAFE-CARB (ALL GRADES) Weighting Chemical PLO 10.00 10.00 - E - CNA BAT/CTN 1097482

SAFE-SCAV NA Oxygen Scavenger PLO 1.00 1.00 - E - CNA BAT/CTN 1244147Soda Ash Other PLO 2.00 2.00 - E - CNA BAT/CTN 336795Sodium Bicarbonate Cement or Cement Additive PLO 2.00 2.00 - E - CNA BAT/CTN 336787Sugar Thinner PLO 1.00 1.00 - E - CNA BAT/CTN 1899864Ven Fyber 201 Lost Circulation Material PLO 2.00 2.00 - E - CNA BAT/CTN 1130529Sodium Chloride Brine Water based Drilling Fluid Additive PLO 200.00 200.00 - E - CNA BAT/CTN 1120443

Sodium Chloride Powder (Salt PVD or Granular Salt) Water based Drilling Fluid Additive PLO 50.00 50.00 - E - CNA BAT/CTN 701625

MEG Gas Hydrate Inhibitor PLO 10.00 10.00 - E - CNA BAT/CTN 1365010METHANOL (all grades) Gas Hydrate Inhibitor PLO 5.00 5.00 - E - CNA BAT/CTN 1248770

12.25" SECTION

WBM OPF





basis (m3) 458.00




(tonnes)

Estimated

Discharge

(tonnes)

Dosage

(lb/bbl)

HQ* RQ CHARM

Algorithm

Code

Discharge

Code



Potassium Chloride Brine Water based Drilling Fluid Additive PLO 300.00 300.00 - E CNA BAT/CTN 1164884





















8.5" SECTION

WBM OPF





basis (m3) 479.00






(tonnes)

Estimated

Discharge

(tonnes)

Dosage

(lb/bbl)

HQ* RQ CHARM

Algorithm

Code

Discharge

Code










Contingency Chemicals








ULTRAFREE NS Drilling Lubricant - 48.00 48.00 9.00 GOLD - DR BAT/CTN 1607278FLO-TROL Fluid Loss Control Chemical PLO 9.00 9.00 - E - CNA BAT/CTN 1154790POLYPAC - All Grades Viscosifier PLO 9.00 9.00 - E - CNA BAT/CTN 920684DUO-VIS Viscosifier - 9.00 9.00 1.20 GOLD - DR BAT/CTN 2180317Caustic Soda Water based Drilling Fluid Additive INORGANIC 1.00 1.00 - E - CNA BAT/CTN 336808Citric Acid Water based Drilling Fluid Additive PLO 2.00 2.00 - E - CNA BAT/CTN 701692



SAFE-SCAV NA Oxygen Scavenger PLO 1.00 1.00 - E - CNA BAT/CTN 1244147Soda Ash Other PLO 2.00 2.00 - E - CNA BAT/CTN 336795Sodium Bicarbonate Cement or Cement Additive PLO 2.00 2.00 - E - CNA BAT/CTN 336787

Soltex® Additive Shale Inhibitor / Encapsulator SUB 6.00 6.00 3.00 GOLD - DR BAT/CTN 1524501Sugar Thinner PLO 1.00 1.00 - E - CNA BAT/CTN 1899864Ven Fyber 201 Lost Circulation Material PLO 2.00 2.00 - E - CNA BAT/CTN 1130529Sodium Chloride Brine Water based Drilling Fluid Additive PLO 200.00 200.00 - E - CNA BAT/CTN 1120443


MEG Gas Hydrate Inhibitor PLO 10.00 10.00 - E - CNA BAT/CTN 1365010

METHANOL (all grades) Gas Hydrate Inhibitor PLO 5.00 5.00 - E - CNA BAT/CTN 1248770


CAIRN Greenland M-I SWACO PONS XVb CHEMICALS - ALPHA Well

36" SECTION

WBM OPF

Mud / fluid name Spud Mud




basis (m3) 254.00




(tonnes)

Estimated

Discharge

(tonnes)

Dosage

(lb/bbl)

HQ* RQ CHARM

Algorithm

Code

Discharge

Code

Danish Product Register

#





26" SECTION

WBM OPFMud / fluid name Spud Mud




basis (m3) 632.00




(tonnes)

Estimated

Discharge

(tonnes)

Dosage

(lb/bbl)

HQ* RQ CHARM

Algorithm

Code

Discharge

Code


#





17.5" SECTION

WBM OPF





basis (m3) 738.00




(tonnes)

Estimated

Discharge

(tonnes)

Dosage

(lb/bbl)

HQ* RQ CHARM

Algorithm

Code

Discharge

Code


#















Greenland ALPHA PON15B CHARM Proposal + DPR submited 11dec.xlsx










12.25" SECTION

WBM OPF





basis (m3) 603.00




(tonnes)

Estimated

Discharge

(tonnes)

Dosage

(lb/bbl)

HQ* RQ CHARM

Algorithm

Code

Discharge

Code


#


Potassium Chloride Brine Water based Drilling Fluid Additive PLO 394.50 394.50 - E CNA BAT/CTN 1164884





















8.5" SECTION

WBM OPF





basis (m3) 622.00






(tonnes)

Estimated

Discharge

(tonnes)

Dosage

(lb/bbl)

HQ* RQ CHARM

Algorithm

Code

Discharge

Code


#









Contingency Chemicals








ULTRAFREE NS Drilling Lubricant - 60.00 60.00 9.00 GOLD - DR BAT/CTN 1607278FLO-TROL Fluid Loss Control Chemical PLO 12.00 12.00 - E - CNA BAT/CTN 1154790POLYPAC - All Grades Viscosifier PLO 12.00 12.00 - E - CNA BAT/CTN 920684DUO-VIS Viscosifier - 12.00 12.00 1.20 GOLD - DR BAT/CTN 2180317Caustic Soda Water based Drilling Fluid Additive INORGANIC 1.00 1.00 - E - CNA BAT/CTN 336808Citric Acid Water based Drilling Fluid Additive PLO 2.00 2.00 - E - CNA BAT/CTN 701692



SAFE-SCAV NA Oxygen Scavenger PLO 1.00 1.00 - E - CNA BAT/CTN 1244147Soda Ash Other PLO 2.00 2.00 - E - CNA BAT/CTN 336795Sodium Bicarbonate Cement or Cement Additive PLO 2.00 2.00 - E - CNA BAT/CTN 336787

Soltex® Additive Shale Inhibitor / Encapsulator SUB 6.00 6.00 3.00 GOLD - DR BAT/CTN 1524501Sugar Thinner PLO 1.00 1.00 - E - CNA BAT/CTN 1899864Ven Fyber 201 Lost Circulation Material PLO 2.00 2.00 - E - CNA BAT/CTN 1130529Sodium Chloride Brine Water based Drilling Fluid Additive PLO 200.00 200.00 - E - CNA BAT/CTN 1120443


MEG Gas Hydrate Inhibitor PLO 10.00 10.00 - E - CNA BAT/CTN 1365010

METHANOL (all grades) Gas Hydrate Inhibitor PLO 5.00 5.00 - E - CNA BAT/CTN 1248770


Annex E

ASA Cuttings and Spill

Modelling Report


1

1 OIL SPILL IMPACT ANALYSIS AND MITIGATION

1.1 INTRODUCTION

This section provides a detailed assessment of potential accidental oil spill events associated with the proposed drilling activity, their likelihood of occurrence and the potential impacts on environmental resources and receptors should an oil spill occur. The measures that will be established to prevent an accidental oil spill, and to respond to any such event that does occur are referenced in the Environmental Management and Mitigation Chapter of the EIA and full details of the procedures in place to respond to oil spills during the drilling campaign are provided in the project specific Oil Spill Response Plan.

1.2 APPROACH OVERVIEW

Oil spill scenarios were considered for development based on historical information on actual spill events and the project description. Scenarios where chosen for further study where they could provide insight into the potential for environmental harm to important receptors in the project area. As part of this consideration the fates of oil spilt in reasonable worst case spill scenarios for different oil types were considered by using stochastic and trajectory modelling and examining the fate of spilt oil. Various hazardous materials will be stored and used in bulk (eg in containers or systems with greater than 1 m3 capacity) during the Project. The most important of these are listed below: crude oil; diesel; bunker oil; lubricating oils; hydraulic oils; and aviation fuel. The spills considered were divided into small, (<1 m3), medium (>1 m3 <10 m3) and large (>10m3). Typical causes of spills in these categories are given below in Table 1.1.

Table 1.1 Indicative oil spill scenarios

Small Spills Medium Spills Large spills

Spill during refuelling of portable plant with diesel

Rupture of hose during vessel bunkering resulting in loss of contents of hose

Vessel collision/foundering rupturing fuel tanks causing loss of inventory, diesel or bunker oil


2

Over turning of lubricating oil container

Helicopter refuelling incident

Loss of well control (blow out).

Break in hydraulic hose

Crude oil entrained in flare during well testing

Data from the International Association of Oil and Gas Producers indicate that for all oil and gas operating areas of the world spills of less than 10 barrels (or 1.6 m3) are the most common with the size of the spill being inversely related to likelihood (1). The potential for major environmental harm is dependent on the context and location of the spill but is closely related to the size. In the context of this development most small spills would be most likely to occur on vessels in areas where fuel is handled and so will be bunded and therefore have little probability of reaching the surface of the sea. The impacts of small spills are correspondingly of lesser potential significance, therefore this assessment concentrates on medium and larger spills. The main risk of a large spill during exploration drilling is either a vessel collision or a loss of well control. These two scenarios have therefore been selected for further consideration which will comprise an assessment of the likelihood of the incident, modelling of oil spill fate, vulnerability and sensitivity of the resources which may be affected.

1.3 LIKELIHOOD AND SPILL SIZE

1.3.1 Spills from Vessels

The risk of spills was considered in more detail by reference to data on oil spills from tanker incidents available from the International Tanker Operator’s Federation (ITOPF) (2). These data were indicative of the type and relative frequency of incidents involving fuel handling. Most spills were due to bunkering and loading. The ITOPF report summarises the data as follows. Most spills result from routine operations such as loading, discharging and

bunkering which normally occur in ports or at oil terminals. The majority of these operational spills are small (between 1974 and 2009

there was an average of 90 spills per year globally) with some 90% involving quantities of less than 7 tonnes.

(1) OGP, 2009. Environmental performance in the E and P Industry 2008 data. Available from: <http://www.ogp.org.uk/pubs/429.pdf> (2) ITOPF Oil Tanker Spill Statistics 2009 http://www.itopf.com/information-services/data-and-statistics/statistics/documents/Statspack2009-FINAL.pdf


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Accidental causes such as collisions and groundings generally give rise to much larger spills, with at least 84% of these incidents involving quantities in excess of 700 tonnes.

No statistics for vessel incidents are available relating to Greenlandic waters but reference to the UK Marine Accident Investigation Branch (MAIB) Annual Report (2008) (1) indicates the following general trends (all data for vessels over 100 gt (2)). Grounding, machinery failure and contact/collision were the commonest

cause of incidents to UK shipping. Within the UK merchant fleet there were 19 collisions reported. Only two total losses were reported in 2008. These data suggest that serious vessel incidents are relatively rare but collisions and contact feature significantly amongst the causes of incidents that did occur. The more serious oil spills that occur from vessels are a result of these causes. In the case of this Project the largest inventory of hydrocarbons would be on board the Stena Forth (11,500 m3 diesel) and the MSV Fennica (1,690 m3 heavy fuel oil) and a total loss of these vessels has been considered as an unlikely but plausible worst case.

1.3.2 Exploration Related Oil Spills

UKCS data for oil spills indicates that between 2000 and 2005, most were between 1 and 100 kg. The probability of such a spill is approximately 0.25 spills per field per year. These data also revealed a trend for fewer spills in newer installations (3). Globally between 1957 and 1996 on average, a blowout (4) has occurred once every 162 wells for exploration drilling. In the UKCS, the frequency was less at 0.006 blowouts per well during exploration drilling (5). Advances in drilling technology and HSE management offshore have continued to reduce the risk of blowouts since these figure were produced. Thus it can be seen that blowouts are relatively rare occurrences, although they have the potential to cause extremely large spills. In this case the worst-case blow out scenario is estimated to be 3,340 (21,000 bbls) m3/day. In order to represent a reasonable worst case drilling related spill a blow out scenario has been modelled.

(1) http://www.maib.gov.uk/cms_resources.cfm?file=/Annual%20Report%202008.pdf (2) Gross Tonnage (3) Report on the Analysis of DTI UKCS Oil Spill Data from the period 1975-2005 October 2006: A report prepared by TINA. (4) It should be noted that this is for all types of blowout including shallow gas and reservoir gas. The figure for blowouts resulting in oil spill would be lower. (5) Hoyland, P., 1997. Offshore Blowouts 1997 based on SINTEF Offshore Blowout Database.


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1.4 OIL SPILL MODELLING

1.4.1 Overview

Following the identification of oil spill release scenarios, three different scenarios were chosen for further consideration by modelling (crude oil, diesel and heavy fuel oil). Two release locations were identified: well sites Alpha and Gamma. These locations were chosen to give a good geographical spread across the exploration block. The fate of oil released at these locations was predicted using stochastic and trajectory modelling as described below.

1.4.2 Stochastic Modelling

Stochastic modelling estimates the probability of oil affecting specific areas and receptors along the shore. The stochastic model runs repeated oil spill predictions based on a specific release scenario and real weather and tide data. By running a large number of predictions a picture of the probability of oil spill behaviour is created. Stochastic modelling uses historical wind data to run a large number of oil spill simulations with random start points within the period being considered. The results of these runs are then combined to generate an overview of the probability of oil affecting the surrounding areas of sea and coast. The wind direction and currents do not show significant seasonal variation so an annual metocean data set was used for the stochastic modelling. Water temperature was set at 5 °C which is conservative in terms of oil spill weathering.

1.4.3 Trajectory Modelling

Based on the results of the stochastic modelling, worst case trajectory modelling scenarios were used to understand the shortest possible beaching times and maximum volumes at sensitive locations.

1.4.4 Assessment of Sensitivity and Vulnerability

For the purposes of this assessment sensitivity is defined as the potential of an oil spill to cause serious harm or damage to a receptor or resource. This will depend on a number of factors such as: the tendency for the receptor to recover; the effect on the receptor of exposure to oil (eg death or serious impairment

of species); the life stage of an organism; and the season with relation to presence or absence of receptors. Recovery from an oil spill depends on two factors:


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rate of removal or weathering of the oil from the environment; and recovery of affected populations and habitats. Vulnerability in this report refers to the tendency of the receptor to be exposed to oil which is present in the immediate vicinity. Thus there will be a physical pathway by which the oil can reach the receptor. This is a combination of proximity to contaminated seawater which is dependent on the dispersion and physical behaviour of the oil and the seasonal presence or absence of the receptor. Receptors for which there is no clear or consistent pathway by which they may be affected by an oil spill are not considered vulnerable. For example there may be very sensitive habitats which are above the high tide line and therefore not reached by beached oil. The sensitivity and vulnerability of major animal groups and habitats which are at risk from an oil spill in the project area are discussed in Section 1.7.

1.5 SCENARIOS MODELLED

No information is available on the type of oil which may be expected from the Capricorn exploration drilling programme. A typical medium crude with a high tendency to emulsify was therefore chosen to base the oil spill modelling on to represent the worst case. The characteristics of the oil types under consideration are summarised in Table 1.2 below.

Table 1.2 Characteristics of oil types modelled

Oil Type Density (g/cm3)

Viscosity (cP)

Surface Tension (dyne/cm)

Maximum Water

Content % Medium Crude 0.8373 33.0 30.0 70 Diesel Fuel 0.8310 2.8 27.5 0 Heavy Fuel Oil 0.9275 17.0 30.2 60

At each site three potential spill scenarios were considered: a blowout of medium crude oil; a maximum release of diesel oil; and a maximum release of heavy fuel oil. All spill scenarios were requested to be simulated during the drilling period: June to November, which corresponds to the ice-free period. Ice cover or oil and ice interaction were not considered in these simulations. Water temperatures were held constant at 5°C. All simulations were run for seven days. The scenarios are summarised below in Table 1.3.

Table 1.3 Summary of Stochastic Modelling Scenarios

Scenario Location Oil Type Release Volume

(L)

Release period (hours)

1 Alpha Medium Crude 3,340 m3 24 2 Alpha Diesel 11,500 m3 1 3 Alpha Heavy Fuel Oil 1,690 m3 1 4 T4 Medium Crude 21,000 m3 24 5 T4 Diesel Fuel 11,500 m3 1 6 T4 Heavy Fuel Oil 1,690 m3 1


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The behaviour of oil when released to water is discussed below.

1.5.1 Behaviour of Oil in Water

Following release of oil into water, a number of processes occur which affect the fate of the resulting slick. These processes are affected by the chemical and physical properties of the oil such as its density, chemical composition (eg relative proportions of different hydrocarbons), viscosity, flash point etc. The most important processes to affect oil following a spill are dispersion and weathering. These processes are described in more detail below. The principal mechanisms of dispersion are as follows. Spreading – tendency to spread on the water surface. This is primarily a

function of the viscosity of the oil and is affected by temperature. Drift – the effect of tidal currents and wind. Oil will drift at the speed and

direction of the tidal current but will be affected by approximately 3% of the wind speed. These two factors will combine to give a drift vector.

Weathering is a complex series of physical, chemical and biological

processes by which the volume of the oil on the water surface reduces. The principal mechanisms involved in weathering are as follows and the potential impacts of these are illustrated in Figure 1.1.

Evaporation – loss of light, low molecular weight fractions. Emulsification – combination with water to form oil -in -water emulsion. Dispersion - breaking up of the slick into small droplets which combine

with suspended particles and allow the oil to be dispersed in the water column and ultimately to sink to the seabed.

Oxidation – chemical/biological processes which break down the oil. The relative importance of these mechanisms is determined by the characteristics of the oil and the ambient conditions.

Figure 1.1 Potential Ecological Impacts of Oil Spills

Low temperatures and presence of ice affects the behaviour of oil that has been released. Oil may be deposited on top of the ice, encapsulated within it or it may collect in pools underneath the ice surface. As the condition of the ice changes so the fate of oil which has been spilt will also change. It has been reported that oil trapped under ice weathers at 10-20% of the rate it would at the open sea surface whilst encapsulated oil hardly weathers at all. Oil trapped within or underneath ice can travel much further than in ice free waters and may migrate to the surface of the ice or in open leads as they form. More detail of the processes which affect the behaviour of oil spills in ice affected waters are given in Box 1.1 below.

Box 1.1 Behaviour of Oil Spilt in Sea Ice

When an oil spill comes into contact with ice there are a number of processes which may occur, affecting the rate of weathering and spread of the oil.

Oil spilt under conditions where sea ice is forming may remain on top of the ice as it forms

beneath it but generally under these circumstances it will become encapsulated within the ice. Oil at the ice/water interface can migrate to the underside of the ice where, given sufficient

current velocity (eg 0.04 m s-1 for diesel) it can travel with the current collecting in pockets or behind ridges on the underside of the ice. Here its fate will be affected by the shape and characteristic of the ice. Trapped oil may reach the surface in leads or holes in the ice surface or it may become encapsulated in the ice.

New ice is formed at the ice/seawater boundary and so oil on the underside of an ice flow

can become trapped within the body of the ice and travel vertically as the surface is eroded by melting and new ice forms below it. By this mechanism oil can be deposited on the surface of the ice or it can be released later when the ice melts. Ice, particularly old or melting ice, is porous and so can absorb oil.

Unless the ice is shore-fast it will move with water and wind currents. As it does so

irregularities such as pressure ridges and rouble fields will form and oil will tend to concentrate in void spaces created by the structure of the ice.


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1.6 BASELINE WIND AND TIDE CONDITIONS

The baseline wind and tidal conditions are described in the EIA (Section 4.1).

1.7 SENSITIVE FEATURES

1.7.1 Introduction

The following sections describe the features sensitive to oil spills that are found within the area identified in the project area. The general sensitivity of the different types of biodiversity receptor and the distribution in the study area are described below.

1.7.2 Coastal Habitat - Soft Sediment Shores

Oil will weather more slowly in muddy sediments which are found in areas subject to less natural weathering by waves and currents. No areas of sheltered muddy sediment have been identified but it is assumed that they would be found in the vicinity of estuaries and at the head of sheltered inlets. The intertidal soft sediments, particularly mud and saltmarsh habitat, are very sensitive to oil contamination. In such finer sediment the effects of toxicity and smothering may last for a number of seasons with a slow recovery. Infaunal communities will be perturbed as oil is broken down and weathers releasing nutrients in the sediment and encouraging colonisation by an adapted community for a period of time (about one year in sandy sediment). Sandy sediments may recover more quickly particularly in areas exposed to wave energy.

1.7.3 Coastal Habitat - Rocky and Boulder Shores

The components of the communities colonising hard substrata will depend on the characteristics of the shore, of which the degree of exposure to wave action and level of tidal exposure are amongst the more important physical factors. Sheltered areas will be colonised by algae, mainly brown algae (eg Fucus species) whereas more exposed locations will have a higher proportion of animals (limpets and barnacles etc). There are no specific locations identified where the intertidal rocky shore communities are particularly rare or of conservation importance. Generally, oil may be weathered from such habitats if they are exposed to wave action, however, in sheltered rocky shore locations where boulders or cobbles overlay softer sediment, contamination may remain for long periods. Oil will cause mortality of species occupying the rocky shore. Recovery will take between one or two seasons. Some effects will be due to ecosystem disturbances, for example the mass mortality of grazing animals reducing grazing pressure on quick growing species of algae. Species will re-colonise by settlement from plankton and emigration from surrounding areas.


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Community composition will alter as the recovery progresses with a succession of species colonising the affected areas.

1.7.4 Coastal Habitat - Sublittoral Habitats

Sublittoral soft sediment habitats in shallow waters may be affected by dispersed oil and oil which has become associated with fine sediment. Diesel spills are likely to affect animal species in the shallow sublittoral, particularly nearer to the coasts where wave action will increase dispersion into the water column. Contaminated sediment may ultimately sink to areas of the seabed where it has the potential to accumulate.

1.7.5 Birds

Sea birds (ie auks, gulls and water fowl) are highly sensitive to oil spill because of their potential exposure to oil on the water surface and tendency to congregate in high density aggregations during critical periods eg breeding and migration. The oil principally affects birds by removal of the natural buoyancy and thermal insulating properties of the feathers and by ingestion during feeding and grooming. Birds that forage at sea are sensitive to oil exposure. This could be particularly damaging to the population during the breeding season when parent birds are feeding unfledged young and subsequently for moulting young. The species most likely to be affected by a spill depends on the circumstances of the incident eg the time of year, location, size and type of oil and type of habitat affected. Severe events can be harmful at the population level (1). Williams et al (1995) (2) proposed a method for assessment of seabird vulnerability to surface pollutants which used the following factors to generate a vulnerability score for the UK coastal waters and the North Sea. Proportion of each species that was oiled of those found dead on the

shoreline and the proportion of the time spent on the surface of the sea by that species (based on UK survey data).

Bio-geographical population. Potential rate of recovery following a reduction in numbers. Reliance on the marine environment.

(1) Piatt, J. F., Carter, H. R. & Nettleship D. N. 1990. Effects of Oil Pollution on Marine Bird Populations. Proceedings from:

the Oil Symposium Herndon, Virginia October 16-18, 1990. (2) Williams, J. M., Tasker, M. L., Carter, I. C. & Webb, A. 1995. A method of assessing seabird vulnerability to surface pollution. IBIS, 137:147-152.


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This approach provides a useful insight into the potential effects of oil spills on sea birds in the study areas and is used below to indicate the general vulnerability of the main types (auks, gulls and water fowl) of seabirds. Auks

Auks (aclids) such as puffins and guillemots feed by catching fish and so can be affected by surface oil whilst foraging. Populations are sensitive due to the low number of young produced per year by each nesting pair and the time to reaching maturity (1). Some auks e.g. puffins only come ashore to breed and thus spend most of their year at sea. Auks are therefore particularly sensitive and vulnerable to oil spills and were amongst the most vulnerable using the method of Williams et al 1995. Gulls, Terns and Skuas

Gulls tend to be less reliant on the sea and also use terrestrial habitat. They can also recover quickly from losses and as a consequence are amongst the least sensitive of seabird species (2). Skuas by contrast have lower clutch sizes, are more reliant on the sea and are considered to more vulnerable to oil spill. Arctic terns are also highly dependent on the sea for the entire year and so are considered to be vulnerable. Water Fowl

Divers are amongst the most vulnerable seabirds mainly due to the amount of time they spend in contact with the water and their low reproductive rate. Most ducks, however have a potential for rapid recovery (3) and are considered less vulnerable.

1.7.6 Sea Mammals - Pinnipeds

The following causes of harm to seals from oil have been identified based on Engelhardt (1983) (4): damage to sensitive tissue through direct contact with lungs following

inhalation or eyes through direct contact; toxic effects following ingestion; effects on thermoregulation; impairment of locomotion in viscous oil; and behavioural modifications due to avoidance.

(1) Piatt, J. F., Carter, H. R. & Nettleship D. N. 1990. Effects of Oil Pollution on Marine Bird Populations. Proceedings from: the Oil Symposium Herndon, Virginia October 16-18, 1990. (2) Willians, J. M., Tasker, M. L., Carter, I. C. & Webb, A. 1995. A method of assessing seabird vulnerability to surface pollution. IBIS, 137:147-152. (3) Willians, J. M., Tasker, M. L., Carter, I. C. & Webb, A. 1995. A method of assessing seabird vulnerability to surface pollution. IBIS, 137:147-152. (4) Engelhardt, F.R. 1985. Petroleum Effects on Marine Mammals. Aquatic Toxicology, 4:199-217.


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Seals may be exposed to surface oil through inhalation, absorption through the skin and ingestion with food. Acute exposure (24 hrs) of harp seals to crude oil indicated that uptake resulted in comparatively low levels of hydrocarbon markers in tissue. Studies on the ingestion of hydrocarbons by ringed seals, however, indicated that residues were deposited in blubber tissue, suggesting the ingestion is a more important exposure route than direct contact with skin (1). Seals rely on their blubber for thermal insulation and do not suffer from a reduction in core temperature due to changes in the thermal properties of their fur following contamination by oil (2). However, very young (1 week old) seals may be susceptible as they rely more on their fur for thermal insulation. There is some evidence for limited toxic effects in ringed seals from experimental exposure to a surface slick. Eye lesions were recorded although they recovered on reintroduction to clean water. Direct mortality of seals following oil spills has been reported but is based on anecdotal evidence; no causal link has been confirmed. Davis and Anderson (1976) (3) reports that grey seal pups experienced difficulty in swimming in viscous oil and may have died of exhaustion. Following the Exxon Valdez spill there is some evidence that disappearance of harbour seals from haul out sites is consistent with avoidance behaviour rather than mortality (4). It can be concluded from the above that pinnipeds are not highly sensitive to oil contamination apart from very young juveniles although the potential for mortality cannot be discounted. The vulnerability of pinnipeds will depend on the following factors. Habitat. Physical contact with oil will be greater where the spill affects the

coast or ice used by seals to breed or haul out. Species which spend proportionately more of their time hauled out will have a greater exposure to oil than those which spend a greater proportion at sea. Oil spilt amongst ice is likely to take longer to weather, may be encapsulated and concentrated in leads or breathing holes. Consequently seals which use ice for breeding and hauling out are more vulnerable than those which do not.

Gregariousness. Potentially a larger proportion of a population could be

affected if a spill contaminates locations where gregarious species congregate.

Feeding habit. Oil spills have the potential to affect inshore, shallow water food resources. Deeper benthic and pelagic resources are less likely to be contaminated. Seals which feed on shallow benthic infaunal prey are more

(1) Engelhardt, F.R. 1985. Petroleum Effects on Marine Mammals. Aquatic Toxicology, 4:199-217. (2) Engelhardt, F.R. 1985. Petroleum Effects on Marine Mammals. Aquatic Toxicology, 4:199-217. (3) Davis, J. E. & Anderson, S.S. 1976 . Effects of oil pollution on breeding grey seals. Marine Pollution Bulletin 7: 115-118. (4) Hoover-Miller, A., Keith, R., Parker, K. R. and Burns, J. 2001. A reassessment of the impact of the Exxon Valdez oil spill on harbour seals (Phoca vitulina richardsi) in Prince William Sound, Alaska. Marine Mammal Science 17 (1): 111-135.


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likely to ingest oil and be affected by a reduction in the availability of their food.

Population status. Population size within a biogeographical area is an

important factor which affects the potential for recovery from natural or anthropogenic impacts. Larger populations are more robust against mortality and or lowered rates of breeding success.

These factors are considered below for the species found in the study area (Table 1.4).

Table 1.4 Summary of Pinniped Vulnerability to Oil in the Waters off West Greenland

Species Habitat Gregariousness Feeding

habit Population

Status in Greenland

Summary

Harbour seal

Stay close to and use land all year. Do not use ice.

Solitary or small groups

Pelagic feeders in near shore conditions

Globally of least concern but locally rare

Moderate vulnerability due to regional rarity, well distributed along coast so proportion of total population vulnerable to any one spill event is likely to be small.

Harp seal

Breed and moult on ice, feed at sea.

Form large moulting assemblages

Pelagic feeders

Very abundant

Low vulnerability unless spill affects ice in which case vulnerability would be moderate due to concentration of species in assemblages. Large population numbers mean that some individuals will be unaffected.

Ringed seal

Breed and moult on ice then migrate to open sea.

Non gregarious Pelagic feeders

Very abundant

Low vulnerability unless spill affects ice in which case vulnerability would be low to moderate. Non gregarious habit and large population numbers mean that a significant proportion of population will be unaffected.

Bearded seal

Breed on ice but may use land in summer.

Non gregarious Benthic feeders

Abundant Moderate vulnerability due to use of ice, land and benthic feeding habit.

Atlantic walrus

Permanently reside in coastal waters breed on ice and haul out on land.

Form large groups

Benthic feeders

Rare Highly vulnerable due to coastal habit use, gregariousness and benthic feeding habitat.


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Species Habitat Gregariousness Feeding

habit Population

Status in Greenland

Summary

Hooded seal

Breed on ice. Non gregarious Pelagic feeders

Abundant Low vulnerability due to population numbers unless spill affects ice in which case it would be moderate to high.

Note: Based on Geraci and Aubin (1988) (1)

1.7.7 Sea Mammals - Cetaceans

A number of potentially harmful effects of oil on cetaceans have been put forward (based on Geraci and Aubin, 1988 (2) and Englehardt, 1985 (3)): damage to sensitive tissue through direct contact with lungs (following

inhalation) or eyes; toxic effects following ingestion; blocking of blow hole; fouling of baleen plates; and behavioural modifications due to avoidance. There is evidence that cetaceans may accumulate hydrocarbon residues in their blubber (4) (Englehardt, 1983) and that the smaller toothed whales accumulate more than baleen whales. Under experimental conditions dolphins (Tursiops truncatus) can detect oil but in the field cetaceans do not tend to avoid oil spills. There is no evidence that any of the identified potential effects of oil has resulted in death or harm to a cetacean species (5) (6) although it has been suggested that a dolphin may have died from a blocked blow hole following a spill of viscous oil (7). Circumstantial evidence also suggests that the Exxon Valdez incident was responsible for mortality in resident killer whales living in the vicinity of the spill (8). There is certainly the potential for individual animals to be harmed by exposure to oil and the most vulnerable are cetaceans that spend time amongst the ice pack where oil could be concentrated in leads and breathing holes increasing the probability of exposure. Of the species regularly present in the project area the most vulnerable species are likely to be belugas and bowhead whales (9).

(1) Geraci, J. R. & Aubin, D. J. 1988. Synthesis of effects of oil on marine mammals. MMS report Contract No. 14-12-0001-30283. (2) Geraci, J. R. & Aubin, D. J. 1988. Synthesis of effects of oil on marine mammals. MMS report. Contract No. 14-12-0001-30283. (3) Engelhardt, F.R. 1985. Petroleum Effects on Marine Mammals. Aquatic Toxicology, 4:199-217. (4) Engelhardt, F.R. 1985. Petroleum Effects on Marine Mammals. Aquatic Toxicology, 4:199-217. (5) Geraci, J. R. & Aubin, D. J. 1988. Synthesis of effects of oil on marine mammals. MMS report. Contract No. 14-12-0001-30283. (6) Engelhardt, F.R. 1985. Petroleum Effects on Marine Mammals. Aquatic Toxicology, 4:199-217. (7) Brownell, R. L., 1971. Whales, dolphins and oil pollution. In : Straughn, D. (ed.) Biological and oceanographic survey of the

Santa Barabara Channel oil spill, 1968 -1970. Vol 1, 255-276. (8) Exxon Valdez Trustees Council (2010). Killer Whale. Available from: http://www.evostc.state.ak.us/recovery/status_orca.cfm. Downloaded: 23rd February 2010. (9) Geraci, J. R. & Aubin, D. J. 1988. Synthesis of effects of oil on marine mammals. MMS report. Contract No. 14-12-0001-30283.


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1.7.8 Polar Bear

The following causes of harm to polar bears from oil have been identified (based on Engelhardt 1985) (1): damage to sensitive tissue through direct contact with lungs (following

inhalation) or eyes; toxic effects following ingestion; affects on thermoregulation; and behavioural modifications due to avoidance. Experimental evidence has indicated that polar bears can take up hydrocarbon residues through their skin and by inhalation but primarily by ingestion (2). Polar bears will groom contaminated fur, resulting in ingestion of oil. This has been shown to have the potential to be fatal (3). Polar bears are reliant on their fur for thermal insulation which is severely affected by the presence of oil. The metabolic rate of bears affected by oil has been shown to increase significantly to counteract the increased heat loss (4). In addition to metabolic effects bears have been shown to avoid oil contaminated water (5). Such avoidance is likely to result in decreased hunting efficiency. From the above it is strongly suggested that polar bears are very sensitive to oil contamination and if a spill affects the ice in which they hunt, they would also be vulnerable.

1.8 OIL SPILL RISK ASSESSMENT

1.8.1 Behaviour of Oil Spills

The results of the modelling are discussed below and are used to predict the overall oil spill risk for the project footprint. Scenario 1 represented a blowout at the Alpha well, releasing 21,000 bbls of medium crude over 24 hours. Figure 1.2 shows the footprint of areas with a greater than 1% probability of impact. The footprint is oriented in a generally north-northwest to south-southeast direction. Oil can reach almost any location within this footprint in 4 days or less (Figure 1.3). There is a very low (<1%) probability of oil being transported to the shoreline of Disko Island. The earliest oil is predicted to arrive onshore is 4.67 days after the spill begins.

(1) Engelhardt, F.R. 1985. Petroleum Effects on Marine Mammals. Aquatic Toxicology, 4:199-217. (2) Engelhardt, F.R. 1985. Petroleum Effects on Marine Mammals. Aquatic Toxicology, 4:199-217. (3) Engelhardt, F.R. 1985. Petroleum Effects on Marine Mammals. Aquatic Toxicology, 4:199-217. (4) Engelhardt, F.R. 1985. Petroleum Effects on Marine Mammals. Aquatic Toxicology, 4:199-217. (5) Geraci, J. R. & Aubin, D. J. 1988. Synthesis of effects of oil on marine mammals. MMS report. Contract No. 14-12-0001-30283.

Figure 1.2 Stochastic Results Probability Contours Scenario 1

Figure 1.3 Stochastic Results Scenario 1 Minimum Travel Times


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Scenario 2 simulated a spill at the Alpha well of 11,500 m3 of diesel fuel, the largest volume considered in this study. However, the area within the water surface oiling probability footprint (Figure 1.4) is smaller than that of Scenario 1 (Figure 1.2) due to the different properties of the oils. Diesel fuel evaporates at a much faster rate than medium crude oil and therefore is removed from the water surface more quickly. The map of minimum travel times (Figure 1.5) indicates that almost any location within the oiled footprint can be reached in 3 days or less. No oil is predicted to reach shore for this scenario.



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Figure 1.5 Stochastic Results Minimum Travel Times Scenario 2

Scenario 3 was a 1,690 m3 spill of heavy fuel oil at the Alpha well. This scenario resulted in a footprint for the water surface at risk of oiling (Figure 1.6) that is approximately the same as that for Scenario 1. The footprint is oriented in a generally north-northwest to south-southeast direction, and oil can reach almost any location within this footprint in 4 days or less (Figure 1.7). Only one of the 500 simulations run for this scenario resulted in oil reaching the shoreline. For that simulation oil reached Disko Island 5.67 days after the spill.


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Scenario 4 represented a blowout at the T4 well, releasing 21,000 bbls of medium crude over 24 hours. Figure 1.8 shows the footprint of areas with greater than a 1% probability of impact. The footprint is oriented in a generally northwest to southeast direction, consistent with the predominant wind directions. There is a higher probability of oil moving south and/or west of the well site; the probability of oil moving northeast from the site is very low. Oil can reach almost any location within this footprint in 4 days or less (Figure 1.9). No oil is predicted to reach shore for this scenario.



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Scenario 5 simulated a spill at the T4 well of 11,500 m3 of diesel fuel. As noted above for Scenario 2, although more oil is spilled in this scenario than in the other scenarios at this location, the water surface oiling probability footprint (Figure 1.10) covers the smallest area due to the faster evaporation rate of the diesel fuel. The map of minimum travel times (Figure 1.11) indicates that almost any location within the oiled footprint can be reached in 3 days or less. No oil is predicted to reach shore for this scenario.


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Scenario 6 was a 1,690 m3 spill of heavy fuel oil at the T4 well. The footprint for the water surface at risk of oiling (Figure 1.12) is approximately the same as that for Scenario 4. The footprint is oriented in a generally northwest to southeast direction with a higher probability of oil moving west of the spill site. Oil can reach almost any location within this footprint in 4 days or less (Figure 1.13). No oil is predicted to reach shore for this scenario.



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1.8.2 Risk Assessment

The probability of a large spill due to a blow out or vessel incident is very low, due to the short duration of the drilling operation and the mitigation measures proposed. The results of oil spill modelling indicate that none of the oil spill scenarios will result in oil reaching the coast over the simulated period (ie 7 days). Pelagic animals are therefore most vulnerable and auks feeding on the water surface or moulting would also be sensitive. Swimming seals and cetaceans are not considered to be at high risk from the effects of a spill in open water. However a spill in July, August and November has a higher probability of reaching the ice margin. In this case it may become entrapped in ice and there is a potential for more significant effects including potential mortality of sea mammals and polar bears if the ice leads and blow holes become contaminated. The most likely scenario of a spill affecting the water surface would be a smaller spill of diesel during refuelling which would cause localised impacts on water quality for a short period of time (eg 2 to 3 days). A small diesel spill during refuelling is assessed to be potentially minor.


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The impact of an oil spill on pelagic animals, particularly those found on the ice during July-November is assessed to be potentially major. However, the mitigation measures in place make a medium or large spill unlikely.

1.9 MITIGATION OF OIL SPILL IMPACTS

1.9.1 Prevention

The most likely spill scenarios will involve small spills to land during fuel handling and storage. Key factors in reducing the likelihood and severity of such spills are listed below: equipment standards; operational control, procedures and training; planning of critical activities; navigational risk control; and meteorological risk control. Equipment Standards

Equipment standards will be maintained through the enforcement of requirements for specific design criteria. Preventive maintenance on critical fuel handling and storage components will be undertaken. Oil spill prevention measures will be incorporated in audit and inspection routines for contractor’s plant and equipment. Operational Control, Procedures and Training

Where necessary, oil spill prevention measures will be incorporated into operational procedures. Specific controls will be adopted for vessel offloading, bunkering and refuelling. The procedures will include specific controls on the supervision and competence of critical roles. Training standards and requirements will also be specified. Specific controls will be adopted in response to circumstances which increase oil spill risk for example, low temperatures or high winds affecting vessel operations at the jetty and non routine events such as heavy lifting operations near oil storage and delivery systems. Specific procedures will be adopted to reduce risk due to operators being unfit to work. Planning

Operations which are subject to a high risk of oil spill will be planned. If necessary specific oil spill risk will be incorporated into job hazard analysis and incorporated into management of change procedures. Local conditions and escalation of bad weather will be closely monitored.

55 Village Square Drive

South Kingstown, RI 02879 Phone: +1 401 789-6224

Fax: +1 401 789-1932 www.asascience.com

DRAFT REPORT

Drill Cuttings Discharge and Oil Spill Modeling - Baffin Bay, Greenland

AUTHOR(S):

Kathy Jayko, Eileen Graham, Tatsu Isaji Project Manager: Eric Comerma

PROJECT NUMBER:

ASA 09-391 VERSION: Draft DATE: February 2010

CLIENT: Jonathan Perry, ERM – UK

ERM 09-391 | Greenland Draft, Feb. 2010

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Applied Science Associates

Table of Contents

Executive Summary ............................................................................................... iv 1. Introduction ........................................................................................................ 1 2. Location and Model Setup ................................................................................. 2

2.1. Study Location ........................................................................................... 2 2.2. Current Data Input ..................................................................................... 4 2.3. Wind Data Input ......................................................................................... 5

3. Drill Cuttings Discharge Simulations ................................................................ 12 3.1. Description of Model ................................................................................ 12 3.2. Discharge Scenarios ............................................................................... 12 3.3. Predicted Seabed Deposition Thickness ................................................. 15

4. Oil Spill Simulations ......................................................................................... 21 4.1. Spill Scenarios ......................................................................................... 21 4.2. Oil Data .................................................................................................... 22 4.3. Stochastic Model Predictions ................................................................... 23 4.4. Deterministic Model Predictions .............................................................. 31

5. Summary ......................................................................................................... 39 6. References ...................................................................................................... 41 Appendix A: MUDMAP Model Description .......................................................... A-1 Appendix B. OILMAP Model Description ............................................................ B-1


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List of Figures

Figure 1. Area of study in Baffin Bay, showing the location of the proposed well sites in the

Sigguk Block. The inset at top identifies the well sites. .......................................................... 3 Figure 2. Average surface currents in the vicinity of the proposed well sites. ....................................... 5 Figure 3. NCEP grid wind locations (black squares) in relation to proposed well locations

(circles). NCEP locations 14729, 14730, 14821 and 14822 were used in this study. ........... 7 Figure 4. Wind roses for NCEP grid 14822 based on 10 years of data from 2000 through 2009. ........ 8 Figure 5. Wind roses for NCEP grid 14730 based on 10 years of data from 2000 through 2009. ........ 9 Figure 6. Wind roses for NCEP grid 14821 based on 10 years of data from 2000 through 2009. ...... 10 Figure 7. Wind roses for NCEP grid 14729 based on 10 years of data from 2000 through 2009. ...... 11 Figure 8. ALPHA - Cumulative seabed deposition thickness contours for discharge from the

Aplha well. The upper left figure is deposition of mud, the upper right figure is depostion of cuttings, and the bottom figure is the combined mud and cuttings deposition. ............................................................................................................................ 17

Figure 9. T4 - Cumulative seabed deposition thickness contours for discharge from the T4 well. The upper left figure is deposition of mud, the upper right figure is depostion of cuttings, and the bottom figure is the combined mud and cuttings deposition. ................... 18



Figure 12. Scenario 1 - Probability contours of water surface oiling for a 21,000 bbl spill of medium crude oil at the Alpha well. ..................................................................................... 25

Figure 13. Scenario 1 – Minimum travel times for a 21,000 bbl spill of medium crude oil at the Alpha well. ............................................................................................................................ 25

Figure 14. Scenario 2 - Probability contours of water surface oiling for a 11,500 m3 spill of diesel fuel at the Alpha well. ................................................................................................. 26

Figure 15. Scenario 2 – Minimum travel times for a 11,500 m3 spill of diesel fuel at the Alpha well. ...................................................................................................................................... 26

Figure 16. Scenario 3 - Probability contours of water surface oiling for a 1690 m3 spill of heavy fuel oil at the Alpha well. ....................................................................................................... 27

Figure 17. Scenario 3 – Minimum travel times for a 1690 m3 spill of heavy fuel oil at the Alpha well. ...................................................................................................................................... 27

Figure 18. Scenario 4 - Probability contours of water surface oiling for a 21,000 bbl spill of medium crude oil at the T4 well. .......................................................................................... 28

Figure 19. Scenario 4 – Minimum travel times for a 21,000 bbl spill of medium crude oil at the T4 well. ................................................................................................................................. 28

Figure 20. Scenario 5 - Probability contours of water surface oiling for a 11,500 m3 spill of diesel fuel at the T4 well. ...................................................................................................... 29

Figure 21. Scenario 5 – Minimum travel times for a 11,500 m3 spill of diesel fuel at the T4 well. ....... 29 Figure 22. Scenario 6 - Probability contours of water surface oiling for a 1690 m3 spill of heavy

fuel oil at the T4 well. ............................................................................................................ 30 Figure 23. Scenario 6– Minimum travel times for a 1690 m3 spill of heavy fuel oil at the T4

well. ...................................................................................................................................... 30 Figure 24. ALPHA - Scenario 1. Predicted water surface signature (top) and mass balance

(bottom) of a 21,000 bbl spill of medium crude oil at the Alpha well in August (left) and November (right). .......................................................................................................... 33

Figure 25. ALPHA - Scenario 2. Predicted water surface signature (top) and mass balance (bottom) of an 11,500 m3 spill of diesel fuel at the Alpha well in August (left) and November (right) .................................................................................................................. 34


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Figure 26. ALPHA - Scenario 3. Predicted water surface signature (top) and mass balance (bottom) of a 1690 m3 spill of heavy fuel oil at the Alpha well in August (left) and November (right) .................................................................................................................. 35

Figure 27. T4 - Scenario 4. Predicted water surface signature (top) and mass balance (bottom) of a 21,000 bbl spill of medium crude oil at the T4 well in August (left) and November (right) .................................................................................................................. 36

Figure 28. T4 - Scenario 5. Predicted water surface signature (top) and mass balance (bottom) of an 11,500 m3 spill of diesel fuel oil at the T4 well in August (left) and November (right) .................................................................................................................................... 37

Figure 29. T4 - Scenario 6. Predicted water surface signature (top) and mass balance (bottom) of a 1690 m3 spill of heavy fuel oil at the T4 well in August (left) and November (right). ................................................................................................................................... 38

List of Tables Table 1. Proposed well locations. ........................................................................................................... 2 Table 2. Specifications for the drill cuttings and mud discharge scenario at the Alpha well (Cretaceous

sediments).............................................................................................................................. 13 Table 3. Specifications for the drill cuttings and mud discharge scenarios at the T4, T8 and T16 wells

(Tertiary sediments). .............................................................................................................. 13 Table 4. Mud size distribution (adapted from Brandsma and Smith, 1999). ........................................ 14 Table 5. Cuttings size distribution (adapted from Brandsma and Smith, 1999). .................................. 14 Table 6. Area within predicted deposition footprint by deposition thickness for each drilling location. 16 Table 7. Scenario specifications for the oil spill modeling scenarios. .................................................. 21 Table 8. Summary of oil characterization data. .................................................................................... 22


iv


Executive Summary ERM contracted with Applied Science Associates, Inc. (ASA) to perform modeling studies for the Sigguk Block, located in Baffin Bay, offshore Greenland. Models were used to predict the seabed deposition of discharged mud and drill cuttings, and to assess potential spills of medium crude oil, diesel fuel and heavy fuel oil. ASA’s MUDMAP and OILMAP models were used for the simulations. Regional currents data from the global HYCOM model was obtained and used to create seasonal first-order Markov transition matrices for use in the MUDMAP simulations. The advantage of this technique is that it avoids the use of specific flow events associated with real dates and times while preserving the speed and direction distribution associated with the original data. The HYCOM velocity time series data, varying in space and time, was used for the OILMAP oil spill simulations. Drill Cuttings Discharge Mud and drill cuttings discharges were simulated using MUDMAP at four well locations. At the Alpha well there is slight preference for transport to the north, with the 0.01 mm deposition thickness contour extending more than 2 km from the discharge location in that direction. The footprint at the T16 site shows a slight northwest-southeast orientation; the 0.01 mm contour extends about 2 km in each direction. The stronger currents at the T4 and T8 sites result in larger deposition footprints. At the T4 site the 0.01 mm contour extends 3.5 km and the 0.02 mm contour extends 2 km to the southwest. Similarly at the T8 site the 0.01 mm contour extends 4 km and the 0.02 mm contour extends 3 km to the west-southwest. At all sites, the majority of the cuttings are deposited within 300-800 m of the well location. For all locations the predicted bottom deposition greater than 1 mm extends less than 200 m from the drill site in any direction, and is primarily due to the discharged cuttings which remain in the vicinity due to their faster settling rates. Deposits greater than 1 mm cover an area of approximately 0.13 km2 at the Alpha well, 0.08 km2 at the T4 and T16 wells, and 0.09 km2 at the T8 well. Oil Spills OILMAP’s stochastic and deterministic models were applied to spills at two sites: the Alpha well and the T4 well. At each site three potential spill scenarios were considered: a blowout of medium crude oil, a release of diesel oil, and a release of heavy fuel oil (HFO). Spills were assumed to occur only during ice-free conditions corresponding to the expected drilling period (June-November); no ice cover was included in the simulations. Simulations were not taking into account oil & ice interaction.


v


The stochastic model predicted the footprint of sea surface oiling for each release, assigning a probability of being oiled and a minimum travel time to areas within the footprint. The stochastic results indicate that spills are most likely to move either to the northwest or the southeast of the release site mainly due to the predominant wind pattern. Spills at the T4 well had a slightly higher probability of moving to the west within the northwest-southeast footprint. No shoreline impacts were predicted for any of the spills at the T4 well. A very low probability (<1%) of shoreline oiling was predicted for the medium crude and heavy fuel oil spills at the Alpha well. Oil could potentially reach Disko Island as soon as 4.7 days after a spill. Deterministic trajectory/fates simulations were done to examine the oil’s behavior under representative seasonal wind and currents conditions. The six spill scenarios were run using winds and currents from the same time periods in August and November. These simulations showed that oil could potentially be transported up to 160 km from the spill site within 7 days. The mass balance graphs indicated approximately one-third of the medium crude oil, one-half of the diesel fuel, and one-third of the heavy fuel oil would be likely to evaporate within 7 days. Only the diesel fuel was predicted to lose significant surface mass via dispersion into the water column in the event of strong wind conditions.


1


1. Introduction ERM contracted with Applied Science Associates, Inc. (ASA) to perform modeling studies to assess the potential impact of drilling and oil spill from the Sigguk Block, located in Baffin Bay, offshore Greenland. ASA was requested to undertake the following numerical model simulations:

• Dispersion of drilling discharges, muds & drill cuttings, to estimate seabed deposition.

• Dispersion of oil, diesel fuel and heavy fuel oil spilled due to different potential situations to determine the areas most likely to be impacted, and the oil weathering and shoreline impacts that could be expected in a worst-case spill.

The following models were applied:

• ASA’s MUDMAP modeling system to simulate the dispersion of drill cuttings discharges and for far-field simulations of chlorine in the cooling water discharge.

• ASA’s OILMAP modeling system to predict surface oiling probabilities and travel time contours (stochastic model) and to predict oil transport and weathering in response to specific environmental conditions (trajectory/fates model).

Several modeling scenarios were defined by the client to represent seasonal current conditions encountered in the study area, as well as to consider different discharge conditions. Currents in the region were assessed based on HYCOM data output. Input data for the models is described in Section 2. The drill cuttings discharge, cooling water discharge and oil spill scenarios and results of the simulations are described in Sections 3, 4, and 5, respectively. Conclusions are in Section 6, and References in Section 7. A brief description of the models used in this study is presented in Appendix A (MUDMAP), and Appendix B (OILMAP).


2


2. Location and Model Setup 2.1. Study Location The Sigguk Block is located in Baffin Bay with Baffin Island, Canada to the west and Greenland to the east. The area is generally ice free from July through November and ice covered (>80% ice) from December through June, although there is considerable interannual variability. Ice forms in early winter along the Canadian coastline and advances south and east across Baffin Bay as the winter progresses. The ice retreat in the summer occurs in the opposite direction, retreating more rapidly to the west than to the north. Icebergs calve from glaciers along the western coast of Greenland throughout the ice-free times of year and are transported in a counter-clockwise direction around Baffin Bay by currents and winds. The western Greenland shelf is an area of relatively low biological productivity. Studies have shown decreasing productivity in zooplankton and several fish stocks (cod, redfish and long rough dab) thought to be in response to changing climate, temperature, and salinity as well as over-fishing. Several sites are under consideration for potential drilling as shown in Figure 1. For this study, drill cuttings discharges were modeled at four of the sites and potential oil spills were modeled at the northernmost (T4 well) and southernmost (Alpha well) of the sites. The locations of these sites are listed in Table 1. Water depths at the sites range from 319-631 m.

Table 1. Proposed well locations.

Well name Latitude Longitude Water depth (m)

Sediment type

Alpha 70.315075o N 58.478112o W 319 Cretaceous

T4 71.129103o N 59.902660o W 485 Tertiary

T8 70.300784o N 59.531483o W 490 Tertiary

T16 70.629521o N 59.104640o W 631 Tertiary


3


Figure 1. Area of study in Baffin Bay, showing the location of the proposed well sites in the Sigguk Block. The inset at top identifies the well sites.


4


2.2. Current Data Input The West Greenland Current flows northward along the shelf and shelf break of western Greenland from roughly 60o-75o N. It is comprised of cooler, fresher water from the East Greenland Current, as well as water from the Labrador Sea and the southern coast of Greenland. The current along the shelf is relatively slow with a speed of approximately 35 cm/sec. Much slower counter-currents with speeds of 3 cm/sec have been observed moving south-eastward offshore of the main current. At its northern extent the West Greenland Current crosses Baffin Bay and joins with waters from the Canadian Arctic Archipelago to form the southward flowing Baffin Current with an average speed of 20 cm/sec. These two currents result in a cyclonic circulation pattern in Baffin Bay that is part of the North Atlantic subpolar gyre. For this study regional currents for the area were assessed from a hindcast analysis using inputs from the HYCOM (HYbrid Coordinate Ocean Model) 1/12 degree global simulation assimilated with NCODA (Navy Coupled Ocean Data Assimilation) by the U.S. Naval Research Laboratory (http://www.hycom.org). The model domain is defined by a grid of 1/12 degree resolution in the horizontal directions, and variable vertical resolution depending on depth. In the vicinity of the proposed sites, the vertical grid has 15 layers and a maximum water depth of 600 m. This HYCOM dataset covers the period from January 2005 through December 2009 with data available on a 24-hour time step. Figure 2 shows the HYCOM surface currents in the vicinity of the proposed well sites averaged over the five year span of the dataset. For use in the MUDMAP simulations, the HYCOM velocity time series data was used to create seasonal first-order Markov transition matrices. The Markov matrix classifies the current data in terms of five speed and eight direction bins, and provides the probability of the current falling within each bin given its previous speed and direction bin. Use of this synthetic current record allows a realistic time series to be generated since correlations between speed and direction are maintained and guarantees that the generated currents result in a speed/direction distribution equivalent to that of the original flow. A particular advantage to this technique is that it avoids the use of specific flow events associated with real dates and times. Since the OILMAP simulations are concerned with oil transport in response to specific environmental forcing, the five years of HYCOM velocity time series data was used without modification for these simulations. The currents vary with both time and location.


5


Figure 2. Average surface currents in the vicinity of the proposed well sites.

2.3. Wind Data Input Wind data is one of the primary forcing parameters for the oil spill model and plays a critical role in determining the oil’s trajectory. For this reason it is important to use the best available dataset that has a proper time and space coverage. For stochastic simulations, the model would ideally be supplied with a minimum of ten years of wind data at the spill location. Since observed data was not available, other resources were utilized. Wind data was obtained from the output of a numerical atmospheric model (the National Center for Environmental Predictions (NCEP) Environmental Modeling Center Regional Spectral Model) provided by the U.S. National Oceanic and Atmospheric Administration - Cooperative Institute for Research in Environmental Studies (NOAA-CIRES) Climate Diagnostics Center (CDC) in Boulder, Colorado. These datasets


6


comprise long-term model wind time series and more accurately describe the wind characteristics than a statistical representation, such as a wind rose, when performing stochastic modeling. Multiple NCEP model grid locations are available in the vicinity of the Sigguk Block. For this application wind speed and direction data (at 10 meters) from the NCEP model grid locations closest to the spill sites was obtained from the NOAA/CDC data server for the ten-year period from January 3, 2000 to January 2, 2010. The four closest model grid locations (Figure 3) were utilized to create the wind record. During oil trajectory simulations the wind affecting the oil’s movement is spatially interpolated from the four grid locations. Figures 4 through 7 present the monthly wind rose averages at the NCEP model grid locations used in this study. Wind roses for the NCEP locations south of the spill sites (NCEP 14822 and NCEP 14730, Figures 4 and 5) show winds are primarily from the northwest in the winter months (November – April) with mean speeds ranging from 5-7.5 m/sec. In the remaining months of the year winds are primarily from the northwest or southeast and mean wind speeds are 3.9-4.4 m/sec. NCEP winds at the locations north of the spill sites (NCEP 14821 and NCEP 14729, Figures 6 and 7) show a significantly different character. Overall wind speeds are lower with the highest mean winds (4.3-5.8 m/sec) occurring from August – December. The directional distribution is not as well defined either. There is still a noticeable preference for winds from the northwest, particularly from November – April, and a strong southeast component from May – October, but a higher percentage of winds from the northeast is evident at these stations. From the standpoint of oil spill trajectory modeling, the events that result in the greatest extent of surface oil movement are characterized by persistent winds from the same general direction. Therefore, the wind rose analysis is useful for uncovering major monthly and seasonal trends, but actual wind record time series are used for running the model simulations.


7


Figure 3. NCEP grid wind locations (black squares) in relation to proposed well locations

(circles). NCEP locations 14729, 14730, 14821 and 14822 were used in this study.


8


Figure 4. Wind roses for NCEP grid 14822 based on 10 years of data from 2000 through 2009.

2010/2/12 2000-2010_69N58W_17665.WNE Lon(Deg) Lat(deg) Start Date End Date days Sample Time-58.13 69.52 1999/1/4 2010/1/1 4015 6hrs

LEGEND

Period Percentage% Calm

(wind from)Northspeed

m/s

Sample CountMax.Speed(m/s)Ave.Speed(m/s)

0.1 0.3

0.5 0.7

0.9 151020

1020

3040

50Yearly% Calm0.35

1460620.6 5.1

January % Calm 0.24

123420.6 5.8

February% Calm0.18

113217.54.7

March% Calm0.73

124013.4 4.9

April% Calm 0.5

120015.4 4.9

May% Calm0.56

124013.43.9

June% Calm0.58

120013.9 4.0

July% Calm 0.48

124014.9 4.1

August% Calm0.4

124015.44.4

September% Calm0.17

120018.5 5.4

October % Calm 0.24

124018.0 5.9

November% Calm0.08

120019.57.1

December% Calm0

124017.5 6.6


9



2010/2/12

2000-2010_69N60W_17665.WNE Lon(Deg) Lat(deg) Start Date End Date days Sample Time-60.00 69.52 1999/1/4 2010/1/1 4015 6hrs

LEGEND


(wind from) North speed

m/s


0.1 0.3

0.5

0.7 0.9151020

1020

3040

50Yearly% Calm0.26

1460619.5 5.4

January % Calm0.08

123417.0 6.4

February% Calm0.18

113218.05.5

March % Calm0.32

124014.9 5.4

April% Calm0.08

120015.4 5.2

May% Calm0.32

124012.94.1

June% Calm0.42

120013.9 4.1

July % Calm0.4

124014.4 4.0

August% Calm0.65

124015.94.4

September % Calm0.33

120018.0 5.6

October % Calm0

124018.0 6.3

November% Calm0.25

120019.57.5

December % Calm0.08

124015.9 6.5


10



2010/2/12


LEGEND


(wind from)North speed

m/s


0.1 0.3

0.5 0.7

0.9 1 510 20

10 20

30 40

50 Yearly % Calm 0.52

1460619.0 4.1

January % Calm 0.73

123418.5 3.7

February% Calm1.15

113219.03.0

March % Calm 0.97

124014.4 3.3

April % Calm 0.5

120014.9 3.2

May% Calm0.73

124013.93.3

June% Calm 0.5

120016.5 4.0

July % Calm 0.65

124016.5 4.3

August% Calm0.48

124016.54.5

September % Calm 0.25

120019.0 4.7

October % Calm 0.16

124014.9 5.3

November% Calm0

120019.05.4

December % Calm 0.16

124017.0 4.6


11



2010/2/12


LEGEND


(wind from) North speed

m/s


0.1 0.3

0.5

0.7 0.9151020

1020

3040

50Yearly% Calm0.64

1460619.0 4.1

January % Calm1.3

123414.9 3.6

February% Calm1.41

113218.03.1

March % Calm0.4

124013.4 3.5

April% Calm0.83

120014.9 3.4

May% Calm1.05

124013.93.3

June% Calm0.42

120014.4 3.8

July % Calm1.05

124016.5 4.0

August% Calm0.89

124017.54.3

September % Calm0.17

120017.5 4.7

October % Calm0

124016.5 5.5

November% Calm0

120019.05.8

December % Calm0.16

124015.4 4.3


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3. Drill Cuttings Discharge Simulations 3.1. Description of Model Mud and drill cuttings discharges were simulated using ASA’s MUDMAP modeling system. MUDMAP is a numerical model developed by ASA to predict the near and far field transport, dispersion, and bottom deposition of drill muds and cuttings. In MUDMAP, the equations governing conservation of mass, momentum, buoyancy, and solid particle flux are formulated using integral plume theory and then solved using a Runge Kutta numerical integration technique. The model includes three stages: convective descent/ascent, dynamic collapse and far field dispersion. It allows the transport and fate of the release to be modeled through all stages of its movement. The initial dilution and spreading of the plume release is predicted in the convective descent/ascent stage. The plume descends if the discharged material is denser than the local water at the point of release and ascends if the density is lower than that of the receiving water. In the dynamic collapse stage, the dilution and dispersion of the discharge is predicted when the release impacts the surface, bottom, or becomes trapped by vertical density gradients in the water column. The far field stage predicts the transport and fate of the discharge caused by the ambient current and turbulence fields. The model’s output consists of the movement and shape of the discharge plume, the concentrations of insoluble (i.e., cuttings and mud) discharge components in the water column, and the accumulation of discharged solids on the seabed. The model predicts the initial fate of discharged solids from the time of discharge to initial settling on the seabed. MUDMAP does not account for resuspension and transport of previously discharged solids; therefore it provides a conservative estimate of the potential seafloor concentrations. The far field, passive diffusion stage is based on a particle based random walk model. This is the same random walk model used in ASA’s OILMAP spill modeling system. More details about MUDMAP are included in Appendix A. The seasonal Markov matrices generated from HYCOM current data (Section 2.2) were used to provide the currents in these dispersion simulations. 3.2. Discharge Scenarios Drill cuttings and mud simulations were conducted for several of the proposed well sites. Table 2 summarizes the discharge scenario for the Alpha well, located in Cretaceous sediments. Table 3 provides the same information for the T4, T8 and T16 wells, located in Tertiary sediments. Tables 4 and 5 provide grain size distribution and settling velocity data for muds and cuttings, respectively. Simulations were run for the four well locations (Table 1) using the discharge scheme summarized in Tables 2 and 3. The Alpha well discharge consisted of 7702 MT of mud and 1671.4 MT of cuttings over 26.5 days. The T4, T8 and T16 well discharges comprised 5531 MT of mud and 1135.3 MT of cuttings over 13.8 days.


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Table 2. Specifications for the drill cuttings and mud discharge scenario at the Alpha well (Cretaceous sediments).

Drill Section

Diameter (in)

Length (ft)

Mud Discharged

(MT)

Cuttings Discharged

(MT) Duration

(days) Depth of

Discharge

1 36 236 233.5 214.2 0.7 seabed* 2 26 1024 468.5 483.9 1.6 seabed 3 17.5 3494

500

561.3 5 surface 4 12.25 3182 250.6 4.1 surface 5 8.5 4905 161.3 15.1 surface

End 6500 surface Total 7702 1671.4 26.5

• * Seabed releases are 5 m above seabed Table 3. Specifications for the drill cuttings and mud discharge scenarios at the T4, T8 and T16

wells (Tertiary sediments).

Drill Section

Diameter (in)

Length (ft)

Mud Discharged

(MT)

Cuttings Discharged

(MT) Duration

(days) Depth of

Discharge

1 36 272 247 247.0 0.5 seabed* 2 26 591 284 279.4 1.3 seabed 3 17.5 2116

500

339.9 2.44 surface 4 12.25 2001 157.7 2.77 surface 5 8.5 3389 111.3 6.75 surface

End 4500 Total 5531 1135.3 13.8

• * Seabed releases are 5 m above seabed


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Table 4. Mud size distribution (adapted from Brandsma and Smith, 1999).

Mud Particle

Size (microns)

Percent Volume

Settling Velocity

(cm/s) (m/day)

3.7 1 0.0003 0.26 5.5 4 0.0006 0.52 8.6 19.2 0.0015 1.30

12.2 19.2 0.0031 2.68 14.8 13.3 0.0045 3.89 16.0 13.3 0.0053 4.58 17.9 10 0.0066 5.70 20.3 5 0.0085 7.34 46.5 8 0.0446 38.53 77.2 7 0.1222 105.58

Table 5. Cuttings size distribution (adapted from Brandsma and Smith, 1999).

Cuttings Particle

Size (microns)

Percent Volume

Settling Velocity

(cm/s) (m/day)

1.0 8 0.0001 0.12

3.5 6 0.0017 1.49

12.5 7 0.0223 19

41.1 3 0.238 206

107.7 2 1.48 1276

217.2 18 4.07 3518

616.8 16 9.90 8552

1049.5 15 13.65 11,792

3585.1 25 26.21 22,647


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3.3. Predicted Seabed Deposition Thickness Figures 8-11 present plan views of the predicted cumulative seabed deposition at the end of the cuttings discharge for the four scenarios. For each location the deposition of mud only, cuttings only, and mud and cuttings combined is shown. As a result of the larger particle sizes and settling velocities of cuttings compared to mud (Tables 4 and 5), cuttings settle much more quickly and cover a smaller area of the seabed. For all scenarios the distribution of cuttings deposited on the seabed is fairly uniform around the discharge location. At the T4 (Figure 9) and T8 (Figure 10) wells there is a slight preference for deposition of the cuttings to the southwest and west-southwest of the site, respectively, in response to the prevailing currents. At the Alpha well (Figure 8), the uniform distribution indicates that the turbulence of the discharge is stronger than the local currents in determining the movement of the discharged cuttings over the short period of time before they settle to the bottom. At all sites, the bulk of the cuttings are deposited within 300-800 m of the well location. In contrast the much smaller particle sizes and settling velocities of the muds result in a much larger deposition footprint as the ambient currents transport muds farther from the release location while they remain in suspension. The HYCOM currents are weakest at the Alpha and T16 well locations and strongest at the T4 and T8 well locations. At the Alpha well (Figure 8) there is slight preference for transport to the north, with the 0.01 mm deposition thickness contour extending more than 2 km from the discharge location in this direction. The footprint at the T16 site (Figure 11) shows a slight northwest-southeast orientation; the 0.01 mm contour extends about 2 km in each direction. The stronger currents at the other sites are clearly evident in their mud deposition footprints. At the T4 site (Figure 9) the 0.01 mm contour extends 3.5 km to the southwest. The 0.02 mm contour for mud extends 1 km to the southwest, while the contour for the combined mud and cuttings deposition extends 2 km. Similarly at the T8 site (Figure 10) the 0.01 mm contour extends 4 km to the west-southwest and the 0.02 mm contour extends 3 km. For all locations the predicted bottom deposition greater than 1 mm extends less than 200 m from the drill site in any direction, and is primarily due to the discharged cuttings which remain in the vicinity due to their faster settling rates. Table 6 provides the areas covered within each deposition contour for the four locations. Deposits greater than 1 mm cover an area of approximately 0.13 km2 at the Alpha well, 0.08 km2 at the T4 and T16 wells, and 0.09 km2 at the T8 well.


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Table 6. Area within predicted deposition footprint by deposition thickness for each drilling location.

Mud and cuttings

thickness (mm)

Cumulative Area (km2)

Alpha T4 T8 T16

0.01 8.17 6.66 7.66 6.51

0.02 4.85 1.59 3.49 1.89

0.05 1.69 0.73 0.66 0.82

0.1 0.61 0.47 0.45 0.52

0.2 0.36 0.30 0.29 0.32

0.5 0.19 0.16 0.16 0.16

1 0.13 0.08 0.09 0.08

2 0.075 0.038 0.045 0.033

5 0.032 0.015 0.018 0.015

10 0.015 0.008 0.008 0.008

20 0.005 0.0025 0.0025 0.0025


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Figure 8. ALPHA - Cumulative seabed deposition thickness contours for discharge from the Aplha well. The upper left figure is deposition of mud, the upper right figure is depostion of

cuttings, and the bottom figure is the combined mud and cuttings deposition.


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Figure 9. T4 - Cumulative seabed deposition thickness contours for discharge from the T4 well. The upper left figure is deposition of mud, the upper right figure is depostion of cuttings, and the

bottom figure is the combined mud and cuttings deposition.


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Figure 10. T8 - Cumulative seabed deposition thickness contours for discharge from the T8 well. The upper left figure is deposition of mud, the upper right figure is depostion of cuttings,

and the bottom figure is the combined mud and cuttings deposition.


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Figure 11. T16 - Cumulative seabed deposition thickness contours for discharge from the T16 well. The upper left figure is deposition of mud, the upper right figure is depostion of cuttings,

and the bottom figure is the combined mud and cuttings deposition.


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4. Oil Spill Simulations 4.1. Spill Scenarios The spill scenarios investigated for this study were specified by the client and are summarized in Table 7. Spills were considered to occur at one of two sites: the Alpha well and the T4 well. The Alpha well is the southernmost drilling location proposed in the Sigguk Block while the T4 well is the northernmost location. Location coordinates of the Alpha and T4 wells are as defined in Table 1. At each site three potential spill scenarios were considered: a blowout of medium crude oil, a maximum release of diesel oil, and a maximum release of heavy fuel oil. All spill scenarios were requested to be simulated during the drilling period, June to November. That corresponds to the ice-free period. Ice cover or oil & ice interaction were not considered in these simulations. Water temperatures were held constant at 5°C. All simulations were run for 7 days.

Table 7. Scenario specifications for the oil spill modeling scenarios.

Scenario Location Oil Type Release Volume

Release Duration

(hrs)

1 Alpha Medium Crude 21,000 bbl 24

2 Alpha Diesel Fuel 11,500 m3 1

3 Alpha Heavy Fuel Oil 1690 m3 1

4 T4 Medium Crude 21,000 bbl 24

5 T4 Diesel Fuel 11,500 m3 1

6 T4 Heavy Fuel Oil 1690 m3 1

OILMAP’s stochastic and deterministic trajectory/fates models were applied to the spill scenarios.

• The stochastic simulations provide insight into the probable behavior of potential oils spills under the met-ocean conditions expected to occur in the study area. The stochastic analysis provides two types of information: 1) sea surface areas that might be oiled and the associated probability of oiling, and 2) the shortest time required for oil to reach any point in the areas predicted to be oiled. In addition, the simulations provide shoreline impact data expressed in terms of minimum and average times taken for oil to reach shore, and the percentage of simulations in which oil is predicted to reach shore.


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• The trajectory/fate simulations provide an estimate of the oil’s weathering under particular environmental conditions. A deterministic trajectory/fate simulation is performed under a specific set of met-ocean conditions identified in the stochastic analysis as resulting in significant impacts. This is typically defined as the simulation that predicts the shortest time for oil to reach shore, but can also be defined by other metrics such as the length of shoreline oiled, or the oiling of sensitive habitats. These simulations provide a time history of oil weathering over the duration of the simulation, expressed as the percentage of spilled oil on the water surface, on the shore, evaporated, and entrained in the water column.

A brief description of OILMAP is provided in Appendix B. 4.2. Oil Data The oils used in the simulations (medium crude, diesel fuel oil and heavy fuel oil) were requested by the client. Physical properties for these oils were available from Environment Canada’s oil database. Selected characteristics of the oils used in the simulations are given in Table 8. Evaporation characteristics for each oil were approximated based on its distillation curve.

Table 8. Summary of oil characterization data.

Oil Type Density (g/cm3)

Viscosity (cP)

Surface Tension

(dyne/cm)

Maximum Water

Content

Medium Crude 0.8373 33.0 30.0 70%

Diesel Fuel 0.8310 2.8 27.5 0%

Heavy Fuel Oil (HFO) 0.9275 17.0 30.2 60%

The maximum water content indicates the emulsion-formation tendency of the oil and the final water content of the emulsion. Oils with a higher water content tend to form emulsions that are persistent on the water surface, thus increasing their shoreline impacts and the volume of oil (mousse) to be cleaned. Refined oils do not tend to form emulsions.


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4.3. Stochastic Model Predictions The OILMAP stochastic model was applied to predict sea surface probabilities of oiling due to oil spills for the six potential spill scenarios in Table 7. The stochastic analysis for each scenario is based on an ensemble of 500 individual simulations, each with a different start time. Start times are selected randomly from within the drilling period / ice-free season (June-November) during the ten-year wind record, thus sampling the variability in the wind forcing. The sum of the sea surface trajectories for the individual simulations defines the expected footprint for each spill stochastic scenario. This footprint represents the likely area of sea surface impact from a spill for that location and season. Any one simulation will encounter a relatively small area of this footprint. Areas within the footprint for each stochastic scenario show the probabilities of water surface and shoreline oiling and minimum travel times based on the individual simulations. The stochastic results provide insight into the probable behavior of potential oil spills under the met-ocean conditions expected to occur in the study area. These results for the six scenarios are shown in Figures 12-23. Only surface oil predicted to be thicker than 200 nanometers was used to generate the probabilities and minimum travel times shown in the figures. This is a conservative minimum thickness at which the amount of oil left on the surface is insignificant. For each scenario, two figures are presented to display: • Probability of surface oiling. This map shows the area in which sea surface oiling

may be expected and the probability of oil reaching the area, based on the trajectories from the 500 independent simulations run for the scenario. The plot does not imply that the entire colored surface presented would be covered with oil in the event of a spill. The plot does not provide any information on the quantity of oil in a given area (water surface or shoreline); it only shows the probability that some oil reaches the area.

• Minimum travel times. The footprint on this map corresponds to the footprint on the probability map, and shows the shortest time required for oil to reach any point within the footprint based on the 500 simulations.

Scenario 1 represented a blowout at the Alpha well, releasing 21,000 bbls of medium crude over 24 hours. Figure 12 shows the footprint of areas with greater than a 1% probability of impact. The footprint is oriented in a generally north-northwest to south-southeast direction, consistent with the predominant wind directions. Oil can reach almost any location within this footprint in 4 days or less (Figure 13). Although not shown by Figure 12, there is a very low (<1%) probability of oil being transported to the shoreline of Disko Island. The earliest oil is predicted to arrive onshore is 4.67 days after the spill begins. Scenario 2 simulated a spill at the Alpha well of 11,500 m3 of diesel fuel, the largest volume considered in this study. However, the area within the water surface oiling


24


probability footprint (Figure 14) is smaller than that of Scenario 1 (Figure 12). The different properties of the oils used in the two scenarios are responsible for the difference. Diesel fuel evaporates at a much faster rate than medium crude oil and therefore is removed from the water surface more quickly. Diesel also reaches the 200 nanometer thickness threshold more quickly. The map of minimum travel times (Figure 15) indicates that almost any location within the oiled footprint can be reached in 3 days or less. No oil is predicted to reach shore for this scenario. Scenario 3 was a 1690 m3 spill of heavy fuel oil at the Alpha well. This scenario resulted in a footprint for the water surface at risk of oiling (Figure 16) that is approximately the same as that for Scenario 1. The footprint is oriented in a generally north-northwest to south-southeast direction, and oil can reach almost any location within this footprint in 4 days or less (Figure 17). Only one of the 500 simulations run for this scenario resulted in oil reaching the shoreline. For that simulation oil reached Disko Island 5.67 days after the spill. Scenario 4 represented a blowout at the T4 well, releasing 21,000 bbls of medium crude over 24 hours. Figure 18 shows the footprint of areas with greater than a 1% probability of impact. The footprint is oriented in a generally northwest to southeast direction, consistent with the predominant wind directions. There is a higher probability of oil moving south and/or west of the well site; the probability of oil moving northeast from the site is very low. Oil can reach almost any location within this footprint in 4 days or less (Figure 19). No oil is predicted to reach shore for this scenario. Scenario 5 simulated a spill at the T4 well of 11,500 m3 of diesel fuel. As noted above for Scenario 2, although more oil is spilled in this scenario than in the other scenarios at this location, the water surface oiling probability footprint (Figure 20) covers the smallest area due to the faster evaporation rate of the diesel fuel. The map of minimum travel times (Figure 21) indicates that almost any location within the oiled footprint can be reached in 3 days or less. No oil is predicted to reach shore for this scenario. Scenario 6 was a 1690 m3 spill of heavy fuel oil at the T4 well. The footprint for the water surface at risk of oiling (Figure 22) is approximately the same as that for Scenario 4. The footprint is oriented in a generally northwest to southeast direction with a higher probability of oil moving west of the spill site. Oil can reach almost any location within this footprint in 4 days or less (Figure 23). No oil is predicted to reach shore for this scenario.


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Figure 12. Scenario 1 - Probability contours of water surface oiling for a 21,000 bbl spill of medium crude oil at the Alpha well.

Figure 13. Scenario 1 – Minimum travel times for a 21,000 bbl spill of medium crude oil at the Alpha well.


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Figure 14. Scenario 2 - Probability contours of water surface oiling for a 11,500 m3 spill of diesel fuel at the Alpha well.

Figure 15. Scenario 2 – Minimum travel times for a 11,500 m3 spill of diesel fuel at the Alpha well.


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Figure 16. Scenario 3 - Probability contours of water surface oiling for a 1690 m3 spill of heavy fuel oil at the Alpha well.

Figure 17. Scenario 3 – Minimum travel times for a 1690 m3 spill of heavy fuel oil at the Alpha well.


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Figure 18. Scenario 4 - Probability contours of water surface oiling for a 21,000 bbl spill of medium crude oil at the T4 well.

Figure 19. Scenario 4 – Minimum travel times for a 21,000 bbl spill of medium crude oil at the T4 well.


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Figure 20. Scenario 5 - Probability contours of water surface oiling for a 11,500 m3 spill of diesel fuel at the T4 well.

Figure 21. Scenario 5 – Minimum travel times for a 11,500 m3 spill of diesel fuel at the T4 well.


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Figure 22. Scenario 6 - Probability contours of water surface oiling for a 1690 m3 spill of heavy fuel oil at the T4 well.

Figure 23. Scenario 6– Minimum travel times for a 1690 m3 spill of heavy fuel oil at the T4 well.


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4.4. Deterministic Model Predictions Two deterministic trajectory/fates simulations were done for each scenario to represent two different climatologic situations during the drilling period. None of these simulations included ice cover or oil & ice interaction. Theses simulations use a typical August wind representative of summer winds from the southeast. The second group of simulations, (“advancing ice simulations”), use a November wind representative of winter winds from the northwest. For each stochastic scenario, a pair of deterministic simulations were performed, always using same dates in August and November. These deterministic simulations provide a time history of oil weathering over the duration of the simulation, expressed as the percentage of spilled oil on the water surface, on the shore, evaporated, and entrained in the water column. Results of the trajectory/fates simulations are shown in Figures 24-29. Two figures are given for each simulation. The first shows the predicted footprint of the spilled oil (in gray) over the 7-day simulation. The oil’s position at the end of 7 days is shown in black. The second shows the oil mass balance over time, indicating the oil left on the water surface (green), in the atmosphere (pink), and in the water column (blue) over the duration of the simulation. Medium Crude Spill – Scenarios 1 and 4 Figures 24 and 27 present the trajectory and mass balance predicted for a 24-hour release of 21,000 bbl of medium crude oil at the Alpha and T4 wells, respectively. The oil is transported 120-140 km over the 7-day simulation, to the northwest in the August simulations and to the southeast in the November simulations. The trajectories are similar between the two locations, varying slightly due to differences in the local winds and currents. The most noticeable difference is in the November trajectories. The spill originating at the T4 well has a much wider swept area due to the varying currents encountered by the oil as it is moved south by the winds. The mass balance graphs indicate that approximately one-third of the oil evaporates by the end of the simulation and that very little oil is naturally dispersed into the water column. Diesel Spill – Scenarios 2 and 5 Figures 25 and 28 present the trajectory and mass balance predicted for a 1-hour release of 11,500 m3 of diesel fuel oil at the Alpha and T4 wells, respectively. The oil is transported 50-60 km to the northwest in the August simulations, and 75-110 km to the southeast in the November simulations over 7 days. The trajectories are similar between the two locations, with slight differences due to the local winds and currents. As noted for the medium crude spills, the most noticeable difference is in the November trajectories. The spill originating at the T4 well again has a much wider swept area due to the varying currents encountered by the oil as it is moved south by the winds.


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The mass balance graphs for the diesel spills show a much different behavior than predicted for the medium crude oil. Approximately one-half of the diesel fuel is predicted to evaporate by the end of the simulation. The most striking difference is in the amount of oil dispersed into the water column. The low viscosity of the diesel fuel allows the strong winds during the period simulated to initially disperse 10-20% of the diesel into the water column. As wind speeds drop off, some of the dispersed oil returns to the surface. However, very strong winds beginning about hour 40 in the August spills and hour 100 in the November spills drive all the remaining surface oil into the water column. Heavy Fuel Oil Spill – Scenarios 3 and 6 Figures 26 and 29 present the trajectory and mass balance predicted for a 1-hour release of 1690 m3 of heavy fuel oil at the Alpha and T4 wells, respectively. Oil trajectories are very similar to those predicted for the medium crude spills. The oil is transported 140-160 km over the 7-day simulation, to the northwest in the August simulations and to the southeast in the November simulations. Differences in the local winds and currents result in slight differences in the trajectories from the two locations, with the most noticeable being the wider swept area of the spill originating at the T4 well in the November. The mass balance graphs indicate that less than one-quarter of the oil evaporates by the end of the 7-day simulation. A small fraction of the oil is naturally dispersed into the water column at the beginning of the simulation, but when the oil emulsifies, it becomes too viscous to be dispersed by the strong winds occurring later in the simulation so the overall amount of oil dispersed remains very low.


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Figure 24. ALPHA - Scenario 1. Predicted water surface signature (top) and mass balance (bottom) of a 21,000 bbl spill of medium crude oil at the Alpha well in August (left) and November (right).


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Figure 25. ALPHA - Scenario 2. Predicted water surface signature (top) and mass balance (bottom) of an 11,500 m3 spill of diesel fuel at the Alpha well in August (left) and November (right)


35


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Figure 26. ALPHA - Scenario 3. Predicted water surface signature (top) and mass balance (bottom) of a 1690 m3 spill of heavy fuel oil at the Alpha well in August (left) and November (right)


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Figure 27. T4 - Scenario 4. Predicted water surface signature (top) and mass balance (bottom) of a 21,000 bbl spill of medium crude oil at the T4 well in August (left) and November (right)


37


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Figure 28. T4 - Scenario 5. Predicted water surface signature (top) and mass balance (bottom) of an 11,500 m3 spill of diesel fuel oil at the T4 well in August (left) and November (right)


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Figure 29. T4 - Scenario 6. Predicted water surface signature (top) and mass balance (bottom) of a 1690 m3 spill of heavy fuel oil at the T4 well in August (left) and November (right).


39


5. Summary Mud and Drill Cuttings Discharge Simulations Mud and drill cuttings discharges were simulated using MUDMAP at four well locations. The Alpha well discharge consisted of 7702 MT of mud and 1671.4 MT of cuttings over 26.5 days. The T4, T8 and T16 well discharges comprised 5531 MT of mud and 1135.3 MT of cuttings over 13.8 days. At the Alpha well there is slight preference for transport to the north, with the 0.01 mm deposition thickness contour extending more than 2 km from the discharge location in that direction. The footprint at the T16 site shows a slight northwest-southeast orientation; the 0.01 mm contour extends about 2 km in each direction. The stronger currents at the T4 and T8 sites are clearly evident in the size of their deposition footprints. At the T4 site the 0.01 mm contour extends 3.5 km and the 0.02 mm contour extends 2 km to the southwest. Similarly at the T8 site the 0.01 mm contour extends 4 km and the 0.02 mm contour extends 3 km to the west-southwest. At all sites, the majority of the cuttings are deposited within 300-800 m of the well location. For all locations the predicted bottom deposition greater than 1 mm extends less than 200 m from the drill site in any direction, and is primarily due to the discharged cuttings which remain in the vicinity due to their faster settling rates. Deposits greater than 1 mm cover an area of approximately 0.13 km2 at the Alpha well, 0.08 km2 at the T4 and T16 wells, and 0.09 km2 at the T8 well. Oil Spill Simulations For this study spills were considered to occur at one of two sites: the Alpha well and the T4 well. The Alpha well is the southernmost drilling location proposed in the Sigguk Block while the T4 well is the northernmost location. At each site three potential spill scenarios were considered: a blowout of medium crude oil, a maximum release of diesel oil, and a maximum release of heavy fuel oil. Spills were assumed to occur only during the drilling period (June - November) which correspond to ice-free conditions; no ice cover or oil & ice interaction were considered in the simulations. OILMAP’s stochastic model was applied to predict the footprint of sea surface oiling for each release. The model assigns a probability of being oiled and a minimum travel time to areas within the footprint. The stochastic results indicate that spills at any location are most likely to move either to the northwest or the southeast. Spills at the T4 well had a slightly higher probability of moving to the west within the northwest-southeast footprint. No shoreline impacts were predicted for the diesel spills due to evaporation and rapid spreading of the oil to an insignificant thickness. No shoreline impacts were predicted for the spills at the T4 well. A very low probability (<1%) of shoreline oiling was predicted for the medium crude and heavy fuel oil spills at the Alpha well. Oil could potentially reach Disko Island as soon as 4.67 days after a spill.


40


The stochastic simulations used winds and currents generated by model hindcasts. Such data is valuable for providing long time series of environmental conditions and is accurate in a statistical sense. However model-generated data may not replicate the very short-term or anomalous behavior that is often seen in observations. Such anomalous conditions represent a very low probability of occurrence and may not be reflected in the oil spill results. Deterministic trajectory/fates simulations were done to examine the oil’s behavior under representative summer and winter wind and currents conditions. The six spill scenarios were run using winds and currents from the same time periods in August and November. These simulations showed that oil could potentially be transported up to 160 km from the spill site within 7 days. The mass balance graphs indicated approximately one-third of the medium crude oil, one-half of the diesel fuel, and one-third of the heavy fuel oil would be likely to evaporate within 7 days. Only the diesel fuel was predicted to lose significant surface mass via dispersion into the water column in the event of strong wind conditions.


41


6. References Brandsma, M.G. and J.P. Smith, 1999. Offshore Operators Committee Mud and

Produced Water Discharge Model — Report and User Guide. Exxon Production Research Company. Production Operations Division. Report EPR.29PR.99.

Other References:

• Environment Canada's Oil Properties Database (2006): http://www.etc-cte.ec.gc.ca/databases/OilProperties/oil_prop_e.html

• NOAA NCEP Database: www.ncep.noaa.gov/

• Currents : http://oceancurrents.rsmas.miami.edu

A-1


Appendix A: MUDMAP Model Description MUDMAP is a personal computer-based model developed by ASA to predict the near and far field transport, dispersion, and bottom deposition of drill muds and cuttings and produced water (Spaulding et al; 1994; Spaulding, 1994). In MUDMAP, the equations governing conservation of mass, momentum, buoyancy, and solid particle flux are formulated using integral plume theory and then solved using a Runge Kutta numerical integration technique. The model includes three stages: convective descent/ascent, dynamic collapse and far field dispersion. It allows the transport and fate of the release to be modeled through all stages of its movement. The initial dilution and spreading of the plume release is predicted in the convective descent/ascent stage. The plume descends if the discharged material is more dense than the local water at the point of release and ascends if the density is lower than that of the receiving water. In the dynamic collapse stage, the dilution and dispersion of the discharge is predicted when the release impacts the surface, bottom, or becomes trapped by vertical density gradients in the water column. The far field stage predicts the transport and fate of the discharge caused by the ambient current and turbulence fields. MUDMAP is based on the theoretical approach initially developed by Koh and Chang (1973) and refined and extended by Brandsma and Sauer (1983) for the convective descent/ascent and dynamic collapse stages. The far field, passive diffusion stage is based on a particle based random walk model. This is the same random walk model used in ASA’s OILMAP spill modeling system (ASA, 1999). MUDMAP uses a color graphics-based user interface and provides an embedded geographic information system, environmental data management tools, and procedures to input data and to animate model output. The system can be readily applied to any location in the world. Application of MUDMAP to predict the transport and deposition of heavy and light drill fluids off Pt Conception, California and the near field plume dynamics of a laboratory experiment for a multi-component mud discharged into a uniform flowing, stratified water column are presented in Spaulding et al. (1994). King and McAllister (1997, 1998) present the application and extensive verification of the model for a produced water discharge on Australia’s northwest shelf. GEMS (1998) presents the application of the model to assess the dispersion and deposition of drilling cuttings released off the northwest coast of Australia. MUDMAP References APASA (2004). “ Modelling studies to assess the fate of sediments released during June

2004 reclaimer jetty dredging operation.” Asia-Pacific Applied Science Associates report to Oceanica Consulting Pty.

Brandsma, M.G. and T.C. Sauer, Jr., 1983. “The OOC model: prediction of short term fate of drilling mud in the ocean, Part I model description and Part II model results”. Proceedings of Workshop on An Evaluation of Effluent Dispersion and Fate Models for OCS Platforms, Santa Barbara, California.

Brandsma, M.G. & Smith, J.P., 1999. Offshore Operators Committee Mud and Produced Water Discharge Model - Report and User Guide. Exxon Production Research Company, December 1999.

A-2


Burns, K., S. Codi, M. Furnas, D. Heggie, D. Holdway, B. King, and F. McAllister, 1999.

Dispersion and fate of produced formation water constituents in an Australian Northwest Shelf shallow water ecosystem. Marine Pollution Bulletin 38(7):593-603.

GEMS - Global Environmental Modelling Services, 1998. Quantitative assessment of the dispersion and seabed depositions of drill cutting discharges from Lameroo-1 AC/P16, prepared for Woodside Offshore Petroleum, prepared by Global Environmental Modelling Services, Australia, June 16, 1998.

King, B. and F.A. McAllister ,1997. Modeling the dispersion of produced water discharge in Australia, Volume I and II. Australian Institute of Marine Science report to the APPEA and ERDC.

King, B. and F.A. McAllister ,1998. Modelling the dispersion of produced water discharges. APPEA Journal 1998, pp. 681-691.

Koh, R.C.Y. and Y.C. Chang, 1973. “Mathematical model for barged ocean disposal of waste”. Environmental Protection Technology Series EPA 660/2-73-029, U.S. Army Engineer Waterways Experiment Station, Vicksburg, Mississippi.

Khondaker, A. N., 2000. “Modeling the fate of drilling waste in marine environment – an overview”. Journal of Computers and Geosciences Vol 26, pp 531-540.

Nedweed, T, 2004. “ Best practices for drill cuttings and mud discharge modelling.” SPE 86699. Paper presented at the Seventh SPE International Conference on Health, Safety and Environment in Oil and Gas Exploration and Production, Calgary, Alberta, Canada. Society of Petroleum Engineers, P6.

Neff, J, 2005. “Composition, environment fates, and biological effect of water based drilling muds and cuttings discharged to the marine environment: A synthesis and annotated bibliography.” Report prepared for Petroleum Environment Research Forum and American Petroleum Institute.

Gallo. A and A. Rocha, 2006. “Computational modelling of drill cuttings and mud released in the sea from E&P activities in Brazil.” 9th International Marine Environmental Modelling Seminar.

Spaulding, M. L., T. Isaji, and E. Howlett, 1994. MUDMAP: A model to predict the transport and dispersion of drill muds and production water, Applied Science Associates, Inc, Narragansett, RI.

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Appendix B. OILMAP Model Description OILMAP is a state-of-the-art, personal computer based oil spill response system applicable to oil spill contingency planning and real time response and applicable for any location in the world (Jayko and Howlett, 1992; Spaulding et al., 1992a,b). OILMAP was designed in a modular fashion so that different types of spill models could be incorporated within the basic system, as well as a suite of sophisticated environmental data management tools, without increasing the complexity of the user interface. The model system employs a Windows based graphics user interface that extensively utilizes point and click and pull down menu operation. OILMAP is configured for operation on standard Pentium PCs and can be run on laptop and notebook computers to facilitate use in the field. The OILMAP suite includes the following models: a trajectory and fates model for surface and subsurface oil, an oil spill response model, and stochastic and receptor models. The relevant models are described in more detail below. The trajectory and fates model predicts the transport and weathering of oil from instantaneous or continuous spills. Predictions show the location and concentration of the surface and subsurface oil versus time. The model estimates the temporal variation of the oil’s areal coverage, oil thickness, and oil viscosity. The model also predicts the oil mass balance or the amount of oil on the free surface, in the water column, evaporated, on the shore, and outside the study domain versus time. The fate processes in the model include spreading, evaporation, entrainment or natural dispersion, and emulsification. As an option OILMAP can also estimate oil-sediment interaction and associated oil sedimentation. A brief description of each process algorithm is presented here. ASA (1997) provides a more detailed description for the interested reader. The oil sedimentation algorithm is described in French et al. (1994), ASA (1996) and Kirstein et al. (1985). Spreading is represented using the thick slick portion of Mackay et al.’s (1980, 1982) thick-thin approach. Evaporation is based on Mackay’s analytic formulation parameterized in terms of evaporative exposure (Mackay et al., 1980, 1982). Entrainment or natural dispersion is modeled using Delvigne and Sweeney’s (1988) formulation which explicitly represents oil injection rates into the water column by droplet size. The entrainment coefficient, as a function of oil viscosity, is based on Delvigne and Hulsen (1994). Emulsification of the oil, as function of evaporative losses and changes in water content, is based on Mackay et al. (1980, 1982). Oil-shoreline interaction is modeled based on a simplified version of Reed et al. (1989) which formulates the problem in terms of a shore type dependent holding capacity and exponential removal rate. For the subsurface component, oil mass injection rates from the surface slick into the water column are performed by oil droplet size class using Delvigne and Sweeney’s (1988) entrainment formulation. The subsurface oil concentration field is predicted using a particle based, random walk technique and includes oil droplet rise velocities by size class. The vertical and horizontal dispersion coefficients are specified by the user. Resurfacing of oil droplets due to buoyant effects is explicitly

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included and generates new surface slicks. If oil is resurfaced in the vicinity of surface spillets the oil is incorporated into the closest surface spillet. A more detailed presentation of the subsurface oil transport and fate algorithm is given in Kolluru et al. (1994). The basic configuration of the model also includes a variety of graphically based tools that allow the user to specify the spill scenario, animate spill trajectories, currents and winds, import and export environmental data, grid any area within the model operational domain, generate mean and/or tidal current fields, enter and edit oil types in the oil library, enter and display data into the embedded geographic information system (GIS) and determine resources impacted by the spill. The GIS allows the user to enter, manipulate, and display point, line, poly line, and polygon data geographically referenced to the spill domain. Each object can be assigned attribute data in the form of text descriptions, numeric fields or external link files. In the stochastic mode spill simulations are performed stochastically varying the environmental data used to transport the oil. Either winds, currents, or both may be stochastically varied. The multiple trajectories are then used to produce contour maps showing the probability of surface and shoreline oiling. The trajectories are also analyzed to give travel time contours for the spill. These oiling probabilities and travel time contours can be determined for user selected spill durations. If resource information is stored in the GIS database a resource hit calculation can be performed to predict the probability of oiling important resources. OILMAP has been applied to hindcast a variety of spills. These hindcasts validate the performance of the model. Hindcasts of the Amoco Cadiz, Ixtoc and Persian Gulf War spills and an experimental spill in the North Sea by Warren Springs Laboratory are reported in Kolluru et al. (1994). Spaulding et al. (1993) also present a hindcast of the Gulf War spill. Spaulding et al. (1994) present the application of the model to the Braer spill where subsurface transport of the oil was critical to understanding the oil’s movement and impact on the seabed. Recently Spaulding et al. (1996a) have applied the model to hindcast the surface and subsurface transport and fate of the fuel oil spilled from the North Cape barge. Integration of OILMAP with a real time hydrodynamic model and the hindcast of the movement of oil tracking buoys in Narragansett Bay are presented in Spaulding et al (1996b).

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OILMAP References Applied Science Associates, Inc. (ASA), 1997. OILMAP users manual Applied Science

Associates, Inc., Narragansett, RI. Delvigne, G.A.L., and C.E. Sweeney. 1988. Natural dispersion of oil, Oil and Chemical

Pollution 4:281-310. Delvigne, G.A.L., and L.J.M. Hulsen, 1994. Simplified laboratory measurement of oil

dispersion coefficient – Application in computations of natural oil dispersion. Proceedings of the Seventeenth Arctic and Marine Oil Spill Program, Technical Seminar, June 8-10, 1994, Vancouver, British Columbia, Canada, pp. 173-187.

French, D., E. Howlett, and D. Mendelsohn, 1994. Oil and chemical impact model system

description and application, 17th Arctic and Marine Oil Spill Program, Technical Seminar, June 8-10, 1994, Vancouver, British Columbia, Canada, pp. 767-784.

Jayko, K. and E. Howlett, 1992. OILMAP an interactive oil spill model, OCEANS 92, October

22-26, 1992, Newport, RI. Kirstein, B., J.R. Clayton, C. Clary, J.R. Payne, D. McNabb, G. Fauna, and R. Redding,

1985. Integration of suspended particulate matter and oil transportation study, Mineral Management Service, Anchorage, Alaska.

Kolluru, V., M.L. Spaulding, and E. Anderson, 1994. A three dimensional subsurface oil

dispersion model using a particle based technique, 17th Arctic and Marine Oil Spill Program, Technical Seminar, June 8-10, 1994, Vancouver, British Columbia, Canada, pp. 767-784.

Mackay, D., S. Paterson, and K. Trudel, 1980. A mathematical model of oil spill behavior,

Department of Chemical Engineering, University of Toronto, Canada, 39 pp. Mackay, D., W. Shui, K. Houssain, W. Stiver, D. McCurdy, and S. Paterson, 1982.

Development and calibration of an oil spill behavior model, Report No. CG-D027-83, US Coast Guard Research and Development Center, Groton, CT.

Reed, M., E. Gundlach, and T. Kana, 1989. A coastal zone oil spill model: development and

sensitivity studies, Oil and Chemical Pollution 5:411-449. Spaulding, M. L., E. Howlett, E. Anderson, and K. Jayko, 1992a. OILMAP a global approach

to spill modeling. 15th Arctic and Marine Oil Spill Program, Technical Seminar, June 9-11, 1992, Edmonton, Alberta, Canada, pp. 15-21.

Spaulding M. L., E. Howlett, E. Anderson, and K. Jayko, 1992b. Oil spill software with a shell

approach. Sea Technology, April 1992. pp. 33-40. Spaulding, M.L., E.L. Anderson, T. Isaji and E. Howlett, 1993. Simulation of the oil trajectory

and fate in the Arabian Gulf from the Mina Al Ahmadi Spill, Marine Environmental Research 36(2):79-115.

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Spaulding, M. L., V. S. Kolluru, E. Anderson, and E, Howlett, 1994. Application of three dimensional oil spill model (WOSM/OILMAP) to hindcast the Braer spill, Spill Science and Technology Bulletin 1(1):23-35.

Spaulding, M.L., T. Opishinski, E. Anderson, E. Howlett, and D. Mendelsohn, 1996a.

Application of OILMAP and SIMAP to predict the transport and fate of the North Cape spill, Narragansett, RI. 19th Arctic and Marine Oil Spill Program, Technical Seminar, June 12-14, 1996, Calgary, Alberta, Canada, pp. 745-776.

Spaulding, M.L., T. Opishinski, and S. Haynes, 1996b. COASTMAP: An integrated

monitoring and modeling system to support oil spill response, Spill Science and Technology Bulletin 3(3):149-169.


25

Navigational Risk Control

Navigational risks will be mitigated by requirements for vessels built and equipped to international standards (eg IMO (International Maritime Organisation) and SOLAS (International Convention on Safety of Life at Sea). Additional requirements for navigational equipment will be implemented for smaller project vessels. Crews will be appropriately qualified and subject to fitness for work assessments. Working procedures and manning levels will be specified, particularly for high risk operations and poor weather. Meteorological Risk Control

Weather and ice conditions will be taken into account for high risk activities such as refuelling at sea and any operations which involve close quarters operations between large vessels. A specific ice management plan will be adopted (see Chapter 7 of the EIA for details). Measures will be put in place to provide accurate weather and ice forecasts for the project area.

1.9.2 Oil Spill Response

Oil Spill Response and Mitigation Plans

A detailed oil spill response and mitigation plan will be produced prior to mobilisation and periodically updated as the project progresses. The level of response will depend on the circumstances of the spill and nature of the resources which are threatened according the following general guidance. Tier 1: a small spill which can be combated using facilities available from

the contractor during drilling or local to the spill site during operations. Tier 2: a medium spill which is estimated to be very unlikely in terms of

probability and which requires the involvement of the project emergency response resources in addition to contractor facilities and manpower.

Tier 3: a large spill which requires external resources to combat. The project oil spill plans will include provision for coordination of external oil spill response contractors, or third party equipment and national response authorities to combat Tier 3 spills. The approach to tactical oil spill response will be to contain the spill, shore line protection and remove where possible any free oil and clean were appropriate however clean up techniques will be managed to avoid additional impacts to sensitive environments.

Annex F

Group HSE and CSR Policies

11

HEALTH, SAFETY & ENVIRONMENT (HSE) POLICY Cairn is committed to protecting the health and safety of everybody involved with our activities, the people who come into contact with our operations and the health and sustainability of the environments in which we operate. We aspire to high standards of practice through a process of continual improvement and the adoption of international codes and standards where practicable.

To meet this commitment we will implement management systems in our operations that accord with the requirements of our health, safety and environmental standards and strive to:

• Ensure that our operations comply with applicable health, safety and environmental laws and regulations

• Implement controls to protect all personnel involved in our activities, to prevent pollution and to protect biodiversity

• Provide health, safety and environmental training to our employees and actively promote awareness of health, safety and environmental issues

• Ensure that contractors are aware of our policies and standards and where necessary, work with our contractors to raise their standards to meet them

• Foster a culture where accidents, incidents and near misses are reported and investigated and the lessons learned are shared throughout the organisation

• Implement a strategy for the conservation and sustainable use of biodiversity, based on the principle of ‘no net loss’

• Monitor our performance and conduct regular audits to ensure our controls are effective and that our health, safety and environmental aspirations are being achieved

• Set objectives and targets for improving health, safety and environmental performance and monitor and report openly on our performance

• Ensure that a high priority is placed on emergency preparedness and contingency planning, and regularly tested, so that any incidents can be responded to in a timely and effective manner

• Work with Government and regulatory bodies in the formulation or improvement of laws, policies, regulations and procedures aimed at protecting health, safety and the environment

• Consult with and respond to the concerns of other stakeholders on our health, safety and environmental performance, including biodiversity

Responsibility for compliance with Cairn's Group HSE Policy and standards lies with the Chief Executive, Directors, Managers and their staff.

Sir Bill Gammell Effective Date: November 2009 Chief Executive

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CORPORATE SOCIAL RESPONSIBILITY (CSR) POLICY

Cairn is committed to maintaining the highest standards of corporate social responsibility in its business activities. To meet this commitment we will seek to respect the rule of law, adopt appropriate international standards, implement management systems, and will strive to:

Business Ethics

• Behave with honesty and integrity in all our activities and relationships with others

• Maintain internal controls adequate to guide and ensure standards are met

Employees

• Respect the rights and dignity of every employee and treat them fairly and without discrimination.

• Encourage team working and the sharing of knowledge throughout the organisation

• Recognise employees’ individual and team contribution and reward them appropriately

Local Communities

• Respect the rights of indigenous peoples in all countries in which we operate

• Assist in the development of local community programmes where we operate, in consultation with local government, public and other appropriate stakeholders

Human Rights

• Identify, assess and manage human rights risks within our sphere of influence and activities

• Develop a culture which supports internationally recognised human rights and seeks to ensure non-complicity in human rights abuses.

Suppliers

• Seek to be honest and fair in our relationships with suppliers and contractors

• Encourage suppliers and contractors to abide by our standards

Responsibility for compliance with Cairn’s Group CSR policy and standards lies with the Chief Executive, Directors, Managers and their staff.

Sir Bill Gammell Effective Date: November 2009 Chief Executive

Documents

Annex A Cover - Naalakkersuisutnaalakkersuisut.gl/~/media/Nanoq/Files/Hearings/2010/Offentlig... · Annex D Drilling and Contingency Chemicals . CAIRN Greenland M-I SWACO PONS XVb