84d79a2be00f4c939805d6f69e017bd3.pdf

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Guidance on performing transportation risk analysis of hazardous materials Spend your time reducing transportation risks rather than spending time producing numbers

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SAFER, SMARTER, GREENER

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

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The hazards and risks of transporting hazardous materials

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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)

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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.

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Transport Risk Assessment

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Questions TRA can answer  What would an accident from my pipeline look like?

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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-locationpictures/#/california-san-bruno-gas-explosion-franciscocars_25824_600x450.jpg

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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-locationpictures/#/california-san-bruno-gas-explosion-franciscocars_25824_600x450.jpg

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(Lutostansky, 2013) – 35 kW/m2 Radiation Contour for San Bruno Pipeline Rupture calculated by Safeti

Questions TRA can answer  What is the risk to people, property and the environment?

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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

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Questions TRA can answer  What is the risk to people, property and the environment?

Risk Contours with impact on surrounding population

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Questions TRA can answer  What are the benefits of prevention measures that I can take? Failure frequency and Wall Thickness

Failure frequency per km.yr

0.0007 0.0006 0.0005

y = 0.0015e-0.333x R² = 0.9755

0.0004 0.0003 0.0002 0.0001 0 0

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Wall thickness (mm)

(EGIG 2015)

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Questions TRA can answer  What mode of transport should I use?        

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Questions TRA can answer  What mode of transport should I use?  Which route should I take?       

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Questions TRA can answer  What mode of transport should I use?  Which route should I take?  What operating conditions optimise production, reliability and safety?      

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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?     

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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?    

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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?   

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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?  

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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? 

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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’?

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TRA Framework (CCPS, 2008)

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TRA Framework (CCPS, 2008)

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TRA workflow (CCPS, 2008)

TRA challenges

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Scope is large

(CCPS, 2008)

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Level of detail needed for accurate modelling is large

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Level of detail needed for accurate modelling is large

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Level of detail needed for accurate modelling is large

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Level of detail needed for accurate modelling is large

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Level of detail needed for accurate modelling is large

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Level of detail needed for accurate modelling is large

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Level of detail needed for accurate modelling is large

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Level of detail needed for accurate modelling is large Operating Procedures

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Level of detail needed for accurate modelling is large Operating Procedures Ignition

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Level of detail needed for accurate modelling is large Operating Procedures Ignition

Regulations

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Level of detail needed for accurate modelling is large Operating Procedures Ignition

Regulations Population

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Level of detail needed for accurate modelling is large Operating Procedures Ignition

Toxicity

Regulations Population

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Level of detail needed for accurate modelling is large Operating Procedures Ignition

Toxicity

Regulations Population

Traffic information 16

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Level of detail needed for accurate modelling is large Operating Procedures Ignition

Toxicity

Regulations Population Maps 16

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Traffic information

Level of detail needed for accurate modelling is large Operating Procedures Ignition

Toxicity MSDS Regulations Population Maps 16

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Traffic information

Level of detail needed for accurate modelling is large Operating Procedures Ignition Meteorology

Toxicity MSDS Regulations Population Maps 16

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Traffic information

Level of detail needed for accurate modelling is large Operating Procedures Ignition Meteorology Failure Rates

Toxicity MSDS Regulations Population Maps 16

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Traffic information

Large x Large = Very Large!

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How do we handle a very large scope?

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How do we handle a very large scope?

TRA study cube (CCPS, 1995)

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What does this mean?   

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What does this mean?  We can’t do everything at once, we need to be strategic  

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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 

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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 

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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

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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

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Mobile Transport Unit TRA Case Study

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Mobile transport unit releases  

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Mobile transport unit releases  We can think of rail cars and tank trucks as vessels which move along a route 

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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)  

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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 

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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

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Route modelling schematic

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Route modelling schematic

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Route modelling schematic

Route

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Route modelling schematic

Route

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Route modelling schematic

Route

Effect Zone

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Route modelling schematic

Route

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Route modelling schematic

Route

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Route modelling schematic

Route

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Route modelling schematic

Route

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Route Model Capabilities  Safeti contains a Route model    

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Route Model Capabilities  Safeti contains a Route model  We define a folder of potential accidents   

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Route Model Capabilities  Safeti contains a Route model  We define a folder of potential accidents  We define routes along which the vehicle may travel  

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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 

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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 

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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

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Route Model Example - CCPS TRA Guidance 1995 Case Study  From ‘Here’ To ‘Eternity’   

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Route Model Example - CCPS TRA Guidance 1995 Case Study  From ‘Here’ To ‘Eternity’   

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Route Model Example - CCPS TRA Guidance 1995 Case Study  From ‘Here’ To ‘Eternity’  Along either Route 27 or Route 46  

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Route Model Example - CCPS TRA Guidance 1995 Case Study  From ‘Here’ To ‘Eternity’  Along either Route 27 or Route 46  Population changes along each 

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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?

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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?

Lets look at this in Safeti

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CCPS TRA 1995 Case Study - Safeti Results Summary   

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CCPS TRA 1995 Case Study - Safeti Results Summary  Route 27 PLL: 30/yr  

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CCPS TRA 1995 Case Study - Safeti Results Summary  Route 27 PLL: 30/yr  Route 46 PLL: 0.011/yr 

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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?

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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? Route 27 section 1 Route 27 section 2 Route 27 section 3 Total PLL/yr

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7.21 8.89 14.09 30.19

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? Route 27 section 1 Route 27 section 2 Route 27 section 3 Total PLL/yr

7.21 8.89 14.09 30.19

pop density?

distance?

frequency? consequence? 26

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How is this helping you to overcome TRA challenges?  

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How is this helping you to overcome TRA challenges?  You can define a large, coarse route and get an overview based on semiquantified parameters 

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How is this helping you to overcome TRA challenges?  You can define a large, coarse route and get an overview based on semiquantified parameters  For example – Put in broad population locations

– Put in broad ignition values – Put in different routing with different failure frequencies   

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How is this helping you to overcome TRA challenges?  You can define a large, coarse route and get an overview based on semiquantified 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  

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How is this helping you to overcome TRA challenges?  You can define a large, coarse route and get an overview based on semiquantified 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 

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How is this helping you to overcome TRA challenges?  You can define a large, coarse route and get an overview based on semiquantified 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

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Pipeline TRA Case Study

(adapted from CCPS 1995 case study 7.1)

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Pipeline releases 

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Pipeline releases 

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Release

Pressure Front

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Pressure Front

Pipeline releases  Pipelines are continuously variable along their length – Friction causes pressure drop – Pipe construction may be variable – Proximity to ESD 

Release

Pressure Front

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Pressure Front

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

Release

Pressure Front

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Pressure Front

New pipeline risk modelling capabilities in Safeti 7.2 

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New pipeline risk modelling capabilities in Safeti 7.2  Define your pipeline

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New pipeline risk modelling capabilities in Safeti 7.2  Define your pipeline – Draw it on a map

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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

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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:

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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

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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   

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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   

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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  

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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 

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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

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Pipeline Case Study – adapted from CCPS 1995  

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(adapted)

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Pipeline Case Study – adapted from CCPS 1995  Sour Gas transported 50 miles, past populations from Facility A to Facility B 

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(adapted)

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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 

(adapted)

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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?

(adapted)

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Case Study Data      

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Case Study Data  Pipe: 5 inch OD, 0.337 wall, 4.663 ID     

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Case Study Data  Pipe: 5 inch OD, 0.337 wall, 4.663 ID  Flowrate 0.2 kg/s    

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Case Study Data  Pipe: 5 inch OD, 0.337 wall, 4.663 ID  Flowrate 0.2 kg/s  Pressure: 80 barg   

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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  

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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) 

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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

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Frequency Estimation  

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Frequency Estimation  Using (EGIG 2015) we can obtain failure frequency information 

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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   

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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  

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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  

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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 

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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 

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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

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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

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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

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before we get started…

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The problems with modelling pipelines 

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The problems with modelling pipelines  For the reasons discussed above, every outcome location has different consequences

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The problems with modelling pipelines  For the reasons discussed above, every outcome location has different consequences Pipeline

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The problems with modelling pipelines  For the reasons discussed above, every outcome location has different consequences Pipeline

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The problems with modelling pipelines  For the reasons discussed above, every outcome location has different consequences Pipeline

Village

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The problems with modelling pipelines  For the reasons discussed above, every outcome location has different consequences Pipeline

Village

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The problems with modelling pipelines  For the reasons discussed above, every outcome location has different consequences Pipeline

Village

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The problems with modelling pipelines  For the reasons discussed above, every outcome location has different consequences Pipeline

Village

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The problems with modelling pipelines  For the reasons discussed above, every outcome location has different consequences Pipeline

Village

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The problems with modelling pipelines  For the reasons discussed above, every outcome location has different consequences Pipeline

Village

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The problems with modelling pipelines  For the reasons discussed above, every outcome location has different consequences Pipeline

Village

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The problems with modelling pipelines  For the reasons discussed above, every outcome location has different consequences Pipeline

Village

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The problems with modelling pipelines  For the reasons discussed above, every outcome location has different consequences Pipeline

Village

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The problems with modelling pipelines  For the reasons discussed above, every outcome location has different consequences Pipeline

Village

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The problems with modelling pipelines  For the reasons discussed above, every outcome location has different consequences Pipeline

Village

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The problems with modelling pipelines  

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The problems with modelling pipelines  We need to create individual scenarios for continuously changing release locations 

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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 

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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

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Manually sectioning the pipeline…

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Manually sectioning the pipeline…

Pipeline

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Manually sectioning the pipeline…

Section 1

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Manually sectioning the pipeline…

ESD Valves

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Manually sectioning the pipeline…

Section 1

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Section 2

Section 3

Section 4

Manually sectioning the pipeline…

Local wall thickness

Section 1

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Section 2

Section 3

Section 4

Manually sectioning the pipeline…

Culverted section

Section 1

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Section 2

Section 3

Section 4

Automatically Sub-sectioning the pipeline

Section 1

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Section 2

Section 3

Section 4

Automatically Sub-sectioning the pipeline

Section 1

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Section 2

Section 3

Section 4

Automatically Sub-sectioning the pipeline Δ pressure drop

Section 1

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Section 2

Section 3

Section 4

Automatically Sub-sectioning the pipeline Δ pressure drop

Section 1

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Section 2

Section 3

Section 4

Automatically Sub-sectioning the pipeline Δ pressure drop

Section 1

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Section 2

Section 3

Section 4

Automatically Sub-sectioning the pipeline Δ pressure drop

Section 1

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Section 2

Section 3

Section 4

Automatically Sub-sectioning the pipeline Δ pressure drop

Section 1

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Section 2

Section 3

Section 4

Automatically Sub-sectioning the pipeline Δ pressure drop

Section 1

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Section 2

Section 3

Section 4

Automatically Sub-sectioning the pipeline Δ pressure drop

Section 1

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Section 2

Section 3

Section 4

Automatically Sub-sectioning the pipeline Δ pressure drop

Section 1

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Section 2

Section 3

Section 4

Automatically Sub-sectioning the pipeline Δ pressure drop

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Automatically Sub-sectioning the pipeline Δ pressure drop

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Sub section 8

Sub section 7

Sub section 6

Sub section 5

Sub section 4

Sub section 3

Sub section 2

Sub section 1

Automatically Sub-sectioning the pipeline Δ pressure drop

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Sub section 8

Sub section 7

Sub section 6

Sub section 5

Sub section 4

Sub section 3

Sub section 2

Sub section 1

Automatically Sub-sectioning the pipeline Δ pressure drop

Δ mass flowrate

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Sub section 8

Sub section 7

Sub section 6

Sub section 5

Sub section 4

Sub section 3

Sub section 2

Sub section 1

Continuously variable scenarios







Continuously variable scenarios  We can now calculate release scenarios for every sub-section 

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Sub section 8

Sub section 7

Sub section 6

Sub section 5

Sub section 4

Sub section 3

Sub section 2

Sub section 1



Continuously variable scenarios  We can now calculate release scenarios for every sub-section  Each sub-section will comprise location specific properties

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Sub section 8

Sub section 7

Sub section 6

Sub section 5

Sub section 4

Sub section 3

Sub section 2

Sub section 1



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

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Sub section 8

Sub section 7

Sub section 6

Sub section 5

Sub section 4

Sub section 3

Sub section 2

Sub section 1

– Downstream valve fails to close

Lets take a look at the results…

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Study set up

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Pipeline construction

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Pipeline construction

Lets look at this in Safeti

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Auto sectioning results

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Pressure drop along pipeline

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1 Inch Breach Average Release Duration vs Downstream Distance 1800

Average Release Duration (s)

1600 1400 1200 1000

No Isolation Full Isolation

800

Successful Upstream Isolation Successful Downstream Isolation

600 400 200 0 0

10000

20000

30000

40000

50000

Downstream distance (m)

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60000

70000

80000

1 Inch Breach Average Mass Flowrate vs Downstream Distance 20 18

Mass flowrate (kg/s)

16 14 12 No Isolation

10

Full Isolation Successful Upstream Isolation

8

Successful Downstream Isolation

6 4 2 0 0

10000

20000

30000

40000

50000

Downstream Distance (m)

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60000

70000

80000

Full Bore Rupture Release Duration vs Downstream Distance 700

Release Duration (s)

600

500

400

No Isolation Full Isolation

300

Successful Upstream Isolation Successful Downstream Isolation

200

100

0 0

10000

20000

30000

40000

50000

60000

Downstream Distance (m)

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70000

80000

Full Bore Rupture Average Release Rate vs Downstream Distance 100

Average Release Rate (kg/s)

90 80 70 60 No Isolation

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Full Isolation Successful Upstream Isolation

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Successful Downstream Isolation

30 20 10 0 0

10000

20000

30000

40000

50000

60000

Downstream Distance (m)

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70000

80000

Risk Contours

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FN Curve

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FN Curve

Is this as low as reasonably practicable?

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Risk reduction options

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Failure frequency effects of pipe wall thickness (EGIG 2015)

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Failure frequency effects of pipe wall thickness (EGIG 2015)

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Failure frequency effects of pipe wall thickness (EGIG 2015)

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Failure frequency effects of pipe wall thickness (EGIG 2015)

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Failure frequency effects of pipe wall thickness (EGIG 2015)

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Failure frequency correlations 



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Failure frequency correlations Discrete  Wall thickness (mm) wt<5 5<wt<10 10<wt<15

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Failure Frequency (/km.yr) 0.00056 0.000167 0.00002

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

Failure Frequency (/km.yr)

Discrete Failure Frequency and Wall Thickness

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0.0006 0.0005 0.0004 0.0003 0.0002 0.0001 0

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0

5

10 Wall Thickness (mm)

15

20

Failure frequency correlations Exponential interpolation

Discrete

Wall thickness (mm)

Failure Frequency (/km.yr)

wt<5

0.00056

5<wt<10

0.000167

10<wt<15

0.00002

Failure Frequency (/km.yr)

Discrete Failure Frequency and Wall Thickness

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0.0006 0.0005 0.0004 0.0003 0.0002 0.0001 0

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0

5

10 Wall Thickness (mm)

15

20

Failure frequency correlations Exponential interpolation

Discrete Failure Frequency (/km.yr)

wt<5

0.00056

5<wt<10

0.000167

10<wt<15

Failure frequency and Wall Thickness

0.00002

0.0007 Failure frequency (/km.yr)

Wall thickness (mm)

Failure Frequency (/km.yr)

Discrete Failure Frequency and Wall Thickness

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0.0006 0.0005 0.0004 0.0003 0.0002 0.0001 0

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0.0006 0.0005 0.0004 0.0003 0.0002

0.0001 0

0

5

10 Wall Thickness (mm)

15

20

0

5

10

Wall thickness (mm)

15

Failure frequency correlations Exponential interpolation

Discrete Failure Frequency (/km.yr)

wt<5

F = 0.0015e-0.333t

0.00056

5<wt<10

0.000167

10<wt<15

Failure frequency and Wall Thickness

0.00002

0.0007 Failure frequency (/km.yr)

Wall thickness (mm)

Failure Frequency (/km.yr)

Discrete Failure Frequency and Wall Thickness

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0.0006 0.0005 0.0004 0.0003 0.0002 0.0001 0

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0.0006 0.0005 0.0004 0.0003 0.0002

0.0001 0

0

5

10 Wall Thickness (mm)

15

20

0

5

10

Wall thickness (mm)

15

Failure frequency correlations Exponential interpolation

Discrete Failure Frequency (/km.yr)

wt<5

F = 0.0015e-0.333t

0.00056

5<wt<10

0.000167

10<wt<15

Failure frequency and Wall Thickness

0.00002

0.0007 Failure frequency (/km.yr)

Wall thickness (mm)

Failure Frequency (/km.yr)

Discrete Failure Frequency and Wall Thickness

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0.0006 0.0005 0.0004 0.0003 0.0002 0.0001 0

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0.0006 0.0005 0.0004 0.0003 0.0002

0.0001 0

0

5

10 Wall Thickness (mm)

15

20

0

5

10

Wall thickness (mm)

15

Failure frequency correlations Exponential interpolation

Discrete Failure Frequency (/km.yr)

wt<5

F = 0.0015e-0.333t

0.00056

5<wt<10

0.000167

10<wt<15

Failure frequency and Wall Thickness

0.00002

0.0007 Failure frequency (/km.yr)

Wall thickness (mm)

Failure Frequency (/km.yr)

Discrete Failure Frequency and Wall Thickness

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0.0006 0.0005 0.0004 0.0003 0.0002 0.0001 0

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0.0006 0.0005 0.0004 0.0003 0.0002

0.0001 0

0

5

10 Wall Thickness (mm)

15

20

0

5

10

Wall thickness (mm)

15

Failure frequency correlations Exponential interpolation

Discrete Failure Frequency (/km.yr)

wt<5

F = 0.0015e-0.333t

0.00056

5<wt<10

0.000167

10<wt<15

Caution!

Failure frequency and Wall Thickness

0.00002

0.0007 Failure frequency (/km.yr)

Wall thickness (mm)

Failure Frequency (/km.yr)

Discrete Failure Frequency and Wall Thickness

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0.0006 0.0005 0.0004 0.0003 0.0002 0.0001 0

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0.0006 0.0005 0.0004 0.0003 0.0002

0.0001 0

0

5

10 Wall Thickness (mm)

15

20

0

5

10

Wall thickness (mm)

15

Translating trends into practical tools

Wall thickness (mm) wt<5 5<wt<10 10<wt<15

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Failure Frequency (/km.yr) 0.00056 0.000167 0.00002

Translating trends into practical tools

Wall thickness (mm) wt<5 5<wt<10 10<wt<15

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Failure Frequency (/km.yr) 0.00056 0.000167 0.00002

We didn’t use this failure frequency for our base case

Translating trends into practical tools

Wall thickness (mm) wt<5 5<wt<10 10<wt<15

Failure Frequency (/km.yr) 0.00056 0.000167

We didn’t use this failure frequency for our base case

0.00002

We used a failure frequency of 0.401/1000 km.yr as per EGIG table 3.

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Translating trends into practical tools

Wall thickness (mm) wt<5 5<wt<10 10<wt<15

Failure Frequency (/km.yr) 0.00056 0.000167

We didn’t use this failure frequency for our base case

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.

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Translating trends into practical tools

Wall thickness (mm) wt<5 5<wt<10 10<wt<15

Failure Frequency (/km.yr) 0.00056 0.000167

We didn’t use this failure frequency for our base case

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, not including pin holes. 5” pipes are easier to break!

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Translating trends into practical tools

Wall thickness (mm) wt<5 5<wt<10 10<wt<15

Failure Frequency (/km.yr) 0.00056 0.000167

We didn’t use this failure frequency for our base case

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.

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Translating trends into practical tools

Wall thickness (mm) wt<5 5<wt<10 10<wt<15

Failure Frequency (/km.yr) 0.00056 0.000167 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.

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Translating trends into practical tools

Wall thickness (mm) wt<5 5<wt<10 10<wt<15

Failure Frequency (/km.yr)

Factor

0.00056

3.35

0.000167

1

0.00002

0.12

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.

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Translating trends into practical tools

Wall thickness (mm) wt<5 5<wt<10 10<wt<15

Failure Frequency (/km.yr)

Factor

0.00056

3.35

0.000167

1

0.00002

0.12

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

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Risk reduction measure 1 – use 12mm pipe wall everywhere

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Risk reduction measure 1 – use 12mm pipe wall everywhere

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Risk reduction measure 1 – use 12mm pipe wall everywhere

x10 reduction

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Risk reduction measure 2 – use 12mm pipe wall near towns

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Societal Comparison

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Societal Comparison

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Societal Comparison

Can make a cost benefit decision about steel costs and risk reduction

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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

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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

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SAFER, SMARTER, GREENER

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