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SS: MODU Risk- MODU Mooring Comparative Risk Assessment
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Abstract
After the hurricanes in the GoM that resulted in several semi-submersible MODUs breaking adrift, one task of the MODU Mooring Strength and Reliability JIP was to develop a comparison between the risks associated with MODUs drilling within the Gulf of Mexico and other industries, including other aspects of the offshore oil industry. The paper reviews the baseline risks associated with moored MODUs in Hurricane season, and extrapolates to include the calculated potential risks of a drifting MODU causing major damage to critical infrastructure. It compares these historical and calculated risks, on a consistent basis, to the risks associated with offshore fixed platforms, offshore collisions, blowout events, pipelines, dams, and trains. It will then show how the industry enhancements in MODU mooring systems (brought about since the 2005 hurricane season) lower the operating risks and contrasts them with other industries and other sectors of the offshore industry.
The perception after the 2005 hurricane season was that semi-submersible MODUs were operating at too high a risk in hurricane season and that mooring design codes should be severely tightened since " Rigs Adrift are not Acceptable?? (Chris Oynes, MMS, 26-Jun-05). The paper, which is based on a JIP report [ref. 1], shows that the enhancements in mooring systems and codes brought about by industry significantly lower the mooring failure1 risks which are now similar to tolerability levels in other industries.
Whitman (Farmer) Diagram: a two dimensional view
One of the complexities of a comparative risk assessment is determining how the derived data should be presented so that it conveys the correct message both easily and honestly. One common method is to use a Risk Assessment Matrix (RAM) which has color-coded blocks of differing " Risk?? (defined as the product of probability of an event with its consequence). An example RAM is given in Figure 1. The difficulty with using a RAM for the JIP comparative risk assessment (CRA) is that it is hard to show the ranges of risk associated with any event: it is not a simple case of one probability and one consequence; there are ranges of both. Hence a more flexible graphical representation was needed.
Another way of presenting comparative risk data is in the form of a diagram used by Robert Whitman in 1984 [ref 2], but probably developed by Reg Farmer in 1967 [ref. 3]. This diagram plots the probability of an event on the ordinate vs. the consequences on the abscissa (see Figure 2). (For reasons of simplicity and familiarity in the offshore industry, this paper will refer to this type of diagram as a Whitman Diagram, even though this may be an imprecise attribution.) As can be seen, it allows for a range of probability and consequence to be associated with any individual risk. It can be extremely effective in giving a visual image of the comparative risks, but must be constructed with enormous care, or can lead to misrepresentations and incorrect conclusions. The comparison of the consequences is relatively straightforward, although combining different consequences on the same diagram can cause confusion, but it is the probabilities that are the major challenge.
Title: SS: MODU Risk- MODU Mooring Comparative Risk Assessment
Description:
Abstract
After the hurricanes in the GoM that resulted in several semi-submersible MODUs breaking adrift, one task of the MODU Mooring Strength and Reliability JIP was to develop a comparison between the risks associated with MODUs drilling within the Gulf of Mexico and other industries, including other aspects of the offshore oil industry.
The paper reviews the baseline risks associated with moored MODUs in Hurricane season, and extrapolates to include the calculated potential risks of a drifting MODU causing major damage to critical infrastructure.
It compares these historical and calculated risks, on a consistent basis, to the risks associated with offshore fixed platforms, offshore collisions, blowout events, pipelines, dams, and trains.
It will then show how the industry enhancements in MODU mooring systems (brought about since the 2005 hurricane season) lower the operating risks and contrasts them with other industries and other sectors of the offshore industry.
The perception after the 2005 hurricane season was that semi-submersible MODUs were operating at too high a risk in hurricane season and that mooring design codes should be severely tightened since " Rigs Adrift are not Acceptable?? (Chris Oynes, MMS, 26-Jun-05).
The paper, which is based on a JIP report [ref.
1], shows that the enhancements in mooring systems and codes brought about by industry significantly lower the mooring failure1 risks which are now similar to tolerability levels in other industries.
Whitman (Farmer) Diagram: a two dimensional view
One of the complexities of a comparative risk assessment is determining how the derived data should be presented so that it conveys the correct message both easily and honestly.
One common method is to use a Risk Assessment Matrix (RAM) which has color-coded blocks of differing " Risk?? (defined as the product of probability of an event with its consequence).
An example RAM is given in Figure 1.
The difficulty with using a RAM for the JIP comparative risk assessment (CRA) is that it is hard to show the ranges of risk associated with any event: it is not a simple case of one probability and one consequence; there are ranges of both.
Hence a more flexible graphical representation was needed.
Another way of presenting comparative risk data is in the form of a diagram used by Robert Whitman in 1984 [ref 2], but probably developed by Reg Farmer in 1967 [ref.
3].
This diagram plots the probability of an event on the ordinate vs.
the consequences on the abscissa (see Figure 2).
(For reasons of simplicity and familiarity in the offshore industry, this paper will refer to this type of diagram as a Whitman Diagram, even though this may be an imprecise attribution.
) As can be seen, it allows for a range of probability and consequence to be associated with any individual risk.
It can be extremely effective in giving a visual image of the comparative risks, but must be constructed with enormous care, or can lead to misrepresentations and incorrect conclusions.
The comparison of the consequences is relatively straightforward, although combining different consequences on the same diagram can cause confusion, but it is the probabilities that are the major challenge.
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