Persistence Of Crude Oil Spills On Open Water 

Project Number 1435-01-02-RP-85091 

Final Report 

for:

U.S. Department of the Interior 

Minerals Management Service 

Alaska Outer Continental Shelf Region 

Anchorage, AK 

by: 

SL Ross Environmental Research Ltd. 

Ottawa, ON 

Alun Lewis Oil Spill Consultancy 

Staines, UK 

Bercha Group 

Calgary, AB 

PCCI 

Alexandria, VA 

U.S. Department of the Interior 

 Minerals Management Service

 Alaska Outer Continental Shelf Region 

OCS Study MMS 2003-047 
The Department of the Interior Mission 

As the Nation's principal conservation agency, the Department of the Interior has responsibility for most of our 

nationally owned public lands and natural resources. This includes fostering sound use of our land and water 

resources; protecting our fish, wildlife, and biological diversity; preserving the environmental and cultural values of 

our national parks and historical places; and providing for the enjoyment of life through outdoor recreation. The 

Department assesses our energy and mineral resources and works to ensure that their development is in the best 

interests of all our people by encouraging stewardship and citizen participation in their care. The Department also 

has a major responsibility for American Indian reservation communities and for people who live in island territories 

under U.S. administration. 

The Minerals Management Service Mission 

As a bureau of the Department of the Interior, the Minerals Management Service's (MMS) primary responsibilities 

are to manage the mineral resources located on the Nation's Outer Continental Shelf (OCS), collect revenue from 

the Federal OCS and onshore Federal and Indian lands, and distribute those revenues. 

Moreover, in working to meet its responsibilities, the Offshore Minerals Management Program administers the 

OCS competitive leasing program and oversees the safe and environmentally sound exploration and production of 

our Nation's offshore natural gas, oil and other mineral resources. The MMS Royalty Management Program

meets its responsibilities by ensuring the efficient, timely and accurate collection and disbursement of revenue from 

mineral leasing and production due to Indian tribes and allottees, States and the U.S. Treasury. 

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1) being responsive to the 

public's concerns and interests by maintaining a dialogue with all potentially affected parties and (2) carrying out its 

programs with an emphasis on working to enhance the quality of life for all Americans by lending MMS assistance 

and expertise to economic development and environmental protection. 
Persistence Of Crude Oil Spills On Open Water 

Final Report

for:

U.S. Department of the Interior 

Minerals Management Service 

Alaska Outer Continental Shelf Region 

Anchorage, AK 

by: 

Ian Buist1, Randy Belore1, Alun Lewis2, Frank Bercha3, Milan Cerovöek3, Justin Wilson4 and 

Bob Rinelli4

1

SL Ross Environmental Research Ltd. 

200-717 Belfast Rd. 

Ottawa, ON 

2

Alun Lewis Oil Spill Consultancy 

121 Laleham Road 

Staines, Middx, 

 United Kingdom 

3

Bercha Group 

2926 Parkdale Blvd 

Calgary, AB 

4

PCCI 

300 North Lee Street Suite 201 

 Alexandria, VA 

November, 2003 

This study was funded by the U.S. Department of the Interior, Minerals Management Service 

(MMS), Alaska Outer Continental Shelf Region, Anchorage, Alaska under Contract No. 1435-

01-02-85091, as part of the MMS Alaska Environmental Studies Program 

The opinions, findings, conclusions, or recommendations expressed in this report are those of the 

authors and do not necessarily reflect the views of the U.S. Department of the Interior, nor does 

mention of trade names or commercial products constitute endorsement or recommendation for 

use by the Federal Government. 
 i 

EXECUTIVE SUMMARY 

The primary objective of this study was to develop mathematical descriptions of the persistence 

of crude oil spills at sea, as a function of spill size, based on a statistical analysis of historical 

spill data. These results could be used by the Minerals Management Service (MMS) to estimate 

defensible durations for spill trajectories in the MMS Oil Spill Risk Analysis for Alaska Outer 

Continental Shelf (OCS) waters. 

The following definition of oil slick persistence was used for this study: 

ìAn oil slick is considered to be persisting on the sea surface when it can be observed to 

be a coherent slick, or perceptible segments of a coherent slick, by normal methods of slick 

detection, such as aerial surveillance.î 

A survey of reports of oil spill incidents throughout the world was completed. Major oil spill 

incidents from the Torrey Canyon in 1967 to the Erika in 1999/2000 have generated an immense 

amount of literature, but the information on oil slick persistence (the critical parameter to this 

study) has seldom been detailed. The number of useable incidents was reduced, from an initial 

154 to 84, by first removing the spills that occurred in inland or restricted waters (ports and 

harbors) then reduced further to 20 by applying other criteria (information availability, crude oil 

only). Of the final incident list, 13 were releases from tankers and 7 were oil well blowouts. In 

addition to these, a database of 12 experimental spills was compiled, for which good persistence 

data existed. These experimental spills all involved much smaller oil volumes. 

Correlation analyses were carried out on three data sets and, although they by no means gave 

definitive results because of the small size of the sets, they did indicate the relative importance of 

different variables and their dependencies for each of the three data sets. Regression analysis 

with the three data sets showed that:

1. Wind speed did not have a statistically significant effect on persistence. 

2. Countermeasures effort did not have a statistically significant effect on persistence. 

3. The following regressions of historic spill data should be used by MMS to estimate the 

mean persistence of slicks on open water for modeling purposes: 
 ii 

For spills greater than or equal to 1000 barrels in size: 

PD≥1000bbl = 0.0001S - 1.32T + 33.1

Where, 

! PD = Spill persistence in days 

! S = Spill size in barrels 

! T = Water temperature in degrees Celsius 

For spills less than 1000 barrels in size: 

PD<1000bbl = 0.0034S + 2.02 

Estimates of the statistical significance and 95% confidence intervals for these predictors are 

given in the report. Cumulative distribution function plots were prepared for several different 

discrete spill sizes. 

The above conclusions apply to spills of most crude oils. Some crude oils (or more specifically, 

condensates) are very light, with an API Gravity exceeding 45.5°. Some of these very light 

crudes, or condensates have high Pour Points, and these would persist if they were spilled on 

water colder than their Pour Point. Many very light crudes, or condensates, have Pour Points that 

are less than ambient temperature and these will not persist as slicks on the open ocean. These 

very light crude slicks are likely to dissipate in less than one day. 

To improve the data set for future statistical studies of oil persistence it is recommended that spill 

responders be encouraged to conduct frequent over-flights of large slicks and monitor weather 

and sea state conditions in the vicinity of the slicks until they dissipate offshore. As well, 

consideration should be given to studies of the basic mechanisms of slick dissipation at sea. The 

processes that eventually render persistent slicks undetectable may be the continuous 

fragmentation of the oil down to small particle sizes combined with horizontal dispersion of the 

ìslickî over the sea surface, rather than conventional natural dispersion of small droplets into the 

water column. Until the mechanisms that control dissipation of a slick are understood, computer 

modeling cannot reasonably be expected to predict slick persistence or impacts. 
 iii 

TABLE OF CONTENTS 

Executive Summary......................................................................................................................... i 

Table of Contents...........................................................................................................................iii 

List of Tables .................................................................................................................................. v 

List of Figures................................................................................................................................ vi 

1. Project Background..................................................................................................................... 1 

2. General Description, Objective and Goals of Project................................................................. 2 

2.1 Discussions with Client......................................................................................................... 3 

3. Project Methodology................................................................................................................... 4 

3.1 Literature/Database Review and Collation of Spill Persistence Parameters ........................ 4 

Data Sources ........................................................................................................................... 4 

Spill Persistence Parameters ................................................................................................... 9 

3.2 Oil Spill Database Development........................................................................................... 9 

3.3 Statistical and Numerical Analysis of Spill Persistence ..................................................... 11 

Regression Analysis : Overview........................................................................................... 12 

Monte Carlo Simulation : Overview..................................................................................... 12 

4. Results....................................................................................................................................... 14 

4.1 Final Database..................................................................................................................... 14 

4.2 Correlation Analysis ........................................................................................................... 17 

Tanker Spill Data Correlations ............................................................................................. 17 

Experimental Spill Data Correlations ................................................................................... 18 

Blowout Spill Data Correlations........................................................................................... 18 

Summary............................................................................................................................... 18 

Conclusions from Correlation Analysis................................................................................ 22 

4.3 Regression Analysis............................................................................................................ 22 

Tanker Spill Data Regression Analysis ................................................................................ 23 

Experimental Spill Data Regression Analysis ...................................................................... 26 

Blowout Spill Data Regression Analysis.............................................................................. 28 

Effect of Wind Speed on Spill Persistence ........................................................................... 30 

Effect of Countermeasures on Spill Persistence ................................................................... 31 

4.4 Monte Carlo Approach to Spill Persistence Analysis......................................................... 33 

Tanker Spill Monte Carlo Analysis ...................................................................................... 33 

Experimental Spill Monte Carlo Analysis ............................................................................ 33 

Blowout Spill Monte Carlo Analysis.................................................................................... 35 

5. Refinement of Empirical Predictors for Spill Size Categories Used by MMS......................... 36 

5.1 Use of Equations for Smaller (< 1000 bbl) Spills............................................................... 36 

5.2 Blowouts vs. Tanker Spills ................................................................................................. 37 

5.3 Additional Considerations: Use of Oil Properties in Prediction of Spill Persistence ......... 39 

5.4 Effect of Spilled Oil Properties on Spill Persistence .......................................................... 39 

Evaporation........................................................................................................................... 39 

Natural Dispersion ................................................................................................................ 40 

Slick Fragmentation.............................................................................................................. 41 

5.5 Existing Computer Models for Predicting Oil Weathering ................................................ 42 

5.6 Oil Spill Persistence Classification Schemes...................................................................... 43 
 iv 

International Oil Pollution Compensation Funds Classification .......................................... 43 

International Tanker Owners Pollution Federation Limited................................................. 43 

United States Coast Guard Oil Classification System .......................................................... 45 

5.7 Correlation of Oil Properties in Environment Canadaís Catalogue with Persistence ........ 46 

5.8 Crude Oils in Present Study................................................................................................ 47 

6. Conclusions and Recommendations ......................................................................................... 49 

6.1 Conclusions......................................................................................................................... 49 

6.2 Recommendations............................................................................................................... 51 

7. References................................................................................................................................. 53 

Appendix A ñ Oil Property Database ........................................................................................... 55

Appendix B ñDatabase of References Cited in Microsoft Access Database of Spills ................ 60 
 v 

LIST OF TABLES 

Table 1: Comprehensive Data List.............................................................................................10 

Table 2: Tanker Spills................................................................................................................15 

Table 3: Experimental Spills......................................................................................................16 

Table 4: Blowout Spills..............................................................................................................16

Table 5: Coefficient of Correlation Summary ...........................................................................18 

Table 6: Summary of Different Regression Analysis Results for Tanker Spills .......................24 

Table 7: Summary of Different Regression Analysis Results for Experimental Spills.............27 

Table 8: Summary of Different Regression Analysis Results for Blowout Spills.....................29 

Table 9: ITOPF Oil Classification System ................................................................................44 

Table 10: Oil Groupings Used in the USCG Vessel Response Plan Regulations .....................45 

Table 11: Properties of Crude Oils from Historic Spills Used in this Study .............................48
 vi 

LIST OF FIGURES 

Figure 1: Correlations Among Tanker Spill Persistence Variables............................................. 19 

Figure 2: Correlations Among Experimental Spill Persistence Variables................................... 20 

Figure 3: Correlations Among Blowout Spill Persistence Variables .......................................... 21 

Figure 4: 95% CI Spill Persistence Variation with Spill Size for Water Temperature of 0°C.... 25 

Figure 5: 95% CI Spill Persistence Variation with Spill Size for Water Temperature of 10°C.. 25 

Figure 6: 95% CI Spill Persistence Variation with Spill Size for Water Temperature of 20°C . 26 

Figure 7: 95% CI Spill Persistence Variation with Spill Size for Experimental Spills............... 28 

Figure 8: 95% CI Variation in Spill Persistence with Spill Size for Blowout Spills................... 30 

Figure 9: Linear Multiple Regression Plot of Effect of Wind Speed on Spill Persistence.......... 31 

Figure 10: Linear Multiple Regression Plot of Effect of Countermeasures on Spill Persistence 32 

Figure 11: Probability Distributions of Spill Persistence for Different Sizes of Tanker Spills... 34 

Figure 12: Probability Distributions of Persistence for Different Sizes of Experimental Spills . 35 

Figure 13: Probability Distributions of Spill Persistence for Different Sizes of Blowout Spills 36 

Figure 14: Persistence Correlation Comparison .......................................................................... 37 

Figure 15: Comparison of Predictions for Tanker Spills and Blowouts...................................... 38 

Figure 16: ITOPF Oil Persistence Model .................................................................................... 44 
 1 

1. PROJECT BACKGROUND 

The Minerals Management Service (MMS) has environmental impact assessment responsibilities 

under both the National Environmental Policy Act (NEPA) and the Outer Continental Shelf 

Lands Act (OCSLA), which are partially addressed through the use of oil spill modeling. In such 

modeling, the MMS typically uses standard time periods such as 1, 3, 10, and 30 days to analyze 

the effects of open water crude oil slicks. The analytical approach used by MMS includes a 

combination of trajectory modeling, oil-weathering modeling, and the results of a study 

completed for MMS in 1985 entitled “Oil Slick Sizes and Length of Coastline Affected: A 

Literature Survey and Statistical Analysis” (Ford, 1985). In this study, correlations are derived 

between spill size and length of coastline affected. Unfortunately, the correlations do not take 

into account spill persistence, and small spills, even far offshore, are presumed to be as likely to 

reach shorelines, as are large spills. This does not make intuitive sense and goes against the 

commonly held views of spill experts. The MMS in Alaska is particularly concerned about the 

issue because scenarios currently considered in Lease Sale Environmental Impact Statements 

(EISs) involve relatively small spills in the size range of 500 to 1000 barrels. MMS scientists 

may be asked in a court of law to defend statements regarding spill persistence and impact, and 

cannot easily defend their approach, especially as it related to relatively small spills. 

Hypothetical spill trajectory shoreline contacts are collated and reported in the MMS oil spill 

trajectory model analysis, but the model analysis does not take into account the oil spill behavior 

processes of spreading, evaporation, emulsification and natural dispersion. These processes are 

dealt with separately by the use of a spill-behavior, or spill-weathering, model. There are a 

number of oil spill behavior models available internationally, but none is considered accurate in 

predicting the long-term fate of spills at sea. Knowing how long it takes for spills to dissipate at 

sea is crucial in accurately determining spill impact. 

State-of-the-art oil weathering models are believed to be reasonably accurate in predicting the 

short-term behavior of marine spills (within hours or a day or two). This is because modelers can 

draw from a broad database on the short-term behavior of spills, actual or experimental. This is 

not true for the long-term behavior of spills. Consequently, most modelers today must arbitrarily 
 2 

select a point at which an oil spill finally ìdissipatesî. This is usually the point at which the 

effective thickness of the spill (meaning the volume of the oil on the surface, whether in slick or 

particle form, divided by the area of coverage) reaches some, low arbitrary value, such as 1 μm 

(1 x 10-3 mm) or 10 μm. None of the existing models predict surface-oil breakup and diffusion 

on the basis of empirical oil spill evidence, nor do databases for existing weathering models and 

other databases maintained by MMS and others compile the necessary spill information as to 

when slicks visibly dissipate as function of time and spill size. Spill size ranges of interest to 

MMS include 500 to 999 bbl; 1,000 to 9,999 bbl; 10,000 to 50,000 bbl; 50,000 to 150,000 bbl; 

and >150,000 bbl. 

2. GENERAL DESCRIPTION, OBJECTIVE AND GOALS OF PROJECT 

The primary objective of the study was to develop mathematical descriptions of the persistence 

of crude oil spills at sea, as a function of spill size, based on a statistical analysis of historical 

spill data. These results would then be used by MMS to estimate reasonable durations for spill 

trajectories in the MMS Oil Spill Risk Analysis for Alaska Outer Continental Shelf (OCS) 

waters. The correlation must be defensible in a court of law and must be based on actual spill 

experience and not theory. A secondary objective of the study was to refine the spill-size/spillpersistence correlation in terms of other variables such as oil type, weather and sea conditions, 

and spill type (e.g., batch spill versus blowout). It was recognized by MMS that satisfying this 

objective would be very difficult because of a perceived dearth of good historical spill data. 

The goals of the project were to: 

1. Compile and collate historical data on the persistence of crude oil slicks on open water; 

2. Apply statistical methods to analyze persistence of crude oil slicks as a function of initial 

spill volume, environmental conditions, crude oil properties, and response effort; and, 

3. Develop correlations, equations, or other empirical predictors of slick duration for the 

size categories used by MMS. 
 3 

2.1 Discussions with Client 

A post-award conference/teleconference was held in Anchorage with Alaska OCS Region and 

other MMS staff to ensure a clear understanding by all of study tasks, concerns, availability of 

relevant data and reports, and timelines. A brief summary of the key points discussed and 

outcomes from the meeting are provided below. These are included here because they guided 

much of the subsequent research and correlation. 

A table of oil persistence factors that would be considered in the analysis of spill databases was 

presented, discussed and revised. The final table is presented below in the project methods 

section. 

The issue of spill dissipation was discussed at length. Its definition was of obvious importance to 

the study. MMS was unsure what definition should apply and was open minded about what the 

project team might decide. It was agreed that spills would be considered ìdissipatedî even 

though some oil in fine-particle, or ìtar ballî, form were still on the surface. It was decided that 

the project team would derive a definition. The project team subsequently derived the following 

working definition: 

ìAn oil slick is considered to be persisting on the sea surface when it can be observed to be 

a coherent slick, or perceptible segments of a coherent slick, by normal methods of slick 

detection, such as aerial surveillance.î 

It was further discussed what to do about spilled oil that hits shoreline and then, after some time, 

washes back to the marine environment. It was agreed that such oil should not be considered as 

part of the same spill, from a modeling point of view. For the purposes of this study, the 

persistence of a spill was the time taken for the spill to ìdissipateî while still at sea before 

contacting any shore (note that the project dealt only with spills on open water, not in icecovered waters). 
 4 

It was agreed that a rational screening system would have to be developed for eliminating the 

bulk of reported spills that are of no use to the study. It was agreed to eliminate all spills for 

which either spill size or some measure of spill persistence could not be determined or estimated. 

To include as many spills as possible in the screening process, it was decided to define 

ìpersistenceî or ìdissipationî in terms of either discreet ranges or exact times depending on the 

available data. By choosing to define persistence as spills that persist for greater than 7 days, 

greater than 34 days, etc., spills could be included in the final data set that survived for at least X 

days before they were lost to observers. This substantially increased the number of spills in the 

final database. Without this refinement, a significant number of spills would have been rejected 

that were tracked for some time but were lost afterwards. 

3. PROJECT METHODOLOGY 

3.1 Literature/Database Review and Collation of Spill Persistence Parameters 

Data Sources 

A survey of reports of oil spill incidents throughout the world was completed. Data sources 

included MMS, NOAA, United States Coast Guard, and Environment Canada reports, Marine 

Pollution Bulletin, and the Oil Spill Intelligence Report. Sources of international experience and 

expertise, such as ITOPF, were also consulted. Much of the detail on the spills selected for 

inclusion in the database was obtained from technical papers published in the AMOP Technical 

Seminar proceedings and the Proceedings of the biennial Oil Spill Conference. The available 

information was assessed and is collated in a Microsoft Access database (Spill Database.mdb), 

which includes the specific references for the source of the data. 

The U.S. Coast Guard Report ìPast In-Situ Burning Possibilitiesî (Yoshioka et al. 1999) 

provided the starting point for the development of the database. The database developed for this 

report contains considerable information on historical oil spills occurring between the years of 

1967 and 1997, for spills of over 10,000 barrels for North America and over 50,000 barrels for 

Europe and South America. For Europe and South America, this dataset only includes spills 

within 200 miles of shore for. For North America, spills within 200 miles of shore (including 
 5 

Alaska, but excluding Hawaii) and spills in the Gulf of Mexico and the Caribbean Sea were 

included. The report analyses 141 total spills and provides information on such criteria as date, 

latitude and longitude, state/country, continent, volume spilled, and oil type. 

Other oil spill data sources consulted in the search included; NOAAís Historical Incident 

Database (http://www.incidentnews.gov/incidents/history.htm), which provides details on oil and 

chemical spills around the world of over 100,000 barrels internationally and 10,000 barrels in US 

water. NOAAís Oil Spill Case Histories (http://response.restoration.noaa.gov/oilaids/spilldb.pdf) 

gives information on significant U.S. and international spills between 1967 and 1991. NOAAís 

Oil and Hazardous Materials Response reports detail oil and chemical incidents in the US coastal 

zones (http://response.restoration.noaa.gov/oilaids/spillreps/spillreps.html) to which NOAA 

provided technical or operational assistance. The MMS Spill Incident Database 

(http://www.mms.gov/incidents/pollution.htm) has information on spills from 1974 through 

2003. Environmental Canadaís Oil Spill Database (http://www.etcentre.org:8080/cgiwin/TankerSpill_e.exe?Path=\Website\river\) provides details on international tanker oil spills of 

over 1,000 barrels. 

In addition, data from training exercises that have involved the intentional release of oil at sea 

and the numerous experimental releases of oils at sea that have been conducted during the last 30 

years in European and Canadian waters were collected. 

A listing of data sources consulted and the type of information they contain is given below. 

1) U.S. Coast Guard Report CG-D-17-99 ìPast In-Situ Burning Possibilitiesî 

 

· Name, date 

· Location (latitude and longitude) 

· Volume (bbls) 

· Product 

· City, state, country, continent 

 

2) NOAA Historical Incident Database 

 

· Name, date 

· Location 
 6 

· Volume 

· Product 

· Cause 

· Weather information 

· Spill summary 

· Spill behavior 

· Response methods 

 

3) NOAA Oil Spill Case History (US spill >10,000 gallons; International spills <100,000) 

 

· Name, date 

· Location 

· Volume 

· Product 

· Cause 

· Weather information 

· Spill summary 

· Spill behavior 

· Response methods 

· References 

4) Environment Canada Spill Database (International ship spills > 1,000 barrels) 

 

· Name, date 

· Volume (bbls) 

· Oil Type 

· Cause 

· Location of spill 

· Short narrative 

 

5) NRC Database 

 

· Name, date 

· Location 

· Product 

· Cause 

· Volume 

· Incident Summary 

 

6) MMS Oil Spill Incident Database 

 

· Name, date 

· Location 

· Product 

· Volume 

· Cause 
 7 

· Distance to shore 

· Depth of water 

 

7) NOAA Oil & HM Spill Reports (93-99) 

 

· Name, date 

· Location 

· Product 

· Cause 

· Volume 

· Response methods 

· Incident summary 

· References 

8) MSRC Report: An Analysis of Historical Opportunities for Dispersants and In-Situ Burning 

Use in the Coastal Waters of the United States, except Alaska 

 

· Name, date 

· Cause 

· Location 

· Product 

· Volume 

· Spill response methods 

 

9) Oil Spill Intelligence Report 

 

· Name, date 

· Location 

· Product 

· Cause 

10) USGC Marine Casualty and Pollution Database 

 

· Name, date 

· Product 

· Source 

· Response Information 

 

11) International Oil Spill Conference Proceedings 1969-97 

 

· Name, date 

· Product 

· Cause 

· Weather Information 

· Persistence 

· Response Information 
 8 

12) AMOP Technical Seminar Proceedings (1976 to 2002) 

 

· Name, date 

· Product 

· Cause 

· Weather Information 

· Persistence 

· Response Information 

 

Sources for environmental data, including such factors as wave height, wave period, wind speed, 

and air/water temperature, included NOAAís Environmental Buoy Database and the 

Comprehensive Ocean ñ Atmosphere Data Set (COADS). The NOAA database contains 

weather and ocean related information collected from buoys and Coastal-Marine Automated 

Network stations around the Atlantic Ocean, Gulf of Mexico, central and western Pacific Ocean, 

North Pacific Ocean above 50°N, and the Eastern Pacific Ocean. 

COADS is a joint effort between a NOAAís Climate Diagnostics Center (CDC), the Cooperative 

Institute for Research in Environmental Sciences (CIRES), the National Center for Atmospheric 

Research (NCAR), and NOAAís National Climatic Data Center (NCDC). It provides ocean data 

that has been compiled from ship reports over the global oceans. 

1) NOAA Marine Environmental Buoy Database 

· Wave height, period, spectra 

· Wind speed, direction, gust 

· Sea surface temperature 

· Air temperature 

· Air pressure 

2) Comprehensive Ocean-Atmosphere Data Set 

· Sea surface temperature 

· Wind speed 

· Air temperature 
 9 

Spill Persistence Parameters 

The prevailing conditions that influence the drifting, spreading and ultimate break-up of oil 

slicks can reasonably be expected to include wind speed, current speed, underlying water 

sediment load and wave conditions. Severe weather events, such as intense storms, will exert 

large influences on oil slick persistence. 

The spilled oil properties that influence persistence likely include volatility, density, viscosity of 

the (perhaps emulsified) oil, and Pour Point of the spilled oil on the sea surface at a particular 

time. 

Environmental data about a specific accidental oil spill was often sparse. Additional information 

to fill/fix data gaps was gathered by attempting to access other marine weather data sources. In 

those cases where weather data was available, it was difficult to determine a number that would 

adequately characterize a variable, particularly for those spills that persisted for a long time, or 

drifted for long distances from their origin. 

Table 1 is a comprehensive list of the spill characteristics that were of interest for each oil spill. 

These data items formed the basis for the Access database field structure. 

3.2 Oil Spill Database Development 

A Microsoft Access Database (Spill Database) was compiled that included the spill information 

parameters identified in Table 1. Data sources were acquired and the spill information 

parameters found for each spill were entered into the database. Major oil spill incidents from the 

Torrey Canyon in 1967 to the Erika in 1999/2000 have generated an immense amount of 

literature, but the information on oil slick persistence (the critical parameter to this study) was 

seldom detailed. As well, many of the recorded spills occurred in inland or coastal waters that 

resulted in the slicks coming ashore, which removed them from consideration in the study. This 

severely limited the number of recorded spill incidents that could be included in the study. 
 10 

Table 1: Comprehensive Data List 

General Weather Sea Conditions Oil Properties Cleanup 

Methods 

Date of Spill Wind Speed Current Speed Type Physical 

Recovery 

Time of Day of 

Spill 

Wind Direction Current 

Direction 

Flash Point Dispersant 

Use 

Volume of Spill Air Temperature Tides 

(Ebb/Flood) 

Specific/API Gravity Shoreline 

Cleanup 

Precipitation Water 

Temperature 

Viscosity 

Air Pressure Water Density Emulsification 

Factor 

Location 

- Latitude 

- Longitude 

Severe Weather 

Conditions 

Water Depth Pour Point 

Cause Visibility Initial Oil 

Layout/Distribution 

Spill Source 

City/State/ 

Country/ 

Water Body 

 

Distance from 

Shore 

Sea State 

Wave Height 

Wave Period 

Wave 

Spectra 

 

Persistence 

Information 

Source 

 

Meta Data 

The number of usable spills was reduced based upon location (spills had to be offshore, in open 

water), information availability (if no information was available from which a persistence 

meeting the criteria listed above could be determined, the event was rejected) and oil type (only 

crude oil spills were used for the final statistical analyses). Discussions of why individual spills 

were rejected are included in the main Access database (Spill Database). 

Selection of the spills was accomplished by first compiling a list of spills using the twelve report 

and database sets listed in Section 3.1, primarily the ìPast In-situ Burning Possibilitiesî report, 

and any available NOAA and USCG online databases. The Internet was then searched for all 

spills listed, using several different search engines, noting the availability of spill data for each. 

Once completed, spills were eliminated where insufficient data was available. Each of the data 
 11 

sources described above was reviewed as possible sources of data. Although there were a 

number of sources listed, most were discounted for one reason or another. An example would be 

the Environmental Canada database, which has quite a comprehensive list of spills, but has very 

limited data fields. Where weather and sea state data were not available, attempts were made to 

procure that data from the National Weather Service and the NOAA weather buoy archives. This 

proved successful for spills in North American and European waters; however, additional 

weather and sea state data were not available for spills in other areas of the world. Overall, wind 

speed data could not be found for six of the 20 incidents on the final list. 

3.3 Statistical and Numerical Analysis of Spill Persistence 

The collected data was analyzed to determine statistically significant relationships between spill 

persistence and spill size, and spill persistence and other factors. A detailed account of the 

analyses completed can be found in the report accessible digitally through the following link 

(BerchaGroupFinalReport.pdf) or in separate hard-copy. The primary goal of the statistical 

assessment was to identify possible relationships between the dissipation times for spills and 

spill size. These times can be used by MMS as the end points for trajectory modeling. The spill 

size-range categories most useful to MMS are 500 to 999 bbl; 1,000 to 9,999 bbl; 10,000 to 

50,000 bbl; 50,000 to 150,000 bbl; and >150,000 bbl, and these were considered in the analysis. 

A secondary goal was to statistically analyze the data set to determine quantitative relationships 

between on-water spill persistence and (1) associated environmental factors; (2) the physical and 

chemical properties of the spilled oil; and (3) response effort parameters. 

Following a review of the oil spill data sets it was ascertained that certain relationships or 

correlations are apparent among them. An investigation of these correlations utilizing classical 

statistical correlation and regression analysis indicated that certain combinations of parameters 

gave statistically significant relationships. Accordingly, use of statistical correlation and 

regression, together with appropriate tests of significance was conducted. 
 12 

Regression Analysis : Overview 

Regression analysis is a method of determining how one variable, the dependent variable, is 

numerically related to other independent variables. Regression analysis can be simple linear 

regression, logarithmic linear regression, polynomial regression, and multiple linear regression. 

Each of these methods was applied to various combinations of independent variables and both 

the regression coefficients and the P values were generated. All calculations were carried out for 

a 95% common confidence interval, CI. The magnitude of the Pearsonís correlation coefficient, 

R2

, is an indicator of the accuracy of the regression. The P value is the estimated probability that 

the hypothesis, in this case the regression, is statistically significant. The smaller P values are an 

indicator of increasing statistical significance. In general, regressions giving P values less than 

0.05 may be considered to be statistically significant. 

The regression analyses were carried out utilizing a proprietary statistical analysis software 

package called ìAnalyse-Itî. This package provides the Pearsonís correlation coefficients, 

standard error (SE), the slope and intercept values for the best-fit and their bounds within the 

95% confidence interval. In addition, several graphical displays are given. The first graphical 

display is simply a plot of the best-fit regression line for which the coefficients were estimated. 

Next, the standardized residuals, which are the differences between the observed and predicted 

values, divided by the standard error are given, together with their histogram and fitted normal 

distribution. The primary measures utilized from these regression analyses were the values of R2

, 

the P-value, and the 95% confidence limits for each of the coefficients. All of the detailed 

regression analysis printouts are given in (BerchaGroupFinalReport.pdf). 

Monte Carlo Simulation : Overview 

Certain generalized distributions were found to characterize the correlation coefficients among 

the dependent variable, spill persistence, and one or more of the independent variables. To obtain 

measures of the probability of exceedence of the spill persistence, numerical or Monte Carlo 

simulation was applied to evaluate cumulative distribution functions from these probability 

distributions. 
 13 

Monte Carlo simulation can be used to obtain the outcome of a set of interactions for equations 

in which the independent variables are described by distributions of any arbitrary form. The 

Monte Carlo simulation is a systematic method for selecting values from each of the independent 

variable distributions and computing all valid combinations of these values to obtain the 

distribution of the dependent variable. Naturally, this is done utilizing a computer, so that 

thousands of combinations can be rapidly computed and assembled to give the output 

distribution. 

Consider the example of the following equation: X = X1S + X2. Where, X is the dependent 

variable (such as spill persistence in days), S is the size of the spill in barrels, and X1 and X2 are 

correlation coefficients. Suppose now that X1 and X2 are some arbitrary distributions that can be 

described by a collection of values x1 and x2. What is done in the Monte Carlo process, 

figuratively, is to put the collection of the X1 values into one hat, the X1 hat, and the same for the 

X2 values ñ into an X2 hat. A value is then randomly draw from each of the hats and the resultant 

value of the dependent variable, X, is computed. This is done several thousand times. Thus, a 

resultant or dependent variable distribution, X, is estimated from the computations of all valid 

combinations of the independent variables (X1 and X2), for a given S. 

The resultant can be viewed as a cumulative distribution function. Such a cumulative distribution 

function (CDF) is also a measure of the accuracy or, conversely, the variance of the distribution. 

If the CDF is a vertical line, no matter where one draws on the vertical axis, the same value of 

the variable will result ñ that is, the variable is a constant. At the other extreme, if the variable is 

completely random then the distribution will be represented as a diagonal straight line between 

the minimum and maximum value. 

Two important concepts related to the CDF enter into Monte Carlo modeling: autocorrelation 

and cross-correlation. Suppose the variables X1 can vary only within a specified interval over the 

simulation time increment. Then, after the first random draw, the next draw would be restricted 

within certain limits of the initial draw simply as a result of the physical restrictions of the 

problem. Such a restriction is represented as an auto-correlation coefficient. Now, suppose that 

not only are the X1 restricted, but also the X2. Suppose further, however, that given a certain X1, 
 14 

a restriction were placed on the range of X2 associated with that X1. Say, only small X1 could 

associate with the full range of X2, while large X1 could only be associated with certain lower X2. 

Then, such a relationship would be expressed as a cross-correlation factor and certain limits 

would be imposed for the drawing on both X1 and associated X2. 

4. RESULTS 

4.1 Final Database 

The original USCG database (Yoshioka et al. 1999) contains 141 incidents. This was eventually 

increased to a total of 154 by accessing other spill incident data sources. The number of useable 

incidents was reduced from 154 to 84 by removing the spills that occurred in inland or restricted 

waters (rivers, ports and harbors) and then reduced to 20 by applying the other criteria 

(information availability, crude oil only) listed in Section 3.2. Of the final 20 incidents, 13 were 

releases from tankers and 7 were oil well blowouts. In addition to these, a database of 12 

experimental spills was compiled, for which good persistence data existed. These spills all were 

much smaller oil volumes than the tanker releases, and were thus kept in a separate database. A 

listing of all the data included in each of the 32 incidents is in the back of the hard copy version 

of the Bercha report. 

The final data sets used for the analysis are summarized in Table 2, Table 3 and Table 4 for 

tanker, experimental, and blowout spills respectively. One tanker incident (Atlantic Empress)

contained in the database was not included in the final list for statistical analysis, because the 

reported spill volume was highly questionable (likely the entire shipís cargo of 1,000,000 bbls, 

rather than the unknown amount released at the end of the incident that was tracked until it 

dissipated). One blowout (Norwuz) was likewise not included in the final list in Table 4 because 

the only information recorded was the estimated flow rate of the well early in the year-long 

incident, not the total discharged over the entire time span of the release. Spill #19 in Table 4 is a 

pipeline spill, but is included with the blowouts because a ìliveî pipeline spill has a similar 

release behavior to that of a sub-sea blowout. 
 15 

Table 2: Tanker Spills 

No1 Spill Name 

Spill 

Persistence 

(days) 

Year Spill 

Occurred 

Spill Size 

(bbl) 

Oil Specific 

Gravity 

Water 

Temperature 

(°C ) 

Wind 

Speed 

(m/s) 

2 Asimi 11 1983 379000 0.851 25.0 8.0 3 Texaco Caribbean 9 1987 60000 0.851 23.8 - 4 Torrey Canyon 60 1967 366500 0.869 10.3 8.8 6 Kirki 4 1991 135000 0.823 21.1 18.0 7 Glacier Bay 18 1987 3800 0.896 4.9 6.6 9 Aragon 21 1989 175000 0.922 19.0 7.6 

10 Khark 5 18 1989 454000 0.871 18.9 - 11 Burmah Agate 9 1979 254761 0.828 20.0 8.1 12 Exxon Valdez 56 1989 257142 0.894 4.0 7.0 15 Stuyvesant (II) 8 1987 14285 0.896 8.0 8.4 17 Mega Borg 15 1990 100000 0.832 28.0 4.7 20 Seki 14 1994 47619 0.865 23.8 - 

1 Spill number identifier as assigned in Berchaís study for tank vessel and blowout spills. 
 16 

Table 3: Experimental Spills 

No1 Spill Name 

Spill 

Persistence 

(days) 

Year Spill 

Occurred 

Spill Size 

(bbl) 

Oil Specific 

Gravity 

Water 

Temperature 

(°C ) 

Wind 

Speed 

(m/s) 

1 5 1972 840 0.858 10 - 2 1 1976 70 0.850 11 1.3 3 0.5 1977 70 0.850 7 7.5 4 Haltenbanken 82 5 1982 700 0.820 12 7.0 5 Forties 1987 4 1987 140 0.835 15 6.5 6 Haltenbanken 89 4 1989 210 0.850 10 6.5 7 4 1992 105 0.835 11 6.5 8 AEA 94 3 1994 105 0.835 15 2.5 9 Charlie 95 3 1995 105 0.835 15 6.5 

10 AEA '97 2 1997 350 0.822 18 5.5 11 AEA '97 2 1997 350 0.822 18 5.5 12 AEA '97 2 1997 230 0.893 18 5.0 

1 Spill number identifier as assigned in Berchaís study for experimental oil spills. Table 4: Blowout Spills 

No1 Spill Name 

Spill 

Persistence 

(days) 

Year Spill 

Occurred 

Spill Size 

(bbl) 

Oil Specific 

Gravity 

Water 

Temperature 

(°C ) 

Win

Spee

(m/

1 Uniacke Blowout 1 1984 3000 0.802 1 11.

13 Trinimar Well 327 9 1973 36650 0.820 24.6 - 14 PEMEX/YUM II 32 1987 56000 0.860 19.7 - 16 Chevron Main Pass Block 41 1 1970 65000 0.855 14 7.1 18 Ekofisk Bravo Oil Field 54 1977 202381 0.846 6 8.5 19 Piper/ Claymore 12 1986 18000 0.850 3 - 

1 Spill number identifier as assigned in Berchaís study for tank vessel and blowout spills. 
 17 

4.2 Correlation Analysis 

The oil spill data sets were delivered to Bercha Group for statistical analysis. An inspection of 

the data revealed that the main quantitative variables relating to spill persistence were: 

• Spill size 

• Oil specific (or API) gravity 

• Water temperature 

Wind speed and countermeasures effects were also considered. 

All of the spill records contained spill persistence; however, several records did not include 

specific gravity and water temperature. Specific gravities for those oils for which these data were 

not given were estimated from the qualitative description of the oil. Missing water temperatures 

were estimated using a correlation with spill latitude and water temperature. 

An examination of the effects of wind speed and spill countermeasures on spill persistence was 

carried out. Wind speed, when available, was included in the data sets. The wind speed most 

often quoted was an average value over the period that the oil slicks persisted. This average 

value may conceal episodes of high or low wind speed or changes in wind direction that would 

contribute to slick break-up. For statistical analysis purposes, countermeasures were simply 

noted as being included (Y) or excluded (N) for tanker and blowout spills. 

Tanker Spill Data Correlations 

Figure 1 summarizes the values of the coefficient of correlation (R2

) and shows the least squares 

linear regression straight lines for the relationship between spill persistence in days and each of 

the three independent variables for the tanker spill data. As can be seen, the highest value of the 

correlation coefficient occurs for the water temperature, at a value of 0.32, with the next highest 

occurring for the spill size at a value of 0.17. The wind speed correlation coefficient at 0.07 is 

very low, indicating virtually no correlation with wind speed. 
 18 

Experimental Spill Data Correlations 

Figure 2 shows the correlation between spill persistence and the independent variables for 

experimental spill data. Here, clearly, the best correlation with a correlation coefficient of 0.34 

occurs for the spill size variable. Again, wind speed correlation is low. Temperature and specific 

gravity give insignificant correlations. 

Blowout Spill Data Correlations 

Finally, Figure 3 shows the correlations among spill persistence and the independent variables 

for blowout spills. Again, the strongest correlation is shown for the spill size variable here. Wind 

speed correlation is very low; temperature correlation is non-existent. 

Summary 

Table 5 summarizes the values of the coefficient of correlation (R2

) for the relationships between 

the spill persistence in days and each of the four main independent variables for the three spill 

categories. Countermeasures could not be included in this analysis since it was recorded as a 

Yes/No value only. 

Table 5: Coefficient of Correlation Summary 

R2

 for Independent Variable Spill Category Spill Size Specific Gravity Temperature Wind Speed 

Tanker Spills 0.167 0.137 0.374 0.069 

Experimental 0.344 0.039 0.013 0.133 

Blowouts 0.729 0.183 0.000 0.026 
 19 

Tanker Spills 

No Spill Name PD S SG T W CM 

2 Asimi 11 379000 0.851 25.0 8.0 N 

3 Texaco Caribbean 9 60000 0.851 23.8 - N 

4 Torrey Canyon 60 366500 0.869 10.3 8.8 Y 

6 Kirki 4 135000 0.823 21.1 18.0 N 

7 Glacier Bay 18 3800 0.896 4.9 6.6 Y 

9 Aragon 21 175000 0.922 19.0 7.6 N 

10 Khark 5 18 454000 0.871 18.9 - Y 

11 Burmah Agate 9 254761 0.828 20.0 8.1 Y 

12 Exxon Valdez 56 257142 0.894 4.0 7.0 N 

15 Stuyvesant (II) 8 14285 0.896 8.0 8.4 N 

17 Mega Borg 15 100000 0.832 28.0 4.7 Y 

20 Seki 14 47619 0.865 23.8 - N 

 R2 0.167 0.137 0.324 0.069 

Figure 1: Correlations Among Tanker Spill Persistence Variables 

Persistence days VS Spec gravity

y = 219.19x - 169.67

R2 = 0.1374

0

10

20

30

40

50

60

70

0.800 0.820 0.840 0.860 0.880 0.900 0.920 0.940

Spec Gravity

Days

Persistence days VS Temp

y = -1.2607x + 41.977

R2

 = 0.3239

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30

Temp C

Days

Persistence days VS Spill Size (bbl)

y = 5E-05x + 11.131

R2

 = 0.1667

0

10

20

30

40

50

60

70

0 50000 100000 150000 200000 250000 300000 350000 400000 450000 500000

Size [bbl]

Days

Persistence days VS Wind Speed

y = -1.4641x + 35.003

R2

 = 0.069

0

10

20

30

40

50

60

70

0 2 4 6 8 10 12 14 16 18 20

Wind Speed [m/s]

Days
 20 

Experimental Spills 

No Spill Name PD S SG T W CM 

1 5 840 0.858 10 - - 

2 1 70 0.850 11 1.3 - 

3 0.5 70 0.850 7 7.5 - 

4 Haltenbanken 82 5 700 0.820 12 7.0 - 

5 Forties 1987 4 140 0.835 15 6.5 - 

6 Haltenbanken 89 4 210 0.850 10 6.5 - 

7 4 105 0.835 11 6.5 - 

8 AEA 94 3 105 0.835 15 2.5 - 

9 AEA 94 3 105 0.835 15 6.5 - 

10 AEA '97 2 350 0.822 18 5.5 - 

11 AEA '97 2 350 0.822 18 5.5 - 

12 AEA '97 2 230 0.893 18 5.0 - 

 R2 0.344 0.039 0.013 0.133 

Figure 2: Correlations Among Experimental Spill Persistence Variables 

Persistence days VS Spec gravity

y = -14.446x + 15.122

R2

 = 0.0388

0

1

2

3

4

5

6

0.810 0.820 0.830 0.840 0.850 0.860 0.870 0.880 0.890 0.900

Spec Gravity

Days

Persistence days VS Temp

y = -0.046x + 3.5712

R2

 = 0.013

0

1

2

3

4

5

6

0 2 4 6 8 10 12 14 16 18 20

Temp C

Days

Persistence days VS Spill Size (bbl)

y = 0.0034x + 2.0207

R2

 = 0.3441

0

1

2

3

4

5

6

0 100 200 300 400 500 600 700 800 900

Size [bbl]

Days

Persistence days VS Wind Speed

y = 0.2654x + 1.3181

R2

 = 0.1329

0

1

2

3

4

5

6

012345678

Wind Speed [m/s]

Days
 21 

Blowout Spills 

No Spill Name PD S SG T W CM 

1 Uniacke Blowout 1 3000 0.802 1 11.0 N 

13 Trinimar Well 327 9 36650 0.820 24.6 - N 

14 PEMEX/YUM II 32 56000 0.860 19.7 - N 

16 Chevron Main Pass Block 41 1 65000 0.855 14 7.1 Y 

18 Ekofisk Bravo Oil Field 54 202381 0.846 6 8.5 N 

19 Piper/ Claymore 12 18000 0.850 3 - N 

 R2 0.729 0.183 0.000 0.026 

Figure 3: Correlations Among Blowout Spill Persistence Variables 

Persistence days VS Spec gravity

y = 391.28x - 310.04

R2

 = 0.1834

0

10

20

30

40

50

60

0.790 0.800 0.810 0.820 0.830 0.840 0.850 0.860 0.870

Spec Gravity

Days

Persistence days VS Temp

y = -0.0305x + 18.514

R2

 = 0.0002

0

10

20

30

40

50

60

0 5 10 15 20 25 30

Temp C

Days

Persistence days VS Wind Speed

y = -2.4893x + 40.739

R2

 = 0.0258

0

10

20

30

40

50

60

0 2 4 6 8 10 12

Wind Speed [m/s]

Days

Persistence days VS Spill Size (bbl)

y = 0.00024x + 2.3817

R2

 = 0.7288

0

10

20

30

40

50

60

0 50000 100000 150000 200000 250000

Size [bbl]

Days
 22 

Conclusions from Correlation Analysis 

Although these correlation analyses have by no means given definitive results, they do indicate 

the relative importance of different variables and their dependencies for each of the three data 

sets. The following preliminary conclusions can be reached from the correlation analyses: 

! For tanker spills, correlation between persistence and spill size and water temperature should 

be investigated further. 

! For experimental spills, the only promising correlation is that between spill persistence and 

spill size. 

! For blowout spills, the most promising correlation is also between spill persistence and spill 

size. 

! Although correlations of the effect of wind on spill persistence are very low, the effect of 

wind could be further investigated by plotting two data subsets of spill persistence for high 

and low wind speeds. 

! A similar approach for the effect of countermeasures on tanker and blowout spills could be 

attempted. 

4.3 Regression Analysis 

Regression analyses were completed using five different methods (linear, log linear, polynomial, 

multiple linear, and log multiple linear) for each of seven combinations of independent variables: 

1. Spill size (S); 

2. Specific gravity (SG); 

3. Temperature (T); 

4. S, SG, T; 

5. S, SG; 

6. S, T; and, 

7. SG, T 

for the three classes of spills. 
 23 

Tanker Spill Data Regression Analysis 

Table 6 summarizes the results of the regression analyses using four different methods for each 

of seven combinations of independent variables for tanker spills. As can be seen, the sixth 

combination, persistence and spill size and water temperature manifests the highest statistical 

significance. Both the multiple linear regressions and the logarithmic multiple linear regressions 

show high values (0.52) of the regression coefficient R2

, and values less than 0.05 of the P value, 

suggesting statistical significance. Accordingly, the multiple linear regression analysis between 

spill persistence, spill size and temperature (S, T) was selected for further statistical 

investigation. This additional statistical investigation consisted of plotting the expected spill size 

variation within the 95% confidence interval for representative water temperatures of 0°, 10°, 

and 20°C. The multiple regression equation for this combination of variables is given below: 

PDTv = 0.0001S - 1.32T + 33.1 (1)

Where, 

• PDTv = Spill persistence in days for tank vessel spills 

• S = Spill size in barrels 

• T = Water temperature in degrees Celsius 

This equation was used as a basis to determine the envelope of spill persistence expectations 

within the 95% confidence interval. Figures 4, 5, and 6 show graphs of the bounds for these 

expectations for the 95% confidence interval for temperatures of 0°C, 10°C, and 20°C, 

respectively. For a spill size of 200,000 barrels, the persistence at 95% confidence interval 

ranges from approximately 20 to approximately 80 days, for the low temperature case. Although 

this is quite a large range, it must be kept in mind that the confidence interval is quite significant 

as this means that there is a 95% chance that the persistence will be within that boundary with an 

increasing probability that it will be at the predicted value of approximately 50 days for that spill 

size. A similar interpretation can be given for the higher temperatures. 
 24 

Table 6: Summary of Different Regression Analysis Results for Tanker Spills 

(Lowest value of P highlighted) 

No Independent 

 Variable Method R2 P 

Linear 0.17 0.188 

1 Log Linear 0.07 0.423 Size 

(S) Polynomial 0.20 0.364 

Linear 0.14 0.236 

2 Log Linear 0.27 0.084 Spec. Gravity 

(SG) Polynomial 0.22 0.326 

Linear 0.32 0.054 

3 Log Linear 0.27 0.082 Temperature 

(T) Polynomial 0.33 0.164 

Multiple 

Linear 0.53 0.098 

4 S, SG, T Log M 

Linear 0.61 0.049 

Multiple 

Linear 0.34 0.153 

5 S, SG 

Log M Linear 0.45 0.067 

Multiple 

Linear 0.52 0.036 

6 S, T 

Log M Linear 0.52 0.038 

Multiple 

Linear 0.32 0.171 

7 SG ,T 

Log M Linear 0.34 0.157 
 25 

Vessel Spills LMR 95% CI T=0 C

0

20

40

60

80

100

120

140

0 100000 200000 300000 400000 500000

Spil Size (S) [bbl]

Persistence Days (PD)

Max

Pred

Min

Figure 4: 95% CI Spill Persistence Variation with Spill Size for Water Temperature of 0°C 

Vessel Spills LMR 95% CI T=10 C

0

20

40

60

80

100

120

140

0 100000 200000 300000 400000 500000

Spil Size (S) [bbl]

Persistence Days (PD)

Max

Pred

Min

Figure 5: 95% CI Spill Persistence Variation with Spill Size for Water Temperature of 10°C 
 26 

Vessel Spills LMR 95% CI T=20 C

0

20

40

60

80

100

120

140

0 100000 200000 300000 400000 500000

Spil Size (S) [bbl]

Persistence Days (PD)

Max

Pred

Min

Figure 6: 95% CI Spill Persistence Variation with Spill Size for Water Temperature of 20°C 

Experimental Spill Data Regression Analysis 

For experimental spills, a similar series of different linear, logarithmic, polynomial, and multiple 

linear and multiple logarithmic regressions were carried out. Table 7 summarizes the results of 

these regression analyses. 

In this case, the first linear regression analysis between spill size and persistence gives the most 

favorable indicator of statistical significance of R2

, at a value of 0.34, and a P value of 0.045. 

The resultant equation is: 

PDE = 0.0034S + 2.02 (2) 

Where, 

• PDE = Spill persistence in days for experimental spills 

• S = Spill size in barrels 
 27 

Table 7: Summary of Different Regression Analysis Results for Experimental Spills

(Lowest value of p highlighted) 

No Independent 

 Variable Method R2 

p 

Linear 0.34 0.045 

1 Log Linear 0.31 0.626 Size 

(S) Polynomial 0.36 0.133 

Linear 0.04 0.539 

2 Log Linear 0.04 0.512 Spec. Gravity 

(SG) Polynomial 0.04 0.832 

Linear 0.01 0.725 

3 Log Linear 0.07 0.393 Temperature 

(T) Polynomial 0.45 0.067 

Multiple 

Linear 0.38 0.261 

4 S, SG, T Log M 

Linear 0.34 0.311 

Multiple 

Linear 0.37 0.129 

5 S, SG 

Log M Linear 0.33 0.169 

Multiple 

Linear 0.35 0.141 

6 S, T 

Log M Linear 0.33 0.167 

Multiple 

Linear 0.06 0.768 

7 SG, T 

Log M Linear 0.10 0.614 

The 95% confidence interval expectations from this linear regression are shown in Figure 7. The 

experimental spill data provide persistence in a small spill size range (less than 1,000 barrels) for 

which there was no data on tanker spills. 
 28 

Experimental Spills Lin Reg 95% CI 

0

1

2

3

4

5

6

7

8

9

10

0 100 200 300 400 500 600 700 800 900 1000

Spil Size (S) [bbl]

Persistence Days (PD)

Max

Pred

Min

Figure 7: 95% CI Spill Persistence Variation with Spill Size for Experimental Spills 

Blowout Spill Data Regression Analysis 

Table 8 summarizes the results of various regression analyses for blowout spills. Again, the first 

combination of dependent and independent variables for the simple linear regression method 

gives the most favorable levels of statistical significance with R2

 and P values of 0.73 and 0.031, 

respectively. 

The associated equation is: 

PDB = 0.0002S + 2.38 (3) 

Where, 

• PDB = Spill persistence in days for blowout spills 

• S = Spill size in barrels 
 29 

The spill persistence 95% confidence interval expectation envelope is illustrated for blowout 

spills in Figure 8. 

Table 8: Summary of Different Regression Analysis Results for Blowout Spills 

(Lowest value of p highlighted) 

No Independent 

 Variable Method R2 p 

Linear 0.73 0.031 

1 Log Linear 0.39 0.185 Size 

(S) Polynomial 0.73 0.137 

Linear 0.18 0.397 

2 Log Linear 0.22 0.354 Spec. Gravity 

(SG) Polynomial 0.23 0.669 

Linear 0.00 0.979 

3 Log Linear 0.11 0.512 Temperature 

(T) Polynomial 0.12 0.832 

Multiple 

Linear 0.74 0.362 

4 S,SG,T Log M 

Linear 0.41 0.741 

Multiple 

Linear 0.74 0.132 

5 S,SG 

Log M Linear 0.39 0.475 

Multiple 

Linear 0.73 0.141 

6 S,T 

Log M Linear 0.41 0.458 

Multiple 

Linear 0.19 0.724 

7 SG,T 

Log M Linear 0.23 0.676 
 30 

Blowout Lin Reg 95% CI 

0

20

40

60

80

100

120

140

0 40000 80000 120000 160000 200000

Spil Size (S) [bbl]

Persistence Days (PD)

Max

Pred

Min

Figure 8: 95% CI Variation in Spill Persistence with Spill Size for Blowout Spills 

Effect of Wind Speed on Spill Persistence 

The tanker spills data set was subdivided into two data sets, one with the higher set of wind 

speeds, and the second with the lower set. Essentially, the data points with wind speeds from 8 to 

18 m/s were considered to be associated with higher winds, while the balance were considered to 

be associated with lower winds. The rate of natural dispersion is thought to undergo a stepchange at around 4 to 7 m/s wind speed with the onset of cresting waves. A linear multiple 

regression was then carried out, resulting in the following equation for high wind speed: 

PDTv,HW = 0.0001S ñ 2.74T + 33.38 (4) 

And the following equation for the low wind speed sub-data set: 
 31 

PDTv,LW = 0.0001S ñ 0.90T + 22.39 (5) 

The plot of these two equations on the same graph, for representative temperatures (in this case, 

for 10°C), is shown in Figure 9. Although the low wind speed shows slightly higher persistence 

tendency, as one would intuitively expect, the results have no statistical significance. The 95% 

confidence interval is so large that it lies outside the axis of the graph, indicating that both plots 

do not show any difference within the 95% confidence interval. 

Vessel Spills T=10 C - Wind 

0

20

40

60

80

100

120

140

0 100000 200000 300000 400000 500000

Spil Size (S) [bbl]

Persistence Days (PD)

Hi

Low

Figure 9: Linear Multiple Regression Plot of Effect of Wind Speed on Spill Persistence 

Effect of Countermeasures on Spill Persistence 

In the case of countermeasures, the tanker spill data set was subdivided into two subsets; one for 

spills where countermeasures were present, and one for those in which they were absent. Again a 

multiple linear regression analysis of the tanker spills was carried out, resulting in the following 
 32 

two equations, with Equation 6 corresponding to the presence of countermeasures, and Equation 

7 corresponding to the absence of countermeasures. 

PDTv,C = 0.00005S ñ 1.14T + 31.12 (6)

PDTv, NC = 0.00006S ñ 1.46T + 34.82 (7)

Again, the results were plotted for a representative temperature of 10°C, yielding the graph 

shown in Figure 10. The difference shown in this graph is again statistically insignificant, with 

the confidence interval lying well outside the scale of the graph. Again, the two lines intuitively 

show a similar physically plausible trend, with the ëno countermeasuresí line showing slightly 

higher persistence than the line at the bottom with countermeasures. For both of these analyses, 

alternative representative temperatures of 0°C and 20°C were also investigated, with no 

improvement in statistical significance. 

Vessel Spills T=10 C - Counter Measures

0

20

40

60

80

100

120

140

0 100000 200000 300000 400000 500000

Spil Size (S) [bbl]

Persistence Days (PD)

No

Yes

Figure 10: Linear Multiple Regression Plot of Effect of Countermeasures on Spill Persistence 
 33 

4.4 Monte Carlo Approach to Spill Persistence Analysis 

The Monte Carlo method was used to generate probability densities that were then transformed 

into cumulative distribution functions (CDFs), or probabilities of exceedence, for oil spill 

persistence. Numerical distributions for each of the regression coefficients were generated from 

statistical confidence limit data and probability distributions of the spill persistence associated 

with the most statistically significant regression equations for each of the three data sets 

(Equations 1, 2, and 3). 

Tanker Spill Monte Carlo Analysis 

Figure 11 shows a graphical form of the cumulative distribution functions generated for each of 

three spill size ranges: 10,000 (10K); 100,000 (100K); and, 1,000,000 (1000K) barrels. Each of 

the graphs in Figure 11 show three lines, one each for 0°C, 10°C, and 20°C water temperatures. 

These cumulative distribution functions can be used to assist in the prediction of the likelihood 

of the persistence of spills of various sizes. For instance, consider the case of the 100,000-barrel 

(100K) spill illustrated in the second graph in Figure 11. Consider the middle line, corresponding 

to a temperature of 10°C. First, we can see at the 0.5 probability level, one could expect the 

longest that such spills would persist is approximately 32 days. However, there is a 10% (0.1) 

chance that such spills would persist no more than 10 days and a 90% chance that the spills will 

persist no more than 55 days. Similar conclusions can be drawn for the low temperature and the 

high temperature spills, and the other representative sizes. 

Experimental Spill Monte Carlo Analysis 

Figure 12 shows similar cumulative distribution functions (CDF) for the experimental spills, for 

sizes ranging from 100 to 2,000 barrels. This CDF is useful because it covers a range of spill 

sizes (< 1,000 bbl) for which data were inadequate from the tanker spills. 
 34 

Figure 11: Probability Distributions of Spill Persistence for Different Sizes of Tanker Spills 

 (a) CDF Vessel Spills Size 10K

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 10 20 30 40 50 60 70

 Persistence Days

Prob of Value <= X-axis Value

S10K-T0

S10K-T10

S10K-T20

 (b) CDF Vessel Spills Size 100K

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 10 20 30 40 50 60 70 80

 Persistence Days

Prob of Value <= X-axis Value

S100K-T0

S100K-T10

S100K-T20

 (c) CDF Vessel Spills Size 1000K

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 20 40 60 80 100 120 140 160 180 200 220

 Persistence Days

Prob of Value <= X-axis Value

S1000K-T0

S1000K-T10

S1000K-T20
 35 

Figure 12: Probability Distributions of Persistence for Different Sizes of Experimental Spills 

Blowout Spill Monte Carlo Analysis 

Figure 13 shows the spill persistence CDF for blowout spills from 10,000 to 300,000 bbl size. 

Although the blowout spills are associated with a different spill mechanism than the tanker spills 

(a continuous release over days for blowouts, as opposed to the near-instantaneous release, or 

series of releases, expected from tankers), there appears to be a similarity between the 

comparable spill sizes for both tankers and blowouts. 

 CDF Experimental Spill Sizes

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 2 4 6 8 10 12 14 16 18 20

 Persistence Days

Prob of Value <= X-axis Value

S100

S500

S1000

S2000
 36 

Figure 13: Probability Distributions of Spill Persistence for Different Sizes of Blowout Spills 

5. REFINEMENT OF EMPIRICAL PREDICTORS FOR SPILL SIZE 

CATEGORIES USED BY MMS 

The linear and multiple linear regressions that predict the relationship between spill persistence, 

spill size and temperature, for the three spill types, provide the best basis for assigning 

persistence values to the size ranges of interest to MMS. 

5.1 Use of Equations for Smaller (< 1000 bbl) Spills 

The equations (1 and 2) predicting spill persistence for tanker and experimental spill data sets 

have been plotted on a single graph for comparison. Figure 14 shows the best-fit regressions and 

their 95% confidence interval (dashed lines) for these data sets. The average temperature of the 

experimental spills (13.3°C) was used in equation 1 for predicting the tanker spill persistence. It 

is obvious that there is a discontinuity between the two ìbatchî spill predictions (experimental 

and tanker). The tanker spill equation predicts significantly higher spill persistence for spills 

smaller than about 3000 barrels, than does the experimental spill predictions. This difference in 

CDF Blowout Spill Sizes

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 20 40 60 80 100 120 140 160 180 200

 Persistence days

Prob of Value <= X-axis Value

S10K

S50K

S100K

S300K
 37 

persistence can possibly be explained as follows. The edges of slicks are subjected to more 

energy and more rapid breakup of coherent oil slicks into smaller and smaller particles than are 

the interior portions of the slick. Another way to think of this is that breaking waves, which 

cause natural dispersion, affect only the edges of a slick ñ their propensity to break is attenuated 

as they propagate into the middle of a slick. Large slicks have smaller edge to area/volume ratios 

than small slicks and so their breakup and dispersion is slower. Conversely, smaller slicks will 

dissipate relatively more quickly. 

Persistence Comparison: Experimental vs Tanker Spills

0

10

20

30

40

50

60

0 5000 10000 15000

Spill Size (barrels)

Persistence (days)

Experimental

Exptl Data

95% CI Exptl

Tanker @ 13.3°C

Tanker Data

95% CI Tanker

CI - Confidence Interval

Figure 14: Persistence Correlation Comparison 

It is recommended that the persistence predictions from the experimental spill results be used for 

batch spills less than 1000 barrels. The tanker spill correlation should be used for all batch spills 

greater than or equal to 1000 barrels. 

5.2 Blowouts vs. Tanker Spills 

Figure 15 compares the predictions for tanker spill persistence and blowout spill persistence. 
 38 

Persistence Comparison: Tanker Spills vs 

Blowouts

0

25

50

75

100

125

150

0 100000 200000 300000 400000 500000

Spill Size (barrels)

Persistence (days)

Tanker @ 11.4°C

95% CI Tanker

Tanker Data

Blow out

Blow out Data

95% CI Blow outs

Cl- Confidence Interval

Figure 15: Comparison of Predictions for Tanker Spills and Blowouts 

The graph shows the data, best-fit regressions and their 95% confidence interval (dashed lines) 

for these data sets. The average temperature of the blowout spills (11.4°C) was used in equation 

1 for predicting the tanker spill persistence. Although the prediction appears to be that blowout 

spills less than 150,000 bbl dissipate faster than tanker spills of the same size, the small data sets 

and large scatter mean that there is no statistically significant difference. It is recommended that 

the tanker spill prediction be used for spill persistence predictions because it is based on a larger 

data set. In hindsight, the blowout and tanker data could perhaps have been combined in the 

statistical assessments. They were separated for the statistical analysis because it was thought 

that the very different release conditions and subsequent different initial slick characteristics 

from these two types of spills would generate significantly different slick persistence 

characteristics. 
 39 

5.3 Additional Considerations: Use of Oil Properties in Prediction of Spill 

Persistence 

None of the historical spill data sets gave the lowest P value for correlations of spill persistence 

with oil properties (specifically oil density or gravity). Due to the limited size of the data sets 

(N=12 was the largest) it is not possible to conclude whether or not the lack of a correlation was 

due to the small number of data points or the true absence of a correlation. However, it seems 

reasonable that there must be some effect of oil properties on spill persistence, at least in the 

extremes (light condensates will not persist; heavy asphaltic crude oils will persist for very long 

times). Justification for the use of oil property information to establish spill persistence limits for 

very light crude oils, based on the understanding of basic spill behavior processes and the efforts 

by other researchers in the classification of oils follows. 

5.4 Effect of Spilled Oil Properties on Spill Persistence 

The major processes that lead to the disappearance, dissipation or lack of persistence of a 

coherent slick on the sea surface include evaporation, natural dispersion and slick fragmentation. 

Brief descriptions of these processes and the oil property most affecting the rate of each follows. 

Evaporation 

Evaporation of the more volatile oil components will remove oil volume from the sea surface by 

transferring it as hydrocarbon vapors into the atmosphere. The rate of this process will depend on 

ambient environmental conditions and slick characteristics, and the extent will depend on the 

proportion of oil that is volatile under the prevailing conditions. 
 40 

Oil property most affecting evaporation. Oil components that have boiling points below 

200oC, including all n-alkanes with less than 12 carbon atoms (this includes gasoline, naphtha 

and most kerosene), will evaporate quite rapidly under most conditions. The proportion of crude 

oil that consists of these components will also rapidly evaporate, leaving behind a non-volatile 

residue on the sea surface. If the oil contains a very high proportion of volatile components very 

little residue will be left behind and it will be left behind as a thin layer. 

Natural Dispersion 

Natural dispersion is the conversion of the surface slick into small oil droplets by the prevailing 

breaking wave action. The small droplets will remain indefinitely suspended (or dispersed) in the 

water column under the existing conditions of turbulence generated by wave action and currents. 

The oil in the slick is removed from the sea surface and transferred into the water column. The 

size of the oil droplets that will be indefinitely suspended is around 100 microns diameter or less 

(somewhat smaller for calmer seas, larger for rougher seas). 

Oil property most affecting natural dispersion. The disruption of the oil slick into small oil 

droplets by breaking wave action is resisted by the increasing viscosity of the oil residue that 

remains after evaporation or by the soaring viscosity of the emulsified oil. Although no accurate 

data apparently exists, it is possible that natural dispersion effectively ceases (that is, no 

significant volume of the spilled oil is converted into very small droplets) when the slick 

viscosity exceeds some limiting value around 1,000 to 5,000 cP. This is not exactly the same as 

the limit for chemical dispersion because that involves the drastic (but temporary) reduction of 

interfacial tension to promote small droplet formation. 

A breaking wave passing through a thin layer of low viscosity oil will create a droplet size 

distribution that includes a high proportion of very small, permanently dispersible, oil droplets. 

As the slick viscosity increases, the proportion of these small oil droplets decreases. At some 

point, the proportion of droplets small enough to stay in suspension becomes negligible, and 

natural dispersion effectively ceases for the given breaking wave energy applied to the slick. 

Above some limiting viscosity, even repeated, large breaking waves will cause no significant 

natural dispersion; the slick merely fragments into smaller patches, mats or particles. 
 41 

Slick Fragmentation 

Slick fragmentation is the conversion of a coherent slick into progressively smaller pieces 

without significant natural dispersion. The oil volume on the sea surface remains the same but is 

distributed in smaller and smaller pieces over an increasing area of sea surface. 

Slick fragmentation is a particularly relevant process for the thicker layers of oil (several 

millimeters to several centimeters) formed by high viscosity oils (e.g. heavy fuel oils or stable 

emulsions). Heavy fuel oils probably do not naturally disperse to any significant extent, yet these 

slicks eventually dissipate in fragments that ultimately yield tar balls. This also occurs with high 

viscosity (and highly stable) emulsions formed by high asphaltene content crude oils. Lower 

viscosity and lower stability ('weaker') emulsions formed from crude oils containing less 

asphaltenes break up more easily than highly stable emulsions. 

Slick fragmentation appears to happen mainly at the edges of slicks. Thick layers of high 

viscosity oil dampen out breaking waves except at the edges of a slick or a patch. Large coherent 

slicks are broken up into smaller (but still relatively large) pieces by the shearing action of wind 

and currents. Slicks with high Pour Points that have gelled at ambient temperatures can be 

ìfracturedî into many small pieces by wave action. Both processes slowly yield more slick edge 

to be broken up into tiny, non-detectable fragments, or tar balls. 

Oil property most affecting fragmentation. The main oil property controlling slick 

fragmentation is likely oil viscosity, or more accurately, some rheological property such as yield 

stress or the elastic component, that will allow the slick to resist shear and/or reform after neardisruption by a breaking wave. Slick thickness is also involved. Since high viscosity oils (and 

emulsions) tend to form much thicker layers, the two are connected. Waxes content (roughly 

estimated by Pour Point) also plays a role in determining at what point some oils will gell, or 

solidify, at ambient temperatures. 
 42 

5.5 Existing Computer Models for Predicting Oil Weathering 

Most oil 'weathering' computer models normally consider the main processes affecting oil slick 

persistence to be: 

1. Evaporative loss. This is the volume decrease of the surface oil as some components are 

transferred to the atmosphere. The consequential increase in oil residue viscosity, 

compared to the viscosity of the original oil, is calculated. 

2. Water-in-oil emulsification. The oil residue that remains on the sea surface incorporates 

water as droplets that may be stabilized by precipitated asphaltenes. This process is 

normally considered in terms of the consequent volume and viscosity increase. 

3. Natural dispersion. The spilled oil is removed from the sea surface by transfer into the 

water column as very small droplets. This proceeds most rapidly in the thin layers of 

sheen that cannot emulsify because they are too thin for emulsification to occur. Sheen 

formation is therefore probably an important intermediary step in natural dispersion. 

Evaporation can be modeled with a high degree of accuracy. Emulsification requires specific lab 

studies. The theoretical basis of algorithms for natural dispersion - which normally relate droplet 

size produced by energy dissipation (related to wind speed and calculated wave energy) to oil 

viscosity is known to be weak. Viscosity (especially emulsion viscosity) is very likely to be an 

inadequate description of the oilís rheological parameters that control droplet formation. 

Provided that the spilled oil is not transferred in other compartments (evaporated to the 

atmosphere or dispersed into the water column) it is modeled to remain on the sea surface until it 

reaches shore. Different models have been calibrated, or 'validated', against results from specific 

sea-trials, but all models have inherent weaknesses in that they involve extrapolations into sea 

and weather conditions where they have not, and cannot, be tested. 

In particular, few, if any, existing models model the horizontal distribution of the oil on the sea 

surface in a realistic way; slick fragmentation by wind and current shear is not included. In 

particular, slick persistence (according to this studyís definition) is not modeled. 
 43 

5.6 Oil Spill Persistence Classification Schemes 

There have been many attempts to classify oils (crude oils and refined products) into groups 

based on common characteristics in attempts to indicate the probable behavior and persistence of 

the oils if spilled at sea. The main oil properties used in these classification schemes are: 

1. Distillation characteristics. Can be used to estimate the proportion of oil that will 

evaporate. 

2. Viscosity. Indicates spreading behavior and is indirectly indicative of other behaviors. 

3. Pour Point. Oils at temperatures well below their pour point will be solid. 

4. Asphaltene content. High asphaltene content oils form more stable, more viscous 

emulsions more readily than low asphaltene content oils. 

It is not always possible to obtain all of this information about an individual crude oil or refined 

product. The most readily available oil property is density (Specific Gravity or oAPI). Attempts 

have therefore been made to relate probable spilled oil persistence to density. 

International Oil Pollution Compensation Funds Classification

The persistence of spilled oil is relevant to the compensation schemes administered by the 

International Oil Pollution Compensation (IOPC) Funds because these schemes only apply to 

'persistent' crude oils and fuel oils from tankers. The term 'persistent oil' is not precisely defined 

in any of the Conventions, but the IOPC Funds use a guideline that states: "An oil is considered 

non-persistent if - at the time of shipment - at least 50% of the hydrocarbon fractions (by 

volume) distill at 340oC and at least 95% distill at 370oC". Oils that are normally classified as 

'persistent' include: crude oils, fuels oils, heavy diesel and lubricating oils. 'Non-persistent' oils 

include gasoline, light diesel oil and kerosene. 

International Tanker Owners Pollution Federation Limited 

The International Tanker Owners Pollution Federation Limited (ITOPF) have classified a 

number of oils and refined oil products according to their Specific Gravity into four groups as 
 44 

shown in Table 9. Additional oil properties also given for each group include viscosity at 15oC, 

% boiling below 200oC and % boiling above 370oC. 

Table 9: ITOPF Oil Classification System 

Oil Group Oil Descriptions 

Specific 

Gravity 

& 

(°API) 

Viscosity 

Range or 

Average 

@ 15 °C 

(mm2

/s=cSt) 

% Boiling 

below 200°C 

% Boiling 

above 370°C 

1 Gasoline, naphtha and kerosene <0.8 

(>45) 0.5 to 2.0 50 to 100 0 

2 47 crude oils plus Gas Oil. 26 oils with PP 

> +5°C, 4 oils solid @ 15 °C 

0.8 - 0.85 

(35 - 45) 8 19 to 48 12 to 50 

3 43 crude oils plus Medium Fuel Oil, 8 oils 

with PP > +5°C, 8 oils solid @ 15 °C 

0.85 - 0.95 

(17.5 - 35) 275 14 to 34 28 to 50 

4 21 crude oils plus Heavy Fuel Oil > 0.95 

(<17.5) 1500 to solid 3 to 24 33 to 92 

ITOPF have related the grouping of the oils to persistence by means of computer models and 

practical experience and represent this as in Figure 16. The volume of oil and oil-in-water 

emulsion remaining on the sea surface over time is shown as a percentage of the volume spilled.

Note that greater than 100% is shown on the graph for some oil groups due to slick volume 

increases as a result of the formation of water-in-oil emulsions. 

Figure 16: ITOPF Oil Persistence Model 
 45 

United States Coast Guard Oil Classification System 

The United States Coast Guardís (USCG) system for classifying oils, shown in Table 10, is very 

similar to the ITOPF system. The difference in the schemes is that non-persistent oils (Group I) 

are classified explicitly by volatility/boiling range (using the IOPC Funds criteria) rather than 

density. The other groups are defined by density. 

Table 10: Oil Groupings Used in USCG Vessel Response Plan Regulations 

Oil Group Specific Gravity 

Group I i) at least 50% (volume) distill at 340°C; and 

ii) at least 95% (volume) distill at 370°C 

Group II SG < 0.85 

Group III 0.85 ≤ SG ≥ 0.95 

Group IV 0.95 ≤ SG ≥ 1.0 

Group V SG > 1.0 

The density of a crude oil is a broad indicator of other oil properties. Higher density (low oAPI) 

crude oils contain a higher proportion of 'heavier' (higher molecular weight) hydrocarbons than 

lower density (higher oAPI) crude oils. For comparison purposes, crude oil composition can be 

greatly simplified to a 3-component blend based on distillation range: 

Higher density crude oils will contain a greater proportion of mid-range and residue components 

than lower density crude oils. The relative proportions of mid-range and residue may also vary, 

Volatiles - Distils at less 

than 200oC boiling point

Mid-range - Distils between 

200oC and 370oC

Residue - Distils 

above 370oC

200oC

370oC
 46 

giving rise to a very wide range of densities as the residue is of higher density than the midrange. 

The proportion of volatiles indicates the proportion of crude oil that will easily evaporate when 

the oil is spilled at sea. The properties of the oil residue that will remain on the sea surface can be 

inferred from the relative proportions of mid-range and residue components. Remaining oil 

residues with a higher proportion of mid-range components will be of lower viscosity than those 

with a higher proportion of residue components. Higher residue contents are broadly indicative 

of higher asphaltene contents. The ITOPF and USCG groupings therefore have a broad 

correlation with more specific oil 'weathering' properties. 

5.7 Correlation of Oil Properties in Environment Canadaís Catalogue with 

Persistence 

In order to determine whether or not it is possible to defensibly define a certain category of crude 

oils as non-persistent (since there is only one actual non-persistent spill data point ñ the Uniacke 

blowout) the Environment Canada oil properties database (http://www.etcentre.org:8080/cgiwin/OilPropspill_e.exe?Path=\Website\river\) was used to construct a spreadsheet with key 

Low density

crude oil 

Medium density 

crude oil

High density 

crude oil
 47 

persistence-related properties of the 222 crude oils listed (see Appendix A, or use the following 

hyperlink to access the original spreadsheet file EC and ITOPF Crudes.xls). Of these, it was 

determined that 10 fell into the ITOPF Group 1 category (API > 45°). It is worthy of note that 

none of them would fall into the USCG Group I category (>95% boiled off at 370°C). Of the 11 

with API > 45°, only one contained any asphaltenes, which are a strong indicator of 

emulsification tendency. Of the 222 crude oils investigated, only 2 had no asphaltenes and 

formed emulsions in standard laboratory tests. Both of these (Issungnak and Nektoralik) had 

Pour Points (at the degree of evaporation and temperature at which they formed emulsions) 

higher than the ambient temperature. Thus, in order to be reasonably certain that a crude oil will 

not persist, it would be necessary to state that itís API Gravity must exceed 45.5° and its Pour 

Point must be less than ambient temperature. Nine of the crude oils in the list would meet this 

criteria at 25°C and seven at 0°C. 

5.8 Crude Oils in Present Study 

The crude oils involved in the spills considered in the current study have a broad range of density 

(oAPI Gravity) and other properties as seen in Table 11. The Uniacke condensate, having a very 

low density, consists almost entirely of volatile components and would (and did) almost totally 

evaporate and be non-persistent on the sea surface. 

The crude oils classified as ITOPF Group 2 would be expected to form emulsions, but these 

would be relatively weak and would not be very persistent.

The crude oils classified as ITOPF Group 3 are of a type that would be expected to be very 

persistent, having relatively high asphaltene contents that would stabilize the emulsions as they 

are formed. These oils would be expected to be more persistent than the other crude oils. This is 

particularly the case for the Maya crude oil that has a very high viscosity. 

The conundrum is that the statistical analysis of the spill persistence data for these oils does not 

detect the expected trends described above. The available data set is too small and incomplete for 

these trends to be evident. The best that can be concluded with the available data is that crude 
 48 

oils with API > 45.5° and Pour Points below ambient temperature will not persist because they 

will evaporate rapidly and their residue will not form emulsions that could persist. 

Table 11: Properties of Crude Oils from Historic Spills Used in this Study 

Spill No.1 Oil Type oAPI 

Gravity 

Viscosity 

(cSt) 

Emulsion 

Formation 

Pour 

Point 

(oF) 

ITOPF Group 1 

1 Uniacke Condensate 45.0 No 

ITOPF Group 2 

105, 107, 108 Forties Blend 41.0 10 Yes -10 

104 Statfjord crude 41.0 4.5 Yes -29 

9 Murban Light crude 40.5 7 @15oC -31° 

15 Nigerian Blend Crude 39.3 Yes 

22 Palanca Crude Oil 38.6 Yes 10 

110, 111 Forties 38.0 8 Yes -10 

24, 102, 103 Ekofisk Crude Oil 35.7 5 @ 15oC Yes -12 

28 Claymore Tartan blend 35.0 Yes 

106 Oseberg crude 35.0 12 Yes -9 

ITOPF Group 3 

2, 5, 101 Light Iranian Crude 34.7 6.6 Yes 

8 Arabian Light Crude 33.4 

7 Kuwait Crude Oil 31.4 22 @15oC Yes -18 

13 Iranian Heavy 31.0 9.4 Yes -20 

109 Troll 27.0 27 Yes -18 

10, 19, 16, 112 North Slope Crude 26.8 23 @ 15°C Yes -8 

12 Mexican Maya crude 22.0 280 @ 15oC Yes -15 

11 Iranian Heavy ?20.0? 250 @15°C Yes -26 

1 Spill identifier as in original Microsoft Access database (Spill Database.mdb) 
 49 

6. CONCLUSIONS AND RECOMMENDATIONS 

6.1 Conclusions 

• The following definition of oil slick persistence was developed for this study: 

ìAn oil slick is considered to be persisting on the sea surface when it can be observed 

to be a coherent slick, or perceptible segments of a coherent slick, by normal methods 

of slick detection, such as aerial surveillance.î 

• An MS Access database of 154 historic spill incidents was developed for the study after 

an extensive worldwide search of data sources, from which a total of only 20 offshore oil 

spill incidents that contained adequate persistence and other information were uncovered. 

Of these, 13 were crude oil tanker spills and 7 were oil well blowouts. 

• In addition to the 20 spill incidents, a database of 12 crude oil experimental spills was 

compiled, for which good persistence data existed. These spills all involved much smaller 

oil volumes than the tanker releases, and were thus kept in a separate database. 

• An inspection of the data revealed that the main quantitative variables relating to spill 

persistence were: 

• Spill size 

• Oil specific (or API) gravity 

• Water temperature 

• Correlation analyses were carried out on the data sets and, although they by no means 

gave definitive results because of the small size of the sets, they did indicate the relative 

importance of different variables and their dependencies for each of the three data sets. 

a. For tanker spills, correlation between persistence and spill size and 

water temperature seemed promising; 

b. For experimental spills, the only promising correlation was that 

between spill persistence and spill size; 

c. For blowout spills, the most promising correlation was also 

between spill persistence and spill size. 
 50 

• Regression analysis with the three data sets showed that: 

a. A multiple linear regression of persistence, spill size and water temperature 

manifested the highest statistical significance (R2

 = 0.52, and a P value of 0.036) 

for the tanker spills and yielded: 

PDTv = 0.0001S - 1.32T + 33.1 

Similar equations to predict the 95% confidence limits for the tanker spills were 

also derived. 

b. In the case of the experimental spill data, a simple linear regression analysis 

between spill size and persistence gave the most favorable indicator of statistical 

significance, with R2 at a value of 0.34, and a P value of 0.045. 

PDE = 0.0034S + 2.02 

Similar equations to predict the 95% confidence limits for experimental spills 

were also derived.

c. For the blowout spills, the simple linear regression method gave the most 

favorable levels of statistical significance with R2 and P values of 0.73 and 0.031, 

respectively. 

PDB = 0.0002S + 2.38 

Similar equations to predict the 95% confidence limits for blowout spills were 

also derived.

d. Wind speed did not have a statistically significant effect on persistence 

e. Countermeasures effort did not have a statistically significant effect on 

persistence. 

• Cumulative distribution function plots were prepared for several different discrete spill 

sizes using the three data sets. 

• There was a discontinuity between the persistence predictions from the experimental spill 

data set and the tanker spill data set. The experimental spill data set prediction best 

represents batch spills less than 1000 barrels. The tanker spill correlation should be used 

for all batch spills greater than 1000 barrels. 

• Comparison of the blowout and tanker spill predictions appears to indicate that blowout 

spills less than 150,000 bbl dissipate faster than tanker spills of the same size; however, 
 51 

the small size of the two data sets and large scatter mean that there is no statistically 

demonstrable difference. 

• A review of worldwide oil persistence classification schemes, and an analysis of crude oil 

property databases concluded that, in order to be reasonably certain that a crude oil will 

not persist, it would be necessary to specify that itís API Gravity must exceed 45.5° and 

its Pour Point must be less than ambient temperature. 

6.2 Recommendations 

The following equations should be used by MMS to estimate the mean persistence of slicks on 

open water for modeling purposes: 

For spills greater than or equal to 1000 barrels in size: 

PD≥1000bbl = 0.0001S - 1.32T + 33.1

Where, 

! PD = Spill persistence in days 

! S = Spill size in barrels 

! T = Water temperature in degrees Celsius 

For spills less than 1000 barrels in size: 

PD<1000bbl = 0.0034S + 2.02 

Spills of crude oils (or more specifically, condensates) whose API Gravity exceeds 45.5° and 

whose Pour Points are less than ambient temperature will not persist as slicks on the open ocean 

and are likely to dissipate in less than one or two days. 

To improve the data set for future statistical studies of oil persistence it is recommended that spill 

responders be encouraged to conduct frequent over-flights of large slicks and monitor weather 

and sea state conditions in the vicinity of the slicks until they dissipate offshore. These 

observations should be published in the open oil spill literature, such as the International Oil 

Spill Conference proceedings. 
 52 

As well, consideration should be given to studies of the basic mechanisms of slick dissipation at 

sea. It is possible that there are as yet unknown, oil-property driven constraints on the processes 

involved in natural dispersion that limit it to moderately viscous slicks. The processes that 

eventually render persistent slicks undetectable may be the continuous fragmentation of the oil 

down to small particle sizes combined with horizontal dispersion of the ìslickî over the sea 

surface, rather than conventional natural dispersion of small droplets into the water column. 

Until the mechanisms that control dissipation of a slick are understood, computer modeling 

cannot reasonably be expected to predict slick persistence or impacts. 
 53 

7. REFERENCES 

Citations in text of main report

Ford, R.G. 1985. Oil slick sizes and length of coastline affected: a literature survey and statistical 

analysis. Final Report. Prepared for U.S. Department of the Interior, Minerals 

Management Service, Pacific OCS region. OCS Study MMS 85-0105. 

Yoshioka, G., E. Wong, B. Grossman, W. Drake, B. Urban and T. Hudon. 1999. Past In-situ 

Burning Possibilities. USCG R&D Center Report CG-D-17-99. Groton, CT. 347 pgs. 

Citations in Microsoft Access database

The references cited in the database of spills developed for this project are available as a ProCite 

compatible database, reproduced as Appendix B. 

Data sources in main report

ITOPF Handbook 2003 / 2004, International Tanker Owners Pollution Federation Limited, 

London UK. 

http://www.itopf.com/fate.html

United States Coast Guard, Regulations on Vessel Response Plans, Oil Group Definitions, 33 

CFR Part 155 

http://www.uscg.mil/vrp/reg/33cfr155d.shtml

National Response Center Database- http://www.nrc.uscg.mil/foia.html

 

MSRC Report ñ Kucklick, J., and D. Aurand., 1995. An Analysis of Historical Opportunities for 

Dispersants and In-Situ Burning Use in the Coastal Waters of the United States, except Alaska 

Marine Spill Response Corporation Technical Report Series 95-005, Herndon, VA 

 

USGC Marine Casualty and Pollution Database - 

http://transtats.bts.gov/Tables.asp?DB_ID=610&DB_Name=Marine%20Casualty%20And%20P

ollution%20Database&DB_Short_Name=Marine%20Casualty/Pollution

NOAA Marine Environmental Buoy Database - http://www.nodc.noaa.gov/BUOY/buoy.html

Comprehensive Ocean-Atmosphere Data Set - http://www.cdc.noaa.gov/coads/products.html

Other oil spill data sources consulted in the search included; NOAAís Historical Incident 

Database (http://www.incidentnews.gov/incidents/history.htm), which provides details on oil and 

chemical spills around the world of over 100,000 barrels internationally and 10,000 barrels in US 

water. NOAAís Oil Spill Case Histories (http://response.restoration.noaa.gov/oilaids/spilldb.pdf) 

gives information on significant U.S. and international spills between 1967 and 1991. NOAAís 

Oil and Hazardous Materials Response reports detail oil and chemical incidents in the US coastal 

zones (http://response.restoration.noaa.gov/oilaids/spillreps/spillreps.html) to which NOAA 
 54 

provided technical or operational assistance. The MMS Spill Incident Database 

(http://www.mms.gov/incidents/pollution.htm) has information on spills from 1974 through 

2003. Environmental Canadaís Oil Spill Database (http://www.etcentre.org:8080/cgiwin/TankerSpill_e.exe?Path=\Website\river\) provides details on international tanker oil spills of 

over 1,000 barrels. 
 55 

APPENDIX A ñ OIL PROPERTY DATABASE 

Crude oil Viscosity at 

15oC 

API Density Pour Point 

(oC) 

Asphalten

es 

% below 

200C 

% midrange % above 

370C 

ITOPF Group 1 - Specific Gravity <0.8 (oAPI >45) 

Griffin (1 @20o) 55.0 -48 66 27 7 

Margham Light (1 @20o) 50.3 -8 0.0 52 34 14 

Arabian Super Light 3 49.3 -29 0.0 36 22 39 

Bekok (2 @38o) 49.1 -3 45 33 22 

Thevenard Island 1 48.6 -70 0.0 49 41 10 

Bass Straight 15 47.0 15 40 40 20 

Gippsland Mix Solid (3 @20o) 47.0 15 0.0 40 40 20 

Nkossa 46.5 -4 0.0 27 29 44 

Sahara Blend 4 45.5 -18 0.0 48 25 27 

Belida (8 @21o) 45.1 16 1.1 40 35 25 

ITOPF Group 2 - Specific Gravity 0.8 - 0.85 (oAPI 

35 - 45) 

Tapis (3 @21o) 44.3 4 0.0 37 45 18 

Anoa Solid 44.0 19 

Bu Attifil Solid 43.6 39 19 34 47 

Ekofisk 4 43.4 -12 1.0 46 29 25 

Kerapu 43.3 26 1.4 

Liverpool Bay (7 @20o) 43.3 -18 0.0 33 31 36 

Sirtica 7 43.3 -3 44 29 27 

Zarzaitine (7 @10o) 43.0 -12 40 28 32 

Kutubu Light 42.7 5 0.0 43 38 19 

Kimkol (7 @30o) 42.5 10 31 25 44 

Pulai (2 @21o) 42.5 -5 0.0 45 47 8 

Attaka 2 42.3 -23 48 39 13 

Skua (3 @20o) 41.9 12 34 59 7 

Qatar Land 

(Dukhan) 

9 41.7 -9 0.0 36 31 33 

Tartan (12 @4o) 41.7 -9 40 28 32 

Badak (6 @38o) 41.3 -26 

Bent Horn 24 41.3 -18 0.0 19 28 53 

Zuetina 9 41.3 9 35 35 30 

Brass River 4 40.9 2 38 45 17 

West Texas 

Intermediate 

7 40.8 -29 1.0 28 37 35 

Lower Zakum 40.6 -9 0.0 34 31 35 

Forties 8 40.5 -12 0.0 32 32 36 

Murban 7 40.5 4 0.0 32 34 34 

Brega 9 40.4 -1 30 38 32 

Marib Light (Alif) 40.3 -4 0.0 40 40 20 

Montrose 7 40.1 -9 36 33 31 

Bekapi (2 @38o) 40.0 -21 0.0 36 50 14 

Olmeca (4 @20o) 39.8 -32 0.0 32 36 32 

Lucina Solid (16 

@20o) 

39.5 16 0.9 26 33 41 

Bombay High Solid 39.4 30 29 37 34 

Fulmar (3 @40o) 39.3 -12 51 16 33 
 56 

Magnus (5 @21o) 39.3 -3 30 31 39 

Dulang Solid 39.0 29 35 40 25 

East Zeit Mix 7 39.0 -1 36 26 38 

Federated 4 39.0 -15 1.0 34 36 30 

Maureen 38.8 7 40 20 40 

Beatrice 32 38.7 12 25 40 35 

Bach Ho Solid 38.6 35 0.0 21 32 47 

Draugen 38.6 -3 

Norman Wells 5 38.4 -85 27 28 45 

Brent Blend 6 38.3 -42 30 32 38 

Sarir Solid (5 @50o) 38.3 24 24 37 39 

Argyll 11 38.0 9 29 32 39 

Khalda Solid 38.0 29 1.0 

Murchison 7 38.0 7 36 44 20 

Pitas Point 2 38.0 -60 54 43 3 

Salawati (4 @38o) 38.0 -26 30 39 31 

Siberian Light (7 @20o) 37.8 -6 0.3 24 24 52 

Statfjord 6 37.8 -3 2.0 30 35 35 

Soyo Solid 37.6 17 0.4 20 30 50 

Hydra (5 @38o) 37.5 10 29 41 30 

Nanhai 37.5 32 0.8 

Abu Dhabi 7 37.4 36 33 31 

Tembungo (2 @38o) 37.4 -4 38 48 14 

Auk 9 37.2 9 33 32 35 

Berri (Saudi Arabian 

Light) 

9 37.2 -29 35 30 35 

Nemba 37.2 3 0.0 

Lalang Solid 37.1 33 0.4 19 32 49 

Kittiwake 8 37.0 -24 30 34 36 

Mubarek (3 @38o) 37.0 -12 35 34 31 

Thistle 9 37.0 9 35 27 38 

Dai Hung Solid 36.9 25 0.0 30 37 33 

Alberta 6 36.8 -24 1.0 

Barrow Island 7 36.8 -30 0.0 37 48 15 

Bonny Light 25 36.7 12 30 40 30 

Es Sider 11 36.7 6 28 30 42 

Palanca 36.6 4 0.0 30 35 35 

Pennington 36.6 6 34 39 27 

Beryl 9 36.5 45 21 34 

Miri Light (4 @21o) 36.3 -6 0.0 25 50 25 

Oman 36.3 -23 3.0 23 32 45 

Escravos 9 36.2 10 35 50 15 

Escravos 36.2 10 30 38 32 

Seria Solid 36.2 18 37 48 15 

Amna Solid 36.1 18 25 45 30 

Arabian Extra Light 36.1 -26 0.0 26 35 39 

Rincon de Los 

Sources 

16 36.1 -4 30 40 30 

Statfjord C 36.1 1 0.0 

Avalon 11 36.0 12 3.0 15 39 46 

Rostam (3 @50o) 35.9 -23 30 24 46 

Umm Shaif 35.9 -9 0.3 29 32 39 
 57 

Qua Iboe 7 35.8 15 0.5 29 39 32 

Ninian Blend 8 35.6 2 23 37 40 

Qatar Marine 53 35.3 -12 0.6 29 32 39 

Ardjuna Solid 35.2 27 37 48 15 

ASMB 35.1 -8 2.0 33 22 45 

Kirkuk (13 @10o) 35.1 -22 35 29 36 

Medanito 16 35.1 -1 25 35 40 

Isthmus 

ITOPF Group 3 - Specific Gravity 0.85 - 0.95 (oAPI 17.5 - 35) 

Forms emulsions at 0°C but not 15°C despite 0% 

asphaltenes 

Hibernia 49 35.0 2 3.0 26 21 50 

Issungnak 35.0 11 0.0 20 55 25 

Statfjord B 35.0 4 0.0 

Cormorant 13 34.9 12 32 30 38 

Dunlin 11 34.9 6 29 35 36 

Kole Marine (11 @10o) 34.9 -6 34 31 35 

Flotta 11 34.7 -21 34 40 26 

Danish North Sea (9 @20o) 34.5 -30 0.0 33 28 39 

Minas (Sumatran 

Light) 

 34.5 37 2.0 14 29 57 

Loreto 34.0 -18 7.7 17 33 50 

Rabi Solid 34.0 30 2.3 

Gorm (5 @40o) 33.9 -37 32 38 30 

Salmon (Sassan) (52 @25o) 33.9 -21 31 17 52 

Empire 11 33.8 -41 1.0 19 35 46 

Iranian Light 33.8 -9 0.7 26 31 43 

Basrah Light (15 @10o) 33.7 -15 26 29 45 

Buchan 14 33.7 6 31 30 39 

Oseberg 10 33.7 -4 0.5 28 33 39 

Rangely 33 33.7 17 4.0 17 36 47 

Soyo Blend Solid (10 

@38o) 

33.7 15 21 31 48 

Brae 14 33.6 -6 0.0 40 21 39 

Dorrood (6 @38o) 33.6 -20 35 20 45 

Arabian Light (Berri) 14 33.4 -53 3.0 24 31 45 

Cinta Solid 33.4 43 10 36 54 

Lucula 43 33.4 18 4.0 18 17 65 

Lavan 33.3 -7 0.8 

Labuan (4 @21o) 33.2 11 0.0 33 45 22 

Daquing (Taching) Solid 33.0 36 12 22 66 

Taching Solid 33.0 35 12 39 49 

Handil Solid (4 @38o) 32.8 35 23 44 33 

Hout 15 32.8 -18 4.6 24 28 48 

Trinidad (Galeota 

Mix) 

Solid 32.8 14 23 49 28 

Isthmus 13 32.7 -15 0.9 28 30 42 

Upper Zakum 32.7 -24 2.5 26 30 44 

Takula 110 32.2 15 2.0 17 28 55 

Tia Juana Light 2,500 32.1 -43 24 31 45 

Arimbi Solid 31.8 38 

Gamba Solid 31.8 23 11 35 54 

Miri 31.8 -1 0.0 25 50 25 
 58 

Bunyu Solid, (3 @38o) 31.7 18 29 59 12 

Cabinda Solid 31.7 17 18 26 56 

Zaire Solid (362) 31.7 15 18 27 55 

Abu Al Bu Khoosh 31.6 -12 27 32 41 

Lagomedio 41 31.5 -26 15 35 50 

Espoir 31.4 -15 26 31 43 

Kuwait Export 30 31.4 -15 2.5 23 25 52 

Widuri Solid 31.4 46 2.6 7 23 70 

Foroozan (15 @20o) 31.3 -37 24 27 49 

Basrah Medium (41 @10o) 31.1 -30 24 48 28 

Iranian Heavy 25 31.0 -18 2.5 24 28 48 

Malongo Solid 31.0 21 4.0 15 25 60 

Mars Blend 33 31.0 -36 32 33 45 

Sirri (20 @10o) 30.9 -9 35 19 46 

Arabian Medium 

(Khursaniyah) 

25 30.8 -15 6.0 22 27 51 

Cano Limon 46 30.8 16 8.0 23 37 40 

Masila Blend 30.8 8 1.2 

Alaskan North 

Slope 

23 30.6 -55 5.0 26 27 47 

Mandji 70 30.5 9 21 26 53 

Dan (9 @40o) 30.4 -43 27 9 64 

Point Arguello Light 22 30.3 -22 7.0 22 33 45 

West Texas Sour 13 30.2 -27 26 34 40 

Dubai (13 @20o) 29.9 -7 2.0 24 31 45 

Forcados 12 29.7 -20 17 46 37 

Al Shaheen 29.6 -7 

Suez Mix 30 29.6 10 1.4 24 27 49 

Sockeye Sweet 20 29.4 -20 4.0 23 47 40 

Gullfaks 13 29.3 -32 21 39 40 

Oriente 85 29.2 -4 20 32 48 

Ashtart (24 @20o) 29.0 9 2.0 16 32 52 

Jatibarang Solid 29.0 43 14 21 65 

Heidrun 18 28.6 -48 1.0 19 36 45 

Khafji 80 28.5 -30 4.1 21 24 55 

Troll 27.9 -39 0.3 

Belayim Solid 27.5 15 22 23 55 

Arabian Heavy 

(Safaniya) 

55 27.4 -28 4.0 20 24 56 

Lago Solid 27.3 21 12 24 64 

Bahrgansar / 

Norwuz 

(20 @38o) 27.1 -33 19 22 59 

Aboozar (37 @20o) 26.9 -34 25 21 54 

Djeno (220 @10o) 26.9 6 16 22 61 

Bintulu Neat Solid 26.5 17 24 42 34 

Canadon Seco (279 @20o) 26.3 -3 2.0 16 26 58 

Sockeye 45 26.2 -12 8.0 21 31 48 

Odudu 26.1 -39 0.2 

Sanga Sanga (151 @38o) 25.7 -15 19 48 33 

Dos Cuadros 51 25.6 -30 6.0 19 31 50 

La Rosa 180 25.3 -46 6.0 17 28 55 

Bonny Medium (12 @38o) 25.2 -27 14 47 39 

Souedie (88 @10o) 24.9 -30 20 23 57 
 59 

Prudhoe Bay 68 24.8 0 2.0 16 31 53 

Basrah Heavy (86 @10o) 24.7 -30 19 39 42 

Forms emulsions at 0°C but not 15°C despite 0% asphaltenes 

Nektoralik (21@10°) 24.5 3 0.0 7 58 35 

Shengli Solid 24.2 21 9 21 70 

Escalante 2120 24.1 -1 12 21 67 

Leona (31 @38o) 24.0 -36 1.7 14 30 56 

Champion Export 18 23.9 15 47 28 

BCF 24 125 23.5 -51 7.0 13 17 70 

Burgan 23.3 -21 13 24 63 

Syrian Blend 23.3 0 25 32 43 

Endicott 84 23.0 -2 4.0 11 24 65 

Kuparuk 23.0 -48 19 25 56 

Lago Treco 272 22.6 -20 16 29 55 

Maya 500 22.2 -27 5.8 17 22 61 

Santa Clara 304 22.1 -3 13.0 15 21 64 

Emerald 170 22.0 -29 7 37 56 

Point Arguello 

Comingled 

533 21.4 -12 14 21 65 

Duri Solid 21.1 14 5 21 74 

Duri (Sumatran 

Heavy) 

Solid (13300) 21.1 18 5 20 75 

Hondo Blend 511 20.8 -21 20 30 50 

Alba (259 @20o) 20.0 -30 4 35 61 

Hondo 735 19.6 -15 12.0 14 22 64 

Sockeye Sour 821 18.8 -22 13.0 15 22 63 

Wafra Eocene 3000 18.6 -29 11 26 63 

Point Arguello 

Heavy 

3250 18.2 -4 19.0 11 20 69 

Cyrus (Soroosh) 10000 18.1 -12 12 22 66 

Soroosh (1381 @20o) 18.1 -12 15 15 70 

ITOPF Group 4 - Specific Gravity > 0.95 (oAPI < 

17.5) 

Bachequero 5000 16.8 -20 10 30 60 

Port Hueneme 4131 14.8 -9 12.0 5 23 72 

Merey 7000 14.7 -18 5.4 7 23 70 

Belridge Heavy 12610 13.6 2 3.0 2 26 72 

Tia Juana Heavy (2983 @50o) 12.1 -1 3 15 82 

Tia Juana Pesado Solid 12.1 -1 3 19 78 

California 34000 10.3 0 7 18 75 

Boscan Solid 10.1 15 18.0 4 16 80 
 60 

APPENDIX B ñDATABASE OF REFERENCES CITED IN MICROSOFT 

ACCESS DATABASE OF SPILLS 

 1. Alejandro, Anthony C. and Buri, Jack L. M/V Alvenus: Anatomy of A Major Oil Spill. In: Ludwigson, John 

O., Editor. Prevention, Behavior, Control, Cleanup; Baltimore, Maryland. Washington, D.C.: 

American Petroleum Institute; 1987; c1987: 27. 662 Pages. 1987 Oil Spill Conference Proceedings. 

v. 10). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4452; Spill not selected 

 2. Allers, Jan (NOSCA - Norwegian Oil Spill Control Association). Oil Recovery and Oil-On-Water Exercises 

in High Seas, Offshore Norway. Oil Spill Symposeum 1997; Tokyo, Japan. Petroleum Association 

of Japan; 199725 pages. 

Notes: Selected spill: Piper/Claymore/Flotta 

 3. Audunson, Tore. The Fate and Weathering of Surface Oil From The Bravo Blowout. Marine Environmental 

Research. 1980; 3:35-61. 

Notes: Selected spill: Ekofisk Bravo; 

 4. ---. Fate of Oil Spills on the Norwegian Continental Shelf. In: Ludwigson, John O., Editor. Prevention, 

Behavior, Control, Cleanup; Los Angeles, California. Washington, D.C.: American Petroleum 

Institute; 1979; c1979: 675. 755 Pages. 1979 Oil Spill Conference Proceedings. v. 6). ISBN: LCCN 

75-4161. 

Notes: API Publication No. 4308; Selected spill: Ekofisk Bravo; The text of the article was not 

printed in the proceedings but was said to be "distribted at the conference by the author" 

 5. Barber, Mary C.; Merther, Susan M.; Ireland, George F., and Breslau, Lloyd. An Analysis of Historical Oil 

Spills and Current Cleanup Requirements to Aid in Selecting New Technologies for Spill Cleanup 

Operations. Washington, D.C.: Marine Spill Response Corporation; 1992; 92-001. 57(MSRC 

Technical Report Series). Marine Spill Response Corporation; Research & Development; 1350 I 

Street N.W., Suite 300; Washington, D.C. 20005. 

Notes: Selected spill: Glacier Bay 

 6. Bergueiro, J. R.; Morales, N., and Dominguez (Dept. of Chemical Engineering, University of Balearic 

Islands, Palma de Mallorca, Spain). Research On a Dispersing Solution for Burnt Crude Oils: 

"Aegean Sea" Oil Spill. In . Proceedings of the Sixteenth Arctic and Marine Oilspill Program 

(AMOP) Technical Seminar; Edmonton, Alberta. Ottawa, Ontario : Emergencies Science Division, 

Environment Canada; 1993; c1993: 1065-1071. 1209 Pages. Proceedings of the Sixteenth Arctic and 

Marine Oilspill Program (AMOP) Technical Seminar. v. 16-2). 

Notes: Spill not selected 

 7. Brodie, Donald. The KIRKI Incident. In. Prevention, Preparedness, Response; Tampa, Florida. Washington, 

D.C.: American Petroleum Institute; 1993; c1993: 201. 936 Pages. 1993 Oil Spill Conference 

Proceedings. v. 13). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4580; Selected spill: Kirki 

 8. Carter, J.; MacGregor, C.; Tidmarsh, W. G.; Chandler, P. C. P.; Parsons, J., and Bonke, C. Monitoring 

programs associated with the Uniacke G-72 gas/condensate blowout on the Scotian shelf. In. 

Proceedings of the Eighth Annual Arctic Marine Oilspill Program Technical Seminar; Edmonton, 

Alberta. Ottawa, Ontario: Environmental Protection Service; 1985; c1985: 403-422. 442 Pages. 

Proceedings of the Eighth Annual Arctic Marine Oilspill Program Technical Seminar. v. 8). 

Notes: Selected spill: Uniake 

 
 61 

 9. Cormack, D. and Nichols, J. A. (Warren Spring Laboratory). Investigation of Factors Affecting the Fate of 

North Sea Oils Discharged at Sea. Part 2: The Ekofisk Blow-out April/May 1977. Stevenage, 

Hertfordshire: Warren Spring Laboratory; 1978Warren Spring Laboratory, Dept. of Industry, 

Gunnels Wood Road, Stevenage, Hertfordshire SG1 2BX. 

Notes: Selected spill: Ekofisk blow-out 

 10. Cormack, D. ; Nichols, J. A., and Lynch, B. (Warren Spring Laboratory). Investigation of Factors Affecting 

the Fate of North Sea Oils Discharged at Sea. Part 1: Ekofisk Crude Oil, July 1975-Feb 1978. 

Stevenage, Hertfordshire: Warren Spring Laboratory; 1978; Report LR 273 (OP). Warren Spring 

Laboratory, Dept. of Industry, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2BX. 

Notes: Selected spill: Ekofisk blow-out 

 11. Daniel, Pierre. Numerical Simulation of The Aegean Sea Oil Spill. In: Ludwigson, John O., Proceedings 

Editor. Achieving and Maintaining Preparedness; Long Beach, California. Washington, D.C.: 

American Petroleum Institute; 1995; c1995: 894. 1085 Pages. 1995 International Oil Spill 

Conference Proceedings. v. 14). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4620; Spill not selected 

 12. Finley, J. Heather; Hanifen, James G.; Roscigno, Pasquale F.; Debusschere, Karolien; Newell, Maura J., and 

Julius, Brian E. A Cooperative Damage Assessment: Lessons From The Greenhill Spill. In: 

Ludwigson, John O., Proceedings Editor. Achieving and Maintaining Preparedness; Long Beach, 

California. Washington, D.C.: American Petroleum Institute; 1995; c1995: 355. 1085 Pages. 1995 

International Oil Spill Conference Proceedings. v. 14). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4620;Spill not selected 

 13. Galt, J. A. Transport, Distribution, and Physical Characteristics of the Oil Part I: Offshore Movement and 

Distribution. Hooper, Craig H., Editor. The Ixtoc I Oil Spill: The Federal Response. Boulder, 

Colorado: NOAA Hazardous Materials Response Project; 1981 Dec; pp. 13-39. 

Notes: Spill not selected: Ixtoc 1 

 14. Galt, J. A.; Watabayashi, G. Y.; Payton, D. L., and Petersen, J. C. Trajectory Analysis for The Exxon Valdez: 

Hindcast Study. In: Ludwigson, John O., Editor. Prevention, Behavior, Control, Cleanup; San Diego, 

California. Washington, D.C.: American Petroleum Institute; 1991; c1991: 629. 769 Pages. 1991 

Oil Spill Conference Proceedings. v. 12). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4529; Selected spill: Exxon Valdez 

 15. Gill, S. D.; Bonke, C. A., and Carter, J. Management of The Uniacke G-72 Incident. In: Ludwigson, John O., 

Editor. Prevention, Behavior, Control, Cleanup; Los Angeles, California. Washington, D.C.: 

American Petroleum Institute; 1985; c1985: 311. 691 Pages. 1985 Oil Spill Conference Proceedings. 

v. 9). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4385; Selected spill: Uniake Blowout 

 16. Gundlach, Erich R.; Finklestein, Kenneth, and Sadd, James L. Impact and Persistence of Ixtoc I Oil on the 

South Texas Coast. In: Parrotte, Rita B., Editor. Prevention, Behavior, Control, Cleanup; Atlanta, 

Georgia. Washington, D.C.: American Petroleum Institute; 1981; c1981: 477. 759 Pages. 1981 Oil 

Spill Conference Proceedings. v. 7). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4334; Spill not selected 

 17. Harris, Chris. The Braer Incident: Shetland Islands, January 1993. In: Ludwigson, John O., Proceedings 

Editor. Achieving and Maintaining Preparedness; Long Beach, California. Washington, D.C.: 

American Petroleum Institute; 1995; c1995: 813. 1085 Pages. 1995 International Oil Spill 

Conference Proceedings. v. 14). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4620; Spill not selected 

 
 62 

 18. Hayes, Terence M. The Tanker Assimi-A Case History. In: Ludwigson, John O., Editor. Prevention, 

Behavior, Control, Cleanup; Los Angeles, California. Washington, D.C.: American Petroleum 

Institute; 1985; c1985: 307. 691 Pages. 1985 Oil Spill Conference Proceedings. v. 9). ISBN: LCCN 

75-4161. 

Notes: API Publication No. 4385; Selected spill: Assimi-A 

 19. Horn, Stuart A. and Neal, Captain Phillip. The Atlantic Empress Sinking - A Large Spill Without 

Environmental Disaster. In: Parrotte, Rita B., Editor. Prevention, Behavior, Control, Cleanup; 

Atlanta, Georgia. Washington, D.C.: American Petroleum Institute; 1981; c1981: 429. 759 Pages. 

1981 Oil Spill Conference Proceedings. v. 7). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4334; Selected spill: Atlantic Empress 

 20. Hurford, N. and Buchanan, I. Results of The 1987 Forties Crude Oil Trial In The North Sea. In: Ludwigson, 

John O., Editor. Prevention, Behavior, Control, Cleanup; San Antonia, Texas. Washington, D.C.: 

American Petroleum Institute; 1989; c1989615 Pages. 1989 Oil Spill Conference Proceedings. v. 

11). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4479; Experimental spill: Forties 1987 

 21. Jeffery, P. G. (Warren Springs Laboratory). Large-Scale Experiments on the Spreading of Oil at Sea and Its 

Disappearance by Natural Factors. In: Washington, D.C. Washington, D.C.: American Petroleum 

Institute; 1973; c1973: 469. 841 Pages. Proceedings of Joint Conference on Prevention and Control 

of Oil Spills. v. 3). ISBN: LCCN 74-124324. 

Notes: Experimental Spills; 

 22. Johansen, Oistein (IKU). Physical Behavior. The Halten Bank Experiment: Observations and Model Studies 

on Drift and Fate of Oil in Marine Environment; Edmonton, Alberta. Ottawa, Ontario: Technical 

Services Branch, Environmental Protection Service; 1984; c1984: pp 18-36. 554 pages. Proceedings 

of the Seventh Annual Arctic Marine Oilspill Program Technical Seminar. v. 7). 

Notes: Experimental spill: Haltenbanken 1982 

 23. Kantin, Roger; Albrecht, Michel; Blanc, Joseph, and Tiercelin, Claudine Le. Emergency Response Activities 

At Cedre. In: Ludwigson, John O., Proceedings Editor. Achieving and Maintaining Preparedness; 

Long Beach, California. Washington, D.C.: American Petroleum Institute; 1995; c1995: 850. 1085 

Pages. 1995 International Oil Spill Conference Proceedings. v. 14). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4620; Selected spill: Seki 

 24. Kelly, Frank J. Jr.; Hann, Roy W. Jr., and Young, Harry N. Surveillance, Tracking, and Model Correlation of 

the Spill from the Tanker Burmah Agate. In: Parrotte, Rita B., Editor . Prevention, Behavior, 

Control, Cleanup; Atlanta, Georgia. Washington, D.C.: American Petroleum Institute; 1981; c1981: 

147. 759 Pages. 1981 Oil Spill Conference Proceedings. v. 7). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4334; Selected spill: Burmah Agate 

 25. LaBelle, R.; Price, J. M.; Johnson, W. R., and Marshall, C. F. Surface drifter deployment in experimental oil 

spill. In. Proceedings of the Eighteenth Arctic and Marine Oilspill Program (AMOP) Technical 

Seminar; Edmonton, Alberta. Ottawa, Ontario: Emergencies Science Division, Environment Canada; 

1995; c1995: 493-503. 1309 Pages. Proceedings of the Eighteenth Arctic and Marine Oilspill 

Program (AMOP) Technical Seminar. v. 18). 

Notes: AEA 94; Experimental spill 

 26. Lange, Rolf. A 100 Tons Experimental Oil Spill At Halten Bank, Off Norway. In: Ludwigson, John O., 

Editor. Prevention, Behavior, Control, Cleanup; Los Angeles, California. Washington, D.C.: 

American Petroleum Institute; 1985; c1985: 503. 691 Pages. 1985 Oil Spill Conference Proceedings. 

v. 9). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4385; Experimental spill: Haltenbanken 1982 
 63 

 27. Leveille, Thomas P. The Mega Borg Fire and Oil Spill: A Case Study. In: Ludwigson, John O., Editor. 

Prevention, Behavior, Control, Cleanup; San Diego, California. Washington, D.C.: American 

Petroleum Institute; 1991; c1991: 273. 769 Pages. 1991 Oil Spill Conference Proceedings. v. 12). 

ISBN: LCCN 75-4161. 

Notes: API Publication No. 4529; Selected spill: Mega Borg 

 28. Lewis, A.; Crosbie, A.; Davies, L., and Lunel, T. (AEA Technology). Large Scale Field Experiments into Oil 

Weathering at Sea and Aerial Application of Dispersants. In. Proceedings of the Twent-First Arctic 

and Marine Oilspill Program (AMOP) Technical Seminar; Edmonton, Alberta. Ottawa, Ontario: 

Emergencies Science Division, Environment Canada; 1998; c1998: 319-344. 962 Pages. 

Proceedings of the Twent-First Arctic and Marine Oilspill Program (AMOP) Technical Seminar. v. 

21-1). 

Notes: AEA 97; Experimental spill 

 29. Lunel, T. The Braer Spill: Oil Fate Governed By Dispersion. In: Ludwigson, John O., Proceedings Editor. 

Achieving and Maintaining Preparedness; Long Beach, California. Washington, D.C.: American 

Petroleum Institute; 1995; c1995: 955. 1085 Pages. 1995 International Oil Spill Conference 

Proceedings. v. 14). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4620; Spill not selected 

 30. McAuliffe, Clayton D.; Smalley, Alfred E.; Groover, Robert D.; Welsh, W Marvin; Pickle, W. Stewart, and 

Jones, George E. Chevron Main Pass Block 41 Oil Spill: Chemical and Biological Investigations. In: 

San Francisco, California. Washington, D.C.: American Petroleum Institute; 1975; c1975: 555. 620 

Pages. 1975 Conference on Prevention and Control of Oil Spills - Proceedings. v. 4). ISBN: LCCN 

74-124324. 

Notes: Selected spill: Chevron Main Pass Block 41 

 31. Nichols, J. A. and Moller, T. H. International Cooperation in Oil Spill Response. In: Ludwigson, John O., 

Editor. Prevention, Behavior, Control, Cleanup; San Diego, California. Washington, D.C.: American 

Petroleum Institute; 1991; c1991: 61. 769 Pages. 1991 Oil Spill Conference Proceedings. v. 12). 

ISBN: LCCN 75-4161. 

Notes: API Publication No. 4529; Selected spill: Aragon 

 32. Nichols, J. A. and Parker, H. D. Dispersants: Comparison of Laboratory Tests and Field Trials With Practical 

Experience At Spills. In: Ludwigson, John O., Editor. Prevention, Behavior, Control, Cleanup; Los 

Angeles, California. Washington, D.C.: American Petroleum Institute; 1985; c1985: 421. 691 Pages. 

1985 Oil Spill Conference Proceedings. v. 9). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4385; Spill not selected 

 33. NOAA / HMRAD (Hazardous Materials Response and Assessment Division, National Oceanic and 

Atmospheric Administration). Harris, Lori, Editor. Oil Spill Case Histories, 1967 - 1991: Summaries 

of Significant U.S. and International Spills. Seattle, Washington: NOAA / Hazardous Materials 

Response and Assessment Division; 1992 Nov; HMRAD 92-11. Approx.250 pages. 

Notes: Selected spills: Torrey Canyon; PEMEX/YUM II; Mega Borg; Trinimar Well 327; Khark 5; 

Aragon; Kirki. Spills not selected: Shell Platform 26; Oregon Standard; Bayou Lafousche; Olympic 

Alliance; Alvenus; Nord Pacific; Ocean Eagle; Haven; 

 34. NORPOL. Blowout Nowruz Oilfield. Norway: NORPOL Environmental Services A/S; 1984. 

Notes: Selected spill: Nowruz Field 

 35. OSIR and Rowland, Helena, editors. Oil Spill Intelligence Report. New York, N.Y.: Aspen Publishers. ISSN 

0195-3524. 

 
 64 

 36. Pardo, Fernando. Response Operations to The Aegean Sea Accident. In: Ludwigson, John O., Proceedings 

Editor. Achieving and Maintaining Preparedness; Long Beach, California. Washington, D.C.: 

American Petroleum Institute; 1995; c1995: 583. 1085 Pages. 1995 International Oil Spill 

Conference Proceedings. v. 14). ISBN: LCCN 75-4161. 

Notes: API Publication No. 4620; Spill not selected 

 37. Patton, John S.; Rigler, Mark W.; Boehm, Paul D., and Fiest, David L. Ixtoc 1 Oil Spill: flaking of surface 

mousse in the Gulf of Mexico. Nature. 1981 Mar 19; 290(19 March 1981):235-238. 

Notes: Spill not selected 

 38. Perry, Robin . The Braer Oil Spill. In: Ludwigson, John O., Proceedings Editor. Achieving and Maintaining 

Preparedness; Long Beach, California. Washington, D.C.: American Petroleum Institute; 1995; 

c1995: 874. 1085 Pages. 1995 International Oil Spill Conference Proceedings. v. 14). ISBN: LCCN 

75-4161. 

Notes: API Publication No. 4620; Spill not selected 

 39. Ross, S. L. ; Ross, C. W.; Langtry, E. K., and Lepine, F. (Canadian Government). Ixtoc 1 Oil Blowout. 

Ottawa, Ontario: Fisheries and Environment Canada; 1979 Jul 1013 pages. 

Notes: Spill not selected 

 40. Saudi MEPA. The Nowruz Oil Spill January 1983: The Saudi Arabian Response, Interim Report No. 1. 

Meteorology & Environmental Protection Administration, Kingdom of Saudi Arabia; 1983 Oct16 

pages. Meteorology & Environmental Protection Administration, Kingdom of Saudi Arabia, 

Ministry of Defense and Aviation, Jeddah. 

Notes: Selected spill: Nowruz Field 

 41. Smith, J. E. "Torrey Canyon" Pollution and Marine Life . Cambridge, England: Marine Biological 

Association of the United Kingdom; 1968. 

Notes: Chapters 4 & 5 and pages 168-173 referenced; Selected spill: Torrey Canyon 

 42. Steinhart, Carol E. and Steinhart, John S. Blowout: A Case Study of the Santa Barbara Oil Spill. Belmont, 

California: Duxbury Press; 1972. 150 pages. 

Notes: Chapters 1 & 4 referenced; Spill not selected 

 43. Strom-Kristiansen, T; Daling, P. S., and Brandvik, P. J. NOFO Exercise 1995; Dispersant and Underwater 

Release Experiment. Surface Oil Sampling and Analysis. 1995 May; Escost Report No. 25; IKU 

Report No. 41. 60 pages. IKU. 

 44. Thomas, Dick and McDonagh, Madeleine. Underwater Releases of Oil. In: Ludwigson, John O., Editor. 

Prevention, Behavior, Control, Cleanup; San Diego, California. Washington, D.C.: American 

Petroleum Institute; 1991; c1991: 724. 769 Pages. 1991 Oil Spill Conference Proceedings. v. 12). 

ISBN: LCCN 75-4161. 

Notes: API Publication No. 4529; Selected spill; Claymore/Piper/Flotta 

 45. USCG. Federal On Scene Coordinator's Report: T/V Exxon Valdez Oil Spill; Vol I & II. Washington, D.C.: 

U.S. Coast Guard, U.S. Department of Transportation; 1993 Sep; DOT-SRP-94-02. Vol. I: 592; Vol. 

II: 1026 pages. National Technical Information Service, U.S. Department of Commerce, Springfield, 

VA 22161; (703)487-4660. 

Notes: Selected spill: Exxon Valdez 

 46. Vanderkooy, N. The Ekofisk Bravo Blowout. Spill Technology Newsletter. 1978 Jan; 2 - 1977(EPS-3-EC-78-

1):123-133. 

Notes: Selected spill: Ekofisk Barvo 1977 
 65 

 47. Yoshioka, Gary ICF; Wong, Eva ICF; Grossman, Beverly ICF; Drake, Wendy ICF; Urban, Bob PCCI, and 

Hudon, Tom PCCI. Past In-Situ Burning Possibilities. Groton, Connecticut: U.S. Coast Guard 

Research and Development Center; 1999 Jan; CG-D-17-99. 345 pages. National Technical 

Information Service, Springfield, VA 22161. 

Notes: Spill not selected: Shell Platform 26