2
1 Introduction to the Concepts and Use of ERA Acute
for risk assessments in the Arctic is given in Wenning et al. (2018). Some available models for use in a risk assessment process are oil spill trajectory models.
The Bureau of Ocean Energy Management in the US uses “Oil Spill Risk Analysis Model” (OSRAM) which calculates probabilities of surface oiling at specific
locations (Guillen et al. 2004, BOEM webpage for OSRAM). The “Blowout and
Spill Occurrence Model” (BLOSOM) was developed by the U.S. Department of
Energy’s National Energy Technology Laboratory, and has e.g. been used to evaluate
the coastal communities’ vulnerability to oil spills in the Gulf of Mexico area from
deepwater and ultra-deepwater blowouts (Nelson et al. 2015; NETL Factsheet 2019).
Readily available also is the GNOME-model (General NOAA Operational Modeling
Environment) by the US National Oceanographic and Atmospheric Administration. The OSCAR (Oil Spill Contingency and Response) model by SINTEF is
frequently used on the Norwegian Continental Shelf (NCS). Also available is the
MIKE model by DHI for surface and water column modelling. These oil spill trajectory models make use of Geographical Information Systems (GIS) to geographically
locate areas of potential concern but may vary with respect to the number of environmental compartments they include. Several include response option modelling, e.g.
GNOME and OSCAR. Taking it a step further, some established models include an
impact assessment of potentially harmed Valued Ecosystem Components (VECs).
The SIMAP model by RPS ASA has oil spill trajectory modelling integrated and
is a coupled oil spill modelling and risk assessment by also including exposure and
impact modelling to wildlife groups. Links to the home pages of the mentioned
models are given in the reference section to this chapter.
On the NCS the quantitative “MIRA” method has been used as an industry standard since the 1990s (NOROG 2007), for execution of ERAs for Authority applications, planning and other pre-spill analyses. MIRA uses input of external oil spill
fate and transport modelling, for example by using input from the OSCAR model,
or similar trajectory models to calculate potential population losses of wildlife and
impacted shoreline habitats. The industry and regulators in Norway have seeked an
improvement over MIRA, and a model that could be globally applicable in line with
“Guideline for oil spill risk assessment and response planning for offshore installations” (IPIECA-IOGP 2013). ERA Acute was developed to meet these requirements.
Like MIRA, ERA Acute does not contain an integrated oil spill trajectory model but
allows the user to select a preferred oil spill model giving the required input format.
The methodology is expected to replace MIRA on the NCS following comprehensive
testing, validation and a series of case studies comparing the two methods. The case
studies are not described in this book. Like MIRA, ERA Acute uses the results of oil
spill trajectory statistics, primarily aimed at assessments for pre-activity planning.
1 Introduction to the Concepts and Use of ERA Acute
for risk assessments in the Arctic is given in Wenning et al. (2018). Some available models for use in a risk assessment process are oil spill trajectory models.
The Bureau of Ocean Energy Management in the US uses “Oil Spill Risk Analysis Model” (OSRAM) which calculates probabilities of surface oiling at specific
locations (Guillen et al. 2004, BOEM webpage for OSRAM). The “Blowout and
Spill Occurrence Model” (BLOSOM) was developed by the U.S. Department of
Energy’s National Energy Technology Laboratory, and has e.g. been used to evaluate
the coastal communities’ vulnerability to oil spills in the Gulf of Mexico area from
deepwater and ultra-deepwater blowouts (Nelson et al. 2015; NETL Factsheet 2019).
Readily available also is the GNOME-model (General NOAA Operational Modeling
Environment) by the US National Oceanographic and Atmospheric Administration. The OSCAR (Oil Spill Contingency and Response) model by SINTEF is
frequently used on the Norwegian Continental Shelf (NCS). Also available is the
MIKE model by DHI for surface and water column modelling. These oil spill trajectory models make use of Geographical Information Systems (GIS) to geographically
locate areas of potential concern but may vary with respect to the number of environmental compartments they include. Several include response option modelling, e.g.
GNOME and OSCAR. Taking it a step further, some established models include an
impact assessment of potentially harmed Valued Ecosystem Components (VECs).
The SIMAP model by RPS ASA has oil spill trajectory modelling integrated and
is a coupled oil spill modelling and risk assessment by also including exposure and
impact modelling to wildlife groups. Links to the home pages of the mentioned
models are given in the reference section to this chapter.
On the NCS the quantitative “MIRA” method has been used as an industry standard since the 1990s (NOROG 2007), for execution of ERAs for Authority applications, planning and other pre-spill analyses. MIRA uses input of external oil spill
fate and transport modelling, for example by using input from the OSCAR model,
or similar trajectory models to calculate potential population losses of wildlife and
impacted shoreline habitats. The industry and regulators in Norway have seeked an
improvement over MIRA, and a model that could be globally applicable in line with
“Guideline for oil spill risk assessment and response planning for offshore installations” (IPIECA-IOGP 2013). ERA Acute was developed to meet these requirements.
Like MIRA, ERA Acute does not contain an integrated oil spill trajectory model but
allows the user to select a preferred oil spill model giving the required input format.
The methodology is expected to replace MIRA on the NCS following comprehensive
testing, validation and a series of case studies comparing the two methods. The case
studies are not described in this book. Like MIRA, ERA Acute uses the results of oil
spill trajectory statistics, primarily aimed at assessments for pre-activity planning.