EVALUATION  OF THE OIL SPILL
             RISK ANALYSIS AS PRESENTED  IN
              ST. GEORGE BASIN SALE 89 EIS
JONES & STOKES ASSOCIATES, INC. 12321 P STREET I SACRAMENTO, CA. 95816

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     EVALUATION OF THE OIL SPILL
    RISK ANALYSIS AS PRESENTED IN
     ST. GEORGE BASIN SALE 89 EIS
            Submitted to:

U. S. Environmental Protection Agency
              Region 10
            Prepared by:

   Jones & Stokes Associates, Inc.
         1802 136th Place NE
     Bellevue, Washington  98005
             31 May 1985

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                        TABLE OF CONTENTS

                                                              Page

EXECUTIVE SUMMARY                                               i

CHAPTER 1 - INTRODUCTION                                        1

     Purpose and Objectives                                     1
     Overview of MMS Model Approach                             2
     Summary of St. George Basin EIS                            3
          Spill Rates for Proposed Action                       3
          Oil Spill Trajectories                                4
          Overall  (Combined) Spill Risk Probability             4
          Cumulative Spill Risk                                 5

CHAPTER 2 - OIL SPILL RISKS                                     7

     Summary                                                    7
     General Discussion                                         7
     Size of Spill                                              8
     Approach to Estimating Spill Frequency                     9
          The Applicability of Past Experience                 10
          Independent Events                                   10
          Spills Rates and Volume of Production                11
          Mean Case Estimates                                  14
          Reasonableness of the Approach                       14
     MMS-Assumed Frequencies of Oil Spills                     14
          Platforms                                            14
          Pipelines                                            15
          Tankers                                              18
          Single Buoy Moorings                                 21
          Summary                                              21
     Verification  of Spill Risk Estimates                      21
     Alternative Approaches                                    26
          The North Sea: A High Latitude Oil                   26
             Field Model
          Alternative Exposure Indices                         26
     Implications for Impact Assessment                        28

CHAPTER 3 - OIL SPILL TRAJECTORIES                             31

     Summary                                                   31
     Overview                                                  31
          Trajectory Evaluation                                31
          General Climatology of the Bering Sea                32
     Simulation of Bering Sea Winds                            33
     Simulation of Bering Sea Circulation                      35
          Oceanography of the Bering Sea                       35
          The Numerical Model                                  36
          Verification                                         38

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                                                             Page

          Ice                                                 38
     Influence of Numerical Model on Trajectory               39
        Analyses
          Batedinic Currents                                 39
          Freshwater Runoff                                   39
     Interpretation of the Trajectory Studies                 40
     Number of Trajectories                                   44

CHAPTER 4 - CONCLUSIONS AND RECOMMENDATIONS                   47

     Overview                                                 47
     Sources of Uncertainty                                   47
     Interpretation of the Oil Spill Risk Analysis            48
     Recommendations                                          49

REFERENCES                                                    51

     Literature Cited                                         51
     Personal Communications                                  53

APPENDIX A - EVALUATION OF OIL SPILL RISK

APPENDIX B - DEPICTION OF OIL SPILL STATISTICS

LIST OF PREPARERS

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                         LIST OF FIGURES

Figure                                                       Page

 3-1      February and August Wind Roses for Designated       34
          Locations in the Bering Sea

 3-2      MMS-Calculated Risk to Pribilof Islands             41
          Resource Areas

 3-3      Launch Points Used in MMS Oil Spill Risk            42
          Analysis

 3-4      Biological Resource Areas                           43

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                         LIST OF TABLES

Table                                                         Page

 2-1      Confidence Intervals for the Expected Value         12
          of the Poisson Distribution

 2-2      Platform Spills (>1,000 bbl) in U. S. Waters,       16
          1955-1980

 2-3      Pipeline Spills (>1,000 bbl) in U. S. Waters        17

 2-4      Summary of Data on Oil Spills from Vessels          19
          Carrying Petroleum as Cargo

 2-5      Crude Oil Spills of ^.1,000 bbl from Tankers         20
          Worldwide

 2-6      MMS-Calculated Expected Number of Spills per        22
          Bbbl

 2-7      Cook Inlet Spill Data                               24

 2-8      Reported Impact Assessment for Marine Birds         29
          and Marine Mammals

 3-1      Monte Carlo Error as a Function of Number of        45
          Trials and Estimated Probability

 3-2      Percent Error for a Biased and Unbiased Coin        45

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                        EXECUTIVE SUMMARY
     An EIS must fully disclose  the information and analytical
procedures used in assessing impacts.  The oil spill  risk
analysis  is a key  component of EISs prepared for OCS  oil and gas
lease sales.   EPA  Region 10 became concerned about  the
reasonableness and adequacy of the oil spill risk analysis  used
in EISs for oil and gas lease sales in the Bering Sea.  This
report describes and evaluates the current approach to the  oil
spill risk analysis as  conducted for St. George Basin Sale 89.


              Overview of Approach to  Risk Analysis

     An oil spill trajectory analysis (OSTA)  model was developed
for MMS to calculate the risk of oil spills  damaging
environmentally sensitive  resources.   The model has three
distinct parts. These parts produce a combined probability that
one or more large (J>1,000 bbl) oil spills will  occur  during the
life of the field and contact a  sensitive resource  area.

     The first part of the  OSTA model uses historical spill data
from all  U. S. OCS areas to estimate  the expected number of
spills  for different  types  of activities (e.g./ platform spills,
modes of transportation).   The second part of the model predicts
trajectories  of spilled oil given that a spill  occurs at selected
points.  The  third part  of  the model combines the probability
that a spill  will occur with the probabilities  associated with
trajectory simulations.   Each launch  point is weighted by  the
volume of oil  handled.   The total risk to a  resource area  is the
probability that spills will occur at each launch point combined
with the probability  that those  spills will  reach the target.


                         Oil Spill Risks

     As might be expected,  the oil spill  risk evaluations  are
inherently uncertain.  Uncertainty arises from a number of
sources:  the estimates of  mean-case resource levels, the
statistics derived from historical spill records, and the
scenarios  chosen for development of the field.  Because the risks
are based  solely on the mean-case  resource estimate,  the
probability of a spill is derived from a single exposure index
tied to production.

     The uncertainty in frequency  of very large spills
0100,000  bbl)  is  extremely large  because it is based on an
extrapolation of statistics outside of the range  of observations.

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     There is no unique way to pose a spill  risk model.   The
approach taken is as reasonable as can be if it is a. j3r_.ipj:i
constrained that the risks are to be based on a single exposure
index.
                     Oil Spill Trajectories

     The ocean and atmosphere  modeling  studies used by MMS  appear
in principle to be capable of generating a reasonable and
adequate impact assessment.  The ocean  model  is  capable  of
describing oceanic circulation,  and the meteorological model  is
capable of simulating winds.  There remains concern about the way
the two models are coupled since the coupling is done at the
expense of faithful reproduction of baroclinic currents.
However, the potential  errors are not considered serious since
baroclinic currents are not a major concern with regard  to
surface dispersion of oil.

     The most serious concerns are .that the number of
trajectories used may be insufficient for adequate projection of
risks and that summer conditions may be under-represented in  the
statistics reproduced in the EIS.


                 Conclusion and Recommendations

     Insufficient information is presented in the EIS and its
support documents to evaluate the reasonableness and  adequacy of
the oil spill risk analysis.  Personal communication  with the
analysts was necessary  to obtain important information about  some
basic features of the risk analysis.

     Although the oil spill risk analysis appears to  be  capable
of providing a reasonable and adequate  assessment of  impacts,  its
interpretation and use in the EIS  should be modified  in  several
key points.   The reason for  this finding is based primarily on
the recognition of great uncertainties  in the risk assessment and
the subsequent need for a reasonable and adequate worst-case
analysis.  We recommend that:

     •    Sufficient trajectories  should be run  to assess error
          bounds on their probability distributions.

     •    Greater consideration should be given to use of
          conditional probabilities in assessing the
          environmental consequences of the project.

     •    The EIS should include a discussion of the  impact of
          season on conditional  probabilities, particularly the
          summer season with reference  to the Bering  Sea lease
          sale EISs.

     •    Little credence should'be given  to the EIS's summary of
          overall risk as currently presented.
                               11

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The EIS should better document what  meteorological and
oceanographic features of  the  environment are
incorporated in the trajectory simulation model.   A
separate document should be published that describes
how these features are modeled.

The EIS should clearly document the  assumptions made
and their implications when appropriate.   In
particular, those implications that  may ultimately
compromise the worst-case  analysis must be clearly
stated.
                     111

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                            Chapter 1


                          INTRODUCTION


                     Purpose and Objectives
     The Minerals Management Service  (MMS) of  the U. S.
Department of the Interior  conducts  oil  and gas leasing  on the
U.  S.  outer continental shelf (DCS).   The  leasing process is
subject to the National Environmental  Policy  Act (NEPA),  which
requires that MMS evaluate potential environmental impacts such
as an oil spill damaging environmentally sensitive resources.
NEPA also requires that the Environmental Impact Statement  (EIS)
be a full disclosure  document,  i.e.,  that  the assumptions and
analytical methodologies be clearly described in plain language
such that decision makers and  the  public can  understand  how the
findings of fact have been derived from the description  of the
proposed action and the affected environment  (40 CFR Section
1502.1 and Section 1502.8).

     EPA Region 10, pursuant  to  NEPA and Section 309  of  the Clean
Water Act, reviews draft and  final EISs  prepared by MMS  for
proposed oil  and gas lease sales.  During review of the DEIS (DOI
1984)  for Lease Sale 89 (St. George  Basin), EPA Region 10 became
concerned about the documentation  for  the  oil  spill  risk analysis
and the reasonableness and adequacy  of the models used to  develop
the risk analysis.  With respect to  biological resources in the
affected environment,  the oil spill risk analysis is one of the
most important features of the impact  assessment.

     The purpose of this report  is to  review the  MMS  oil  spill
risk analysis as presented in the Lease  Sale 89  EIS  (DOI 1985)
and its supporting documents.  The objectives of the report are
to:

     •    Describe the MMS approach  to the risk analysis.

     •    Determine whether the  underlying assumptions and
          structure of the risk  analysis are  reasonable.

     •    Provide recommendations to EPA regarding the
          interpretation and use of  the  oil spill risk analysis
          in assessing the environmental consequences of the
          proposed action and alternatives.

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                 Overview of MMS Model Approach

     An oil spill trajectory analysis (OSTA)  model  was developed
for MMS to calculate the risk of oil  spills contacting
environmentally  sensitive  resources.  The OSTA model  is  described
by Smith et al.  (1982).   The  primary  concern  in  using  the  OSTA
model are  spills of 1,000 barrels (bbl) or larger.   The"
1,000 bbl cutoff  was  selected to limit evaluations  to  those
spills large enough to travel long distances on the ocean surface
and have the potential to do serious  damage  (Smith  et  al.  1982;
Lanfear and Amstutz 1983).

     The model used by MMS has three distinct steps that are
taken to calculate probable risk to resources.   The first  step is
common to all  oil spill  risk analyses prepared for  offshore oil
and gas lease  sales.  It calculates  the  unit risk,  i.e.,  the
expected number  of oil spills of 1,000 barrels  (bbl)  or  greater
per unit volume  of oil handled during certain types of
activities.  As might be expected,  there  is considerable
uncertainty in forecasting whether spills will occur and,  if so,
how many and how  large.  Thus, the model  uses a probability
distribution partially based on historical data from other  U. S.
OCS lease  sales.   Spill  occurrence rates for  different activities
were developed by Lanfear  and Amstutz (1983) and Nakassis  (1982).
Spill  rates are  assumed to be directly proportional to the  volume
of oil produced  and are reported as  expected  number of spills per
billion  barrels  (Bbbl) produced or handled.

     The second part of the model  addressees the conditional
probability of an oil  spill  hitting  a specified target,  i.e., it
assumes that a large spill occurred at a specified  location.  The
likely  paths or  trajectories of  an oil spill are also
probabilistic  because they depend on wind and current  conditions
during and following the spill.   Conditional  probabilities
reported in the  EIS are averaged out for the  life of the project,
i.e.,  seasonal  variations in trajectories are averaged.   Thus,
the conditional  risk reported in the EIS  is likely  to  be greater
or less than the true conditional risk for a given  season.

     The output  from the first and second parts of  the model are
then used  to estimate the combined risk to specified sensitive
resource areas.  The MMS OSTA model  uses matrix algebra
(specifically, matrix multiplication) to calculate the combined
probability of an oil spill occurring and making contact with a
target.  One matrix lists the conditional probabilities  derived
from the trajectory analysis, i.e.,  each  element in the  matrix
represents the mean probability that target "i" is hit by a spill
occurring  at point "j".   The  second  matrix represents  spill
occurrence, i.e., each  element  in the matrix  represents  the
expected number  of spills occurring at "j" as a result of
production of  a  unit volume of  oil  at site "k".  The points  "j"
and  "k" are distinguished because some launch  points  ("j")
represent  locations along  a  pipeline or  tanker  route.

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     The resulting product matrix is then multiplied by the
volume of  oil  expected to be found at production site "k" to
obtain a final product matrix containing the expected number of
oil spills that  occur  and contact  target "i".  Thus, each launch
point is weighted by the amount of  oil handled over the life of
the project at that point.  Thus,  the combined probability is
used to determine the overall combined probability, which
expresses  the risk to a specified target from all the launch
points.  It is this latter figure  (sum of the combined
probabilities) that is instrumental  in the EIS assessment of
impact to environmental resources.   The  mathematical derivation
of combined probability for  each launch  point is important to
understand because this probability is less than the conditional
probability unless the volume  of oil handled at that launch point
is high enough that the expected number  of spills  is 21.

     Before examining the details of the oil  spill  risk analysis,
it should  be noted that large spills  (>1,000 bbl) are assumed by
MMS to be  rare,  random, independent events.  The Poisson
probability distribution is a mathematical method of describing
the probability  of such rare events.  The Poisson distribution is
defined by only  one parameter:  in this case, the expected number
of spills.  Thus, the elements  in  the final product matrix are
inserted in the Poisson distribution formula  to  calculate the
probability of one or more spills  occurring and hitting a
specified  target ("i").


                 Summary of St. George Basin EIS

     The following discussion briefly describes  the oil  spill
risk analysis as used (Samuels  1984) for the proposed action
described  in the St.  George Basin EIS.   The  proposed action  calls
for pipelines connecting production platforms north of 56ฐN
Latitude with a facility on  St. George Island.  Oil would then be
tankered south through Unimak Pass.  Production  platforms south
of 56ฐN Latitude would connect to an offshore collection platform
where  oil  would  be loaded and  tankered south.

Spill Rates for__Proposed Action

     Unless specified otherwise, spill rates  described in this
chapter refer to spills ฃ1,000  bbl,  i.e.,  those  treated in the
oil spill  risk analysis for  the EIS.  Spill occurrence rates were
calculated separately for  the northern and southern sectors of
the lease  sale with  the assumption that  the mean case production
scenario for the field (1.124 Bbbl) would be  equally divided
between northern and  southern  sectors  (Hale  pers.  comm.).

     For transportation activities in the northern sector,  spill
rates of 1.6/Bbbl for pipelines, 0.2/Bbbl for tankers in port,
and 0.9/Bbbl  for  tankers at sea were used.   For  tankers at sea,
MMS assumed that the rate  would be 0.45  rather than 0.9/Bbbl
because of a 50 percent  chance that the spill would not occur in
the lease  sale  area  (Hale pers.  comm.).   Thus, for the northern

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sector, transportation-related spill rates were assumed to be
2.25/Bbbl (1.6 for  pipelines,  0.2 for tankers  in  port,  and 0.45
for tankers at sea),  or  1.26 transportation-related spills
expected for the northern sector of the lease  sale  (2.25  x  0.56).

     Similar calculations were made for the southern sector;
however, spill rates for tankers  loading from  collection
platforms were assumed to be included  in spill statistics for
tankers at  sea (Prentki  pers. comm.; DOI 1985, p.  IV-7).   Thus,
for the southern sector,  transportation-related spill rates were
assumed to  be 2.05/Bbbl  (1.6 for  pipelines and 0.45  for tankers
at sea), or 1.15 spills  expected (2.05  x  0.56).

     Platform spills 11,000 bbl were assumed to occur at a rate
of 1.0/Bbbl, i.e.,  1.12 spills for  the  entire  lease  sale.  Thus,
for the St.  George Basin lease sale, 3.54  spills are  expected
(2.42  for all transportation plus 1.12  for platforms) for the
proposed action.   Using  the  MMS-assumed probability  distribution
(the Poisson distribution),  the expected number of spills yields
a probability of 0.97 that there will  be one or more spills of
^1,000  bbl.

Oil Spill Trajectories

     In Atlantic,  Gulf of Mexico, and California DCS  lease sale
EISs,  MMS used the full OSTA model  to predict  oil  spill
trajectories.  Wind and current data were  used and applied to as
many as 100 launch points and  500 hypothetical  spills from each
launch point  for  each season of the year.

     In Bering and Beaufort Sea lease sale EISs,  a numerical
model  developed  by the  Rand Corporation (Liu and Leendertse 1982)
was used to predict trajectories.   Oil  spill trajectories were
generated from 29  launch points within the lease sale boundary
with 26  trajectories each during  ice-free  conditions  and 36
trajectories each  for winter conditions of average ice cover.
The trajectories were used by  MMS with  the OSTA model  to  evaluate
the probability  of risk  to targeted resources.

Overall  (Combined) Spill Risk Probability

     The volume of oil assumed at each launch point  is critical
to the calculation of the combined  probability.   In  the case of
Sale 89,  the  lease sale  area can be divided into subregions based
on the resource estimates.  The Pribilof Island and  Unimak Pass
deferral areas are assumed to contain 10  and 5 percent of the
total  resource, respectively.  The  remaining subregions of the
northern and southern sectors contain 40 and 45 percent of the
resource, respectively.  Within each subregion, the  expected
production  is divided equally between the  launch points (Hale
pers.  comm.).

     For transportation-related spills, the expected number of
spills associated  with the volume produced at "k" is  divided
equally among the  launch points carrying that volume.  The

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expected number of spills at a particular  launch point is the sum
of these allocated fractions.   This sum is then multiplied by the
volume of oil produced at "k" to give the weighted spill risk.
Thus, the spill is geometrically accumulative as oil moves
through a collection system to a storage  or  processing area.
However, the allocation and weighting procedure does not result
in "multiple counting"  of  the transported volumes for that
particular mode of transportation.  In other words, the expected
number of transportation-related spills for  a particular
transportation mode in the  lease sale does not exceed the unit
risk for that mode of transportation multiplied by the mean-case
production estimate for  the field.  "Multiple counting" occurs
only in the sense that the  same barrel of oil may be collected
and transported by pipeline and then shipped by tanker out of a
storage facility and, therefore, that oil  is exposed to two
separate transportation-related risk calculations.

Cumulative Spill Risk

     As part of its evaluation,  MMS includes an analysis of oil
spill  risk from the cumulative activities in the Bering Sea,
i.e.,  other Bering Sea lease  sales  and transportation of oil from
Arctic production fields.   In developing the cumulative-case
scenario for the DEIS,  MMS assumed that  the  proposed action for
Sale 89 will be replaced by the pipeline transportation
alternative  because the North Aleutian Basin lease sale  (Sale 92)
involves pipeline activity adjacent to St. George Lease Sale 89,
and it would be more reasonable to tie the southern sector of
Sale 89  to the Sale 92 pipeline system (Hale pers.  comm.).
Expected number of spills  in the cumulative case  (DOI 1984,
Table  IV-9) is calculated from the  sum of  the operations
(DOI 1984,  Table IV-5)  and used in  the Poisson distribution
formula.  In the FEIS, MMS assumed  that the  proposed action for
Sale 89 would also be valid for the cumulative case, and the
expected number of spills  (DOI  1985, Table IV-10)  is readily
obtained from data in Table IV-5 of the  FEIS.

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                            Chapter 2


                         OIL SPILL RISKS


                             Summary
     MMS uses a log-normal distribution to estimate the
probability distribution for  sizes of  spills.   The agreement
between calculated and observed frequencies  of spill volume
appears reasonable.   Although the approach provides the best
estimate that can be made,  it should be noted that the approach
can result in extremely large uncertainties.

     Similar conclusions are made for the frequency of large
spills.  MMS assumes large spills occur as a Poisson process and
that the expected number of spills can be derived from past DCS
history and the mean-case resource estimate  for  the proposed
field.  The approach taken by MMS is reasonable,  but the expected
number of spills that is derived by the approach  is characterized
by great uncertainty.   An important  element  of the risk analysis
is the selection of  the exposure index used  to develop the  mean
expected number of spills.  The assumptions  made  about the
exposure index are not demonstrably  better than alternative
assumptions, and alternative  assumptions very  likely would  alter
the expected number  of spills.  However, the degree of
uncertainty at all steps in the calculations is  so large that
only great changes in the expected number  of spills are likely to
have any real meaning.

     The large uncertainty inherent in the calculation of
expected number of spills is  of greater significance to the
impact assessment than small  modifications of  the calculated
number.
                       General Discussion

     Risk assessments are generally probabilistic in nature.
Three fundamental elements in a risk assessment model are:
1) the probability distribution function,  which provides the
probabilities of events; 2) an exposure index,  which sets the
probability parameters; and 3)  the ability to predict future
events using the exposure indices.  A sufficient number of
historical events, each with the same exposure  index,  allows
evaluation of the probability function.  With a small database,
however, assumptions on the nature of the  probability function
must be made.   These  assumptions are  not  verifiable in an
absolute sense and can only be judged by their reasonableness.

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To determine the exposure index requires first  that  the
distribution function is  known and then that  it is known  for  a
number of different exposures.   Exposure indices that  are good
fits to the  data but not predictable into the future are  of
little value.   If the exposure index is not predictable,  then
forecasting is not possible.

     The MMS oil spill risk analysis model must be considered
within the constraints mentioned above.   Because vastly different
inferences can be drawn  from  similar data, the  burden  of  proof is
always on the declarer to present  a thorough  description  of  the
steps leading to conclusions.

     Data on spill incidents are  kept by a number of sources:
the U. S. Coast  Guard, the U.  S. Geological Survey,  EPA,  and
Lloyds of London.  The recent compilation of  data for  the DOI by
The Futures Group (1982)  represents  the  most-  complete  set of
publically  available information.   The Futures Group report
classifies spills by five sources:  platforms,  pipelines,  single
buoy moorings,  tankers at sea,  and tankers in ports.  These
categories, where possible, are further  subdivided by  cause  of
spill.  The Futures Group,  however, was not able to  complete  the
analysis of appropriate exposure indices; therefore, their
conclusions are tentative and expressed as suggestions for
further  study.

     The technique adopted by MHS  to highlight  the
disproportionate importance of  the rare,  large  spill is to
analyze the problem in terms  of spill frequency models and spill
volume  distributions.  The former  are used to predict  the number
of spills that might occur, the latter to predict the  volume
spilled  given the event.   The predictions are made in  terms  of
probabilities,  i.e.,  "n" spills will occur with the  probability
P(n)  and less than "x" gallons will  be spilled  with  probability
P(x) .
                          Size of Spill

     MMS selects spills of volume ^1,000 bbl for its risk
analysis model based on the belief that portions of  spills of
this size or larger remain on the water surface  long enough
(30 days) to be transported away from the source to
environmentally  sensitive areas  (Lanfear and Amstutz 1983).
These authors also point out that a 1,000 bbl spill  is  large
enough not to go unnoticed, so reporting records tend to be
reliable.

      A  10,000 bbl  spill is likely to have the  same  environmental
impact as a 12,000  bbl spill.  Thus, in terms of risk analysis,
the size of a spill need  not be  defined to great precision.  In
the oil  spill risk analysis, therefore, the primary  effort is
devoted  to frequencies of spillage.  The frequency distribution
for spill volumes  is  of interest, however, from the  standpoint of
worst-case risk analyses.   MMS assumes that  spill  volumes

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2100,000 bbl constitute a worst-case;  smaller spills are more
frequent but also tend to be more  localized in their  adverse
effect.  To determine the probability of an event requires a
great deal of data.  It is impossible to determine with a small
data set the correctness of any of the distribution  functions
chosen.  Unfortunately for the statistician,  there have been too
few spills  of 2100,000 bbl  for proper study.  Thus,  a means is
needed to estimate the probability of these events.

     Lanfear and Amstutz (1983) compare  the distribution of  spill
sizes  (spills 21*000 bbl) for both platform and pipeline spills
with a log-normal  distribution.   The agreement is reasonable and
the log-normal distribution is probably the best  assumption that
can be made.  It should  be noted  that the log-normal  distribution
is a generic distribution used to fit widely scattered  data.
Deriving rates for this distribution, particularly outside of the
range of data used to evaluate the distribution,  can  result in
extremely  large  uncertainties.

     In the FEIS for Lease Sale 89 (DOI 1985),  spills of
21,000 bbl and 2100,000 bbl were  considered.   Because there have
been no platform spills  of 2100,000 bbl, the log-normal
distribution function was used to  evaluate the probability of
platform spills  2100,000 bbl.  The extrapolated rate constant is
0.036/Bbbl.  Of  eight pipeline spills in the DCS  records,  one was
>100,000 bbl.  Using the same distribution function  as for
platform spills,  MMS evaluated a  spill  rate  of 0.065/Bbbl  for
pipeline spills  2100,000 bbl  (DOI 1985, p. IV-7).


             .Approach to Estimating Spill Frequency

     The following critical  assumptions about spill  frequency are
used by MMS in its risk analysis:

     •    Future spill frequencies can  be  based on past DCS
          experience (DOI 1985, p. IV-6).

     •    Spills occur independently of each other (DOI 1985,
          p. IV-6).

     •    The spill  rate is dependent on the volume  of oil
          produced or transported (DOI 1985, p. IV-6).

     •    The mean-case  estimate  of the oil  resource can be used
          to estimate the volume  of  oil produced  (DOI 1985,
          p. IV-6).

The reasons for  and  implications  of  these assumptions are  useful
in interpreting the reasonableness of the oil spill  risk
analysis.

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The Applicability of Past Experience

     It would be unreasonable to presume that  the  physical
environment  (or working conditions)  of the harsh,  stormy  Bering
Sea is comparable to existing  U.  S. DCS  conditions  (i.e.,  the
Gulf of Mexico and southern California).  Even Cook  Inlet does
not experience the storm and wave conditions typically found in
the Bering Sea.

     It is reasonable,  however, to presume that production
platforms will be engineered to meet the environmental rigors of
the lease  sale area.   This implies  a  second assumption,  i.e.,
that the engineering of platforms has  met the  environmental
rigors of  existing oil   fields.  The implied assumption has not
been strictly satisfied, but improvements have been made  in
technology as a resu-lt of previous accidents.   Since oil  recovery
technology is not starting from a new basis in each new
development,  it  is  permissible to take  into account trends in
reduction  of spill rates in the risk evaluation.   MMS currently
evaluates  risk based on a model  (Nakassis 1982) which maintains
the critical assumptions in the risk analysis  but  allows  for
industry improvement.  Analysis  shows that platforms and  tankers
have shown improvements in spill rates over time;  pipelines have
not.

Independent  Events

     A basic assumption of the approach taken by MMS is that
spills  occur as a Poisson process, with volume of oil produced or
handled as the exposure  variable.   One  of the key supporting
documents  for the MMS oil spill  risk analysis is the Nakassis
(1982) study of platform spills, discussed in detail in
Appendix A.  Nakassis was unable to  conclude that  large oil
spills occur as a Poisson process, but he assumed a  Poisson
process in order to test the  hypothesis  that platform spill rates
have  decreased over time.  Subsequent work (Lanfear and Amstutz
1983) continues to assume large oil  spills occur as a Poisson
process.

      One way of  understanding the Poisson distribution is to
understand how it may be derived from games of chance.  The
outcome of such  games  is a win or lose situation with a
probability  of winning  or losing.   How  does one fare after  "n"
trials  in  a  game with  a probability "p"  of winning and a
probability  "q" of losing?  The answer is given by the binomial
distribution function,   but this function is complicated.   If the
gambler seldom wins and wins are random  and independent events,
success is approximated  by a Poisson process,  which is a
considerably simpler mathematical expression  to deal with.  In a
number  of  problems,  rare and  random events are counted and the
average rate (expected value)  of some process is thereby
estimated  using  the hypothesis that they are  Poisson  distributed.

      Oil spills are approximated by this Poisson distribution on
the assumption that they are rare events.  Several oil spills in
                                10

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the Gulf of Mexico occurred in October  1964  as  a  result of a
single hurricane.  Consistent with  the  Poisson  assumption,  MMS
treats these as one event.   In accident work,  the fact that
people are more careful after a series of accidents causes
deviations from the Poisson law.   MMS modifies  the Poisson law
parameters to include industry improvement in its rate
predictions (Nakassis 1982).

     Table 2-1 shows how well the mean expected value of a
Poisson process is estimated as a function of  the number of
observed events.  The table illustrates the difficulty of
estimating a true mean even if it is known that the information
comes from a Poisson process.   Verifying that  a process is
Poisson would be even more difficult.

     Occasionally it will  be possible to test  directly the
Poisson assumption in its entirety.   If there are numerous
observations, each with the same exposure,  then the associated
numbers of spills represent independent observations. from_ a^ „ J,',",
single Poisson distribution, and the standard statistical te^sts
for goodness-of-fit can be employed.   A possible  case is tanker
spills, where a contemplated exposure index is tanker-years.
Every tanker which has been in service  for the  same  period will
have  the  same exposure.  Stewart and Kennedy (1978, p. 24)
performed goodness-of-fit tests in  this situation and concluded
the Poisson model was acceptable.

Spill Rates and Volume of Production

     A risk assessment model cannot be formulated without an
exposure index.   Criteria for choosing an exposure index (Smith
et al. 1982)  are:

     •    It should be simple.

     •    It should not intuitively violate to any significant
          extent technical assumptions made  in  the analysis (this
          refers primarily  to  the Poisson assumption).

     ซr    It should be a quantity that is predictable in the
          future.

     MMS  has chosen to  characterize the risk associated with
production of oil from a lease sale area with a single number:
the expected number of spills of 21/000 bbl  during the life of
the field.  This number is related to experience  by the
assumption that the number of spills is proportional to the total
expected production from the oil field.  Thus,  if the anticipated
production is a tenth of the production of the  "past experience
production,"  then the  risk  is  a tenth of that  in  the "past
experience data."

     The approach scales all risks according to anticipated
volume of production.   This is a simplification of oil recovery
operations that avoids consideration of the many  factors that
                                11

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     Table 2-1,
OBSERVED
 COUNT

   0
   2
   4
   6
   8
  10
  20
Confidence Intervals for the Expected Value of
the Poisson Distribution

              LEVEL OF CONFIDENCE

LOWER
LIMIT
90%


UPPER
LIMIT

LOWER
LIMIT
98%


UPPER
LIMIT
 0.0
 0.355
 1.37
 2.61
 3.98
 5.43
13.25
 3.0
 6.3
 9.15
11.84
14.43
16.96
29.06
 0.0
 0.149
 0.823
  ,79
  ,91
 4.13
11.08
1,
2,
 4.61
 8.41
11.60
14.57
17.40
20.14
33.10
SOURCE:  Wilson 1952
                                 12

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bear on possible causes of  spills.   There is  some  justification
for this approach.   At the  time of  leasing,  it is  not  known if
oil will be discovered and, if so,  where in the lease sale area
the oil would be found.  Because the risk statistics are  based  on
interpretation of past experience independent of causal  factors,
scenarios depicting the number of wells, tanker traffic,  or
lengths of pipelines are used  only  to allocate "share of the total
risk.   A consequence of this choice of exposure index  is  the
important assertion that all activities  involved in the
production of oil over the  lifetime of the field are directly
related to total production.   Thus,  even spills associated with
transportation of oil are directly  related to volume of oil
produced.

     It may not be practical to find an  alternative exposure
index to total production.  It can never be  known  if oil  is
present in commercially marketable quantities until a discovery
has been found by drilling.   It is  also  reasonable to  assume that
environmental regulation and environmental awareness of the
industry will result in technology  meeting the environmental
rigors.  However,  adoption  of  the above  assumptions precludes the
inclusion into the risk analysis those factors which individually
constitute real elements in the  risk.  Such  factors are:

     •    rapid changes in  production as a result  of world oil
          demand, which might  result in the  use of  developing
          technology;

     •    production with  a few very large platforms rather than
          many smaller platforms;

     •    rapid or slow development based on economic  conditions
          of the industry;

     •    large local  tank storage because the marine  climate
          shortens "windows" during which tankers  can be  safely
          loaded;

     •    tectonics of the  region;

     •    use of the lease  area in  activities other than  oil
          production,  e.g.,  fishing in the Bering  Sea;

     •    comparison of alternate modes  of oil transportation in
          terms of net  risk; and

     •    important variations  in  transportation  modes,  e.g.,
          long and large, pipelines instead of many short  and
          small pipelines, and/or  the use of a few large  tankers
          instead of many small  tankers.
                                13

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Mean Case Estimates

       Anticipated resource levels and volume of production
during the lifetime of  an  oil  field are  derived  from geological
information and information from neighboring regions.   It is
difficult to place a strict confidence limit on this  number.   The
resource estimates are based on primary production methods, thus
volume of production and the expected number of  spills are also
based on primary production methods.   Limitation of the  oil spill
risk analysis to primary production is perhaps  reasonable since
it is not certain that  oil will be found let alone whether
secondary production is economically practical.  It is important,
however,  to recognize  that this limitation may have a bearing on
the interpretation of the mean case and maximum case  resource
estimates and production life of the field.

Reasonableness of the Approach

     It is clear from  the foregoing discussion of the assumptions
of the spill risk analysis that estimations of  expected number of
spills are  characterized  by great  uncertainty.   The degree of
confidence in the calculated expected number of  spills decreases
as one examines in detail its derivation.  This is not to say
that the approach is unreasonable, rather it means that
unquestioning acceptance of the expected number of spills at face
value is  unreasonable.  The approach is reasonable to the extent
that the calculated values  can  be  used to determine whether the
probability of a spill is relatively  high or low.  Thus, the
great deal  of effort expended in refining the expected number of
spills  (even by as much as a factor of two or so) may  not be
warranted in view of the large confidence interval inherent in
the Poisson statistics  (Table 2-1).
              MMS—Assumed Frequencies of Oil Spills

     The MMS OSTA model uses risk estimates for different
operations in offshore oil production.   There are two main
categories:  platform and transportation spills.   Included in
platform spills are blowout, tank,  and miscellaneous  spill
categories.  Included in transportation  are pipelines and tankers
at sea and in port.   The spill rates are derived from historical
data using assumptions about the industry.   In  all cases
discussed below, only spills 11,000 bbl are considered unless
otherwise noted.   The number of  spills  this large in the
historical record is not uniformly agreed upon by the various
analysts who have used the spill records.

Platforms

     Before 1981, OSTA model runs used DCS  platform spill rates
based on studies by Devanney and Stewart (1974)  and Stewart
(1975).   The  database for  this work was  10  spills of  21,000 bbl
in handling 5.338 Bbbl, yielding a rate  of 1.87  spills/Bbbl.
                                14

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     Samuels et al.  (1981)  used U.  S.  Geological  Survey (USGS)
accident records (1979a,  1979b), which  reported nine spills  of
11,000 bbl from 1964-1975,  and used a 1964-1980 federal  DCS  oil
production of 4.386  Bbbl  to compute a rate  of 2~.Q5 spills/Bbbl  of
oil produced.

     Nakassis (1982)  examined  the  spill record  and concluded that
a trend existed that indicated improvement  in the platform spill
rate (Appendix A).   Using a maximum-likelihood  approach,  he
estimated that the present  spill rate for U.  S. OCS platforms is
0.79 spills/Bbbl.   Nakassis began with the  assumption that spills
can be represented by a Poisson process.  He did  not prove that
oil spills  come from a Poisson process  (Appendix A).

     To help update its own estimates of spill  rates, DOI
contracted with The Futures Group to prepare  a  database  of oil
spills and to perform a preliminary analysis  of spill  rates.
Completed in September 1982, the database contains detailed
records of platform, pipeline, and tanker spills. The  Futures
Group database contains records of 462 platform accidents
worldwide from 1955-1980,  including 15 spills of  11,000  bbl  in
U. S.  waters.

     Lanfear and Amstutz (1983) used Nakassis1  methodology on the
database in Table 2-2  (i.e., excluding  three spills that were
included by The Futures Group:  two spills in 1964 [2,559 bbl and
6,387 bbl] and one spill in 1969 [18,363 bbl]) and computed  a
spill  rate of 1.0  spills/Bbbl  of production.   This is the figure
used in the EIS for the St. George Basin lease  sale.  Lanfear and
Amstutz (1983)  modified The Futures Group data  somewhat  for  their
analysis.  There were several  spills in October 1964 that
occurred during a single hurricane.  Since  these  spills  occurred
as a result of the same event,  Lanfear and  Amstutz chose to make
these spills a single  event because, if spills are not
independent events, they will not be modeled  by a Poisson
process.   Because  these spills came early in  the  study  period
(1964-1980),  the analysis would have indicated  greater  industry
improvement had they not been grouped together.   Thus,  the
analysis is conservative in the way  it treated this  information.

Pipelines

     Most U. S.  OCS oil produced is transported via  pipelines.
The MMS database for U. S.  OCS pipeline spills is in  Table 2-3.

     Because anchor dragging is the prime cause of large pipeline
spills  (The Futures Group  1982), there is reason  to expect that
pipeline length is  the important indicator of risk.  The Futures
Group tentatively concluded that pipeline length was  a  more
accurate predictor of failure rate than volume transported.   The
Futures Group considered  all failures regardless  of spill size:
a total of 235 accidents and a mean spill size of 190 bbl.   The
Futures Group concluded that corrosion-related failures were on
the increase; however,  most corrosion-related failures  result in
small spills.
                               15

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     Table 2-2.
Platform Spills OlfOOO bbl) in U.
1955-1980
S.  Waters,
 DATE         LOCATION

4/8/64       Eugene Island 208
10/3/64      (7 Platforms)
7/19/65      Ship Shoal 29
1/28/69      Santa Barbara
3/16/69      Ship Shoal 72

8/17/69      Main Pass 41

2/10/70      Main Pass 41
12/1/70      South Timbalier 26
7/20/72      (Unspecified, Gulf
               of Mexico)
1/9/73       West Delta 79
11/23/79     Main Pass 51
11/17/80     Galveston
                    SIZE (bbl)

                       5,108
                      17,500
                       1,688
                      77,000+
                       2,500

                      16,000

                      30,500
                      53,000
                       4,300

                       9,935
                       1,500
                       1,500
   CAUSE

  Collision
  Hurricane
  Blowout
  Blowout
  Blowout
   (weather)
  Tank Spill
   (weather)
  Blowout
  Blowout
  Unspecified

  Tank Spill
  Tank Spill
  Tank Spill
+ = Estimates vary.

SOURCE:  Lanfear and Amstutz 1983.
                                  16

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     Table 2-3.  Pipeline Spills  (>1,000 bbl) in U. S. Waters

  DATE            LOCATION             SIZE           CAUSE

10/17/67     West Delta 73           160,638      Anchor Dragging
3/12/68      South Timbalier 131       6,000      Anchor Dragging
2/11/69      Main Pass 299             7,532      Anchor Dragging
5/12/73      Grand Island 73           5,000      Corrosion
4/18/74      Eugene Island 317        19,833      Anchor Dragging
9/11/74      Main Pass 73              3,500      Environmental
12/18/76     Eugene Island 297         4,000      "Damaged"
7/17/78      Eugene Island 215         1,000      Anchor Dragging
SOURCE:  Lanfear and Amstutz 1983.
                                   17

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     Samuels et al. (1981),  using USGS accident data from 1964 to
1979, computed a rate  of 1.82 spills/Bbbl produced for spills of
2lrOOO bbl.   For large spills  (21,000 bbl),  Lanfear  and  Amstutz
(1983)  stated,  "On a likelihood basis, volume  of  oil is  better
than km-yr in explaining the spill record.   The length of
pipelines has increased more than threefold  since 1969,  with no
corresponding increase in spill  occurrences.  Perhaps km-yr,
adjusted for some experience factor,  may yet prove to be a
superior exposure variable.   However, such  an  adjustment would
cost a statistical analysis at least two degrees of  freedom (for
shape and parameter value),  making its superiority very  difficult
to demonstrate with only eight spill  occurrences."   On the basis
of data through 1980,  Lanfear and Amstutz  suggested  that the
expected spill  rate should be 1.6 spills/Bbbl  produced.

Tankers

     Tankers present a problem in assigning risks.   The
difficulties of finding exposure indices for tankers are
considerable.  For-example,  1983 had  the  lowest rate of  tanker
accidents in 16 years.  The  world tanker fleet shrank in 1983,
and  more restrictive  operating rules were in effect  for  this
year; nevertheless, recent figures in the 1983  Oil Spill
Intelligence  Report  (New York  Times,  Oct. 7, 1984,   p. 34) show
a 930 percent increase in spill volume in 1983 vs. 1982.
Approximately 241.8 million gal of oil were  lost by  spillage,
fire, or sinking in 1983.  This is  the largest  amount  of  oil lost
since 1979.  Of the total,  80 million gal were associated with
the  Middle  East conflict and an additional  78.5 million  gal were
lost when  a tanker burned and  sank near South Africa.

     The DOI did not maintain a database of  tanker accidents as
it did for platforms and pipelines.   All  tanker spill rates were
derived from published world-wide spill data.   Devanney  and
Stewart (1974), examining spills on major trade routes,  reported
99 spills  of  2.1,000 bbl  in  transporting 29.326 Bbbl  of oil.
Stewart (1976) reported 178  spills in transporting  45.941 Bbbl of
oil, for a rate of 3.87  spills/Bbbl;  all  of  these spills occurred
before  1976.

     The Futures Group  (1982) database provided the  DOI  with the
first opportunity since 1976 to review and  update the tanker
spill  rates.  Because of the difficulty and  expense  of collecting
spill  data, primary emphasis was placed on  collecting data on
spills of 21,000 bbl  since 1974, although spills of  all dates and
sizes were  included.  The data summarized in Table 2-4 contain
855  records of accidents involving vessels engaged in
transporting oil as a product.

     Spills of  crude  oil of 21,000 bbl,  from tankers worldwide
are  shown in Table 2-5.   That at  least 31 percent of the spills
occurred in harbors or at piers is particularly important for
evaluating  environmental impacts, as these  spills would not be
subject to  the  same advective  and weathering effects of winds and
currents as spills in open water on the  DCS.   Earlier  analyses
                               18

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     Table 2-4.  Summary of Data on Oil Spills from Vessels
                 Carrying Petroleum as a Cargo

                       	NUMBER OF SPILLS	
   YEAR                ANY SIZE               >1.000 bbl
Pre-1969                 49                       33
    1969                 20                       13
    1970                 40                       22
    1971                 47                       19
    1972                 89                       44
    1973                 78                       49
    1974                 82                       30
    1975                 67                       27
    1976                 57                       26
    1977                 88                       34
    1978                 81                       27
    1979                111                       43
    1980                 76                       27

TOTAL                   885                      394
SOURCE:  Lanfear and Amstutz 1983.
                                19

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     Table 2-5.   Crude  Oil  Spills of 21fOOO bbl  from  Tankers
                 Worldwide,  by  Location


 YEAR          AT SEA          IN PORT     UNSPECIFIED    TOTALS

 1974           10                 82
 1975            9                 43
 1976           16                 41
 1977           12                 40
 1978            8                 12
 1979           11                 91
 1980           _ฃ                _5_
TOTAL           69                35             10           114
SOURCE: Lanfear and Amstutz  1983.
                                20

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did not make this important distinction.   The  Futures Group  could
not find a statistically significant exposure  index.  The MMS
analysis  relies on the Lanfear and Amstutz (1983) paper on spills
21,000 bbl.   Using an exposure of approximately 88 Bbbl of oil
transported between 1974 and 1980, the calculated spill  rate
becomes 0.90 spills/Bbbl for spills  at sea (open,  restricted,  or
unknown waters) and 0.40  spills/Bbbl for  spills in port  (harbors
and piers),  for a  total  of 1.3 spills/Bbbl.  Spills  in port  must
be assumed to be divided evenly between the inbound and outbound
portions of the voyage,  as the database  does  not make this
distinction.

     The tanker spill rate since 1974 appears to be only a third
of that before 1973.   Stewart (1976)  reports more spills  before
1976 than are contained in The Futures Group  database, but this
could be due to the latter group's incomplete  collection  of  data
from the  earlier  years  (emphasis was on years 1974 and  later).
Goldberg  et al. (1981) also report more  incidents for the years
before 1972 than does The Futures Group  but about the same number
for later years.  (Their classification  scheme, however,  is  not
exactly the same;  individual records are not available,  so the
comparison is only approximate.)  Unless  the databases are very
much in error, it appears that the tanker  spill rate for spills
of 2.1,000 bbl dropped significantly  sometime  between 1972 and
1974.

Single Buoy Moorings

     The  St. George Basin Sale 89 FEIS (DOI 1985) assumes that
the risk for single buoy moorings is included in the risk for
tankers at  sea.  The Futures Group  considered  it a  separate
category.  There have not been any spills of J>1000 bbl
associated with si-ngle  buoy moorings.

       The Futures Group noted a significant  increase (above
port-related spills)  in spill volume associated with loading at
single buoy moorings, although the  spill rates were about the
same as for ports.  Ship calls were  deemed the best  exposure
index.  Spill  rates were three times larger at unloading  than  at
loading; however, volumes lost during unloading were considerably
smaller  than during loading.

Summary

     The unit risk values used by MMS in the  oil spill risk
analysis  are summarized in Table 2-6.


              Verification of Spill  Risk Estimates

     One of the major objectives of this study has  been to
determine whether the oil spill risk analysis is reasonable  and
adequate for impact assessment.   Two possible  ways  of achieving
this are:  1)  testing the projected  values against  the experience
record for a comparable oil field,  or 2)  examining the effects of
                                21

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        Table 2-6.  MMS-Calculated Expected Number of Spills
                    per Billion Barrels „

                    21,000 bbl              2100,000 bbl
SOURCE:  DOI 1985
                                22
Platforms              1.0                      0.036
Pipelines              1.6                      0.065

Tankers
  At Sea               0.9                      0.190
  Per Port Call        0.2                      0.042

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alternative sets of assumptions or exposure indices on the
projected values.

     The FEIS for Sale 89 (DOI 1985)  compares the Alaskan  spill
record with the expected  number  of spills  generated  by  the oil
spill  risk analysis.  Oil production began in Cook Inlet  in 1964.
By 1980 about 0.7 Bbbl of oil had been produced.  Spill
statistics for 1965-1980  are shown in Table 2-7.   In Cook  Inlet,
there were 3 years with total spillage in excess of1,000  bbl:
1966f 1967, and 1968.  These  spill volumes resulted  from 9 spills
in 1966, 9 spills in  1967, and  32 spills  in 1968 (DOI 1984).
Included in data for  these 3 high-spill years were  two tanker
spills  of >1,000 bbl and one pipeline spill of >1,000 bbl.  These
numbers correspond to pipeline spill  rates of 1.42/Bbbl and total
transportation spill  rates of 2.84/Bbbl  if the  data are not
adjusted for  industry improvements.

     The following paragraphs are quoted from  the Lease Sale  89
FEIS (DOI 1985, p.  IV-8). The projected values in  the quoted
material apparently have been adjusted for improvement  in  spill
rates.

     "Because DCS statistics are compiled as  'number  of spills
per volume produced,1  the only comparisons of  DCS  statistics with
Alaskan data  are for  the state-leased offshore Cook  Inlet  and
Prudhoe Bay/Kuparuk fields.   Based on OCS  spill statistics, and
assuming that Alaska also experienced the post-1974  improvement
in platform and tanker (not  pipeline) performance  seen in  OCS
statistics, the number of spills which would be projected  for  the
Cook Inlet and Prudhoe Bay/Kuparuk fields are  shown  below:


            Number of 1,000-Barrel or Greater  Spills
                      (through August 1983)

                    Cook Inlet            Prudhoe Bay/Kuparuk
             Projected     Observed       Projected     Observed

Platforms      1.79          0              3.0        1-  3(*)
Pipelines      1.28          2(**)          4.8           6
Tankers        2.06          2              3.9           1
Total          5.13          4             11.7         10
 (*)  The 3 includes two airfield spills.
 (**) From Gulf Research and Development Company 1982.

 [Note:  The information cited at this point in the FEIS includes
        spill  statistics through 1983.  Table 2-7, also from the
        FEIS, includes data only through 1980.]

 SOURCE:  MMS, Alaska OCS Region, 1984.
                                23

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     Table 2-7.  Cook Inlet Spill Data
YEAR
PRODUCTION
  (Mbbl)
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
0.03
2.65
15.9
52.5
60.9
70.1
66.2
63.7
61.7
59.9
60.0
54.5
49.8
45.0
38.4
32.3
SPILLAGE3
 (bbl)

    87
  2467
  1982
  2278
   246
    28
    75
    22
   131
   150
    23
    76
    10
    14
     4
     3
a  Spills of known volume.

SOURCE:  DOI 1984
NO. OF
SPILLSa

   1
   9
   9
  32
  12
   9
  10
  11
  12
  25
  13
  15
  12
   9
   5
   3
                           24

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     "For Cook Inlet,  the  probability  (0) of observing platform
spills is 17 percent.   The above  calculations  indicate that we
would have projected  5.1 spills to occur as a result of
production and transportation of  oil  in Cook Inlet.  In  fact,
4 spills were observed.  The probability of  observing only
0 to 4 spills overall is 42 percent, almost an even chance.
Thus, the OCS oil-spill-occurrence statistics applied to Cook
Inlet production shows a reasonable agreement with the observed
number of spills.

     "The OCS statistics project  11.7 spills for  Prudhoe
Bay/Kuparuk production and transportation; we observed 10.  OCS
statistics projected 3 platform spills,  and  1 to 3 spills
(depending upon inclusion  of airfield spills) were observed.  We
projected 4.8 pipeline spills; we observed 6.  We projected
3.9 tanker spills; we  observed 1.   The probability of observing
0 to 1 platform spills is 20 percent,  and the probability of
observing 1 to 3 platform spills is 60  percent.  The probability
of observing 5 to 7 pipeline spills is 41 percent.  The
probability of observing 0 to 2 tanker spills  is  25 percent.  Th(
probability of observing 0 to 10  spills overall is 38 percent.
     "In conclusion,  Alaska has not  produced enough oil to  ,
statistically demonstrate that it has a different spill rate  than
the rest of the OCS."

     The comparison between OCS statistics  and  the Prudhoe
Bay/Kuparuk fields illustrates one of the difficulties in
extrapolating data.  Of eight  pipeline spills (21,000 bbl)  in the
Gulf of Mexico, five were the  result of anchor  dragging.  Anchor
dragging is not a significant  problem for the trans-Alaska
Prudhoe Bay/Kuparuk pipelines.   Since anchor  dragging is not  a
possible cause of pipeline spills in the Prudhoe Bay/Kuparuk
fields, it is misleading to use OCS statistics  as was done  above.
If the projected pipeline spill rate is adjusted downwards  by
5/8, then 1.8  spills would have been predicted  instead  of  4.8 as
listed in the FEIS.  This would suggest that  the observed
pipeline spill rate is much more than the projected  rate.
However, even this manipulation is challengeable because the
Prudhoe Bay and Kuparuk fields are not offshore environments.

     Cook Inlet, although a harsh environment for  ice and  tides,
is not an open ocean environment.   It does  not  have the same  risk
of high wave conditions  as the Bering Sea OCS nor does it have
the shipping and fishing traffic of the Gulf  of Mexico.  Apart
from these two important differences, use of Cook Inlet data
would be reasonable as a rough approximation  of the adequacy  of
the projected values.

     Given these considerations, use of Alaskan data  to test  the
applicability of U. S. OCS data should be done  with  care.
                               25

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                     Alternative Approaches

     Apart from using different models for U.  S.  DCS  production
(e.g., the recent North Sea spill  record), probably the  only
alternative approach to the  current  oil  spill  risk analysis is to
re-examine the selection of  exposure indices.   As noted  earlier
in this  chapter,  this would  entail a more  critical examination of
the causes of  oil  spills and less emphasis on  the volume of oil
produced.   The way  to achieve  this is to identify historical data
with regard to cause,  i.e.,  to determine if  engineering
improvements have lessened! the probabilities of certain  types of
accidents, to  determine relationships between  certain types of
accidents and  the  environment, and to relate these findings to
developments  in the proposed lease area.  Accomplishing  this
analysis might require use of  the  data for spills of  all sizes,
although  the  final focus of the subsequent impact assessment
would be only on the larger  spills.  Appendix B has been prepared
for the purpose of  allowing  the reader  to  visually examine the
spill record  and relate spill  data to different factors.

     The primary difficulty  with applying a single concept to a
risk analysis situation is that confidence limits cannot be
derived.  One  way  to achieve a measure of confidence  is  to
consider different models based on different assumptions and then
compare risks.  If alternative approaches have no major  effects
on the expected number of spills, then it can be concluded that
the spill  risk estimates are  reasonably accurate.  This  section
is prepared as a preliminary effort to recommend different models
or approaches.  It  is not presented  to  prove or disprove a risk
model.

The North Sea; A High Latitude Oil Field Model

     The North Sea  oilfield  is particularly attractive as an
alternative model  because the marine environment is similarly
harsh, and it is a new production field that underwent rapid
development.   Use  of the model assumes that industry  technology
and practices are not likely to be significantly different from
U. S. DCS practices.   Unfortunately,  the data were not available
for this  review.  The Futures Group obtained 3 years  of  North Sea
data, which proved to be insufficient for analysis.   The main
deterrent for further examination of North Sea data appears to be
its  high  cost from Lloyds (Prentki pers.  comm.).

Alternative Exposure Indices

     One  could also  develop models  that use more than one
exposure  index as the predictor of spill  frequency.  This
approach would allow for more  flexibility in defining senarios
for oil field development.  The following is a brief  list of the
types of  variables that  may be important  in determining  the
exposure  index.

     Production.  This could  be further refined to define the
number and types of  tankers,  production platforms, and pipelines.
                               26

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     Changes in Production.   This could be an important factor in
determining risk,  particularly in a region where rapid changes in
production are anticipated.   Spill data have been graphically
related to rate of change in Appendix B.  These figures suggest
rate of change may have some influence on spill rate, but the
necessary statistical evaluation has not yet been done.

     Size of Platforms.  The database has not been examined for
the relative safety records of small vs. large platforms.

     Seasons.  Risks to mammals,  birds, crabs,  and migrating fish
may have a seasonal dependence.   Smith et al.  (1982)  state that
such factors can be put into the  MMS model using trajectory data.
It may be worthwhile to examine effects of season on  spill
frequency.

     Infrequent Severe  Storms.  No analysis has yet been done to
ascertain the influence of severe storms.   If platforms are
designed for 50-year storms, what is the probability  of the
100-year storm striking during the lifetime of  the oil field?

     Geological Features.  Oil production in highly fractured
areas is more hazardous than in more stable  areas.

     Size of Holding Tanks.  The risk of a large spill could
increase with increases in average container size.

     Size and Frequency of Tanker Traffic.  Spills of all sizes
from U. S. flag tankers have been shown (Stewart and Kennedy
1978) to occur at  a rate  of  about one  per 3  tanker-years.  Thus,
an alternative exposure index could  be compared with the current
values in the oil  spill risk analysis.

     Pipelines.  Here there are variables such as length, size,
ability to quickly detect and respond to problems, and frequency
of anchoring along the  pipeline  route.

     Other Activities.  The interactions between other activities
(e.g.,  a large  commercial  fishery) and  oil production activities
could  change risk  estimates.  In some cases, these may be closely
linked to season.

     Incorporating an alternative or even a multivariate exposure
index into a risk model would require changes to the present risk
analysis  and investigation  into  a number  of scenarios.  The
assumptions operating under the  current approach are not
demonstrably more  reasonable than the alternatives.  However, the
outcome of such analyses may serve no purpose other than to
provide analysts  with  better  information  on the  causes of spills.
The inherent nature of probability functions means that
uncertainty will  occur  about the expected number of spills
irrespective of which  assumptions are  used.  It is doubtful that
the use of a more accurate exposure  variate, for example, will
result in greater  confidence in the expected number of spills
because the  confidence  interval  is  so  large.
                                27

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               Implications for Impact Assessment

     The foregoing discussion indicates that spill risk
predictions may be sensitive to different sets of assumptions
used to calculate the risk and that there is no compelling reason
for accepting any particular set of assumptions over another.
The use of a "superior" exposure index may change the expected
mean number of  spills which, in turn,  may significantly alter the
probability function.  However, decision makers and the public
should recognize that the inherent uncertainties in the process
are such that changes of  this scale are  unlikely to substantively
reduce the uncertainty.   What is more  important is recognition of
how the uncertainty might affect interpretation of the impact
assessment.

     Marine birds and mammals are perhaps most susceptible to the
effects of oil  spills because these animals have frequent and
regular exposure to  the water surface.  Table 2-8 summarizes
findings of the impact assessment on these organisms as reported
in the FEIS  (DOI 1985) or readily derived from the assessment.
The four whale  species included in Table 2-8 have been selected
because they are known to occur regularly and in significant
numbers in the St. George lease sale  (Jones & 'Stokes Associates
1984), and they are  designated as endangered species.   Other
endangered cetaceans are  either not likely to occur or are so few
in number that the probability of contact with an oil  spill is
particularly remote.

     The table shows the  MMS-calculated combined probability that
a spill will occur and contact the Pribilof Islands area,  the
Unimak Pass area, or the shelfbreak south of the Pribilofs
(Resource Areas 10 and 11 in the EIS analysis).   These numbers
can then be compared to risk criteria  established by MMS for
marine birds and gray whales.  (It is  not clear why the criteria
are different.   One  could interpret "low" and "unlikely" to be
similar categories,  but MMS assumes that a  "medium" risk for
birds is a probability of 11-25 percent and an "unlikely" risk
for gray whales is a probability of 11-30 percent.  The important
point is that the two sets of numbers  for resource areas and
species can be compared.)   Clearly,  the risk to birds and mammals
is high in the vicinity of the Pribilof Islands and borders on
high in areas along the shelfbreak south of  the Pribilofs.   Given
the uncertainties involved  in  the estimated expected number of
spills,  the  combined probability may have a confidence range that
is wide enough to include more than one of the probability risk
criteria.

     This conclusion is particularly important when evaluating
the summary of the FEIS and comparing  it to the supporting
documentation.   The impact rating categories are clearly defined
in the EIS, but it is not immediately  clear that the rating in
the summary  integrates impacts from all  activities (e.g.,  noise
disturbance and oil spills).  Using the definitions established
by MMS and examining only conditional probabilities as discussed
in the EIS, significantly different categories are noted even
                               28

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                               Table 2-8.   Reported Impact Assessment for Marine Birds and Marine Mammals

                                                                             PROBABILITY RISK CRITERIA*1
                                                             COMBINED                 MEDIUM      HIGH
                                                           PROBABILITY8  .    LOW    (UNLIKELY)   (LIKELY)
                                                                                        *	     %
NJ
VO
     RESOURCE/
   RESOURCE AREA

Prlbilof Islands
Unimak Pass
Shelfbreak south of Islands
Marine birds
Marine mammals6
Gray whale
Fin whale
Humpback whale
Sperm whale
                                                          33-34
                                                             13
                                                           5- 9
44-46
   15
14-34
                                                                             0-10
                                                                                       11-25

                                                                                       11-30
                              26-50

                              31-60
                                          DEIS
                                         IMPACT
                                         RATINGC
Mod.-Kaj.
  Mod.
 Minor
 Minor
 Minor
 Negli.
             LARGE
             SPILL
            EFFECTS0
Major
Major
Major
 Mod.
 Mod.
 Mod.
                             From Table E-10, probability a spill occurs from proposed action And hits target in "x" days.
                             Averaged over ail modeled launch (feints.  Does not include cumulative case.
                             Qualitative scale used in discussion of marine birds (DOI 1985, p. IV-46) and gray whales
                             (in parens) (DOI 1985, p. IV-72) .
                             From Table S-l, integrates all activities (e.g., noise disturbance and oil spills) and classified
                             as major, moderate, minor, negligible.
                             Conditional probability, i.e., assumes a large spill occurs during summer in the vicinity of a
                             species-specific high use area in lease sale.
                             Noncetacean species.

-------
when it is recognized that the reported conditional  probabilities
are annual means of the trajectories.
                                30

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                            Chapter 3


                     OIL SPILL TRAJECTORIES


                             Summary
     Winds are more important than ocean currents  in determining
spill trajectories.  This chapter begins,  therefore,  with a  brief
discussion of the general climatology of the Bering Sea and  how
wind observations at the Pribilof Islands relate to the
climatology.  The purpose of the discussion is  to  describe major
features of the weather  that must be  simulated  if  the trajectory
analysis is to be adequate.   It is not possible to discern from
the EIS or its supporting documents what meteorological data are
used or how they are used.  Personal  communication with Liu  and
Leendertse was necessary to  determine that the  information used
in the model to simulate winds could  result in  reasonable
trajectory simulations.   The numerical model's  ability to portray
ocean circulation also appears reasonable,  although apparently
minor concerns remain about  the model's  "as-run" ability to
accomodate monthly changes in density—driven currents.

     Significant concerns remain about the adequacy of the number
of trajectories currently used in the oil spill risk analysis.
Evaluation of the  risk assessment in the St. George Basin Sale 89
EIS suggests that the risk to targets during summer periods  may
be under-represented.   Over  half  (58  percent) of the trajectory
simulations are run for winter conditions, which may account for
the apparent under-representation. It is not clear,  however,
whether under-representation also results from  the manner in
which overall  risk is calculated  or from the error inherent  in
the trajectory simulations.


                            Overview

Trajectory Evaluation

     An important fact that  stands out when one attempts to
predict oil spill  risks for a proposed OCS lease area is that the
problem is fundamentally  probabilistic.   A great deal of
uncertainty exists not only  with regard to the  location, number,
and size of spills that will  occur during the course of
development,  but also with regard to  wind and current conditions
that give direction to the oil at the particular times that
spills occur.  While some of the uncertainty reflects incomplete
or imperfect data for which it is difficult to  assign error
bounds,  the trajectory should be amenable to error analysis.
                                31

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     The purpose of the oil spill trajectory studies done for
EISs is to assess  the  probabilities  that  oil  spills from
locations within a proposed lease area will reach specific
targets.  To accomplish this goal, a simulation is made of a
number  of trajectories of oil spills and then these trajectories
are treated statistically.  Oil spill trajectories in the
Beaufort and Bering Sea regions are computed using a numerical
model developed by Liu and Leendertse  (1982)  for the Rand
Corporation.  The  model consists of two major features:  one
dealing with meteorological conditions, and the other with ocean
circulation.  The trajectories are then inserted into the MMS
OSTA model  (which is described by Smith et al. 1982)  to evaluate
the conditional probability of  oil  reaching the targets.  The use
of the  Liu and Leendertse numerical  model to predict trajectories
is unique to Beaufort and Bering Sea EIS documents.

General Climatology of the Bering Sea

     Movement  of  oil  on  the sea surface is directed by local wind
and wave  conditions and  ocean currents.  Should ice be present,
it would modify the response of oil to the appl ied forces.  The
most important element in determining the trajectory of an oil
spill,  in both  cases,  is the wind.

     The Bering Sea is affected by arctic, continental, and
maritime  air masses.  In summer, the entire region is normally
under the influence of maritime air from the Pacific.  The
southern-portion  of the Bering Sea is most frequently under the
influence  of maritime  air, except during January and February
(Grubbs and McCollum  1968) when normally a strong flow of air
from the north and east brings in continental and  arctic  air.
For the remainder of the year,  the movement of low-pressure
centers and associated winds dominate the a-tmospheric
circulation in the southern Bering Sea.

     A  major influence on the general atmospheric circulation in
the area  is the region of low pressure normally located in the
vicinity  of  the Aleutian Chain, referred to as the Aleutian Low.
On monthly  mean-pressure  charts,  this appears as a low-pressure
cell  normally oriented with the major axis in an east-west
direction.  This is a statistical low, indicating only that
pressures are generally  lower along  the major axis as a result of
the passage of low-pressure centers  or storms.  Storms are most
frequent  and more intense in this area than in adjacent  regions.
The most  frequent track  or trajectory of movement  of these storms
is along  the Aleutian  Islands and into the Gulf of Alaska in
winter, and along the same general path in the west, but curving
northward  into the Bering Sea in summer (Overland  1981).  The
monthly frequency of  low-pressure centers  in the southern Bering
Sea is  slightly higher in winter  (generally four-five) than in
summer  (three-four).  Winter storms are much more  intense than
summer  stoxms.

     In winter, the most  frequent airflow is northeasterly around
the northern side of the low-pressure cell that is present at
                                32

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some location along  the  Aleutian Chain.   In summer, with the
movement of lows into the Bering Seaf a more southwesterly mean
flow develops  over the  lower two-thirds of  the  region.
Climatology of  the southern Bering Sea is characterized by a
progression of  storms rather than fixed weather types (Overland
1981; Overland  and Pease 1982).  These storms produce increased
cloudiness, reduced diurnal  temperature  range, and winds that
rotate through  the compass.  During the summer in the southern
Bering Sea, frontal activity can be severe as very cold arctic or
continental air comes in contact with the warm air from the
Pacific Ocean,  forming a  sharp discontinuity and localized winds.

     Figure 3-1 shows wind roses for selected locations and
marine regions  in  the Bering Sea during  February and August.   The
wind roses show the percentage of observations from each of eight
possible directions.  (The data were from Brower et al.  [1977]
and Grubbs and  McCollum  [1968] and compiled by Overland [1981].)

     In winter  ("February" in Figure 3-1), the northern stations
show a high percentage of winds >17 kn from the north and
northeast, whereas the winds over Bristol Bay (Marine Area C) are
uniformly  distributed over direction with moderately  high speeds.
This is indicative of a  fairly  continuous progression of storms
through the area.  Wind speeds over the Bering Sea in summer
("August"  in Figure 3-1)  are  generally lower than in winter
although conditions are seldom calm.  Marine Area A to the north
shows little  preferred  direction,  but  the other stations show
predominance of south and southwest winds in the summer.


                 Simulation  of Bering Sea Winds

     This  section  concerns the procedures used by the Rand
Corporation to  simulate winds for use in the oil spill trajectory
studies.  Because  the EIS and its support documents contain
insufficient detail  to permit reconstruction of the technical
details, we met with Liu  and Leendertse  at Rand Corporation in
order to obtain information  on the simulation procedures.   The
discussion below is based on personal communications with Liu and
Leendertse but  does not constitute a formal review of the Rand
programs.   It should be noted that statements in the EIS and
support documents  can be  misleading; for example, they state that
Putnins1 (1966)  study  is  used to model the winds.   The data are
used but in a highly  modified way; no mention is made of the
modification or its form  in  the EIS.  Many of the questions
concerning the  simulation of wind  conditions that concerned this
review  team were discussed at the  meeting.  Based on  these
discussions, we conclude  the approach taken by the Rand
Corporation could  be capable of wind simulations adequate for
trajectory studies.  Until a full  documentation of the procedures
is provided for peer review, the simulations will remain a
concern to the  scientific community.

     Rand Corporation has identified 11  basic weather patterns
for the Bering  Sea region.  These weather patterns are similar  to
                                 33

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      NORTHEAST CAPE


FEBRUARY

SPEED CLASSES

1-6 KN	
7-16 KN •==
17- KN  ——
o  10  10 so 
-------
the more predominant patterns selected by Putnins  (1966);
however, they are also based on more recent meteorological data.
The wind model uses these basic patterns and their frequencies
and patterns of occurrence to simulate the  large scale
atmospheric features.

     Since the Bering Sea region is one of  frequent storm
activity, the Rand simulation procedure interrupts the large
scale weather patterns and inserts a traveling storm.   For winter
months,  the simulated storms occur at a rate of  four-five per
month, and for summer months at a rate of three-four per month.
In addition,  Rand has carried out a study of the intensity and
statistics of storm events in the Bering Sea region.   These
statistics are incorporated in the simulation procedures.

     The simulation procedure has been shown by Rand Corporation
(unpublished)  to  reproduce the  observed wind roses at  selected
locations in  the Bering  Sea  region.  In the DEIS for the St.
George Basin lease sale, it is stated that the wind model is
verified because it reproduces the average wind speed and
direction at Nome,  Alaska.  This  is  a gross  over-simplification
of the work that has been done at Rand Corporation.   The
simulation procedure contains more reality  than  is referenced in
the EISs for the Bering Sea lease areas.  However,  the  simulation
procedure has not been documented nor has it been demonstrated
that the analysis reproduces both the spatial and time scales
which occur in actual Bering Sea winds.'

     The question concerning spatial and time scales  of simulated
winds is important to the trajectory analysis.   When  a trajectory
evaluation is made, trajectories from a number of launch points
are evaluated using the same simulated winds.   If these winds
have  larger spatial patterns than the launch point separations,
then adjacent trajectories will not be statistically independent.
Consequently, it is important that the scales of atmospheric
forcing be simulated correctly.


              Simulation of Bering Sea Circulation

Oceanorah  of the Berin
     The reason that a numerical model of ocean circulation was
used for Bering Sea leases is that the region has strong tidal
currents, is subjected to strong wind events,  and has domains in
which horizontal density gradients are large.   Furthermore, the
shelf circulation has regimes which are distinctly different as
to the influences of bottom friction, thermohaline processes, and
oceanic influence.   The inclusion of a dynamic model in the risk
analysis study is intended to increase the reliability of  oil
spill trajectory estimates.

     The Bering Sea continental shelf is broad  (500 km) and the
bottom grades smoothly offshore to a relatively deep  (170 m)
shelf break, with the 50 and 100 m isobaths dividing the shelf
                                35

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into three zones with  distinguishable water  column
characteristics.  The  50  m contour  lies 80-150-km from shore,  and
the 100 m contour is 100-150 km landward of the shelf break,
leaving  a central region over 200 km wide with  intermediate
depths.

     The  dominant water motion on the shelf is  by tidal currents,
which are relatively  strong (20-50 cm/sec) and  account for
60-90 percent of the horizontal kinetic energy.   Turbulent energy
for mixing the water column comes from only  two  sources:   tidal
currents  (up from the bottom)  and wind  (down from the  surface).
During winter and spring,  the  wind-mixed layer  is 10-70 m thick,
with an  average  thickness of about 50 m.  In summer, this layer
is 5-20  m thick, with  an  average value  of about 10 m.   The
tidally  mixed  bottom  layer is 30-50 m thick.  Over the inner
shelf  (depths <50 m),  these wind and tidal boundaries  merge,
creating  a well-mixed water column.   Over the middle shelf
(depths 50-100 m),  the boundary  layers are separated.   The layer
between  has no significant source of turbulent  mixing  energy.   It
is within this central part of  the water column  that shelfwater
is working its way seaward.  Over  the outer  shelf,  oceanic water
penetrates inward beneath  the  outflowing water from the middle
shelf.

     The  circulation described above is weak with horizontal
currents  generally <4 cm/sec.   Tidal currents are much stronger;
however,  tidal  motions are elliptical.  It terms of transport of
oil over time spans of 3-30 days, neither  the weak mean flows
associated with  the distribution of temperature  and salinity over
the Bering Sea shelf nor  the tidal  currents  are  of  predominant
importance.   The wind-driven currents dominate  in the  transport
of oil on the sea surface.   The  wind-driven  currents fluctuate on
periods  of 2-10  days,  with magnitudes up to 20  cm/sec   (Schumacher
pers.  comm.).

     In  addition to the wind-driven circulation over the Bering
Sea shelf, there are strong currents along the continental shelf
between  Unimak Pass and the Pribilof Islands.  These currents
fluctuate at periods longer than 10 days,  and the fluctuations
are not  apparently related to  local wind events.  The  average
speed of  the currents  is  about 6 cm/sec; however, currents as
high as  20 cm/sec and as  small as 2 cm/sec may  be found
(Schumacher pers. comm.).   The  general direction  of flow  is  along
the shelfbreak  to the northwest.  During the summer  of 1977, six
satellite-tracked drifters (drogued at 17  m)  were deployed over
the shelf/slope  break.  Vector mean speeds were 5-15 cm/sec  over
deeper water and 1-3 cm/sec in  the  shallower shelf waters.  The
buoys drifted towards the northwest.

The Numerical Model

     The  basic 3-dimensional model  developed by Liu and
Leende-rtse solves the equations of motion for water and ice,
continuity, mass, heat, salt,  pollutant, and turbulent energy
balance.  Implicit numerical solution methods are used in the
                                36

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vertical  so that cross-layer transfer of momentum, energy, and
constituents can be computed accurately without any numerical
stability problems.  The model was first described by Leendertse
et al.  (1973) and again by Leendertse and  Liu  (1977).
Improvements to the model which have been made since these
publications include:

     •    The horizontal grid structure includes the ellipsoidal
          curvature of  the earth.

     •    Ice dynamics  (including melting, salt rejection, and
          ice-ice interactions)  are included in the formulation.

     •    A parameterization of oil movement under  ice is in the
          formulation.

     •    The model incorporates a closed form for the generation
          and decay of turbulence.  This form no longer depends
          on the Richardson number and related parameters.

     •    The model includes the kinetic energy content and
          dissipation associated with short wind waves.

No substantial changes in the model have been made  since  1981.
Consequently, all  studies of oil spill trajectories have been
carried out with the same formalism.

     The 3-dimensional model can be used directly for simulating
oil movements for a duration of several days.  For longer periods
(such as several months), a much more economical method is
needed.  To accomplish this,  Rand uses  a method called the unit
response  function method.  Response functions,  after being
generated by the 3-dimensional model under four wind directions
(N,E,S,W),  are  used  to  synthesize,  through  a convolution  process,
the drift currents due to winds from various weather scenarios.
Response  functions are generated by the difference in currents in
the 3-dimensional field with and without the wind stress under
identical  tide conditions.  The model is run for 5 days; the
first 3 days of information are discarded because they are
contaminated by start-up transients in the model.  The last
2 days of data are used to evaluate the response functions and
average flows.

     Boundary conditions used in the simulations are based on
field observations.  For the shelfbreak currents between Unimak
Pass and the Pribilof Islands, historical hydrographic data have
been used to determine the density field and geostrophic
currents.  The  model uses a shelfbreak current of 6 cm/sec
flowing towards the northwest.  Over the shelf proper, the
initial data for the model's density field are based primarily on
observations gathered in 1976 by the NOAA research ships Mo ana
Wave and  Miller Freeman.  Tide data at the open boundaries, which
are based on observations, complete the required suite of
boundary  conditions.
                                37

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Verification

     Mofjeld  (1984) has compared observations of tides with
predictions made by the 3-dimensional  Liu  and Leendertse model
and by a vertically integrated hydrodynamic model (Sundermann
1977).   Mofjeld concludes that the quantitative agreement  with
observations  is better for the 3-dimensional model.  He states,
"This may be due to the more  complete  dynamics in the
3-dimensional model as well as the tuning of this [3-dimensional]
model to a larger set of tide  observations than was available to
Sundermann  (1977)."  The  simulations  were for summer conditions.
The comparison employed predictions from a preliminary
calculation using the 3-dimensional model; more recent work may
demonstrate further improvements.  Even without further
improvements, the 3-dimensional  model  has been shown to predict  a
reasonably  accurate tide picture for  the Bering Sea region.  This
could not result from the calculations if the model was
incorrectly evaluating tidal  dissipation.

Ice

     The Rand model allows for ice cover in the oil spill
trajectory  studies.  Ice,  particularly near the ice edge,  adds
several  complications  to  the  prediction problem.  Muench (1983)
and Muench  and Schumacher (1985)  discuss the results  of recent
experiments.  One important process is the melting of the  ice,
which creates a fres.hwater lens  near the limit of ice flow.  The
density contrast between this fresh water  and the more saline
waters of the ice-free regions of the  continental  shelf generates
a northward-flowing baroelinic current parallel to the edge of
the ice zone.   This current,  coupled with  reduced mobility of sea
ice  (relative to open water),  diminishes the importance of  local
winds relative to baroclinc flows in transporting oil.,
Trajectories  of oil spills generated by the Rand model show this
effect.

     An average ice cover year is assumed for the model runs.
The actual  marginal ice zone  is not fixed in space but varies
with wind conditions.   It does not appear  that the Rand model can
include this  variability  in the  trajectory calculations.   This is
because the calculations  use  response  functions that are fixed by
the ocean state at the beginning of the simulation.  Thus,  the
variability in  trajectory paths  related to the position of the
marginal ice  zone may be underestimated in the calculations.

     The EIS  and its supporting documents inadequately describe
the model run with ice cover.   The documents state only that the
model was run with  ice cover.  In fact, the model  'with ice
cover1 refers to a model  formulation  capable of incorporating ice
cover.  For the trajectory studies, winter conditions were
simulated by  using six trajectory simulations for each of
6 winter months.  For  each simulation,  the marginal ice zone was
located at  its multi-year average location for  the month.
                                38

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       Influence of Numerical  Model  on Trajectory  Analyses

     A review of the trajectory analysis  is severely  hampered  by
a lack of documentation of the details of the  calculations.  When
tests and checks have been made,  they should  be  cited in the
references in the EIS.   Because our  major goal has been to
consider reasonableness of the approach,  we have looked at  the
results as the  output  of  a 'black box1 and tried to understand
its significance in predicting conditional risk  percentages.   It
is not necessary that details  of  the calculations  or  formulations
be described in the EIS.   It is necessary, however, that the EIS
provide enough  information about  what data are used in model
formulation and an overview of how  they are used so that it is
possible to determine whether  important factors  in trajectory
simulations are accounted for.  Our  review suggest that the model
and resulting trajectory analyses are reasonable,  although  two
concerns remain:  first,  regarding the model's ability to address
short-term (monthly) changes in baroclinic currents;  and second,
regarding the model's ability  to  address  effects of freshwater
runoff.

Baroclinic Currents

     The evaluation of trajectories  of  oil spills  relies heavily
on the use of response  functions  derived  from  the  continental
shelf circulation model.  These response  functions are derived
from model runs a few days long  (after  allowing  for startup
transients in the calculations).   The use of  these response
functions implies that they contain  all of the relevant physics
and dynamics of  the ocean circulation.

     The purpose of a multi-layered  numerical  model is to include
baroclinic processes in the calculations.  Since the  response
functions are fixed in  space and  derived  from  the  density field
imposed when the model run is  begun, any  effects due  to short-
term advection of the density  field  are lost.  If  wind forcing
moves the density field or alters horizontal  density  gradients,
the resulting changes in circulation cannot be evaluated.   New
response functions would need  to be derived based  on  the new
density field, and this step would negate the  numerical economy
achieved by  using  response functions.

     Since baroclinc currents  are weak ซ10 cm/sec) over most  of
the Bering Sea shelf,  the errors made in  their evaluation may  not
seriously hamper the trajectory evaluations.   If the  model  were
extended to oil in water calculations, however,  errors in
evaluating baroclinc flows could  become more important.

Freshwater Runoff

     If fresh water were  to enter the  system, e.g., from major
rivers such as the Yukon, the  influence of this  lens  of water  is
not accounted for  unless  it is present in the response functions.
Runoff can vary appreciably over  periods  of 30 days,  particularly
in spring time.   This temporal variation  of  input  of  fresh  water
                                39

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would not be included in the response functions,  and the
resulting calculations  could be  in error.  Since effects of
runoff are localized, the errors need not have a major impact on
the shelf as a whole, but they could appreciably alter local
calculations in areas such as Norton Sound or inner Bristol Bay.

     These comments are not to be interpreted as a statement that
the Rand model  is  incapable  of evaluating baroclinic flows or
freshwater runoff.  We note that, as used in the trajectory
analysis, the model cannot properly reproduce short-term changes
in baroclinic flows.  Regions where such flows are important
include:  the marginal  ice zone, regions with high inputs of
fresh water, regions with time-variable input of saline water
(the most predominant is Unimak Pass),  and the region near the
50 m  contour.
             Interpretation of the Trajectory Studies

      Figure 3-2 was prepared from the risk tables in the FEIS for
the  St.  George Basin lease sale  (DOI 1985, Table E-3).  The
launch points in the lease sale area are labeled in Figure 3-3
using the  notation in Table E-3.  Figure 3-2  shows  the overall
combined risk probabilities for the resource areas around  St.
George and St. Paul  Islands  (Figure 3-4).  The resource area is
defined by a circle  of radius 50 km about each island.
Figure 3-2 shows that the majority of the risk comes from within
the  quadrant to the  east of the islands.  Although risk to St.
George Island is minimal from the south and west,  similar
findings are not made for St.  Paul  Island.   Overall, the patterns
of probability are not inconsistent with wind rose data for the
winter (Figure 3-1).   In summer,  however, the wind rose for St.
Paul Island shows more persistent winds  from the south,
southwest,  and west  (Figure 3-1).   These pictures raise a
question concerning  the true risk to the Pribilofs because it
seems that summer weather is not adequately represented in the
statistics.

      One way to interpret Figure 3-2 is to assume that
differences in the probabilities of contact to a target from
nearby locations represent the overall statistical accuracy in the
calculations.  For example, in Figure 3-2 there are two nearby
launch points to the southeast of St. George Island:  one has a
probability of 42 percent of contacting  the St. George resource
area and the other a 30 percent chance.   Since the locations are
nearly the same, it  could be assumed that the internal
consistency in the calculations is about 12 percent.   Similarly,
north of St. Paul Island, there is a launch point with a
14 percent probability.  A launch point nearly an equivalent
distance north of St. George Island has a 0 percent chance of
resulting  in a spill that  reaches  the  St. George resource  area.
This suggests an internal consistency in the simulation procedure
of  about 10-15  percent.
                                40

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NOTE:  VALUES INDICATE PERCENT PROBABILITY THAT A LARGE OIL SPILL OCCURRING AT THAT POINT WILL REACH
      PRIBILOF ISLANDS WITHIN 30 DAYS.
FIGURE 3-2,  MMS-CALCULATED RISK  TO PRIBILOF  ISLANDS  RESOURCE AREAS
SOURCE:   DO I 1985,  TABLE E-3.

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                                        A  ST. GEORGE BASIN DEIS

                                        P  NAVARIN BASIN DEIS
FIGURE 3-3,   LAUNCH POINTS USED  IN MMS  OIL SPILL RISK
              ANALYSIS


SOURCE:  DOI  1984,  GRAPHIC 5,
                                42

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CO
                                                                          BIOLOGICAL RESOURCE AREA

                                                                          TRACTS WITH ACCEPTED BIDS (SALE 70)
          FIGURE  3-4,   BIOLOGICAL RESOURCE AREAS FOR ST, GEORGE BASIN OIL SPILL RISK ANALYSIS
          SOURCE:   DOI 1984, FIGURE E-2,

-------
     For the St.  George  Basin  EIS, simulated oil  trajectories
were generated from 24 launch  points in or near the lease  sale
area.  These were selected to  be representative of platform
locations, pipeline routes, and tanker routes.   In addition,
26 launch points used for the Navarin Basin EIS were  included in
the  statistics.   Twenty-six trajectories were launched from each
point for the ice-free season.  Thirty-six trajectories were
launched from each point during periods when there could be ice
cover.  It is possible that the use  of so  few trajectories from
each launch point can explain  the  differences in  probabilities
noted in the preceding paragraph.

     Because the winds in winter do not tend to drive oil  from
the  south towards the Pribilof Islands,  there is  little risk  to
these islands during this time.  Thus, 58  percent of  the
tractories (36 out of 62} have a negligible  chance  of reaching
the  Islands from the south.  This leaves a maximum chance  left of
42 percent for the summer tractories.  With weaker summer  winds
and  variability in these winds, not  all  summer  trajectories
should move northward.  If a quarter  of  them reached  the Island
resource areas, the risk percentages would be  about 10 percent.
If the inherent statisical  variation in  the  trajectory
evaluations is on the order of 10-15 percent,  then a 0 percent
chance is the same as a 10 percent chance.   Proper decisions  can
be made only when it is recognized that  the analysis  as currently
done has this level of uncertainty.   Some  of this uncertainty may
be eliminated if this question of the number of  trajectories
needed is resolved.
                     Number of Trajectories

      The risk anlaysis calculations for the Bering Sea lease
 areas use the spill trajectories provided by Rand Corporation to
 evaluate the probability that a target area is  hit by  a spill
 from  a given source.  This probability is defined as the  number
 of hits divided by the number of spills expressed as a
 percentage.   Once an oil spill trajectory hits  land, it is no
 longer counted in the calculations.   This raises the question of
 how many trajectories should be calculated for  a specified source
 point and how many source points  should be evaluated.

      Samuels (1984)  documents the trajectory analysis for the
 St. George Basin lease sale and includes a table of errors and
 number of trials in Monte Carlo simulations of  a binomial
 process.  A binomial process is the  equivalent  of a biased coin
 toss  problem.  Table 3-1 summarizes some statistics from Samuels'
 table.  The table gives the error estimates in  estimating the
 true  probabilities  given the  number of trials.   The numbers in
 Table 3-1 are re-examined in Table 3-2 in terms of percentage of
 error and relates it to a biased/unbiased coin toss problem.
 When  the table is expressed in this way, it is  apparent that many
 experiments are needed to determine the bias in an extremely
 biased coin, while  relatively many fewer are needed to determine
 the probabilities in an  unbiased coin.
                                44

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     Table 3-1.   Monte Carlo Error as a Function of Number of
                 Trials and Estimated Probability

                           Number of Trials
Prob
0.02
0.50
SOURCE :

0
0
UL
.07
.26
Samuels
Afl.
0.04
0.13
1984,

0
0
Tabl
5JI
.03
.12
e 2.
100
0.02
0.08
500
0.01
0.04
1000
0
0
.01
.03
2000
0
0
.01
.02
     Table 3-2.  Percent Error for a Biased  (P=0.02) and
                 Unbiased  (P=0.50) Coin.

                	    Number of Trials	
Prob             10_    4J1    5_ฃ   100  500   1000   2000

0.02            350   200   150   100   50     50     50
0.50             52    26    24    16    8      6      4
Note:  Derived from Table 3-1.
                                45

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     Applying this model to the  true probability that oil spills
might strike a specific land segment vs.  the calculated
probabilities that oil strikes the land segment, we  observe that
many trials are needed to adequately model low risk areas and few
trials are needed to model high risk areas.   We note,  however,
that the percentage of error is large  even with 2,000  samples of
a coin with extreme bias.   Table 3-2  suggests  that for  the
trajectory calculations in which 62 trials are used,  there  should
be inherent errors between about 24 and 150 percent.

     An alternative method for viewing trajectory experiments
can be expressed by the following thought experiment, which  is
geometrically similar  to the trajectory problem.   Suppose we
randomly generate 36 spokes radiating  from the center  of  a  circle
that has been divided  into 36 arcs each of 10ฐ width.   For  any
arc, the mean expected number of spokes hitting the arc will be
one; however, the standard deviation in the number of  spokes in a
given arc is four.  Thus,  in a specific experiment,  it  would not
be unusual  to have several arcs with zero spokes and a few  arcs
with four-five spokes.   In terms of  risk  analysis  made  utilizing
a single sample,  one arc would have four-five times the risk of
others, even though its true risk is identical to other arcs. In
the models  of trajectories, the oceanographic  and  meteorologic
influences will  tend to focus trajectories.  Thus, statistics
from thought experiments,  such as this, are" conservative.

     Our concern with regard to  the risk analysis  presented for
Bering Sea lease  areas  is that the analysis of the trajectories
not introduce a bias and that they are sufficient  in number to
eliminate statistical  variations  in the assessment of risk.  The
documents supporting the EIS have failed to address  this
question.

     During the meeting with Rand Corporation, Liu presented an
overhead slide showing that independent calculations of
trajectories by Overland  (PMEL unpublished), which were based on
actual weather sequences,  had the same qualitative character as
Rand predicitions.  The predictions were for an open ocean  region
south of the Pribilofs.  Oneway to look at  this result is  that
the Rand model is not predicting significantly different  results
from what a 3.5  percent of surface wind-speed  model  would give.
In the Rand model, a 1.6 percent of wind-speed rule is used to
account for Stokes drift due to surface waves.  The remaining
difference  of 2.9 percent is included  in the air-sea interaction
simulations.

     The approach to the trajectory analysis appears to be
reasonable.  Although  ambiguities in the risk  analysis procedure
appear  to be of little importance as  the model is now used, we do
not believe  that  these ambiguities should continue.   The  most
serious concern is that the risk to specified resource areas is
not adequately represented because the number  of trajectories is
too few and perhaps biased toward winter conditions.   Whether
this is the case cannot be determined  until  additional
trajectories are  run so that error bounds can be evaluated.
                                46

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                            Chapter 4


                 CONCLUSIONS AND RECOMMENDATIONS


                            Overview
     Our review and evaluation of the oil spill risk analysis as
presented by the St.  George Lease Sale 89 EIS (DOI  1984,  1985)
and its support documents lead us to the  following  major
conclusions:

     •    There is considerable uncertainty in the  spill  risk
          estimate (expected number  of spills) .

     •    Given the inherent uncertainty in the estimate,  the
          approach and the reported  estimates appear to be
          reasonable approximations.

     •    The wind simulation technique appears  to  be  reasonable
          and adequate for offshore  lease sales in  the Bering
          Sea but has not been documented.

     •    It is not clear that the number of simulated
          trajectories is adequate to portray the risk to
          designated targets,  especially once the probability
          distribution for trajectories is  adjusted by mean-case
          production data to generate combined probabilities.

     •    The presentation of  the oil  spill risk analysis  in the
          EIS can and should be improved in several significant
          ways if it is to meet NEPA requirements.


                     Sources of Uncertainty

     There are four main sources of  uncertainty in  the risk
evaluations. These are:

     1)   Uncertainties in the resource estimates.

     2)   Uncertainties in the expected number of spills  because
          of the small database from which means have been
          derived.

     3)   The distribution function  for spill frequencies  is only
          approximated by a Poisson  distribution.   Therefore,
          estimates of the probabilities  of number  of  spills will
          be increasingly uncertain  as the  number differs  from
                               47

-------
          the mean estimate.  Similarly, spill  size forecasts
          will increase in uncertainty as  the forecast  departs
          from the mean spill size.

     4)   The scenarios for  production introduce additional
          uncertainties.

     In the DEIS for Lease Sale 89 (DOI 1984),  the mean resource
estimate was 0.66  Bbbl.   In  the  FEIS for Lease  Sale 89,  the  mean
resource estimate increased to 1.12  Bbbl.   In the 1981  synthesis
meeting, the mean resource was  listed  as 1.12 Bbbl.  Finally in
the Federal Offshore Statistics (Essertier 1984,  p. 98),  the mean
resource estimate for St.  George Basin is  listed at 1.4 Bbbl.
The final risk estimates are linearly  related to these  resource
estimates.   Thus,  the  final  risks  have inherent in them the
uncertainties in  resource estimates.

     For spills ฃ.100,000 bbl,  spill  rates  are much less accurate
and much less deterministic  than for smaller spills.   This is
unfortunate because these  very large spills are more likely  to
have major and long term environmental consequences.   The
projected spill rates are based on an  extrapolation of  a  log-
normal  distribution fit to the frequency distribution for  spill
volume.  Extrapolation of a  log-normal distribution beyond the
set of date it was fit to is  usually not attempted because almost
no confidence can be placed on the extrapolated estimate.  The
FEIS (DOI 1985)  presents the risk  rate for very large spills as
if it had the same certainty as smaller spills.  This  is
misleading, particularly  when one considers that the spill rate
for even smaller  spills are characterized by great  uncertainty.


          Interpretation of the Oil  Spill  Risk  Analysis

     On several points, we conclude that the  EIS interpretation
of the oil spill  risk analysis requires improvement.   The
majority of these points focus on two  major concerns:   1)  the
tendency to average out findings that  either  should not be or
cannot be averaged, and 2) incomplete  presentation  of  information
necessary to judge the reasonableness  and adequacy  of  the
findings.

     By averaging the impact levels from various activities
associated with the lease sale,  the  significance of the impact
from oil spills is  often  obscured.  This problem is most
noticable in the  EIS summary.   For example, although  the EIS may
determine that impacts from seismic  exploration activity may be
negligible  and  impacts from oil spills may be moderate for a
certain species,  the EIS summary concludes that the "overall
risk" to the species is minor.   This approach violates a
fundamental principle  of  ecology  which states that  the presence
and success of an organism or group of organisms depends on that
one condition that most closely approaches or exceeds  the limits
of tolerance.  Thus, the "overall impact"  of a  project  is no less
than the most severe impact  likely from any particular aspect of


                                48

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 the  lease sale activity.   In  a  similar vein,  "overall  risk"  to  a
 higher  taxon (e.g., crustaceans or marine mammals) cannot  be  the
 average of the risk to each species.

      The EIS also averages out  seasonal  differences  in trajectory
 simulations.  Averaging may be  a  reasonable approach in
 calculating combined probabilities as long as this point is
 clearly stated and seasonal differences  are given weight
 proportional to their occurrence.  This  latter  point is
 particularly important because  of significant  summer use of  the
 area by marine birds  and  mammals.   Since 58 percent  of  the
 trajectory simulations for Sale 89 are  run  for winter  conditions,
 a bias  away from summer trajectories  is  incorporated in the
 analysis.  Thus,  the  conditional  probabilities  and combined
 probabilities reported in the EIS are averaged  over  the annual
 meteorological and oceanographic  conditions and weighted more
 toward  winter conditions.

      References to incomplete documentation have  been  noted
 several times in previous chapters.   In  several cases,  the
 missing information is important  to interpreting  the
 reasonableness and adequacy of  the impact assessment.   The most
 important omissions are:

      •     the general features  of the wind simulation  model;

      •     a clear statement of  the important assumptions
           underlying  the  estimated expected number of  spills;

      •     an explanation  of how the launch points have  been
           weighted for volume of  oil  handled;  and

      •     clear statements regarding  the implications  of the way
           certain information is  treated.

 This latter point is  particularly important.   CEQ regulations
 require a worst-case  analysis when great uncertainty is inherent
 in the  analysis (40 CFR 1502.22).   We  believe it is  reasonable to
 present conditional probabilities that have been  averaged  out for
 all seasons, for example, but the decision maker and the public
 must recognize that the conditional probabilities in reality will
 be higher or lower for certain  seasons.   It is  the responsibility
 of the  EIS to clearly state implications such  as  these.


                          Recommendat ions

      We recommend that EPA give greater  emphasis  to  conditional
 probabilities when determining  the environmental  consequences of
 a proposed action.  Greater reliance on  the conditional
•probabilities is, of  course,  predicated  on:   1) a larger number
 of trajectories per launch point, 2) proper balance  between
 seasonal conditions,  and  3) clear understanding that these values
 are  averaged for the life of  the  project. The combined
 probabilities are of  value in that they  provide a rough estimate
                                49

-------
of the likelihood of a particular spill event/impact,  but  they
should not be used in a deterministic manner  because of the large
uncertainties in the expected number of  spills.  We believe our
recommended approach is more  appropriate because it more closely
approximates a reasonable worst-case analysis,  which  is required
by CEQ regulations when great uncertainty  is  involved.

     An adequate worst-case analysis should include a  discussion
of conditional probabilities for the  summer season.  This
represents the period of time when most birds and mammals are at
greatest risk.   Basing the analysis on  conditional probabilities
that have been averaged out over the year would not result in a
reasonable worst-case analysis.

     MMS should  not  continue to report  "overall risk"  to a
species as an average of the  risks posed by different aspects of
the lease sale action.  The practice has no biological value  and
is misleading.

     Sufficient numbers of trajectories should  be simulated so
that error bounds on the probabilities  can be assessed.  We also
recommend that the number of  trajectories  be  proportionately
distributed among the seasons if  annual  averages are going to be
used.

     The EIS must do a better job of  describing the Rand model.
We do not recommend that MMS  describe the mathematical details of
the trajectory simulation models, rather,  it  is recommended that
a brief explanation be given  of  the factors considered in  model
runs.  For example, the reader must know:   traveling  storms are
simulated at a frequency that resembles actual  conditions  in  the
Bering Sea; how the number of trajectories  are  allocated to
different months for the winter simulation; where the marginal
ice zone was for each winter  simulation.  This  level of
information is necessary to judge the adequacy  and reasonableness
of the risk assessment.  We suggest that Rand publish  a document
that concisely states  the model's adaptation  to the trajectory
studies, its meteorological  inputs, and the tests that have been
made of the model.  We also suggest a careful analysis be
conducted of the  error  bounds associated with the  trajectories.

     We recommend that the critical assumptions of the oil spill
risk analysis be clearly stated.   In most  cases, MMS  has stated
the assumptions at various points in  the EIS.   However, we
believe the presentation can  be  significantly improved if  these
are brought together in one location to  clearly reveal  to  the
reader the information necessary to judge  the credibility  of  the
analysis.  As an example, we  believe  Table S-2  (DOI 1985)  is  a
commendable example  of how important assumptions can  be presented
to the reader.   In this context,  the reader can judge for  himself
whether the terms as applied to endangered species  (for example)
are appropriate and should be equivalent to those applied  to
nonendangered  cetaceans.
                               50

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                           REFERENCES


                        Literature Cited
Brower, W. A. Jr, H.  F. Diaz,  A. S. Prechtel,  H.  W.  Searby, and
  J.  L.  Wise.  1977.   Climatic atlas of the outer continental
  shelf waters and coastal  regions  of  Alaska.  AEIDC/NOAA,
  Anchorage,  AK.

Department of Interior.   1984.  Draft environmental impact
  statement.  Proposed outer continental shelf oil and gas lease
  sale, St.  George Basin  (Sale 89).  MMS,  Anchorage, AK.

               1985.   Final environmental  impact  statement.  St.
  George Basin Sale 89.  MMS, Anchorage, AK.

Devanney, J. W. Ill, and R.  Stewart.   1974.  Analysis  of  oilspill
  statistics, April 1974.  Report No. MITSG-74-20.  Prepared for
  the Council on Environmental  Quality.  MIT, Cambridge, MA.

Essertier, E. P.   1984.  Federal offshore statistics.   OCS Report
  MMS 84-0071.  DOI, Washington, D.C.  123 pp.

The Futures Group and World  Information System.   1982.  Final
  technical report:  Outer  continental shelf oilspill  probability
  assessment.  Vol 1:  Data  collection  report.  69 pp.  Vol 2:
  Data analysis report.  Prepared for DOI/BLM under contract
  No. AA851-CTO-69.  The Futures Group, Glastonbury,  CN.

Goldberg, N.  N., V. F. Keith, R. F. Willis, N. F.  Meade, and R.
  C. Anderson.  1981.   An analysis of tanker casualties for the
  10-year period 1969-1978.   Proceedings of  the  1981 Oil  Spill
  Conference.  Am. Petroleum Inst., Washington,  D.C.

Grubbs, B. E.,  and  R.  D.  McCollum Jr.   1968.   A  climatology guide
  to Alaskan weather.  Scientific Services Section, llth Weather
  Squadron, Elmendorf AFB, Alaska.

Jones & Stokes Associates, Inc.   1984.  Preliminary draft Ocean
  Discharge Criteria Evaluation OCS Lease Sale 89 St.  George
  Basin.  Prepared for U. S. EPA Region 10, Seattle, WA.

Lanf ear,  K. J., and D.  E. Amstutz.  1983.  A re-examination of
  occurrence rates for accidental oil spills on  the U.  S. outer
  continental shelf.   Pp. 355-359 In Proceedings of the 1983
  Oilspill  Conference.   Am.  Petroleum Inst.,  Washington,  D.C.
                                51

-------
Leendertse,  J. J.f R.  C.  Alexander, and S. K.  Liu.   1973.   A
  three-dimensional model for estuaries and coastal seas.   Vol.
  1.   Principles of computation.   R-1417-OWRR.   The Rand
  Corp., Santa Monica, CA.

Leendertse,  J. J., and S. K. Liu.   1977.  A three-dimensional
  model for estuaries  and coastal seas.  Vol  IV: Turbulent
  energy computation.   R-2187-OWRT.  The Rand Corp.,  Santa
  Monica,  CA.  59 pp.

Liu, S. K., and J. J. Leendertse.   1982.  A three-dimensional
  shelf model of the Bering and Chukchi  Seas.   Coastal
  Engineering J.  18:598-616.

Mofjeld, H. 0.  1984.   Recent  observations of  tides and  tidal
  currents from the northeastern Bering  Sea shelf.   NOAA Tech.
  Memo. ERL PMEL-57.   Seattle, WA.  36 pp.

Muench, R. D.  1983.   Mesoscale oceanographic features  associated
  with the central Bering Sea ice edge,  Feb-Mar 1981.  J.
  Geophys.  Res.  80:4467-4476.

Muench,  R. D., and J.  D. Schumacher.   1985.  On  the Bering Sea
  ice edge front.  J.  Geophys. Res. 90:3185-3198.

Nakassis, A.   1982.  Has offshore oil  production become  safer?
  USGS Open-File  Report  82-232.   26 pp.

Overland,  J.  E.  1981.  Marine climatology of the Bering Sea.
  Pp. 15-22 In D. W. Hood and  J. A. Calder, eds., The eastern
  Bering Sea  shelf:  Oceanography and resources.  NOAA/OMPA,
  Seattle, WA.

Overland,  J.  E., and C. H.  Pease.   1982.  Cyclone climatology of
  the Bering  Sea and its relation to sea ice extent.   Mon.
  Weather  Rev.  110:5-13.

Putnins, P.   1966.  The sequences of  baric weather  patterns over
  Alaska.  In Studies on the meteorology of Alaska.  DOC.
  Prepared for Dept. of Army,  IVO-14501-B53A-05-03.

Samuels,  W. B., D. Hopkins, and K. J.  Lanfear.   1981.  An
  oil spill risk    analysis  for the southern California (proposed
  Sale 68) outer continental shelf lease area.   USGS Open-File
  Report No.  81-605.  206 pp.

Samuels, W. B.  1984.   An oil spill risk analysis for the St.
  George Basin (December, 1984)  and North  Aleutian  (April, 1985)
  outer continental shelf lease  offerings.   DCS Report 84-0004.
  DOI/MMS.

Smith,  R.  A., J. R. Slack, T.  Wyant, and K. J.  Lanfear.   1982.
  The oilspill  risk analysis model of the U.  S.  Geological
  Survey.  Professional  Paper 1227.   USGS.
                                52

-------
Stewart, R. J.   1975.   Oil  spillage associated with the
  development  of  offshore  petroleum  resources.  In:  Report to
  the Organization for Economic Cooperation and Development.  49
  pp.

	1976.   A survey and critical review of U. S. oil
  spill data resources with application to the tanker/pipeline
  controversy.  Prepared for  DOI.  Martingale,  Inc., Cambridge,
  MA.  75 pp.

Stewart,  R. J., and M. B.  Kennedy.  1978.  An analysis of U.  S.
  tanker and offshore petroleum production oil spillage through
  1975.  Office of Policy  Analysis.   DOI, Washington,  D.C.

Sundermann, J.   1977.   The  semidiurnal  principal lunar  tide M2 in
  the Bering Sea.  Deutsche Hydro.  Zeitschrift 30:91-101.

USGS.  1972.  Outer continental shelf  statistics  1953-1971.
  Conservation Div.

	.  1979a.   Accidents connected with federal oil and
  gas operations on the outer continental shelf, Gulf of Mexico.
  Vol.  I:   1959-1976.  Conservation  Div.,  Metairie, LA.

	.  1979b.  Accidents connected with federal oil and
  gas operations on the outer continental  shelf,  Pacific area.
  Conservation Div., Los Angeles,  CA.

Wilson, E.  B.  1952.  An introduction  to  scientific research.
  McGraw-Hill, NY.
                     Personal Communications
Hale, D.  1984.  MMS, Anchorage, AK.  Telephone conversation,
  December 27.

Prentki, R.  1985.  MMS, Anchorage, AK.  Memo, March 1.

Schumacher, J.  1985.   PMEL/NOAA, Seattle, WA.  Telephone
  conversation, May 3.
                                53

-------
                           Appendix A


                  EVALUATION OF OIL SPILL RISK


              Spill Occurrence as a Poisson Process
     The principal reference for the Poisson model used in the
EIS is a USGS Open File Report by Anastase Nakassis (Nakassis
1982).  The abstract for this paper is short and to the point:

     "ABSTRACT:   In what follows we examine the hypothesis that
     there has been no improvement in the offshore oil production
     safety [versus] the hypothesis that there has been a gradual
     improvement.  Our analysis will show that the second
     hypothesis is much better supported by the available data."

The report is referenced in the supporting documents to the EIS
to indirectly suggest that the modeling of oil spill frequencies
is correctly done with a Poisson process based on a modified oil
production variate as an exposure variate.  For example, Lanfear
and Amstutz (1983)  state that they  treat oil  spill occurrence as
a Poisson process.   Their justification and methodology are based
on Nakassis  (1982).

     We conclude that Nakassis1 report does not show that oil
production is an appropriate exposure parameter.  Rather,
Nakassis first shows that production is not a good exposure
variate; and then he attempts to transform the data to create a
better exposure variate under the assumption that oil  spills are
caused by a Poisson-like process.   The  validity of the
transformation that Nakassis uses rests on the Poisson assumption
but does not verify the assumption.


             Tests of Oil Spill Occurrence Patterns

     The Nakassis report first examined the question,  "Is there
any statistical evidence that we are not dealing with a Poisson
process whose parameter is proportional to the oil produced?"
Four different statistical procedures were used and, without
exception, all suggested that the data &Q not support the
proposed model, i.e.,  the  relationships between the intervals
between spills are not like those predicted for a Poisson
process.

     The underlying property exploited  by Nakassis to test the
model is that the waiting time between Poisson-distributed events
is identically and independently distributed.  This allows the


                               A-l

-------
use of distribution-free statistical  tests  (including  tests  of
runs and rank-order  correlations)  and  tests based on parameter-
free ratios of summations;  all of  these were employed  by
Nakassis.   The database  employed by Nakassis consisted of nine
spills  that occurred between 1 January 1964 and the end  of  1980.
The data consist of  the dates of the nine spills and the amount
of oil  produced in federal OCS waters between  spills.  There is,
however, one important exception:  the amount  of oil produced
prior to the first spill in the database (8 April 1964)  is
presumed to be the amount of oil produced between 1 January  and
8 April 1964.

     Nakassis accepts, without discussion,  1 January 1964 as an
acceptable starting date for calculation  of the exposure prior to
the first spill in his database.   This  is somewhat  controversial.
The selection of 1 January 1964 results in an exposure of
31.3 million barrels (Mbbl) for the first spill, whereas the
cumulative OCS production prior to 1964 was 380 Mbbl.  One could
argue that 31.3  is not a  very  likely  draw from the  population.
More significantly,  the tests  of runs and rank-order calculations
would be less conclusive if the exposure  prior to the  first  spill
were changed only slightly.

     We believe it would be preferable to use the date of the
first spill as the beginning of the first interval  to  be
analyzed.  If we change  the starting  date from 1 January 1964 to
4 April 1964, we change  the statistics  calculated by Nakassis.
Most importantly, we reduce the number of inter-arriVal intervals
from nine to eight.   With respect  to  Lanfear  and Amstutz's
analysis, we reduce their number of  intervals from  12  to 11.
These changes do not alter the conclusion that the  data  are  not
independently and identically distributed.

     Table A-l shows the modified  form of Nakassis1 data.   (We
also recalculated the production values and found some slight
discrepancies with Nakassis1 results.)  The statistical  tests we
applied to the data are Kendall's  *  and  Q  parameters  which
compare rank ordering between  two  lists.  The  rank  orderings we
use are based on  the date of the spill (list 1) and the
inter-arrival prod-uction (list 2). The statistical test suggests
that the two lists are positively linked (the chance  that they
are not is 0.27  percent), which implies that longer inter-arrival
intervals  occur at  later dates.   This is not  consistent  with a
Poisson distribution.   The  same test applied to Lanfear  and
Amstutz's data (Table A-2)  showed  that  the  chance of  independence
of  their data is 2 percent.

     Thus, correcting the starting value  and using  two spills in
October, 1964, we still concur with the paper's conclusions
regarding the import of these  tests.   Specifically, Nakassis
states:

     "Thus it seems that there are excellent  reasons  to  reject
     the hypothesis that the random variables Xj,  X2,  ... Xn are
     identically  distributed  and  independent."
                               A-2

-------
REF
NUM
1
2
3
4
5
6
7
8
9
Table A-l. Test of Poisson
SPILL PRODUCT TON
DATE NAKASSIS
08-Apr-64
03-Oct-64
09-JU1-65
23-Jan-69
16-Mar-69
10-Feb-70
Ol-Dec-70
09-Jan-73
23-Nov-79
55
103
712
43
270
272
804
2054
0
.9
.1
.9
.5
.5
.5
.9
.3
Fit to Arrival
INTERVAL TIME
RECALCULATED
55.9
98
716
42
272
271
811
2064
.8
.9
.0
.6
.5
.2
Times Between Spills: Modified Nakassis Data
RUNS RANK STATISTICS
2 Kendall:
3
+ 6
1
4
5
+ 7
+ 8
T ซ
I =
S =
Tau = 0
Chance
25
3
22
.785
of
being
Poisson = 0.275%
                           Mean =  541.6
First spill noted by Nakassis  is  assumed  to be  the  beginning  point  of  the
modified data set,  i.e., the first spill  noted  by Nakassis  is not included
in determining run  sequence and not  ranked.

-------
Table A-2. Test of Poisson
REF
NUM
1
2
3
4
5
6
7
8
9
10
11
12
SPILL
DATE
08-Apr-64
03-Oct-64
19-Jul-65a
28-Jan-69b
16-Mar-69
17-Aug-69
10-Feb-70
Ol-Dec-70
20-Jul-72
09-Jan-73
23-Nov-79
17-Nov-SO
PRODUCTION
NAKASSIS
0
55.9
103.1
712.9
43.5
270.5
272.5
804.9
2054.3

Fit to Arrival Times Between Spills: Lanfear and Amstutz Data
INTERVAL TIME
RECALCULATED RUNS
55.9
102.6
717.3 +
38.0
124.6
147.9
271.5 +
632.7 +
178.5
2064.0 +
252.5 +
RANK
2
3
10
1
4
5
8
9
6
11
7
STATISTICS
Kendall:
T = 41
I = 14
S = 27
Tau = 0 .490
Chance of
being
Poisson = 2.027%



                           Mean
416.9
Nakassis reports 9 July 1965 (see Table A-l).
Nakassis reports 23 January 1969 (see Table A-l).

-------
Although it is not explicitly stated as such,  a necessary
corollary  to this finding is that the spill process is not well
modeled using a Poisson process with production as an exposure
parameter.   This is because some of the properties that
distinguish Poisson processes are 1)  that they have no memory and
2> that intervals between events are therefore identically and
independently distributed.   Any finding that suggests that  the
data are not independently  and identically distributed also
rejects any hypothesis of Poisson behavior.


            The Idea of Time-Varying Rate Parameters

     Nakassis next asks the question:  "Can a  better model  be
constructed using the idea  that the rate parameter has declined
with time?"  The idea for this approach is reasonable, and  it is
strongly suggested by the analysis.  As a result of the increased
concern over oil spillage,  it is presumed that the industry  has
improved its practices and  consequently its safety record over
time and that the frequency of spills has decreased.  Rather than
create a time-varying rate parameter, Nakassis finds a
transformation of the original production data that more  closely
approximates a Poisson variate when considered from the
standpoint of the OCS spill history.  This is a subtle but
important  distinction that  was not discussed in Nakassis1 report.

     The approach taken by  Nakassis is to formulate three
distinct,  single-parameter  transformations of  the original
production date.  Each of these  transformed variate-families are
then treated as candidates  for a new exposure  variate for
modeling the spill generation process.  Nakassis uses a maximum
likelihood method to select an optimal value  for the fitted
parameter  in each family, and the magnitude of the  likelihood
function is used to determine if one of the transformation
families is a better choice than the  others.

     The maximum likelihood calculation  is based on the
distribution of the number of spills that can occur in an
interval of arbitrary length  of the exposure variate.  At its
most rudimentary form,  the  maximum likelihood  idea can be thought
of as a way of fitting annual exposure to the  original spill
incidence data.   The  probability  of having no  spills over a  small
interval is large  (say close  to 1.0), while the probability  of
having many spills is small.  Each transformation proposed  by
Nakassis will assign its own unique pattern of large and  small
intervals  to the original data.   Inevitably, one of the
transformations will do  a better job of recasting the original
production data in terms of the magnitude of the maximum
likelihood function.  If we have some years with many spills, the
"best" transformation will  cause  these years to have large
increments of exposure.   In years with no spills,  the "best"
transformation will have small increments in  exposure.

     Nakassis selects his intervals to correspond to the
increments of the transformed variates that occur over the


                               A-5

-------
calendar dates 1 January to 31 December in each of the years 1964
through 1980.   (It  is  this  procedure that results  in  the
controversial first spill  interval.)  Since it is  assumed that
the spill generation process  is Poisson,  the  distribution of the
number of events  in an interval of arbitrary  length is given by
the Erlang distribution,  and  Nakassis1 likelihood  function is
based on this functional  form.   The procedure used by Nakassis to
find the maximum  likelihood solution exploits the  properties of
this likelihood function under the assumption that the
transformation is linear  over any given  1-year interval  (Nakassis
pers.  comm.);  this  assumption  is  not explicitly stated in the
open-file report.  The procedure adopted  by Nakassis  is
mathematically rigorous except for this  assumption which
oversimplifies the exposure variate behavior.

     With each transformation that Nakassis postulates,  the
observed spill history creates a unique  set of observations that
consist of the number  of  events that occur  over  intervals that
correspond to a calculable (and variable)  exposure.   This
exposure is determined by the change  in  the transformed variate
over the annual interval.  The transformations proposed  by
Nakassis are one-parameter  functions,  and his optimization
process is directed at finding  the optimal  value of this
parameter for each family.

     For expository purposes,  it is easiest to consider  a
discrete version of Nakassis1  problem.   Imagine  that  we are given
three different transformations with  fixed parameters, and that
we have calculated the increments in these  exposure variables for
each of the 17 years in our sample.  Our data will then consist
of three arrays of numbers.  Each array will  have  2 columns and
17 rows.   In any given row, the first column will contain the
number of spills  observed,  and the second  column will contain the
change in the transformed variate from 1 January to 31 December
in the year corresponding to  the row.  For  each of the
transformations of the original oil production data,  there will
be a best estimate of the Poisson rate parameter;  this estimate
is simply the number of spills divided by the total exposure over
the 17-year period.  Given the exposure,  the  number of events,
and the rate parameter, the Erlang distribution provides us with
a probability for  the outcome of each row.  Assigning such a
probability to each row,  the  arrays can  then  be assigned
cumulative likelihoods that are  the product of the row-wise
probabilities.  Among all  three, one array will have the  largest
likelihood product.  It will  be the maximum likelihood
transformation.   The method employed  by  Nakassis simply  expands
the range of possible transformations  to include the  possibility
of continuous variation in the transformation parameter.

     In its general formulation,  Nakassis1 method  is correct.
But in some details,  methodology  is approximate and prone to
error.  For example, the assumption that the  exposure of  the
transformed variate over a year  can be approximated by the
product of the average production rate and  the value  of  the
transformation function at the beginning of the year  is  not


                               A-6

-------
warranted in the early period of the analysis.  As we show in the
section below,  this assumption predicts that the probability of
having no spills over  a year is about 0.6, whereas a more exact
solution places this  probability at about 0.45.   Nakassis1
approach leads  to a simpler estimation problem but at the expense
of  accuracy.

     There is another  feature of Nakassis' analysis that bears
discussion.   There is no guarantee that his fitting procedure
would reject a  false fit to the data.   The maximum likelihood
method simply ensures that of the possibilities considered,  the
one selected has the closet fit to the pattern implied by the
Erlang distribution.  The maximum likelihood procedure does not
distinguish between good and bad choices except to the extent
that the analyst had included a sufficiently broad selection to
begin with.

     The only real  test of  the model comes from the subsequent
tests of the runs and correlations of the fitted data, but the
eight or nine data points have already been used to reject three
parameters and  to estimate two more.   It  is likely that the fit
is  only  apparent.


       The Model Implied by a Time-Varying Rate Parameter

     The rate of DCS oil production varied greatly with time over
the period 1964-1980  (Figure A-l).   The functional form proposed
by Nakassis (1982)  for  transforming production  also involves
large changes from  one year to the next,  particularly in the
1964-1968 period (Figure A-2).   In this section,  exact
distributions are derived for a process like that prescribed by
Nakassis.  Three generic production histories are  considered:
steady production,   increasing production, and decreasing
production.   This analysis may be used to examine the degree of
error introduced by Nakassis through his assumption that the
production rate was constant -through the year, and that the
transformation  variate could be  approximated with  the product of
the production  rate and the transformation function evaluated at
the beginning of the year.

     Assume that we have a Poisson process that generates spills
according to the quantity of oil produced.  Specifically, assume
that the probability of N spills in an infinitesmal exposure
interval is given by the following equation:

                              1 - AAq  : N=0
                    P[N,Aq] =     AAq   : N=l               ^)
                                 0    : N>1

This states that the probability of observing  one  event  is   AAq,
that the probability of two or  more spills is identically zero,
and that the probability of no spills  is  therefore 1 minus the
probability of  one spill.  The exposure is denoted here by the
                               A-7

-------
               400
I
oo
         o
         3
         T3
         O
CO
3
C
C
                          OIL  (NO CONDENSATE) Mbbl
                                                                              Annual Production
                                                          Date
         FIGURE A-l,  USGS  PRODUCTION DATA
         SOURCE:  NAKASSIS 1982; USGS  1972,

-------
    r
VD
          v>
          3
          .0
          CO

-------
variate "q".   It is presumed to be  production  volume in keeping
with Nakassis1  paper.   Assume that  the  spill  rate constant
changes with time, as is first proposed by Nakassis.

     Provided the inf initesmal spill probabilities are as given
above, then it follows that the probability of observing zero
spills over an  interval  (Ofq) must obey the following ordinary,
first order differential equation:


                          l  + APlO,q] = 0                     (2)
     We may now incorporate Nakassis1  presumption that the rate
constant  " A" is a function of time.  Nakassis estimates that the
best fit  is obtained with the form:

But time and oil production are linked via the production history
of the system that is under observation.  That is,  if we had one
common functional form for the rate parameter but two oil fields,
the field  that  had the greatest production during the high rate
constant times would have more spills on the average (all other
things being equal).  More significantly, they would not share
the same distribution of the inter-arrival times.

     While there may be characteristic field production curves,
it serves  our purpose to consider  the simplest of cases:   a field
producing at a  rate R that is undergoing a linear increase/
decrease of  production rate over some characteristic time.  To
make this  idea  clearer, Figure A-3 shows the inferred rates and
the rate of changes of  oil production rates that would be
required to  exactly model  the annual OCS oil production data from
1952 through 1980.  (In its present form, the approximation is
obviously  unstable.  The  rate  of change  of production rate is
alternately over-estimated and then  under-estimated,  i.e., it
oscillates from one year  to the next.  This suggests that 1 year
is too long a period to approximate OCS oil  production with the
simple model presented here,  but  for shorter periods [e.g.,
0.5 yr], it is acceptable.)
                      dq =  (R + (6R)x  t/Tc)  dt


 and  t =  T - TOf where To is the time at  the beginning of the
 interval.
                                                             (4)
                               A-10

-------

*


CM
i_
>%
•^
J3
a

A
h.
>•
••.
JQ
.O
2





400 -i
350 -
300 -

250 -


200 -

150 -
100 -


50 -
0 -

-50 -
-100 -

. 	
	 Inferred Rate ' x%x>.
• • • • • • • ^} A t ^ A f ^^ (% a n *^ ^ ' •
....... i-(Qje OT onange . . •
/xv/ \/ \
/ \^~^
/ x-^
/
f
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**•* • . • • . •

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ป •• •.ซ••••••*.
•• •. . •. /
• ;


i i i i 1 1 1 1 1 1 1


















Apr-49 Oct-54 Mar-60 Sep-65 Mar-71 Aug-76 Feb-82
Year
FIGURE A-3, LINEAR PRODUCTION MODEL

-------
     Given this model, we then have the following relationships
between time and production:

                       ^o + ^ + ^T                       (5)
                        t =
where,       (q-qQ)
        q'  = 	ฃ	

and qo is  the  cumulative  production  at  the beginning  of the
interval.  The  6 parameter is a nondimensional fractional value
equal to the  normalized change in the production rate over the
time interval Tc.

     Since the  production rate model is only  accurate over short
periods, we need a suitably small characteristic time interval.
A quantity with the units of time that springs naturally  from
parameters at hand is:
                         T  = l/(A R)                           (7)

This quantity is also small.   Nakassis has chosen  XQ to  ke
16.4/Bbbls, and a typical DCS  production rate  is 0.25 Bbbl/yr, so
the characteristic time is on  the order of 0.25 yr.

Defining                  a - i + kT0                          (8a)
                          b = K/R                             (8b)
                          c = -XQ6b/2                          (8c)

the solution  to equation  (2) is:
                                             •~ A
                                               o
                                                              (9)
             P {o,q} =
                       1 + (b + /b2 - 4ac)q/2a
                       1 + (b - /b2 - 4ac)q/2a
                                            /b2 - 4ac
     There are several features of this solution that bear
discussion.

     •    If  the production rate is steady, c = 0, then the
          solution becomes:
                                       A
                                       - o
          This equation does not have the form of a Poisson
          distribution,  but  it is a close relative; and  it
          furthermore has the property that events are
          independently and  identically  distributed when the
          parameter q/a is used as the exposure variate.  The
          parameter q/a is the transformed production variate
          identified  by Nakas"sis.

          If  the  rate  of change of the rate parameter,   Mt),  is
          large and if  the production rate is changing rapidly
                               A-12

-------
          (the case in 1964 in the  problem at hand),  then the
          exact distribution is very different from a Poisson
          distribution that uses the approximation  employed
          by Nakassis.  Figure A-4  compares Nakassis1 Poisson
          process with the exact form.   Note that the exact form
          has a much lower probability  that any  given quantity  of
          oil can be produced without incurring one or more oil
          spills.

     •    Conversely,  if  the rate of change parameter is  small,
          as it is after about 1968 for the form proposed by
          Nakassis, then the exact  solution is well approximated
          by the Poisson equation.   Figure A-5 compares the exact
          solution with the Poisson solution for 1970. It can  be
          seen that there is some difference between the  two, but
          this is probably  not  significant.


                           Conclusions

     Out of the infinitely numerous possibilities,  Nakassis1
"better model" is a priori constrained  to  be a Poisson process
with a time dependent rate parameter and with exposure measured
in oil production.  Considerable ingenuity has gone into  tuning
this model to fit the observations  as closely as possible,  but
the fundamental issue of the model's applicability  has not been
addressed.   If we consider that:  1) the database consists of
nine (or better yet eight)  inter-arrival times for  larger spills,
2) the rate parameter is estimated from the data, and 3)  one of
three one-parameter functional forms are also selected based on
their ability to fit the observations, then it is not too
surprising that the various tests do not reject  the (resultant
four-parameter) model.

     It is important to recall  the  purpose  of the Nakassis study.
The study was undertaken to show that an hypothesis based on an
improvement in DCS oil spill  safety  is better supported than the
hypothesis that no improvement has taken place.   To some  degree,
these problems can be addressed qual itatively without a full
knowledge of the spill generation process; and based on the data,
we have no quarrel with the conclusion  that spills are less
likely  now  than  in 1964.  But Nakassis1 estimate that this
increase in safety amounts to a factor  of  about  three is
dependent on the model that underlies the  analysis.   As we
discussed above, the model has not been verified.  A point of
particular concern is that the spill generation process may have
to do with the break-in of a new development, and the apparent
improvement in OCS spill  rates might really be due to the aging
and maturing of the existing facilities.  Under  this interpre-
tation, each new field may be subject to a pronounced "learning
curve" history.  In this case,  we might postulate spill rates as
high as those seen in the mid to late 1960s  (say 5/Bbbl)  for any
new development.  In any event, it is neither fair nor accurate
to cite this paper as  proof that a Poisson model driven by
production volume  is valid for  the  simulation of OCS oil  spiels.
                               A-13

-------
U)
W

O
z
J3
O
                                                                         Exact  Distribution

                                                                      -4- Exponential
           0.00
0.28
                                       Production (Bbbls)
FIGURE A-4,   PROBABILITY  OF  NO SPILLS OCCURRING, 1964 COMPARISON

-------
I-1
en
J2

'5.
to

o
z
v^

JQ
O
 1.0


 0.9  -


 0.8  -


 0.7  -



 0.6  -


 0.5  -


 0.4  -



0.3  ~


0.2  -



0.1  -


  0
                     0.00
                                 Exact

                                 Exponential
                        I      I
                       0.04
                            I
                           0.08
                                                        I
         I
  0.12

Production (Bbbls)
 I      I
0.16
0.20
 i      r    T
0.24       0.28
          FIGURE A-5,   PROBABILITY OF NO  SPILLS OCCURRING, 1970  COMPARISON

-------
                           Appendix B


                DEPICTION OF OIL SPILL STATISTICS


                             Summarv
     This appendix contains graphical presentations of two sets
of oil spill data:  spill statistics for Cook Inlet, taken from
the DEIS for St.  George Basin (DOI  1984), and oil  spill
statistics for all U.  S.  DCS  platforms,  compiled by The  Futures
Group (1982).   The purpose of this  appendix  is  to  portray  oil
spill statistics graphically, rather than by mathematical
formulae, so the reader can visualize the spill record and
associated trends.

     Although the Cook Inlet observations and The Futures  Group
compilation are independent,  they have sufficient  similarities so
that conclusions drawn from and supported by both data sets may
be assumed to. reasonably represent  the risks of offshore oil
production.   The Cook  Inlet data set includes three spills of
volume  >1,000 bbl.  Two were tanker related and one was  a
pipeline spill.  The data for the smaller spills include spills
from all sources:  platforms, pipelines, and tankers.  Although
The Futures Group report included pipeline,  tanker,  and  single
buoy mooring spill statistics as well as platform statistics,
only platform statistics  are used  in this discussion.  The format
in which the data are  presented in  The Futures  Group report is
not convenient for combining information from different  risk
sources.

     For this discussion, we  do not restrict the initial
discussion to large spills; rather, we focus on all sizes  of
spills.  This allows a large data set to be  visualized,  and
occurences of large spills can be interpreted within the
framework of all spills.

     Four of the fundamental  assumptions in  the MMS oil  spill
risk model are:

     1.   that spill frequency is directly proportional  to
          production;

     2.   that there has been an improvement over time in  the
          spill rate;

     3.   that the frequency  of occurrence  of oil  spills can be
          approximated by a Poisson distribution;  and
                               B-l

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     4.    that the  statistics describing small spills
          ซ1,000 bbl) are different from those describing large
          spills because the causes  of large spills are
          different.

     If  the MMS model assumptions are correct, it follows that
the graphs depicting oil spill statistics would generally show
the following:

     •    Observed spill rates that  increase monotonically and
          linearly with increases in production.

     •    Improvements in spill  rates.

     •    Ability of  the Poisson  distribution to estimate spill
          frequency.

     •    Differences between  statistics for all spills  and
          statistics  for only  large  spills.

However, the graphs present  the  following general patterns:


     •    The Futures Group  data,  in particular, indicate a
          bimodal nature to  spill frequencies with high spill
          rates at  both  low  and high values of production.  Cook
          Inlet data do not  have as clear a bimodal nature.

     •    There is  a  relationship between spill frequencies and
          changes in production.

     •    The Poisson distribution tends to underestimate the
          probability of occurrence  of years with few spills and
          also to underestimate  the  probability of years with
          many spills.

     •    Properties  and dependencies of all spills appear to be
          similar to  those of  only larger spills.


                         Cook  Inlet  Data

     Figure B-l shows the  cumulative number of spills as a
function of the cumulative oil production for Cook Inlet.  All
spills  reported and all  spills of known volume >1  bbl  are  shown.
An immediate observation from these  figures  is that a straight
line fit to the observations would not  pass  through  the  origin.
Care must be exercised in interpreting  this figure since any two
increasing-(cumulative)  functions graphed against each other will
always  show a trend even if  they  are totally unrelated.  The
important point established by this figure is that if there is a
relation between spills and production,  the relation does not
predict zero spills at zero production.  One interpretation of
this observation is that a small  number of spills will occur
which is associated with any startup activity and is not
                               B-2

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   280
CO
                                             • All Spills
                                             D Known Volume
                    200           400           600
                       Cumulative Production (Mbbl)
                                             800
  FIGURE B-l.
COOK INLET:   CUMULATIVE NUMBER  OF SPILLS VS
CUMULATIVE  PRODUCTION
                                B-3

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dependent on the amount of oil  produced.   A  second interpretation
is that there has been an improvement in the spill rate,
particularly  early  in the production period.   A  lessening  of  the
spill rate diminished the slope of the  curves in the figure.
This is seen to occur after a cumulative production  of about
80 Mbbl.  This level of production was  reached in Cook Inlet
about 1968.

     Figures B-2 and B-3 show some of the details of Cook Inlet
incidence of oil spills.  Figure B-2  shows the number of spills
per year as a function of year.   There  are two curves, the lower
curve is the number  of  spills whose volume is known.  The  upper
curve is the total  number of spills including those whose volume
is unknown.  Data  prior to 1972  are from EPA records; 1972 and
later data were furnished by the U. S.  Coast Guard.   The
incidence of spills with unreported volumes  diminished after
1972, probably reflecting changes in record keeping.  There
appears to be a downward trend after  1968 of the annual number of
spills.  Spill volumes  as  a function of time are shown in
Figure B-3.  There is a  definite  reduction in spill volume after
1969.  The Sale 89  FEIS indicates that  there has been an
additional large spill since 1980 in  Cook  Inlet.  This spill  is
not  shown  because we used data  only through 1980.

     Shown in Figure B-4 is the annual spill frequency as  a
function of annual  production.   There is an  indication of
association between high spill  rates and high production.
However, the  highest spill  rates are not associated  with the
highest productions.   The production  history  of  the Cook Inlet
development  is shown in Figure  B-5.   One could ask whether spills
are  related to large changes in production.   Stable,  high
production occurred between 1969 and  1975;  during this time,  the
spill  rate was large but not at its peak.  Figure B-6 shows the
correlation of spills with  changes in production.  A trend of
increasing number  of spills with increasing  rates of  change in
production may exist, but it appears weak  with Cook  Inlet  data.

     In Figure B-7,  we  show a comparison of actual spills  and the
Poisson distribution for spills as a  cumulative function.  The
x-axis plots number  of spills/yr  (X)  and  the y-axis  shows  the
cumulative percent of years with spills less than X.  Two  curves
are  shown, one for  the  Cook Inlet observations  (271 total  spills)
and  one for the Poisson predictor with  a  rate constant of
16.9 spills/yr (the  observed average  spill  rate).  The cumulative
probability graph shows  that for  years with  few spills  (e.g*,
10 spills  or  less),   the Poisson distribution underestimates the
cumulative frequency of  occurrence.  Thus,  the Poisson
distribution predicts that 5 percent  of the years will be
characterized by 10 or fewer spills,  whereas Cook Inlet had
20 percent  (3 of 14 years).  For years  with many spills  (e.g.,
30 spills), the Poisson distribution  overestimates the cumulative
frequency of occurrence; however, it  overestimates the cumulative
frequency  of years with 30 or JLaks spills.   It follows,  then,
that the Poisson distribution underestimates  the cumulative
                               B-4

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  60
  50 -
ป
• All Spills
D Known Volume
1965   1967   1969
                              i    i   r
                         1971   1973   1975
                               Year
  1977   1979
FIGURE B-2,   COOK INLET:  ANNUAL SPILL RATES
                             B-5

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 2.6
   1965
1967   1969
1971   1973
    Year
1975
1977
1979
FIGURE B-3,  COOK INLET:  ANNUAL  SPILL VOLUME
                            B-6

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   60 t
   50 -
   40 -
a.
to  30 -
   20 -
   10 -
          • All Spills

          D Known Volume
                                               Pป   *  *
       D
                            B
                   20            40

                        Annual Production (Mbb!)
60
80
 FIGURE B-4,   COOK INLET:   SPILL RATES VS ANNUAL PRODUCTION
                               B-7

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  80
  70-
  60-
  50-
  40-
ฃm

ฃw
u  .
s o
ฃ    30 H
Q.

     20-

     10-
        • All Spills
       — Production
                      •   •
                              •   •
         i   i    I   I    i   I    i    i   i    )   r    i   I    i
    1965   1967    1969   1971    1973   1975   1977   1979
                            Year
FIGURE B-5,  COOK  INLET:   PRODUCTION AND NUMBER OF SPILLS
             OVER  TIME
                             B-8

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ou
50-
40 -
"5.
CO
. 30 -
0
z
20 -
o -


•
• D
•
D
ftflQ1
B
S
•

D
•
•
•
' S ฐD D * AM Spilte
D Known Volumes
   -10
0         10         20        30
  Annual Production Change (Mbbl)
40
FIGURE B-6,  COOK INLET:  NUMBER OF  SPILLS  VS  CHANGE IN
             PRODUCTION
                            B-9

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                                                Observed
                                                Predicted
                       20                40
                          No. Spills/Year
 NOTE:  PREDICTED CUMULATIVE FREQUENCY DERIVED FROM POISSON

       DISTRIBUTION WITH EXPECTED MEAN = 16.9 SPILLS/YR.
FIGURE  B-7,
COOK  INLET:  CUMULATIVE FREQUENCY OF OBSERVED
AND PREDICTED SPILL RATES
                              B-10

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frequency of years with 30 or more spills, i.e.,  underestimates
the occurrence of years with many spills.

     Thus, in reviewing Cook Inlet data,  it appears  that:

     •    There is an indication  of improvement in spill
          statistics.

     •    Spill  rates may depend on large changes in production
          rate as well as production  level, but the  database for
          Cook Inlet is relatively small and trends  are not
          clearly observed.

     •    The Poisson distribution underestimates the number of
          years with few or many  spills.

Because the Cook Inlet database is relatively  small,  it is
important to determine whether similar observations are evident
with the  larger  database  provided by The Futures Group (1982).


                     The Futures  Group Data

     The Futures Group information includes only  spills larger
than 50 bbl and, furthermore,  breaks  the spills from platforms
into three categories:  blowout,  tank, and  other  spills.  We
consider the sum of all platform spills  independent  of  category.

     Figure B-8 shows the cumulative  number of  spills as a
function of cumulative  oil  production on the U. S. DCS.  Two
curves are shown:  one for all spills >50 bbl  and one for all
spills >1,000 bbl.  A similar trend was  seen in Cook Inlet data
(Figure B-l).   Prior to the production of the first Bbbl, the
curves for all spills and for spills >1,000 bbl are  similar.
This could be due  to a  number of causes, e.g.,  better reporting
of small spills later on in production,  or  improvements which
helped keep spills (when they occurred)  to a small size.

  Figure B-9 is also similar to Cook  Inlet data (Figure B-3)
in showing a dramatic reduction in volume spilled since the early
1970s.

     Figures B-10  and B-ll display some of the details of The
Futures Group platform spills.  Figure B-10 shows the number of
spills per year, with spills defined by  five  categories:   >50,
>100, >250, >500, and >1,000 bbl.   Figure B-ll  shows  the
production history of the production  platforms considered  in The
Futures Group report.

     Spills as  a function of production are shown in Figure B-12.
They show a bimodal pattern of higher spill rates at high and low
production levels.   The bimodal pattern is more pronounced  than
for Cook Inlet data  (Figure B-4).  Thus,  it appears  that
production level alone  is not the sole predictor of  spill  rates.
                              B-ll

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  50
  40 -
ta

'o.
w

d
z

0)
   30 -
ซ  20
3
E
D
O
   10 -
        D > 50 bbl

        • > 1,000 bbl
                  1           2           3

                  Cumulative Production (Bbbl)
 FIGURE B-8,
              U, S, OCS:  CUMULATIVE NUMBER OF PLATFORM

              SPILLS VS CUMULATIVE  PRODUCTION
                             B-12

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   1964  1966  1968   1970
1972
Year
1974  1976  1978   1980
FIGURE B-9,   U.S. DCS:  ANNUAL PLATFORM SPILL VOLUME
                           B-13

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  10

   9 -
   8-

   7 -
co

W
   4 -
   3 -
           X


    X
        X

    O  O
    D     O
    A  D
        A
           D
    ~ T  I   ^"
O
                                                     x
   1964   1966
1968   1970   1972    1974
             Year
      1976   1978   1980
                                              X  > 50 bbl
                                              O  > 100 bbl
                                              D  > 250 bbl
                                              •  > 500 bbl
                                              A  >1000 bbl
FIGURE B-10,   U.  S,  OCS:   ANNUAL PLATFORM SPILL RATES
                            B-14

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360
   1964   1966  1968   1970
1972
Year
1974  1976   1978   1980
  FIGURE B-ll,  U, S, DCS:  PRODUCTION OVER TIME
                             B-15

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  10

   9 -

   8 -

   7 -
 50 bbl
                                            O > 100 bbl
                                            D > 250 bbl
                                            • > 500 bbl
                                            A > 1000 bbl
   FIGURE B-12,
             U, S, DCS:   PLATFORM SPILL RATES  VS
             ANNUAL PRODUCTION
                            B-16

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     Figure B-13 shows spill rates as a function of change in
production.   In this  figure, we see that spill  incidence is
highest for  both positive and  negative  changes  in production and
least for little change  in production.   Figure  B-14 compares
spill rates with the magnitude of  change in production.  Although
there appears to be a relationship indicating increased  risk with
increased change,  the data would not  be significantly  fit with  a
straight line.   The important  point  of  the figure is that highest
spill rates are more closely tied to the larger changes  in
production.   If there is  a year of many spills,  this year is more
likely  to be a  year of high production  change than a year with
production similar to the previous year.

     Figures B-15 to B-19 compare spill  rates with predictions
from a Poisson  distribution.   For  all categories of spill sizes,
the Poisson distribution  underpredicts  the frequency of
occurrence of years with  few spills  and also underpredicts the
frequency of occurrence of years with many spills.  The  same
pattern was observed in  the Cook Inlet observations (Figure B-7).

     The Kolmogorov-Smirnov test is  a crude statistical  method  of
examining goodness of fit of observations to predicted cumulative
frequency distributions.   It is a fairly simple test that has one
advantage of quickly identifying data sets  that do not fit
predicted distributions.   (Because of its nature, it is  not
capable of demonstrating that a set of data .do_e..s. fit the expected
pattern.)  The  calculation on  the data for large spills
(>1,000  bbl)  recorded in  Figure B-19  reveals that the
observations pass the test at  the 85 percent confidence  limit.
At this level  of confidence,  spill rates for >500 bbl
(Figure B-18) also  pass the  test, but spill rates for  >50 bbl,
>100 bblf and  >250 bbl do not pass the test.  Thus, the  pattern
of all spills  is not fit  by a Poisson distribution.

     MMS assumes that the causes  of  large spills are different
from causes of  small  spills.   For example, large spills  tend to
be due  to blowouts or collisions (major accidents), whereas  small
spills tend to be caused by  equipment failure or human error.  On
the other hand, one could argue that major  accidents also  can
result  from human error and  equipment failure,  and the main
difference is  one of magnitude.   Figures B-15 through  B-19  show
that all size classes of  spills have the same bias relative  to
the Poisson distribution.  Figure  B-7,  comparing Cook  Inlet
spills  with a  Poisson model,  shows the same bias.  Furthermore,
Figure B-10 shows that there is a high correlation between
numbers of large spills and  small  spills per year; the
correlation coefficient  is 0.68.  The Kolmogorov-Smirnov test
results indicate that spills  <500 bbl do not fit a Poisson
distribution,  but  it is incapable of demonstrating that  spills
>500 bbl do fit  a  Poisson distribution.  Nakassis (1982) also was
unable  to fit  platform spills >1,000 bbl to a  Poisson  process
(Appendix A).   These observations  suggest that  the assumption
that small  and large spills are different must  be carefully
considered.  It does not  appear that  occurrence  of large spills
                               B-17

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iu -
9 -
8 -
7 -
= 6-
'a
w 5 _
•
o
2 4 -
3 -
2 -
1 -
n -




X



O
c
ฎ XX
ฉ ฉ @
	 S&IHE 	 . ฎi

X

X
X

0 0

D O
D A
Q A
ฎ A XD
OR . , 	 - ,,./&> — iSi iff?) A
  -40
-20          0          20
  Annual Production Change (Mbb!)
40
                                            X > 50 bbl
                                            O > 100 bbl
                                            D > 250 bbl
                                            • > 500 bbl
                                            A > 1000 bbl
FIGURE B-13.
U, S, DCS:  NUMBER OF PLATFORM SPILLS VS
CHANGE  IN PRODUCTION
                            B-18

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  10

   9 -

   8 -

   7 -

ซ  6 -
"o.
w  5 -
o

Z  4-

   3 -

   2 -

   1 -
                                                   D
                             DD
                 D

      D     D           D      D

        D        D                              D

        r-B - , - . - 1 - 1 - rQ - 1 - BT - 1
            ฑ10       -20       -30       ฑ40        ฑ50
               Magnitude of Production Change
FIGURE B-M.  U. S,  DCS:   NUMBER OF PLATFORM SPILLS >50 BBL
              VS MAGNITUDE OF PRODUCTION CHANGE
                            B-19

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           a
           CO
           X
           V
           ฃ
               100
           C
           •>
           o
           fe
           E
           CL
            E
            3
            u
                              No. Spills/Year
FIGURE B-15,
U, S, DCS:   CUMULATIVE FREQUENCY OF  OBSERVED
AND PREDICTED SPILL RATES (PLATFORM  SPILLS
>50 BBL)
           in
           x
           V
 100-1
            c
            V
            u
            k
            t>
            o.
FIGURE B-16,
                             No.SplllE/Year
U, S, OCS:   CUMULATIVE FREQUENCY OF  OBSERVED
AND PREDICTED SPILL RATES (PLATFORM  SPILLS
>100 BBL)
                               B-20

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tn
X
V
ฃ
5
w
fa.
c
>•
0
c
V
o
e
CL
e
c
^
E
U
FIGURE B-17,

m
"o.
CO
X
V
ฃ
i
•
fa.
ซ
e
>
0
c
•
0
fa.
0.
*
ซ
3
E
3
O
FIGURE B-18,


1 UU -
80-

80-

70-
60-

60 -
1

40-

30-
20-
rJ*J CJ 	 ป 	 •










• Observed
O Predicted


• I 1 ' 1 I 1 1















02468
No. Spllls/Yr.
U, S, OCS: CUMULATIVE FREQUENCY OF OBSERVED
AND PREDICTED SPILL RATES (PLATFORM SPILLS
>250 BBL)
100 -i
80-
60-
70-
60-
60-

40 -
30-
>500 bbl ^ฐ~ _ ^/^
f
I • Observed
/ O Predicted






02466
No. Spllls/Yr.
U, S, OCS: CUMULATIVE FREQUENCY OF OBSERVED
AND PREDICTED SPILL RATES (PLATFORM SPILLS
>500 BBL)
B-21

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                                          • Observed
                                          D Predicted
                                                   8
                        No.Spills/Yr.
FIGURE B-19.  U, S, DCS:  CUMULATIVE FREQUENCY OF OBSERVED
              AND PREDICTED SPILL RATES  (PLATFORM SPILLS
              >1,000 BBL)
                           B-22

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is dramatically different from occurrence  of  all  spills (other
than in  magnitude).


                           Conclusions

     Both Cook Inlet and The Futures Group data:

     •    Illustrate significant reductions in oil spill volumes
          since about 1971.

     •    Indicate a weak  dependence  of spill frequency on volume
          of oil produced  at high volumes of  production.

     •    Indicate a weak  dependence of spill frequency on
          changes in oil production levels.

     •    Display more years of few oil spills  than  predicted by
          a Poisson distribution.

     •    Display more years with many oil spills than predicted
          by a Poisson distribution.
                              B-23

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                        LIST OF PREPARERS


     Jones & Stokes Associates, Inc.  accepts full  responsibility
for the organization and content of this report.   Dr.  Harvey Van
Veldhuizen was Project Manager and Dr.  Charles Hazel was the
Program Director and Contract Administrator.   Dr.  Lawrence Larsen
reviewed the oil spill trajectory analysis, made significant
contributions to Chapter 2,  and prepared Appendix B.  Dr.  Robert
J.  Stewart,  special consultant to Jones & Stokes Associates, made
significant contributions to Chapter 2  and prepared Appendix A.
We thank Dr.  Nakassis  for  his review of an early draft of
Appendix A.

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