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DNV GL © SAFER, SMARTER, GREENER DNV GL ©
SOFTWARE
Guidance on performing transportation risk analysis of hazardous materials
1
Spend your time reducing transportation risks rather
than spending time producing numbers
DNV GL ©
Contents
The hazards and risks of transporting hazardous materials
Transport Risk Assessment
TRA challenges
Mobile transport unit TRA Case Study
Pipeline TRA Case Study
– Before we get started
– The results
– Risk reduction options
References
2
DNV GL ©
The hazards and risks of transporting hazardous materials
3
DNV GL ©
Pipeline Accidents
Kaohsiung, Taiwan, 2014: Gas pipeline leak and explosions, 25 fatalities, 257
injured
Qingdao, China, 2013: Oil pipeline leak and explosion, 62 fatalities, 136
hospitalized. (Wikipedia - 2013 Qingdao pipeline explosion, 2014)
Dalian, China, 2010: Oil release to sea from port for 90km, covering 946km2.
Fatalities and injuries occurred, number not reported. Extent of environmental
damage also not reported. (Wikipedia - 2010 Xingang Port oil spill, 2013)
San Bruno, California, natural gas pipeline explosion, 8 fatalities. (Wikipedia -
2010 San Bruno pipeline explosion, 2014)
Ghislenghien, Belgium 2004: 24 fatalities, 120+ injuries. (French Ministry of
Sustainable Development, 2009)
4
DNV GL ©
Mobile Transport Accidents
Oil rail tank car
Lac-Mégantic, Canada, 6/6/2013, 42 fatalities, 5 missing presumed dead. 66 of
69 downtown buildings destroyed (30) or to be demolished (36).
West Virginia, USA, 16/2/15, Fireball, Fires, two towns evacuated, no injuries or
fatalities. Using CPC 1232, not DOT 111 tank cars.
Timmins, Ontario, Canada, 14/2/15, 29 tank cars derailed, fires, no reported
injuries or fatalities.
Road
Kannur, India, 27/8/12, 16 tonne road tanker collision with road divider, 41
seriously injured.
Kannur, India, 13/1/14, 18 tonne LPG tanker car collision and overturned, fire, no
injuries.
5
DNV GL ©
Transport Risk Assessment
7
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Questions TRA can answer
What would an accident from my pipeline look like?
8
DNV GL ©
Questions TRA can answer
What would an accident from my pipeline look like?
http://news.nationalgeographic.com/news/2010/09/photogallerie
s/100910-san-bruno-fire-explosion-nation-gas-location-
pictures/#/california-san-bruno-gas-explosion-francisco-
cars_25824_600x450.jpg
8
DNV GL ©
Questions TRA can answer
What would an accident from my pipeline look like?
http://news.nationalgeographic.com/news/2010/09/photogallerie
s/100910-san-bruno-fire-explosion-nation-gas-location-
pictures/#/california-san-bruno-gas-explosion-francisco-
cars_25824_600x450.jpg
(Lutostansky, 2013) – 35 kW/m2 Radiation Contour for
San Bruno Pipeline Rupture calculated by Safeti
8
DNV GL ©
Questions TRA can answer
What is the risk to people, property and the environment?
9
DNV GL ©
Questions TRA can answer
What is the risk to people, property and the environment?
(UK HSC, 1991) Major Hazard Aspects of the Transport of
Dangerous Substances
9
DNV GL ©
Questions TRA can answer
What is the risk to people, property and the environment?
Risk Contours with impact on surrounding population
9
DNV GL ©
Questions TRA can answer
What are the benefits of prevention measures that I can take?
(EGIG 2015)
10
y = 0.0015e-0.333x
R² = 0.9755
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0 2 4 6 8 10 12 14
Failu
re f
req
uen
cy p
er k
m.y
r
Wall thickness (mm)
Failure frequency and Wall Thickness
DNV GL ©
Questions TRA can answer
What mode of transport should I use?
11
DNV GL ©
Questions TRA can answer
What mode of transport should I use?
Which route should I take?
11
DNV GL ©
Questions TRA can answer
What mode of transport should I use?
Which route should I take?
What operating conditions optimise production, reliability and safety?
11
DNV GL ©
Questions TRA can answer
What mode of transport should I use?
Which route should I take?
What operating conditions optimise production, reliability and safety?
Which sections of my route requires most attention?
11
DNV GL ©
Questions TRA can answer
What mode of transport should I use?
Which route should I take?
What operating conditions optimise production, reliability and safety?
Which sections of my route requires most attention?
Where and how frequently should I place my ESD systems?
11
DNV GL ©
Questions TRA can answer
What mode of transport should I use?
Which route should I take?
What operating conditions optimise production, reliability and safety?
Which sections of my route requires most attention?
Where and how frequently should I place my ESD systems?
What pipeline design shall I use?
11
DNV GL ©
Questions TRA can answer
What mode of transport should I use?
Which route should I take?
What operating conditions optimise production, reliability and safety?
Which sections of my route requires most attention?
Where and how frequently should I place my ESD systems?
What pipeline design shall I use?
What is the cost-benefit of risk reductions measures?
11
DNV GL ©
Questions TRA can answer
What mode of transport should I use?
Which route should I take?
What operating conditions optimise production, reliability and safety?
Which sections of my route requires most attention?
Where and how frequently should I place my ESD systems?
What pipeline design shall I use?
What is the cost-benefit of risk reductions measures?
Do I comply with regulations?
11
DNV GL ©
Questions TRA can answer
What mode of transport should I use?
Which route should I take?
What operating conditions optimise production, reliability and safety?
Which sections of my route requires most attention?
Where and how frequently should I place my ESD systems?
What pipeline design shall I use?
What is the cost-benefit of risk reductions measures?
Do I comply with regulations?
Has anybody encroached into my ‘High Consequence Area’?
11
DNV GL © 12 TRA Framework (CCPS, 2008)
DNV GL © 12 TRA Framework (CCPS, 2008)
DNV GL © 13 TRA workflow (CCPS, 2008)
DNV GL ©
TRA challenges
14
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Scope is large
15
(CCPS, 2008)
DNV GL ©
Level of detail needed for accurate modelling is large
16
DNV GL ©
Level of detail needed for accurate modelling is large
16
DNV GL ©
Level of detail needed for accurate modelling is large
16
DNV GL ©
Level of detail needed for accurate modelling is large
16
DNV GL ©
Level of detail needed for accurate modelling is large
16
DNV GL ©
Level of detail needed for accurate modelling is large
16
DNV GL ©
Level of detail needed for accurate modelling is large
16
DNV GL ©
Level of detail needed for accurate modelling is large
16
Operating Procedures
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Level of detail needed for accurate modelling is large
16
Ignition
Operating Procedures
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Level of detail needed for accurate modelling is large
16
Ignition
Operating Procedures
Regulations
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Level of detail needed for accurate modelling is large
16
Population
Ignition
Operating Procedures
Regulations
DNV GL ©
Level of detail needed for accurate modelling is large
16
Population
Ignition
Operating Procedures
Regulations
Toxicity
DNV GL ©
Level of detail needed for accurate modelling is large
16
Population
Ignition
Operating Procedures
Traffic information
Regulations
Toxicity
DNV GL ©
Level of detail needed for accurate modelling is large
16
Population
Ignition
Operating Procedures
Traffic information
Regulations
Maps
Toxicity
DNV GL ©
Level of detail needed for accurate modelling is large
16
Population
Ignition
Operating Procedures
MSDS
Traffic information
Regulations
Maps
Toxicity
DNV GL ©
Level of detail needed for accurate modelling is large
16
Population
Ignition
Operating Procedures
MSDS
Traffic information
Meteorology
Regulations
Maps
Toxicity
DNV GL ©
Level of detail needed for accurate modelling is large
16
Population
Ignition
Operating Procedures
MSDS
Traffic information
Meteorology
Regulations
Maps
Failure Rates
Toxicity
DNV GL © 17
Large x Large = Very Large!
DNV GL ©
How do we handle a very large scope?
18
DNV GL ©
How do we handle a very large scope?
18
TRA study cube (CCPS, 1995)
DNV GL ©
What does this mean?
19
DNV GL ©
What does this mean?
We can’t do everything at once, we need to be strategic
19
DNV GL ©
What does this mean?
We can’t do everything at once, we need to be strategic
We need to systematically screen a broad study set and then zoom-in
19
DNV GL ©
What does this mean?
We can’t do everything at once, we need to be strategic
We need to systematically screen a broad study set and then zoom-in
“Zoom in” means:
– Use more quantitative methods
– Get more accurate local information
– Smaller “step sizes” in the calculations
19
DNV GL ©
What does this mean?
We can’t do everything at once, we need to be strategic
We need to systematically screen a broad study set and then zoom-in
“Zoom in” means:
– Use more quantitative methods
– Get more accurate local information
– Smaller “step sizes” in the calculations
When should we zoom in?
– Sensitive area
– Uncertain of the results
– When we have detailed data available
19
DNV GL ©
What does this mean?
We can’t do everything at once, we need to be strategic
We need to systematically screen a broad study set and then zoom-in
“Zoom in” means:
– Use more quantitative methods
– Get more accurate local information
– Smaller “step sizes” in the calculations
When should we zoom in?
– Sensitive area
– Uncertain of the results
– When we have detailed data available
We need to be efficient and systematic using consistent, validated models
19
DNV GL ©
Mobile Transport Unit TRA Case Study
20
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Mobile transport unit releases
21
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Mobile transport unit releases
We can think of rail cars and tank trucks as vessels which move along a route
21
DNV GL ©
Mobile transport unit releases
We can think of rail cars and tank trucks as vessels which move along a route
We can assess the reasons why the containment can fail due to:
– Operation
– Accident initiated event (collision, allision, overturn, derailment)
– Non-accident initiated event (corrosion crack, overpressure, valve/fitting leaks)
21
DNV GL ©
Mobile transport unit releases
We can think of rail cars and tank trucks as vessels which move along a route
We can assess the reasons why the containment can fail due to:
– Operation
– Accident initiated event (collision, allision, overturn, derailment)
– Non-accident initiated event (corrosion crack, overpressure, valve/fitting leaks)
This means we can define a fixed set of cases and then move them along the
route
21
DNV GL ©
Mobile transport unit releases
We can think of rail cars and tank trucks as vessels which move along a route
We can assess the reasons why the containment can fail due to:
– Operation
– Accident initiated event (collision, allision, overturn, derailment)
– Non-accident initiated event (corrosion crack, overpressure, valve/fitting leaks)
This means we can define a fixed set of cases and then move them along the
route
We can supplement the route releases with fixed point rest stops or high risk
locations such as cross roads
21
DNV GL ©
Route modelling schematic
22
DNV GL ©
Route modelling schematic
22
DNV GL ©
Route modelling schematic
22
Route
DNV GL ©
Route modelling schematic
22
Route
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Route modelling schematic
22
Route
Effect Zone
DNV GL ©
Route modelling schematic
22
Route
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Route modelling schematic
22
Route
DNV GL ©
Route modelling schematic
22
Route
DNV GL ©
Route modelling schematic
22
Route
DNV GL ©
Route Model Capabilities
Safeti contains a Route model
23
DNV GL ©
Route Model Capabilities
Safeti contains a Route model
We define a folder of potential
accidents
23
DNV GL ©
Route Model Capabilities
Safeti contains a Route model
We define a folder of potential
accidents
We define routes along which the
vehicle may travel
23
DNV GL ©
Route Model Capabilities
Safeti contains a Route model
We define a folder of potential
accidents
We define routes along which the
vehicle may travel
This can be used for road tankers, rail
cars, barges, ships
23
DNV GL ©
Route Model Capabilities
Safeti contains a Route model
We define a folder of potential
accidents
We define routes along which the
vehicle may travel
This can be used for road tankers, rail
cars, barges, ships
The hazard zones are calculated and
then the risk model spreads them
along the routes
23
DNV GL ©
Route Model Capabilities
Safeti contains a Route model
We define a folder of potential
accidents
We define routes along which the
vehicle may travel
This can be used for road tankers, rail
cars, barges, ships
The hazard zones are calculated and
then the risk model spreads them
along the routes
The failure frequency/distance is
applied to the hazard zone when it is
placed in each location
23
DNV GL ©
Route Model Example - CCPS TRA Guidance 1995 Case Study
From ‘Here’ To ‘Eternity’
25
DNV GL ©
Route Model Example - CCPS TRA Guidance 1995 Case Study
From ‘Here’ To ‘Eternity’
25
DNV GL ©
Route Model Example - CCPS TRA Guidance 1995 Case Study
From ‘Here’ To ‘Eternity’
Along either Route 27 or Route 46
25
DNV GL ©
Route Model Example - CCPS TRA Guidance 1995 Case Study
From ‘Here’ To ‘Eternity’
Along either Route 27 or Route 46
Population changes along each
25
DNV GL ©
Route Model Example - CCPS TRA Guidance 1995 Case Study
From ‘Here’ To ‘Eternity’
Along either Route 27 or Route 46
Population changes along each
Which is the best risk option?
25
DNV GL ©
Route Model Example - CCPS TRA Guidance 1995 Case Study
From ‘Here’ To ‘Eternity’
Along either Route 27 or Route 46
Population changes along each
Which is the best risk option?
25
Lets look at this in Safeti
DNV GL ©
CCPS TRA 1995 Case Study - Safeti Results Summary
26
DNV GL ©
CCPS TRA 1995 Case Study - Safeti Results Summary
Route 27 PLL: 30/yr
26
DNV GL ©
CCPS TRA 1995 Case Study - Safeti Results Summary
Route 27 PLL: 30/yr
Route 46 PLL: 0.011/yr
26
DNV GL ©
CCPS TRA 1995 Case Study - Safeti Results Summary
Route 27 PLL: 30/yr
Route 46 PLL: 0.011/yr
Who is being impacted and where?
26
DNV GL ©
CCPS TRA 1995 Case Study - Safeti Results Summary
Route 27 PLL: 30/yr
Route 46 PLL: 0.011/yr
Who is being impacted and where?
26
Route 27 section 1 7.21
Route 27 section 2 8.89
Route 27 section 3 14.09
Total PLL/yr 30.19
DNV GL ©
CCPS TRA 1995 Case Study - Safeti Results Summary
Route 27 PLL: 30/yr
Route 46 PLL: 0.011/yr
Who is being impacted and where?
26
distance? pop density?
frequency?
consequence?
Route 27 section 1 7.21
Route 27 section 2 8.89
Route 27 section 3 14.09
Total PLL/yr 30.19
DNV GL ©
How is this helping you to overcome TRA challenges?
27
DNV GL ©
How is this helping you to overcome TRA challenges?
You can define a large, coarse route and get an overview based on semi-
quantified parameters
27
DNV GL ©
How is this helping you to overcome TRA challenges?
You can define a large, coarse route and get an overview based on semi-
quantified parameters
For example
– Put in broad population locations
– Put in broad ignition values
– Put in different routing with different failure frequencies
27
DNV GL ©
How is this helping you to overcome TRA challenges?
You can define a large, coarse route and get an overview based on semi-
quantified parameters
For example
– Put in broad population locations
– Put in broad ignition values
– Put in different routing with different failure frequencies
Zoom in and apply more details when you see risks are getting relatively larger or
when hazards are near populations
27
DNV GL ©
How is this helping you to overcome TRA challenges?
You can define a large, coarse route and get an overview based on semi-
quantified parameters
For example
– Put in broad population locations
– Put in broad ignition values
– Put in different routing with different failure frequencies
Zoom in and apply more details when you see risks are getting relatively larger or
when hazards are near populations
Phast and Safeti’s discharge, dispersion, pool, fire and explosion models are
validated against a wide range of experiments
27
DNV GL ©
How is this helping you to overcome TRA challenges?
You can define a large, coarse route and get an overview based on semi-
quantified parameters
For example
– Put in broad population locations
– Put in broad ignition values
– Put in different routing with different failure frequencies
Zoom in and apply more details when you see risks are getting relatively larger or
when hazards are near populations
Phast and Safeti’s discharge, dispersion, pool, fire and explosion models are
validated against a wide range of experiments
By systematically approaching this problem you are saving time to apply your
skills to managing safety, not crunching numbers
27
DNV GL ©
Pipeline TRA Case Study (adapted from CCPS 1995 case study 7.1)
28
DNV GL ©
Pipeline releases
29
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Pipeline releases
29
Release
Pressure Front Pressure Front
DNV GL ©
Pipeline releases
Pipelines are continuously variable along their length
– Friction causes pressure drop
– Pipe construction may be variable
– Proximity to ESD
29
Release
Pressure Front Pressure Front
DNV GL ©
Pipeline releases
Pipelines are continuously variable along their length
– Friction causes pressure drop
– Pipe construction may be variable
– Proximity to ESD
Long distances to consider
29
Release
Pressure Front Pressure Front
DNV GL ©
New pipeline risk modelling capabilities in Safeti 7.2
31
DNV GL ©
New pipeline risk modelling capabilities in Safeti 7.2
Define your pipeline
31
DNV GL ©
New pipeline risk modelling capabilities in Safeti 7.2
Define your pipeline
– Draw it on a map
31
DNV GL ©
New pipeline risk modelling capabilities in Safeti 7.2
Define your pipeline
– Draw it on a map
– Specify where valves are and the valve properties
31
DNV GL ©
New pipeline risk modelling capabilities in Safeti 7.2
Define your pipeline
– Draw it on a map
– Specify where valves are and the valve properties
– Define sections of the pipeline which differ:
31
DNV GL ©
New pipeline risk modelling capabilities in Safeti 7.2
Define your pipeline
– Draw it on a map
– Specify where valves are and the valve properties
– Define sections of the pipeline which differ:
– Elevation
31
DNV GL ©
New pipeline risk modelling capabilities in Safeti 7.2
Define your pipeline
– Draw it on a map
– Specify where valves are and the valve properties
– Define sections of the pipeline which differ:
– Elevation
– Pipe wall thickness
31
DNV GL ©
New pipeline risk modelling capabilities in Safeti 7.2
Define your pipeline
– Draw it on a map
– Specify where valves are and the valve properties
– Define sections of the pipeline which differ:
– Elevation
– Pipe wall thickness
– Diameter differences
31
DNV GL ©
New pipeline risk modelling capabilities in Safeti 7.2
Define your pipeline
– Draw it on a map
– Specify where valves are and the valve properties
– Define sections of the pipeline which differ:
– Elevation
– Pipe wall thickness
– Diameter differences
Safeti creates a complete pipeline definition containing segments to be modelled
31
DNV GL ©
New pipeline risk modelling capabilities in Safeti 7.2
Define your pipeline
– Draw it on a map
– Specify where valves are and the valve properties
– Define sections of the pipeline which differ:
– Elevation
– Pipe wall thickness
– Diameter differences
Safeti creates a complete pipeline definition containing segments to be modelled
Create breaches of interest (small, medium, large etc.) which will be modelled for
all sections along the pipeline
31
DNV GL ©
New pipeline risk modelling capabilities in Safeti 7.2
Define your pipeline
– Draw it on a map
– Specify where valves are and the valve properties
– Define sections of the pipeline which differ:
– Elevation
– Pipe wall thickness
– Diameter differences
Safeti creates a complete pipeline definition containing segments to be modelled
Create breaches of interest (small, medium, large etc.) which will be modelled for
all sections along the pipeline
In addition to the systematic breaches you can produce detailed results from a
location of interest along the pipeline
31
DNV GL ©
Pipeline Case Study – adapted from CCPS 1995
32
(adapted)
DNV GL ©
Pipeline Case Study – adapted from CCPS 1995
Sour Gas transported 50 miles, past populations from Facility A to Facility B
32
(adapted)
DNV GL ©
Pipeline Case Study – adapted from CCPS 1995
Sour Gas transported 50 miles, past populations from Facility A to Facility B
Releases:
– One inch holes
– Full bore rupture
– Pinholes are omitted
32
(adapted)
DNV GL ©
Pipeline Case Study – adapted from CCPS 1995
Sour Gas transported 50 miles, past populations from Facility A to Facility B
Releases:
– One inch holes
– Full bore rupture
– Pinholes are omitted
What level of protection
do we need?
32
(adapted)
DNV GL ©
Case Study Data
33
DNV GL ©
Case Study Data
Pipe: 5 inch OD, 0.337 wall, 4.663 ID
33
DNV GL ©
Case Study Data
Pipe: 5 inch OD, 0.337 wall, 4.663 ID
Flowrate 0.2 kg/s
33
DNV GL ©
Case Study Data
Pipe: 5 inch OD, 0.337 wall, 4.663 ID
Flowrate 0.2 kg/s
Pressure: 80 barg
33
DNV GL ©
Case Study Data
Pipe: 5 inch OD, 0.337 wall, 4.663 ID
Flowrate 0.2 kg/s
Pressure: 80 barg
Product temperature: 30°C
33
DNV GL ©
Case Study Data
Pipe: 5 inch OD, 0.337 wall, 4.663 ID
Flowrate 0.2 kg/s
Pressure: 80 barg
Product temperature: 30°C
Valve stations: 7 (evenly distributed)
33
DNV GL ©
Case Study Data
Pipe: 5 inch OD, 0.337 wall, 4.663 ID
Flowrate 0.2 kg/s
Pressure: 80 barg
Product temperature: 30°C
Valve stations: 7 (evenly distributed)
Consequence scenario inputs:
– Elevation 0 ft
– Angle 10° from horizontal
– Weather conditions: 12°C, D11mph, F4.5mph
33
DNV GL ©
Frequency Estimation
34
DNV GL ©
Frequency Estimation
Using (EGIG 2015) we can obtain
failure frequency information
34
DNV GL ©
Frequency Estimation
Using (EGIG 2015) we can obtain
failure frequency information
It is a very sophisticated data
source which allows us to analyse
the frequency of events in detail
34
DNV GL ©
Frequency Estimation
Using (EGIG 2015) we can obtain
failure frequency information
It is a very sophisticated data
source which allows us to analyse
the frequency of events in detail
We can look at total failure rates
per breach size
34
DNV GL ©
Frequency Estimation
Using (EGIG 2015) we can obtain
failure frequency information
It is a very sophisticated data
source which allows us to analyse
the frequency of events in detail
We can look at total failure rates
per breach size
34
DNV GL ©
Frequency Estimation
Using (EGIG 2015) we can obtain
failure frequency information
It is a very sophisticated data
source which allows us to analyse
the frequency of events in detail
We can look at total failure rates
per breach size
Or we can look at rates for pipe
diameters
34
DNV GL ©
Frequency Estimation
Using (EGIG 2015) we can obtain
failure frequency information
It is a very sophisticated data
source which allows us to analyse
the frequency of events in detail
We can look at total failure rates
per breach size
Or we can look at rates for pipe
diameters
34
DNV GL ©
Frequency Estimation
Using (EGIG 2015) we can obtain
failure frequency information
It is a very sophisticated data
source which allows us to analyse
the frequency of events in detail
We can look at total failure rates
per breach size
Or we can look at rates for pipe
diameters
Given that around 5 inch diameters
sees a peak we should use those
values for our case
34
DNV GL ©
Frequency Estimation
Using (EGIG 2015) we can obtain
failure frequency information
It is a very sophisticated data
source which allows us to analyse
the frequency of events in detail
We can look at total failure rates
per breach size
Or we can look at rates for pipe
diameters
Given that around 5 inch diameters
sees a peak we should use those
values for our case
34
DNV GL ©
Frequency Estimation
Using (EGIG 2015) we can obtain
failure frequency information
It is a very sophisticated data
source which allows us to analyse
the frequency of events in detail
We can look at total failure rates
per breach size
Or we can look at rates for pipe
diameters
Given that around 5 inch diameters
sees a peak we should use those
values for our case
34
DNV GL ©
before we get started…
35
DNV GL ©
The problems with modelling pipelines
36
DNV GL ©
The problems with modelling pipelines
For the reasons discussed above, every outcome location has different
consequences
36
DNV GL ©
The problems with modelling pipelines
For the reasons discussed above, every outcome location has different
consequences
36
Pipeline
DNV GL ©
The problems with modelling pipelines
For the reasons discussed above, every outcome location has different
consequences
36
Pipeline
DNV GL ©
Village
The problems with modelling pipelines
For the reasons discussed above, every outcome location has different
consequences
36
Pipeline
DNV GL ©
Village
The problems with modelling pipelines
For the reasons discussed above, every outcome location has different
consequences
36
Pipeline
DNV GL ©
Village
The problems with modelling pipelines
For the reasons discussed above, every outcome location has different
consequences
36
Pipeline
DNV GL ©
Village
The problems with modelling pipelines
For the reasons discussed above, every outcome location has different
consequences
36
Pipeline
DNV GL ©
Village
The problems with modelling pipelines
For the reasons discussed above, every outcome location has different
consequences
36
Pipeline
DNV GL ©
Village
The problems with modelling pipelines
For the reasons discussed above, every outcome location has different
consequences
36
Pipeline
DNV GL ©
Village
The problems with modelling pipelines
For the reasons discussed above, every outcome location has different
consequences
36
Pipeline
DNV GL ©
Village
The problems with modelling pipelines
For the reasons discussed above, every outcome location has different
consequences
36
Pipeline
DNV GL ©
Village
The problems with modelling pipelines
For the reasons discussed above, every outcome location has different
consequences
36
Pipeline
DNV GL ©
Village
The problems with modelling pipelines
For the reasons discussed above, every outcome location has different
consequences
36
Pipeline
DNV GL ©
Village
The problems with modelling pipelines
For the reasons discussed above, every outcome location has different
consequences
36
Pipeline
DNV GL ©
The problems with modelling pipelines
37
DNV GL ©
The problems with modelling pipelines
We need to create individual scenarios for continuously changing release locations
37
DNV GL ©
The problems with modelling pipelines
We need to create individual scenarios for continuously changing release locations
This requires:
– Pipeline pressure at the release location
– Distance to closure valves
– Local pipe wall thickness
– Local burial depth
37
DNV GL ©
The problems with modelling pipelines
We need to create individual scenarios for continuously changing release locations
This requires:
– Pipeline pressure at the release location
– Distance to closure valves
– Local pipe wall thickness
– Local burial depth
The solution in Safeti is to automate this process
37
DNV GL ©
Manually sectioning the pipeline…
38
DNV GL ©
Manually sectioning the pipeline…
38
Pipeline
DNV GL ©
Manually sectioning the pipeline…
38
Section 1
DNV GL ©
Manually sectioning the pipeline…
38
ESD Valves
DNV GL ©
Manually sectioning the pipeline…
38
Section 1 Section 2 Section 3 Section 4
DNV GL ©
Manually sectioning the pipeline…
38
Local wall thickness
Section 1 Section 2 Section 3 Section 4
DNV GL ©
Manually sectioning the pipeline…
38
Culverted section
Section 1 Section 2 Section 3 Section 4
DNV GL ©
Automatically Sub-sectioning the pipeline
39
Section 1 Section 2 Section 3 Section 4
DNV GL ©
Automatically Sub-sectioning the pipeline
39
Section 1 Section 2 Section 3 Section 4
DNV GL ©
Automatically Sub-sectioning the pipeline
39
Section 1 Section 2 Section 3 Section 4
Δ pressure drop
DNV GL ©
Automatically Sub-sectioning the pipeline
39
Section 1 Section 2 Section 3 Section 4
Δ pressure drop
DNV GL ©
Automatically Sub-sectioning the pipeline
39
Section 1 Section 2 Section 3 Section 4
Δ pressure drop
DNV GL ©
Automatically Sub-sectioning the pipeline
39
Section 1 Section 2 Section 3 Section 4
Δ pressure drop
DNV GL ©
Automatically Sub-sectioning the pipeline
39
Section 1 Section 2 Section 3 Section 4
Δ pressure drop
DNV GL ©
Automatically Sub-sectioning the pipeline
39
Section 1 Section 2 Section 3 Section 4
Δ pressure drop
DNV GL ©
Automatically Sub-sectioning the pipeline
39
Section 1 Section 2 Section 3 Section 4
Δ pressure drop
DNV GL ©
Automatically Sub-sectioning the pipeline
39
Section 1 Section 2 Section 3 Section 4
Δ pressure drop
DNV GL ©
Automatically Sub-sectioning the pipeline
39
Δ pressure drop
DNV GL ©
Automatically Sub-sectioning the pipeline
39
Δ pressure drop
DNV GL ©
Automatically Sub-sectioning the pipeline
39
Δ pressure drop
Sub s
ection 1
Sub s
ection 2
Sub s
ection 3
Sub s
ection 4
Sub s
ection 5
Sub s
ection 6
Sub s
ection 7
Sub s
ection 8
DNV GL ©
Automatically Sub-sectioning the pipeline
39
Δ pressure drop
Δ mass flowrate
Sub s
ection 1
Sub s
ection 2
Sub s
ection 3
Sub s
ection 4
Sub s
ection 5
Sub s
ection 6
Sub s
ection 7
Sub s
ection 8
DNV GL ©
Continuously variable scenarios
40
Sub s
ection 1
Sub s
ection 2
Sub s
ection 3
Sub s
ection 4
Sub s
ection 5
Sub s
ection 6
Sub s
ection 7
Sub s
ection 8
DNV GL ©
Continuously variable scenarios
We can now calculate release scenarios for every sub-section
40
Sub s
ection 1
Sub s
ection 2
Sub s
ection 3
Sub s
ection 4
Sub s
ection 5
Sub s
ection 6
Sub s
ection 7
Sub s
ection 8
DNV GL ©
Continuously variable scenarios
We can now calculate release scenarios for every sub-section
Each sub-section will comprise location specific properties
40
Sub s
ection 1
Sub s
ection 2
Sub s
ection 3
Sub s
ection 4
Sub s
ection 5
Sub s
ection 6
Sub s
ection 7
Sub s
ection 8
DNV GL ©
Continuously variable scenarios
We can now calculate release scenarios for every sub-section
Each sub-section will comprise location specific properties
Safety systems are modelled, giving rise to cases for:
– Valves close
– Upstream valve fails to close
– Downstream valve fails to close
40
Sub s
ection 1
Sub s
ection 2
Sub s
ection 3
Sub s
ection 4
Sub s
ection 5
Sub s
ection 6
Sub s
ection 7
Sub s
ection 8
DNV GL ©
Lets take a look at the results…
41
DNV GL ©
Study set up
42
DNV GL ©
Pipeline construction
43
DNV GL ©
Pipeline construction
43
Lets look at this in Safeti
DNV GL ©
Auto sectioning results
44
DNV GL ©
Pressure drop along pipeline
45
DNV GL ©
1 Inch Breach
46
0
200
400
600
800
1000
1200
1400
1600
1800
0 10000 20000 30000 40000 50000 60000 70000 80000
Averag
e R
ele
ase D
urati
on
(s)
Downstream distance (m)
Average Release Duration vs Downstream Distance
No Isolation
Full Isolation
Successful Upstream Isolation
Successful Downstream Isolation
DNV GL ©
1 Inch Breach
47
0
2
4
6
8
10
12
14
16
18
20
0 10000 20000 30000 40000 50000 60000 70000 80000
Mass f
low
rate
(kg
/s)
Downstream Distance (m)
Average Mass Flowrate vs Downstream Distance
No Isolation
Full Isolation
Successful Upstream Isolation
Successful Downstream Isolation
DNV GL ©
Full Bore Rupture
48
0
100
200
300
400
500
600
700
0 10000 20000 30000 40000 50000 60000 70000 80000
Rele
ase D
urati
on
(s)
Downstream Distance (m)
Release Duration vs Downstream Distance
No Isolation
Full Isolation
Successful Upstream Isolation
Successful Downstream Isolation
DNV GL ©
Full Bore Rupture
49
0
10
20
30
40
50
60
70
80
90
100
0 10000 20000 30000 40000 50000 60000 70000 80000
Averag
e R
ele
ase R
ate
(kg
/s)
Downstream Distance (m)
Average Release Rate vs Downstream Distance
No Isolation
Full Isolation
Successful Upstream Isolation
Successful Downstream Isolation
DNV GL ©
Risk Contours
50
DNV GL ©
FN Curve
51
DNV GL ©
FN Curve
51
Is this as low
as reasonably
practicable?
DNV GL ©
Risk reduction options
52
DNV GL ©
Failure frequency effects of pipe wall thickness (EGIG 2015)
53
DNV GL ©
Failure frequency effects of pipe wall thickness (EGIG 2015)
53
DNV GL ©
Failure frequency effects of pipe wall thickness (EGIG 2015)
53
DNV GL ©
Failure frequency effects of pipe wall thickness (EGIG 2015)
53
DNV GL ©
Failure frequency effects of pipe wall thickness (EGIG 2015)
53
DNV GL ©
Failure frequency correlations
54
DNV GL ©
Failure frequency correlations Discrete
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
54
DNV GL ©
Failure frequency correlations Discrete
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
54
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0 5 10 15 20
Failu
re F
req
uen
cy (
/km
.yr)
Wall Thickness (mm)
Discrete Failure Frequency and Wall Thickness
DNV GL ©
Failure frequency correlations Discrete
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
Exponential interpolation
54
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0 5 10 15 20
Failu
re F
req
uen
cy (
/km
.yr)
Wall Thickness (mm)
Discrete Failure Frequency and Wall Thickness
DNV GL ©
Failure frequency correlations Discrete
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
Exponential interpolation
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0 5 10 15
Failu
re f
req
uen
cy (
/km
.yr)
Wall thickness (mm)
Failure frequency and Wall Thickness
54
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0 5 10 15 20
Failu
re F
req
uen
cy (
/km
.yr)
Wall Thickness (mm)
Discrete Failure Frequency and Wall Thickness
DNV GL ©
Failure frequency correlations Discrete
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
Exponential interpolation
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0 5 10 15
Failu
re f
req
uen
cy (
/km
.yr)
Wall thickness (mm)
Failure frequency and Wall Thickness
54
F = 0.0015e-0.333t
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0 5 10 15 20
Failu
re F
req
uen
cy (
/km
.yr)
Wall Thickness (mm)
Discrete Failure Frequency and Wall Thickness
DNV GL ©
Failure frequency correlations Discrete
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
Exponential interpolation
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0 5 10 15
Failu
re f
req
uen
cy (
/km
.yr)
Wall thickness (mm)
Failure frequency and Wall Thickness
54
F = 0.0015e-0.333t
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0 5 10 15 20
Failu
re F
req
uen
cy (
/km
.yr)
Wall Thickness (mm)
Discrete Failure Frequency and Wall Thickness
DNV GL ©
Failure frequency correlations Discrete
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
Exponential interpolation
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0 5 10 15
Failu
re f
req
uen
cy (
/km
.yr)
Wall thickness (mm)
Failure frequency and Wall Thickness
54
F = 0.0015e-0.333t
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0 5 10 15 20
Failu
re F
req
uen
cy (
/km
.yr)
Wall Thickness (mm)
Discrete Failure Frequency and Wall Thickness
DNV GL ©
Failure frequency correlations Discrete
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
Exponential interpolation
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0 5 10 15
Failu
re f
req
uen
cy (
/km
.yr)
Wall thickness (mm)
Failure frequency and Wall Thickness
54
F = 0.0015e-0.333t
0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0 5 10 15 20
Failu
re F
req
uen
cy (
/km
.yr)
Wall Thickness (mm)
Discrete Failure Frequency and Wall Thickness
Caution!
DNV GL ©
Translating trends into practical tools
55
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
DNV GL ©
Translating trends into practical tools
55
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
We didn’t use
this failure
frequency for
our base case
DNV GL ©
Translating trends into practical tools
55
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
We didn’t use
this failure
frequency for
our base case
We used a failure frequency of 0.401/1000 km.yr as per EGIG table 3.
DNV GL ©
Translating trends into practical tools
55
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
We didn’t use
this failure
frequency for
our base case
We used a failure frequency of 0.401/1000 km.yr as per EGIG table 3.
This is important as we wanted to account for the adverse influence of our
5” diameter pipeline.
DNV GL ©
Translating trends into practical tools
55
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
We didn’t use
this failure
frequency for
our base case
We used a failure frequency of 0.401/1000 km.yr as per EGIG table 3.
This is important as we wanted to account for the adverse influence of our
5” diameter pipeline, not including pin holes.
5” pipes are easier to break!
DNV GL ©
Translating trends into practical tools
55
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
We didn’t use
this failure
frequency for
our base case
We used a failure frequency of 0.401/1000 km.yr as per EGIG table 3.
This is important as we wanted to account for the adverse influence of our
5” diameter pipeline.
5” pipes are easier to break!
We must therefore use the Wall Thickness failure frequency effect as a
factored influence on our base case, rather than as an absolute frequency.
DNV GL ©
Translating trends into practical tools
55
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
We used a failure frequency of 0.401/1000 km.yr as per EGIG table 3.
This is important as we wanted to account for the adverse influence of our
5” diameter pipeline.
5” pipes are easier to break!
We must therefore use the Wall Thickness failure frequency effect as a
factored influence on our base case, rather than as an absolute frequency.
DNV GL ©
Translating trends into practical tools
55
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
We used a failure frequency of 0.401/1000 km.yr as per EGIG table 3.
This is important as we wanted to account for the adverse influence of our
5” diameter pipeline.
5” pipes are easier to break!
We must therefore use the Wall Thickness failure frequency effect as a
factored influence on our base case, rather than as an absolute frequency.
Factor
3.35
1
0.12
DNV GL ©
Translating trends into practical tools
55
Wall thickness (mm) Failure Frequency
(/km.yr)
wt<5 0.00056
5<wt<10 0.000167
10<wt<15 0.00002
We used a failure frequency of 0.401/1000 km.yr as per EGIG table 3.
This is important as we wanted to account for the adverse influence of our
5” diameter pipeline.
5” pipes are easier to break!
We must therefore use the Wall Thickness failure frequency effect as a
factored influence on our base case, rather than as an absolute frequency.
Our 0.401/1000km.yr can be factored by 0.12 to 0.04812/1000km.yr
Factor
3.35
1
0.12
DNV GL ©
Risk reduction measure 1 – use 12mm pipe wall everywhere
56
DNV GL ©
Risk reduction measure 1 – use 12mm pipe wall everywhere
56
DNV GL ©
Risk reduction measure 1 – use 12mm pipe wall everywhere
56
x10 reduction
DNV GL ©
Risk reduction measure 2 – use 12mm pipe wall near towns
57
DNV GL ©
Societal Comparison
58
DNV GL ©
Societal Comparison
58
DNV GL ©
Societal Comparison
58
Can make a cost
benefit decision
about steel costs
and risk reduction
DNV GL ©
Conclusions
TRA is vast and complex
There are excellent resources such as EGIG and OGP reports which can provide us
with guidance on how to model accidents and how to predict the effect of risk
reduction measures
Use caution when applying information sources to your cases (E.g. EGIG is for
steel, methane pipelines)
Software tools exist which can speed up systematic work
Making the laborious parts of a TRA more efficient frees us up to ask “What If?”
and make better risk management decisions, and hence improve safety
59
DNV GL ©
References
CCPS. (1995). Guidelines for Chemical Transportation Risk Analysis. New York:
AIChE.
CCPS. (2008). Guidelines for Chemical Transportation Safety, Security and Risk
Management. Hoboken: Wiley.
Lutostansky, E., Shork, J., Ludwig, K., Creitz, L., & Jung, S. (2013). Release
Scenario Assumptions for Modeling Risk From Underground Gaseous Pipelines.
Global Congress on Process Safety. AIChE CCPS.
EGIG. (2015). 9th Report of the European Gas Pipeline Incident Data Group.
Groningen: European Gas Pipeline Incident Data Group.
OGP. (2010 - 434-7). Consequence Modelling Report - 434-7. London, International
Association of Oil & Gas Producers.
Hickey, C., Oke, A., Pipeline Transportation of Hazardous Materials – an Updated
Quantitative Risk Assessment Methodology, CCPS China, Qingdao, 2014
Safeti. DNV GL. dnvgl.com/safeti
60
DNV GL ©
SAFER, SMARTER, GREENER
www.dnvgl.com
61
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