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Evaluating Oil Spill Risks through Stochastic and Deterministic
Modeling
Don Danmeier
Environmental Risk Scientist
San Ramon, March 2 2017
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Outline
• How modeling fits into an oil spill risk assessment
• Define stochastic and deterministic modeling
• Modeling considerations when assessing likelihood & consequence
• Comments on the common practice
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Part of a bigger picture
consultation with stakeholders
establish the context
risk assessment
risk treatment
monitor and review
Ris
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an
ag
em
en
t Pro
ce
ss
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Elements of the risk assessment
Hazard identification
HAZOP, PHA, What-If-Analysis
Likelihood analysis
historical data and extreme value theory
Consequence analysis
Predicted exposure & receptor sensitivity
Evaluate risk
Compare severity/prob. to tolerance criteria
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Hazard identification
event
blowout
penetration
depth
entering
formation
full
penetration
flow path
annulus
open hole
drill pipe
…
BOP status
fully open
partially
closed
…
release
location
sea surface
mudline
…
…
Hypothetical Event Tree Analysis (ETA) for exploratory drilling
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Likelihood analysis
1.E-05
1.E-04
1.E-03
1.E-02
1.E-01
10 100 1,000 10,000 100,000 1,000,000
pro
ba
bil
ity
tons
0.0E+00
2.0E-05
4.0E-05
6.0E-05
8.0E-05
1.0E-04
1.2E-04
pro
ba
bil
ity
flow rate (m3/d)low
high
Characterize volume & probability
For blowout example:
• Model flow for each ETA pathway
• Vol. based assumed intervention time
• Assign prob. based on historical data
Representative spill scenarios
• Evaluating all hazards is intractable
• Select representative scenarios for
consequence analysis
Consider complete risk profile
• Production & transportation risk
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Consequence analysis
Severity of spill determined by:
• Modeling fate & trajectory of spilled oil
• Comparing exposure to environmental sensitives
Importance of metocean inputs
• Drive trajectory…but also affect weathering
• Transient and seasonally variable
Day 8 Day 14 Day 20
spill scenario
oil properties
volume, duration, location
metocean conditions
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Establish & evaluate risk
Establishing risk
• Combine likelihood and consequence
– Establish risk level from one specific set of metocean parameters
– Do different metocean inputs affect severity?
Evaluating risk
• Compare risk level to tolerance criteria
• Are safeguards needed?
• Are risks as low as responsibly practical (ALARP)?
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Deterministic modeling
Simulate fate & transport under specific metocean forcing
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Stochastic modeling
Combine multiple runs under various metocean forcing
• Spatial variation of conditional probability
• Does not show extent of oiling
Histograms describe distribution of low/high exposure
• Rank runs by surface area oiled, shoreline oiled, …
• Shape of distribution indicates importance of metocean variabilty
+⋯ →+
trajectory #1 trajectory #2 probability plot
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A ‘representative’ or ‘worst-case’ run?
Some exposure metrics may not vary much….
but simplification may mask severity
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A ‘representative’ or ‘worst-case’ run?
…but some may vary considerably
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Conflicting guidance & practice
IPIECIA Good Practice Guide
The stochastic analyses should be paired with a most probable deterministic
case that can be utilized to support response planning
Australian Maritime Safety Authority
Stochastic modelling is the recommended method for determining the Zone of
Potential Impact…assess the likely effect of the spill scenarios for each resource
type identified within the ZPI .
BOEM risk assessments
• Simplified consequence analysis (no weathering, 30-day trajectory, no ecology)
• Summation of prob. x extent of oiling over stochastic set
Typical practice
Use P100 run as a (overly?) conservative assumption
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closing thoughts
• Don’t blindly adopt P100 run for consequence analysis
• Consider how severity varies with conditional probability
– Is comparison to tolerance criteria robust?
• When practical, establish risk by weighting consequence by conditional
probability
• If establishing risk only from central portion of stochastic set, confirm ability to
scale-up response
P50P95 P95or
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Questions?