Page 318 - Pipeline Risk Management Manual Ideas, Techniques, and Resources

Failure rates 14/295
.00E-02

.00E-03

.00E-04

.00E-05

.00E-06

.00E-07
1 10 1( 30
Number of Fatalities (N)
Flgure 14.1 FN curve for riskcharacterization.

regarding the best “slice” of data to use. This involves attempts rudimentary frequency-based assessment will simply identify
to extract from an existing database of pipeline failures a the number of fatalities or injuries per incident and use this ratio
subset that approximates the characteristics of the pipeline to predict future human effects. For example, even in a database
being evaluating. Ideally, the evaluator desires a subset of with much missing detail (as is typically the case in pipeline
pipelines with similar products, pressures, diameters, wall failure databases), one can extract an overall failure rate and the
thicknesses, environments, age, operations and maintenances number of fatalities per length-time (i.e., mile-year or km-
protocols, etc. It is very rare to fiid enough historical data on year). From this, a “fatalities per failure” ratio can be calcu-
pipelines with enough similarities to provide data that can lead lated. These values can then be scaled to the length and design
to confident estimates of future performance for a particular life of the subject pipeline to obtain some very high-level risk
pipeline type. Even if such data are found, estimating the per- estimates on that pipeline. A sample of high-level data that
formance of the individual from the performance of the group might be useful in frequency estimates for failure and fatality
presents another difficulty. In many cases, the results of the rates is given inTables 14.1 through 14.4.
historical data analysis will only provide starting points or A recent study [67] for pipeline risk assessment methodolo-
comparison points for the “best” estimates of future failure gies in Australia recommends that the generic failure rates
frequency. shown in Table 14.5 be used. These are based on U.S.,
The evaluator will usually make adjustments to the his- European, and Australian gas pipeline failure rates and are pre-
torical failure frequencies in order to more appropriately sumably recommended for gas transmission pipelines
capture a specific situation. The assumptions and adjustments (although the report addresses both gas and liquid pipelines).
required often put this risk assessment methodology on par Using the rates from Table 14.5 and additional assumptions,
with a relative risk assessment in terms of accuracy and pre- this study produces the more detailedTable 14.6, a table of fail-
dictive capabilities. This underlies the belief that, given some ure rates related to hole size and wall thickness. (Note: Table
work in correlating the two scales, absolute and relative risks 14.6 is also a basis for the results shown later in this chapter for
can be related and used interchangeably. This is discussed Case Study B.)
below. As discussed in earlier chapters, there is a difference between
‘frequency’ and ‘probability’ even though in some uses, they
General failure data are somewhat interchangeable. At very low frequencies of
occurrence, the probability of failure will be numerically equal
As a common damage state of interest, fatality rates are a subset to the frequency of failure. However, the actual relationship
of pipeline failure rates. Very few failures result in a fatality. A between failure frequency and failure probability is often