

OSRA Oil-Spill
Occurrence Rates
Estimates of occurrence rates for offshore oil spills are useful for
analyzing potential oil-spill impacts and for oil-spill response contingency
planning. With the implementation of the Oil Pollution Act of 1990 (U.S.
Public Law 101-380, August 18, 1990), estimates of oil-spill occurrence
became even more important to natural resource trustees and to responsible
parties involved in oil and gas activities.
The Oil-Spill Risk Analysis (OSRA) model, developed in 1975 by the DOI,
is a tool that evaluates offshore oil-spill risks (Smith et al., 1982;
LaBelle & Anderson, 1985). This model is used to develop probabilistic
estimates of oil-spill occurrence and contact. A realistic, objective
methodology for estimating oil-spill occurrence rates is required for the
model's application. The MMS developed and maintains oil-spill databases on
U.S. OCS Spills
1996-2008,
U.S. OCS Spills 1964-1995, and tanker spills, which are used to support these
estimations (Lanfear & Amstutz, 1983; Anderson & LaBelle, 1990, 1994).
Oil-spill occurrence rate
estimates were revised
(Anderson & LaBelle, 2000) based on U.S. Outer
Continental Shelf (U.S. OCS) platform and pipeline spill data (1964 through
1999), worldwide tanker spill data (1974 through 1999), and barge spill data
for U.S. waters (1974-1999). These spill rates are expressed and normalized
in terms of number of spills per volume of crude oil handled. All estimates
of spill occurrence rates were restricted to spills greater than or equal to
1,000 barrels (159 m3, 159 kiloliters, 136 metric tonnes, 42,000 U.S.
gallons). This paper presents a simple approach for estimating oil-spill
occurrence, normalized as a function of the volume of oil handled. For this
paper, volume is reported in barrels (bbl) to assist policy- and
decision-makers in government and industry.
Confidence
Intervals
As a supplement to this paper, 95-Percent Confidence Intervals are
presented. Further statistical information supporting this approach can be
found in documents identified in the Additional Statistical Background
discussion below.
Additional Statistical Background
Anderson & LaBelle (2000) is the fourth of a series of independently
peer-reviewed papers presented in support of oil-spill rate assumptions used
for the DOI OSRA Model, with two earlier Anderson & LaBelle efforts (1994 &
1990) and Lanfear & Amstutz (1983). Lanfear & Amstutz (1983) examines
the cumulative frequency distributions of oil spills, tests pipeline miles
as an alternative exposure variable for pipeline spills, and discusses the
trend analysis of offshore spills performed by
Nakassis (1982). These
spill rate papers tier off earlier work performed by DOI in support of the
OSRA Model, and work performed by other oil-spill researchers, as referenced
in the papers.
Smith et al. (1982) documents the fundamentals of the DOI OSRA Model. It
describes the approach of using lambda, the unknown spill occurrence rate
for a fixed class of spills, as a parameter in a Poisson process, with
volume of oil handled as an exposure variable to predict the probability of
spill occurrence (pages 18-24). A Bayesian methodology, described in
detail in Appendix A, “Distribution Theory of Spill Incidence”, provides one
way to weight the different possible values of lambda given the past
frequency of spill occurrence for a fixed class of spills
(Appendix). Smith et al. (1982) selects volume as an exposure variable in that
it is a quantity that would be more practical to estimate future exposure (a
necessity for using it to forecast future spill occurrence) than the other
exposure variables considered.
In support of using the Poisson process for spill occurrence and
examinations of different exposure variables, Smith et al. (1982) references
the works of Devanney & Stewart (1974), Stewart (1976), and Stewart & Kennedy
(1978). These references, and other pertinent ones, can be found at
Oil Spill Rates - Additional References.
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You can download the linked PDF documents by
right-clicking on the link and then selecting SAVE TARGET AS,
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For more information, contact Cheryl Anderson.