Oil Budget Calculator

Deepwater Horizon

TECHNICAL DOCUMENTATION

November 2010

A Report by:

 The Federal Interagency Solutions Group,

Oil Budget Calculator Science and Engineering Team

Oil Budget Calculator

Deepwater Horizon

TECHNICAL DOCUMENTATION

A Report to the National Incident Command

November 2010
i

This document is the product of several individuals who were consulted on particular aspects 

of the Oil Budget Calculator, provided field data, suggested formulas, analysis methods, and/

or reviewed the algorithms used in the calculator.

LEADS:

Lehr, Bill National Oceanic and Atmospheric Administration (NOAA)

Bristol, Sky U.S. Geological Survey (USGS)

Possolo, Antonio National Institute of Standards and Technology (NIST)

MAJOR CONTRIBUTORS OR REVIEWERS: (See Appendix 9 for short biographies)

 Expert Affiliation Major contribution

 Allen, Alan Spiltec In-situ burning

 Boufadel, Michel Temple University Review

 Coolbaugh, Tom ExxonMobil Review

 Daling, Per SINTEF Field data

 Fingas, Merv Environment Canada (retired) Emulsion

 French McCay, Debbie Applied Science Associates (ASA) Review

 Goodman, Ron Innovative Ventures Ltd. Review

 Jones, Robert NOAA Evaporation

 Khelifa, Ali Environment Canada Dispersion

 Lambert, Pat Environment Canada Review

 Lee, Ken Fisheries and Oceans Canada Field data

 Leifer, Ira University of California Santa Barbara Hydrates

 Mearns, Alan NOAA Biodegradation

 Overton, Ed Louisiana State University Dissolution

 Payne, James Payne Environmental Consultants Review
ii

Many others contributed directly or indirectly to the Calculator development but were not 

necessarily involved in the preparation of this document. Some are listed below:

 Expert Affiliation Major contribution

 Beegle-Krause, C.J. NOAA Subsurface oil

 Farr, Jim NOAA Oil chemistry

 Galt, Jerry NOAA Review

 Hammond, Steve U.S. Geological Survey Interface

 Lasheras, Juan University of California San Diego Natural dispersion

 Mabile, Nere BP In-situ burning

 Miller, Mark NOAA Initial summary

 Svekovsky, Jan Ocean Imaging Remote sensing

 Yapa, Pooji Clarkson University Subsurface oil

The Coastal Response Research Center (CRRC), a partnership between the National Oceanic 

and Atmospheric Administration and the University of New Hampshire, provided logistical 

support with document preparation and the peer-review process. Particular thanks to Nancy 

Kinner, Zachary Magdol and Kathy Mandsager.
iii

EXECUTIVE SUMMARY

An oil budget (ICS Form 209) is normally prepared as part of the Incident Action Plan for 

ordinary oil spills. The Deepwater Horizon spill was anything but ordinary, and a special tool 

had to be developed to construct a workable oil budget useful to the unified command in 

making its response decisions. This report describes that tool.

The National Incident Command (NIC) assembled several interagency expert scientific 

teams to estimate the volume of BP Deepwater Horizon oil that has been released from the 

well and the short-term fate of that oil. The expertise of government scientists serving on 

these teams was complemented by non-governmental and governmental specialists providing 

data, offering suggestions, and reviewing the calculations and conclusions. 

The Flow Rate Technical Group (FRTG) and the Department of Energy (DOE) developed 

estimates of the flow rate and of the total volume of oil released. A second interagency team, 

led by the U. S. Geological Survey (USGS) from the Department of the Interior, and the 

National Oceanic and Atmospheric Administration (NOAA) and the National Institute of 

Standards and Technology (NIST) from the Department of Commerce, developed a tool 

called the Oil Budget Calculator to determine what happened to the oil. 

The Oil Budget Calculator was designed to assist the Situation Unit of the Incident 

Command System (ICS). Therefore, its sole purpose was to inform and advise the response, 

and it should not be used to assess environmental damage or any other purpose. 

The Calculator became operational on June 22, 2010 but continues to undergo modification 

and refinement, not only best to characterize what happened to the oil in this case, but also 

to assist in the response to future, similar spills. The numbers in this report may change as 

new information becomes available. The calculator assumed as an input the estimated 4.9 

million barrels of oil discharged in total from the well from April 20 until July 14, 2010, 

provided by FRTG/DOE, and used both direct and indirect measurements and the best 

scientific estimates available to determine what has happened to the oil. 

This report discusses the methods used to estimate the portions of that volume that were 

recovered directly from the well head, dispersed either chemically or naturally, evaporated 

or dissolved, burned or skimmed, and the portion left over (“other” oil) that may still be 

amenable to response actions. The latest results, by and large, are consistent with early results, 

produced by an early version of the Calculator and announced in a NOAA press release of 
iv

August 4, 2010: the early estimate of the percentage of “other” (or, “residual”) oil was 26%; 

the current version of the Calculator estimates it at 23%, and qualifies this estimate with 

the belief that, with high confidence, the true percentage should be between 11% (best-case 

scenario) and 30% (worst-case scenario). 

The report also describes the methods used to qualify such estimates with uncertainty 

assessments. Processes such as direct capture and burning, that were directly measured onscene, have the smallest uncertainty. Processes such as dispersion that have to be estimated 

based upon limited data, theoretical considerations and expert knowledge from past 

incidents, have the greatest uncertainty. The emphasis was on getting a conservative answer 

so as not to underestimate cleanup requirements. In terms of response, this translates into 

using conservative estimates for cleanup efficiency, particularly with regard to skimmer 

efficiency and dispersant success.

The Calculator does not track the final fate of the spilled oil. Instead it estimates oil that may 

be amenable to response decisions as opposed to oil that is not (e.g. dissolved or evaporated 

oil). No attempt was made to estimate the amount of dispersed oil that reached the 

sediments; nor was degradation rate of any of the oil components evaluated quantitatively. 

The focus was on estimating the fraction of the spilled oil that might still be amenable to 

response actions, so that informed decisions could be made.

The Oil Budget Calculator is believed to be sufficiently accurate to meet the needs of the 

Deepwater Horizon spill response. Thus, while it may have underestimated dispersed oil, it 

predicted sufficient amounts of subsurface oil as to encourage a large-scale water-sampling 

program. Suggestions are provided at the end of the report that would allow improved 

budgets in future large-scale incidents.
v

TABLE OF CONTENTS

LEADS AND CONTRIBUTORS i

EXECUTIVE SUMMARY iii

LIST OF TABLES vi

LIST OF FIGURES vi

LIST OF ACRONYMS vii

1. INTRODUCTION 1

2. PURPOSE OF OIL BUDGET CALCULATOR 3

3. PREVIOUS EXPERIENCE - IXTOC SPILL 8

4. LEAK RATE AND SUBSURFACE BEHAVIOR 10

5. DISSOLUTION 13

6. EVAPORATION 17

7. WEATHERING ESTIMATION BY EMULSIFICATION 20

8. NATURAL DISPERSION 21

9. CHEMICAL DISPERSION 24

10. BURNING 31

11. MECHANICAL RECOVERY 33

12. OTHER OIL 34

13. LONGER TERM PROCESSES 35

14. ASSESSMENT AND FUTURE PLANS 38

15. REFERENCES 41
vi

LIST OF TABLES

Table 1: Mass Balance for Ixtoc I. (page 9)

Table 2: USCG Surface Water Dispersant Study Sample Results. (page 29)

LIST OF FIGURES

Figure 1: Schematic of Deepwater Horizon Oil Spill (page 3)

Figure 2: Ixtoc I Oil Spill (page 8)

Figure 3: Estimated flow rate for Deepwater Horizon Spill (DOE/FRTG) (page 11)

Figure 4: Droplet rise velocity (Yapa et. al, 2010) (page 13)

Figure 5: A typical ambient temperature and thermodynamic equilibrium curve for Methane 

(Chen and Yapa, 2004) (page 14)

Figure 6: Solubility per carbon number for hydrocarbon molecules, (Modified 

from McAuliffe, 1987 IOSC – proc. Pp 275 – 288) (page 15)

Figure 7: Histogram shows the C-10 to C-25 Alkanes of the whole Source Oil (Orange), 

a floating oil sample immediately adjacent to the source (red), and two floating oil samples 

at a relatively highly weathered oil further away from the source (green, purple). The highly 

weathered samples clearly show that virtually all the alkanes up to C-15 have been stripped 

from the original source oil. (page 17)

Figure 8: Cumulative dispersant use at the Deepwater Horizon Spill (page 25)

Figure 9: Multi-spectral images of the surface oil above leak (Ocean Imaging) (page 26)

Figure 10: Results from the MNS testing with Corexit 9500 at different dispersant dosages. 

Position 3 Light brown / reddish emulsion: Viscosity 7200 cP, water content 50%; 

Position 4: Dark Brown emulsion: Viscosity 1250 cP, water content 33 %. (page 28)

Figure 11: In situ burn history for Deepwater Horizon Spill (page 32)

Figure 12: Response estimates produced by the Oil Budget Calculator showing best case, 

expected, and worst case volumes of the seven different portions that the calculator tracks 

individually, of the cumulative volume of oil discharged through July 14, 2010. (page 39)

Figure 13: Response estimates expressed as percentages of the cumulative volume of oil 

discharged through July 14, 2010, in the best, expected, and worst cases. (page 40)
LIST OF ACRONYMS

ASA Applied Science Associates

AVIRIS Airborne Visible Infrared Imaging Spectrometer

BOP Blowout Preventer

DOE Department of Energy

DOI Department of the Interior

DOR Dispersant to Oil Ratio

DWH Deepwater Horizon

FRTG Flow Rate Technical Group

ICS Incident Command System

ITOPF International Tanker Owners Pollution Federation Limited

LISST Laser In Situ Scattering and Transmissometry

MNS Mackay-Nadeau-Steelman Laboratory Test 

NIC National Incident Command

NIST National Institute of Standards and Technology

NOAA National Oceanic and Atmospheric Administration

NOAA/ERD NOAA’s Emergency Response Division

NRC National Research Council

ORR Oil Recovery Rate

PAH Polycyclic Aromatic Hydrocarbon

RE Recovery Efficiency

ROV Remotely Operated Vehicle

SMART Special Monitoring of Applied Response Technologies

TE Throughput Efficiency

USCG United States Coast Guard

USGPA United States Gallon per Acre

USGS United States Geological Survey

WHOI Woods Hole Oceanographic Institute

vii
1. INTRODUCTION

The Oil Budget Calculator grew out of a need to provide status of the spilled oil to the 

National Incident Command. Excel spreadsheets were first created to record volumes of 

skimmed oily water, of oil burned, and of dispersant applied. Later, the number of barrels 

that were directly captured was also recorded. However, there was no accounting for the 

fate of the remaining oil, and as the model became increasingly complex, the spreadsheets 

became difficult to interpret. Therefore, the U.S. Geological Survey (USGS), the U. S. 

Coast Guard, the National Oceanic and Atmospheric Administration (NOAA) and the 

National Institute of Standards and Technology (NIST) developed the Calculator, using 

data and supplementary expertise provided by non-government experts. The Calculator 

became operational, as a response tool, on June 22, 2010. However, it continues to undergo 

modification and refinement.

This document discusses the technical understanding that was captured in the model 

implemented in the Calculator — a simplified model that has evolved over time since it 

was first applied to produce results announced by NOAA on August 4, 2010 —, presents 

an assessment of the oil fate at the time of the well having been capped for good, and gives 

recommendations for future research. The aforementioned early results (published in a 

NOAA press release) included the estimate of 26% as the percentage of residual or “other” 

oil. The latest results presented in this report update this estimate to 23%, and assert that, 

with high confidence, the true percentage should be between 11% (best-case scenario) and 

30% (worst-case scenario). 

Because this matter has drawn the attention of scholars and other interested parties outside 

the oil spill science community, additional background material is included. However, in 

the end, this report is still only the technical documentation of a tool used for response, 

and it is neither a comprehensive review of the relevant technical literature, nor a research 

contribution intended for publication in a professional journal. Interested readers seeking 

more background on oil spill science are referred to Lehr (2001), NRC (2003), or Reed et al. 

(1999). Appendices describe the statistical approach used to qualify the Calculator’s estimates 

with uncertainty assessments, the Calculator itself, the raw data used in the Calculator, and 

supporting findings of laboratory and field studies.

It is important to remember that the Deepwater Horizon incident was an emergency, not 

an experiment. In spill emergencies, decision makers need immediate information that 

1
sometimes requires estimates of unknown quantities without as much data as a scientific 

study normally would demand. Some of the processes governing the fate of the oil are poorly 

understood and knowledge about them mostly consists of the personal experience of skilled 

spill responders. In developing the Calculator, the team handled these poorly understood 

phenomena by constructing a simplified model that the participating experts could reach a 

consensus on, or by choosing compromise values (for rate or other constants, for example) if 

a consensus could not be reached.

The usefulness and accuracy of the Oil Budget Calculator needs to be assessed in light of its 

purpose, further discussed below. The answers that the Calculator provides to the response 

team only need to be accurate to the extent that they correctly inform cleanup decisions 

and do not lead to errors in response actions. Accuracy beyond that level, while desirable 

from a scientific viewpoint, is superfluous for the purpose for which the tool was designed. 

Hence, potentially large errors (e.g., in estimates of volumes of dispersed oil) likely would 

be inconsequential unless they would have led to misdirected response activities: we are not 

aware of instances of such misdirection in this case. 

The reader is cautioned that the numerical values stated in this report are estimates based on 

the information available at the time that the estimates were produced, and are based on the 

simplified understanding that the time constraints of the response allowed: therefore, not 

only are they tentative, but most likely will change as new information becomes available. 

For this reason, the final mass balance calculation of the Deepwater Horizon Spill lies 

somewhere in the future, to be produced only after all the appropriate and necessary studies 

will have been completed.

2
3

2. PURPOSE AND BRIEF DESCRIPTION OF THE OIL BUDGET CALCULATOR

Once spilled into the marine environment and moved from the source, the oil interacts 

with the environment in a number of processes collectively called weathering. Figure 1 

shows some short-term processes that acted on this spilled oil. These processes change both 

the composition and properties of the oil, and can result in the amount of oil in the water 

environment being continually reduced. Other longer-term processes such as biodegradation, 

photo-oxidation, and sedimentation may have an impact on the environment but are less 

amenable to response decisions.

Figure 1: Schematic of Deepwater Horizon Oil Spill
4

The Oil Budget Calculator was designed to assist the Situation Unit of the Incident 

Command System (ICS). ICS was developed to provide federal, state, and local governments, 

as well as private and not-for-profit entities, with a consistent framework for the preparation 

for, response to, and recovery from any incident or event, regardless of the size, nature, 

duration, location, scope, or complexity. The ICS Form 209 provides the mass balance 

information that the unified command needs to assess the size of the threat and make 

informed response decisions. Preparing the mass balance tables for an ICS 209 form is 

usually a straightforward process. Vessel tanks are sounded, reports from the field estimate 

oil amount recovered or beached, and standard fate and behavior models, perhaps coupled 

with trained observer overflights, provide the remaining numbers for the tables. Such was not 

the case for the recent Deepwater Horizon Spill. Instead, the most sophisticated technology, 

involving expertise and apparatus never before used on oil spills, was necessary to construct 

even the most rudimentary mass balance table. The Oil Budget Calculator was a combined 

effort of several federal agencies, leading academics in the field of spill science, and practical 

response experts with years of actual spill experience. Its results are a product of field 

measurement, scientific analysis and practical cleanup expertise. The emphasis was on getting 

a conservative answer. In terms of response, this translates into using conservative estimates 

for cleanup efficiency, particularly with regard to skimmer efficiency and dispersant success.

The application of the tool defined its design requirements:

•	 Calculator must be operable by response personnel, not specialized staff, and use easily 

accessible input data.

•	 Calculator must generate output that provides information similar to the standard ICS 

209 form along with some estimate of the confidence of the answers generated.

•	 Calculator must be able to deal with incomplete, uncertain, or missing data and still 

provide the best estimate available to the unified command.

•	 The Calculator should be conservative in its answers (i.e., it should err on overestimating 

oil that is still available to cleanup activities as opposed to oil that is outside of response 

capabilities).

It is important to understand what the Calculator is not designed to accomplish:

•	 The Calculator is not a spill research tool, although new research has been a product of its 

development. Simplifications were made to make it accessible to response personnel.

•	 The Calculator is not a damage assessment tool and is not applicable to determining 

environmental impact of the spilled oil. Other methods are required for this task.
5

•	 The Calculator does not track the final fate of the spilled oil. Instead it estimates oil that 

may be amenable to response decisions as opposed to oil that is not (e.g. dissolved or 

evaporated oil).

The logical structure of the algorithm that drives the Calculator is straightforward. Mass 

is conserved as processes act upon the spilled oil. The chronological order that is assumed 

for the unfolding of these processes is based on observations from other spills and our 

understanding of the corresponding science. Oil that comes from the well either is directly 

captured or not. The portion that is not captured there, in turn either is dispersed naturally 

or chemically in the jet zone at the leakage points, or rises to the surface. Some of the 

dispersed oil dissolves into the water column. Some of the oil that floats to the sea surface 

either dissolves on the way up or quickly evaporates. Oil that remains on the surface can 

be burned or skimmed. Some of the surface oil disperses naturally or chemically into the 

water column. After all these processes, there will be some oil left. A brief description of the 

governing equations (listed in full in Appendix 1) is given below.

(1) Subtract off direct recovery from the total amount escaping from the reservoir. While this 

oil does not enter the Gulf waters, it was important to record this amount for the Unified 

Command since logistical assignments depend upon it. If 

€ 

VR (t) is the oil volume discharged 

on day t and 

€ 

VDT (t) is the amount of oil directly recovered, then the effective discharge, 

€ 

VRE (t), is given by

€ 

VRE (t) = VR (t) −VDT (t) (1)

 

(2) Determine the lower water column chemical dispersion amount. The amount of oil that 

is dispersed as a result of the injection of dispersants is calculated. To guarantee that mass 

balance is maintained, this amount cannot exceed the effective discharge. Some of the oil 

that is dispersed as small droplets will have a portion of the hydrocarbons dissolve into the 

surrounding water. Subtracting this gives 

€ 

VDC (t), the net chemically dispersed oil:

€ 

VDC (t) = (1− k7)min(90k2VCB (t),VRE (t)) (2)

Here, 

€ 

VCB (t) is the volume of dispersing chemicals injected into the subsurface jet. The rate 

constants 

€ 

k2 and 

€ 

k7 are defined in Appendix 1 along with the other rate constants. 
6

They are treated as random variables whose probability distributions describe our incomplete 

knowledge about their values, which is a consequence of our uncertainty about the exact 

attributes of the physical process the equations attempt to model. 

(3) Determine natural dispersion from the leaking jet. From oil that is not chemically 

dispersed, compute the fraction that is naturally dispersed. Again, subtract oil that dissolves 

from the droplets.

€ 

VDN (t) = (1− k7 )max(0,k1(VRE (t) −VDC (t)/(1− k7 ))) (3)

(4) Calculate the dispersed oil near the bottom. Add the amount that is chemically dispersed 

and naturally dispersed near the bottom. This oil is not available for evaporation, nor is the 

oil that dissolved near the bottom.

€ 

VDB (t) = VDC (t) +VDN (t) (4)

 

(5) Compute skimmed oil as a fraction of oily water recovered. Not all the liquid 

recovered by mechanical recovery, 

€ 

VOW , is oil. The rate constant 

€ 

k6 specifies that fraction. 

€ 

VNW (t) = k6VOW (t) defines the net skimmed oil. Oil at all stages of weathering is skimmed. The 

model makes the assumption that the majority of the skimmed oil is ‘older’ oil. Hence, this 

oil is assumed to have already lost all oil that might evaporate.

(6) Determine oil that evaporates or dissolves. Compute oil that evaporates from surface oil 

during its first day on the surface or dissolves. Add evaporation from the second day on the 

surface plus oil that dissolves from dispersed oil. Let 

€ 

Z = VRE (t) −VDB (t)/(1− k7) be the oil that 

makes it to the surface on day t. The oil that rose to the surface on day t-1 and is still left 

(neglecting natural surface dispersion and skimming) is 

€ 

W (t −1) = (1− k4 )Z(t −1) −VBU (t −1). 

Since evaporation and dissolution are combined, dissolution from the bottom is added to 

€ 

VE , 

the net evaporated or dissolved, where

€ 

VE (t) = k4Z(t) + k5W (t −1) +

k7

1− k7

VDB (t) (5)

 

Here 

€ 

VBU is the volume of oil that is burned in-situ. The model uses reported values for daily 

volumes burned as one of its inputs. Since the burns involve oil that will have reached the
surface at any prior point in time, occasionally the total volume burned on a particular day 

may exceed the amount of oil surfacing on that day.

(7) Determine natural surface dispersion. Surface dispersion, 

€ 

VNS , is a competitive process 

with emulsification and the potential for dispersion decreases as the oil weathers on the 

surface. Disperse the surface oil that is available for surface dispersion, after subtracting 

evaporation and burning: 

€ 

VNS (t) = k8 max(0,W (t)) (6)

 

(8) Determine chemically dispersed oil near the surface. Compute volume of chemically 

dispersed oil from surfactants sprayed on the surface slicks, 

€ 

VDS (t). Check that it does not 

exceed oil volume on the surface, based upon 

€ 

VS (t −1), which is the total volume of surface 

oil remaining from the day before.

€ 

VDS (t) = min(20k3VCS (t),VS (t −1)) (7)

 

Here, 

€ 

VCS (t) is the volume of dispersants used on day t. The remainder is then added to the 

“Other” oil category. For the purpose of the Calculator, this “Other” oil is treated as if it is all 

on the surface, when in fact some of it is not.

Appendix 1 lists all the equations used in the current version of the calculator, and explains 

how the uncertainty assessments for the estimates were produced. The corresponding results 

are described in Appendix 2, and are summarized in Figures 12 and 13.

7
8

3. PREVIOUS EXPERIENCE – THE IXTOC SPILL

The Deepwater Horizon incident is not the first large oil spill in the Gulf of Mexico. In June 

of 1979, the Ixtoc I exploratory well in the Bay of Campeche blew out, releasing oil for 

290 days. It was the largest spill in history, at that time, totaling approximately 3.5 million 

barrels. The well was in much shallower water (165 ft) than the Deepwater Horizon.

In many ways, the present Deepwater 

Horizon spill and response is similar to 

Ixtoc incident. For example, in both cases 

the oil released at the well was saturated 

with gas and formed an emulsion on 

the surface. In terms of the response, 

various devices were employed at the 

Ixtoc and Deepwater Horizon but were 

unsuccessful at stopping the flow of oil 

from the well. Again in both situations, 

eventually a relief well was employed to 

stem the flow. As for countermeasures, 

literature states mechanical recovery removed about 4-5% of the oil from the water surface 

during Ixtoc, which appears similar to the Deepwater Horizon incident. Large-scale dispersant 

operations were undertaken on oil on the water surface. In both events there was an impact on 

the fishing industry from the outright ban or local restrictions on fishing.

As much as the two incidents were similar, there are key differences. It is not the objective 

of this report to directly compare the two spills. However, as it relates to the subsequent 

discussion of the Oil Budget Calculator it is important to mention some pertinent detail.

Based on a limited literature review, the obvious difference is the depth of the oil release 

point where the Ixtoc I release point was approximately 165 ft (51m) below the water surface 

and the Deepwater Horizon release point was approximately 5000 ft. (1500m) below the 

water surface. Second, dispersants were used on the surface oil slick at the Ixtoc I but no 

subsurface application is noted (Jernelov and Linden, 1981). The loss to evaporation to the 

atmosphere of the Ixtoc I oil was reported as 50%. It is postulated that the circumstance of 

the Ixtoc I incident facilitated a larger mass of the oil reaching the water surface enabling 

greater overall mass loss to evaporation into the atmosphere in comparison to the conditions 

at the Deepwater Horizon incident. 

Figure 2: Ixtoc I Oil Spill
There appears to be some differences in the physical and chemical properties of the two 

oils. Literature (Jernelov and Linden, 1981, Linton and Koons, 1983) noted the Ixtoc 

oil was a light type with an API gravity of 32, heavier than the Deepwater Horizon oil. 

Detailed chemical analysis of the Ixtoc I well head oil was not available in the literature 

reviewed preparing for the Oil Budget Calculator report. The literature did report 

that the Ixtoc I oil formed a “chocolate mousse” emulsion on the water surface within 

kilometers of the wellhead (Overton, 1981). Empirical evidence shows that the Ixtoc I and 

Deepwater Horizon Macondo oils differed; this will have some bearing on the mass balance 

determination of the oil spills. 

Finally, literature on the Ixtoc I oil spill reported that the change in the chemical properties 

of the spill oil along with the heavy sediment load in the water column caused a good 

fraction of the oil to settle on the bottom of the Gulf of Mexico. News reports published 

in September 2010 indicated that this might also be true for this spill. However, ongoing 

sampling programs have not confirmed the claim of extensive oil being present on the 

bottom of the Gulf from Deepwater Horizon.

Jernelov and Linden (1981) constructed the following mass balance table for the Ixtoc I oil 

spill which has been included here for the convenience of the readers. It should be noted the 

table prepared by Jenelov and Linden reports values based on information available at the 

time. Readers are referred to the literature for further details on the assumptions used and 

contradictory opinions. 

Table 1: Mass Balance for Ixtoc 1

 Percent M Tons

 Burned at well site 1 5,000

 Mechanically removed 5 23,000

 Degraded biologically, photo, chemically 12 57,000

 Landed on Mexican beaches 6 29,000

 Landed on Texas beaches <1 4,000

 Sank to bottom 25 120,000

 TOTAL 100 476,000

9
10

4. LEAK RATE AND SUBSURFACE OIL BEHAVIOR 

Oil was initially leaking from two major sources several hundred meters apart. After severing 

the riser in early June, oil only leaked from the single location at the blowout preventer 

(BOP). The leak rate varied both in the short-term and in the longer-term. The latter was 

due mainly to the drop in reservoir pressure. Figure 3 shows the estimated flow rate, based 

upon studies done by various teams of experts employing a variety of methods. Details on 

the measurements of the Flow Rate Technical Group (FRTG) Plume Team using image 

correlation velocimetry are available in the Plume Calculation Team Report (2010). At 

the time of this document preparation (August and September 2010), the report by the 

joint effort of the FRTG and Department of Energy (DOE) was still in draft form. The 

source estimates by FRTG/DOE were considered to be accurate to within ten percent. This 

uncertainty level is adopted by the Calculator, with three release rates (FRTG/DOE estimate 

minus ten percent, FRTG/DOE estimate, and FRTG/DOE estimate plus ten percent). 

The leak was a combination of gas and oil. In fact, a key parameter in estimating flow was

the average ratio of these two. The actual value seemed to vary over the time period of the 

spill. Increasing gas increased the velocity of the plume but decreased the mass flow.  Analysis 

of available short movies of the riser flow showed the existence of periods when the flow 

oscillates from pure gas to seemingly pure oil. The DOE/FRTG consensus generally 

accepted an average value of 44% oil percentage by volume for the exiting flow, based upon 

measurements of Woods Hole Oceanographic Institute (WHOI, 2010).

The Deepwater Horizon spill was unique not only for its size but also its location at a mile 

beneath the water surface. In general, when oil and gas are released from a deep-water 

location, they are expected to break into bubbles or droplets of various sizes. These sizes can 

vary widely. In field trials off Norway (Chen and Yapa, 2003), they were generally between 

1 mm and 10 mm. Leifer (2010) has suggested, however, that the gas bubbles for the 

Deepwater Horizon spill were smaller than the North Sea experiments.

The larger droplets have a relatively stronger buoyancy force to friction force than the smaller 

droplets (this ratio increases with diameter) and therefore move faster towards the surface 

than smaller droplets. If the simple form of Stokes’ law is assumed (not valid for larger 

droplets), droplet rise/fall velocity, , is a function of the water viscosity, , relative density 

difference, , and characteristic droplet size, d. The value of 

€ 

cstoke varies, depending upon 

several factors. Figure 4 shows the rise velocity for this oil, according to Yapa et al. (2010).

 

(8)
All the droplets are subject to cross currents that will move them laterally while moving 

upwards. For this reason the larger droplets and the smaller droplets may not come to the 

surface at the same location, but quite a distance apart. If there are droplets of very fine 

scale, it may take weeks or even months for them to come to the surface (Galt, 2010). 

Galt concluded that even for the large droplets, the rise time was several hours, an answer 

consistent with Yapa (2010) estimates. Galt also concluded that droplets in the 100 to 200 

micron diameter range would take such a long time to surface from a depth of one mile 

that they were effectively dispersed. This is considerably larger than the common maximum 

diameter size limit for dispersed oil droplets of around 60-80 microns (Lehr, 2001; NRC 

2005). Spaulding et al. (2000) estimated that the rise time for 200 micron droplets with 

specific gravity of 0.81, less dense than this oil, would have a rise time from this depth on the 

order of a week.

However, the large surface slicks showed that a good percentage of the released oil did make 

it to the surface. The remote sensing effort of the NASA ER/2 equipped with Airborne 

11

0 

10000 

20000 

30000 

40000 

50000 

60000 

70000 

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 

Barrels of oil per day (BOPD) 

Event Days 

Following riser fall to

seabed: ~62,000 BOPD

Riser cut, ~4% increase:

~60,000 BOPD

Stacking Cap installed:

~53,000 BOPD

Figure 3: Estimated flow rate for Deepwater Horizon Spill (DOE/FRTG)
12

Visible InfraRed Imaging Spectrometer (AVIRIS) surveyed about 30% of the core area 

of the surface spill on May 17, 2010. Extrapolating the examined area to the whole slick, 

Clark et al (2010) estimated that a minimum of floating oil of between 66,000 and 120,000 

bbl. Limitations of the instruments do not allow measurement of very thick oil. The report 

suggested that if adjustments were made for this limitation, the surface oil could be as much 

as 500,000 bbl. According to the DOE-FRTG estimates, approximately 1.6 million bbl 

had been released from the well-head by that time. This suggests that somewhat less than a 

third of the oil had reached and remained on the surface if the report’s hypothesis is correct. 

Since sub-surface recovery and dispersant applications were limited preceding this date, this 

estimate is not inconsistent with the later estimated values of remaining oil produced by the 

Oil Budget Calculator.

The plume also contained gas of many bubble sizes. For this incident, a large amount gas 

bubbles dissolved and may never have made it to the surface. Gas bubbles move faster than 

oil bubbles if they are the same size. Because of this, gases can separate from the main plume 

and start going in a slightly different direction (Chen and Yapa, 2004). Gases when released 

in deep-water, also have the potential to be converted into hydrates. Methane has a level of 

hydrate dissociation generally around 550 m of water depth as shown in Figure 5. This is 

not a fixed value. It depends on parameters like water temperature and gas type (Spaulding 

et al, 2000). Natural gas can get converted to hydrates at a much higher level. Therefore, 

gases can get converted into hydrates as they travel up. Hydrates are still buoyant with 

specific gravities of around 0.92 to 0.96. As hydrates travel towards the water surface they 

can get reconverted back into gas when they reach the lower pressure in the shallower regions 

(Leifer, 2010). Where the oil to gas ratio is sufficiently high, rather than an oily bubble, the 

aggregate is better described as a droplet with internal bubble. He concludes this effect could 

be very significant. For a primarily gas bubble (i.e., very low oil to gas ratio), in contrast, the 

effect of surface flow around the bubble pushing surface materials towards the downstream 

hemisphere could minimize any oil effect. This blowout occurred at far greater pressure than 

has been studied in the Deep Spill study (Johansen et al. 2000), or the lab (Masutani and 

Adams (2000), at depths where hydrate formation is far more rapid (Rehder et al. 2009). The 

oil budget calculator does not keep track of the gas bubbles, hydrates or dissolved gases.
5. DISSOLUTION

Unlike a typical spill, oil from the Deepwater Horizon spill entered the marine environment 

5000 feet below the surface of the Gulf, and started weathering before it reached the airwater surface. Because of the nature of the material flowing from the riser, which was a 

miscible mixture of oil and natural gas, significant dispersion of the liquid oil occurred near 

the wellhead. Some of the oil droplets were so small, less than a 100 microns in diameter, 

that the turbulent diffusivity of the water was enough to overcome the oil’s natural buoyancy, 

and caused the micro droplets to be dispersed at depth by ocean currents. Larger droplets 

floated to the surface, however the speed of the transit was determined by the size of the 

drops. Larger accumulations of droplets of oil rose fairly quickly while smaller droplets rose 

slowly and were dispersed further from the spill site by currents in the water column.

The important point about this movement of oil from the wellhead is that oil in tiny droplets 

is exposed to weathering processes both at depth as well as on its transit to the surface, and 

once on the surface, weathering continues. Another important point is that much of the 

oil moved through the marine environment as tiny drops, and these drops were continually 

exposed to uncontaminated Gulf water in their transit. This means that the dissolution of 

compounds from the tiny oil droplets was not controlled by equilibrium factors, and this 

resulted in an almost continuous molecular extraction from the oil droplets by the water 

column. The results of this continuous extraction are that even sparingly soluble compounds 

were extracted from the droplets and dissolution was a much more important factor in the 

weathering of Deepwater Horizon spilled oil than it is in more common surface oil spills.

13

Figure 4: Droplet rise velocity (Yapa et al. 2010)
The proverb that oil and water do not mix is usually scientifically accurate when it relates to 

molecular dissolution of oil into the surrounding water from a surface spill. For the normal 

surface type spill, dissolution is less important for estimating the mass balance of the slick 

(NRC 2003).  However, as described above, because of the nature and depth of the release, 

dissolution of oil into the water column was a significant weathering factor.

The dissolution process is a very complex series of interactions between hydrocarbon 

molecules that are non-polar and surrounding water molecules that are polar. In general, 

polar molecules dissolve in polar solvents, seawater in this case, and non-polar molecules 

dissolve in non-polar solvents, the oil in this case. However, because of their molecular 

shapes, sizes, and chemical bonds, some non-polar compounds are slightly soluble in polar 

solvents. Such is the case when considering oil solubility in seawater. As a general rule, 

small mono-aromatic compounds, such as benzene and the alkyl benzenes, have significant 

seawater solubility compared to equivalently sized saturated compounds. This solubility 

extends to larger aromatic compounds, such as naphthalene and its alkyl homologues, and 

may also slightly extend to three-ringed aromatic compounds such as the phenanthrenes and 

the dibenzothiophenes. As a rule of thumb, saturate compounds are the least water soluble 

of the components of oil, probably because they have no significant interactions with solvent 

water molecules and do have significant interactions with similar non-polar compounds 

in the oil. The conclusions are consistent with experimental results obtained at SINTEF 

(Melbye at al. 1999) which showed that a component needs to have a water solubility of a 

14

Figure 5: A typical ambient temperature and thermodynamic equilibrium 

curve for Methane (Chen and Yapa 2004)
15

minimum 0.3 -1 mg/L in seawater (Figure 6) in order to have any significant potential to 

dissolve out from an oil droplet when rising to the surface. This means that n-alkanes up 

to maximum n-C8-9 may have a potential to dissolve but larger alkanes have very limited 

solubility potentials. However, the mono-aromatic benzene compounds (the BTEX’s), C0-

C3 Napthalenes, and the C0-C1-alkylated 3-rings PAH can be stripped out of oil droplets 

because of their water solubilities. The bottom line is that when tiny droplets of oil transit the 

water column to the surface, many of the low molecular weight one, two and maybe threeringed aromatic compounds can be dissolved into the water column. This dissolution can 

affect both the toxicity and physical properties of surface oil from deepwater releases.

Few models or studies exist for oil dissolution at spills, mostly in dated work and older 

generation spill models. Mackay and Shiu (1975) measured the aqueous solubility of fresh 

and weathered crude oil. Payne et al. (1984) reported that studies of Prudhoe Bay crude 

found that truly dissolved components were almost exclusively alkyl-substituted lower weight 

mono-aromatic hydrocarbons with very little n-alkane dissolution. The dissolution rate 

depends directly upon surface area, which was proportionally larger per volume spilled for 

this spill incident compared to a normal surface spill by factors of 100 to 1000. Mackay and 

Leinonen (1977) concluded that, for droplets less than 100 microns in diameter, dissolution 

is very rapid for any component 

that will dissolve at all. Any 

remaining material in the 

droplet will consist of relatively 

insoluble saturate hydrocarbons, 

i.e. hydrocarbons with a carbon 

number greater than about 8, 

and larger multi-ringed aromatic 

PAHs. While the droplets that 

made it to the surface were 

larger than 100 microns, the 

extended time that it took 

for them to reach the surface 

suggests that dissolution of even 

marginally soluble compounds 

occurred.

Figure 6: Solubility per carbon number for hydrocarbon molecules,

(Modified from McAuliffe, 1987 IOSC - proc. Pp 275-288)
It should be pointed out that initial chemical analysis of some samples collected below the Gulf’s 

surface indicates that significant dissolution did occur. One and two ringed aromatic compounds, 

and some of the three ringed sulfur heterocyclic aromatic compounds, appear to have been 

dissolved during transit from the wellhead. These compounds have carbon numbers above 10, 

and there is some evidence that normal alkanes were also slightly affected by the long transit that 

occurred as oil drops moved through the water column. 

Based upon the chemistry of the oil and the above discussions, it was estimated that any oil 

exposed to conditions that could lead to dissolution would lose between 5-10% of its volume 

this way. Since many of these same molecules would evaporate, this process was included in the 

evaporative losses estimate for any oil that made it to the surface. However, dissolution is subtracted 

from oil dispersed near the bottom that never reaches the air-water interface. These estimates may 

change as further studies underway at SINTEF and elsewhere provide additional information. At 

a minimum, it may be possible to separate dissolved fraction from evaporative fraction. If, on final 

review, we find that water samples with oil droplets were collected just below the water surface, 

then by using GC-MS, it is possible to look at the changes in ratios between the more semi-volatile 

aromatics (e.g. 2-3 rings PAH’s and their alkyl homologues) versus the corresponding n-alkanes 

with same boiling Points (similar vapor pressure, but with far less solubility). In that way, it would 

be possible to say more specifically how much of the depletion of the components in the range of 

C10 -C17 is due to dissolution, and how much is due to evaporation. Such information would be 

useful for impact assessment but, for the purpose of response, would be of limited value. The Oil 

Budget Calculator groups dissolution and evaporation.

16
6. EVAPORATION

Laboratory and field studies of oil weathering under wide ranging conditions and for a 

wide range of crude oils demonstrate that surface slicks quickly lose volatile components 

to evaporation. As the more volatile compounds are lost, the rate of evaporation slows. 

Evaporation is often the most significant loss mechanism from surface slicks during the first 

week following a spill. Generally, after a week at sea, evaporation is no longer a significant 

loss mechanism for surface oil. For light crude oils, such as this oil, the great majority of the 

evaporative loss occurs within a couple days of its exposure to the air.

Empirical methods were used to estimate the evaporation rate during this spill. Samples 

of weathered oil collected from the sea surface were analyzed using GC/MS. Laboratory 

weathering of the source oil provided an independent assessment of evaporative behavior. 

Estimations of evaporation rates were not based on model predictions; rather models were 

used in analyzing data from field measurements.

17

Figure 7: Histogram shows the C-10 to C-25 Alkanes of the whole Source Oil (Orange), a floating oil 

sample immediately adjacent to the source (red), and two floating oil samples at a relatively highly 

weathered oil further away from the source (green, purple). The highly weathered samples show 

that virtually all the alkanes below C-15 have been stripped from the original source oil.

(Blue bar graph is sample from dispersant efficiency testing).
18

Evaporation changes not only the volume of a surface oil slick, but changes the chemical 

composition of the oil as well. Because smaller chemical compounds are preferentially lost 

during evaporation, it affects the relative abundance of individual chemical compounds within 

the oil. These changes can be observed by comparing the composition of unweathered oil 

to that which has weathered on the surface. Other loss processes, such as dissolution, can 

also change the composition of the oil. While dissolution is usually not a dominant loss 

mechanism for surface spills it might be significant for releases in deep water. Without the 

appropriate samples, it was not practical to try to resolve the relative importance of dissolution 

and evaporation, so these two losses are grouped. Oil that never reached the sea surface is not 

susceptible to evaporative loss, so the measured estimates of fraction evaporated only applies to 

the oil that surfaced.

Pseudo-component models (Payne et al. 1984; Kirstein et al. 1984) were used to process 

measured data. These models approximate the oil as an ideal mixture of a relatively small 

number of components, each component representing a range of individual chemical 

compounds with similar vapor pressures. Each component is characterized by a mole fraction 

and a vapor pressure. The rate, per unit area, at which a component evaporates from the 

surface slick is proportional to the wind speed, the mole fraction of that component within the 

oil (which varies with time), and the vapor pressure of that component (which varies with the 

temperature of the oil, assumed to be the same as the water temperature). The rate equations 

for the components are solved simultaneously. The total evaporation rate is set equal to the sum 

of the rates of the individual components. This method has been adapted for an oil-weathering 

model, ADIOS2TM , developed by NOAA (Jones 1997), the Type A model developed by ASA 

(French et al., 1996) and the OWM model developed by SINTEF (Reed, Singsaas et al. 2001).

NOAA’s Emergency Response Division (NOAA/ERD) measured the composition of several 

oils (Figure 7). For the purpose of this discussion on evaporation, in Figure 7 we focus on 

the oil from the reservoir (Source oil, orange) and the two weathered oils (green and purple) 

collected from the sea surface on, or around, 16 May. It is not known how long the weathered 

samples were on the sea surface before being collected. GC/MS analysis was used to measure 

the relative abundance concentrations of chemical compounds that make up the oil. The 

weathered oil samples exhibited an expected depletion of the more volatile compounds. The 

pseudo-component evaporation model used in ADIOS2 (Jones 1997) was initialized with 

oil-composition data provided by BP in conjunction with oil-composition data measured by 

NOAA/ERD. The ratio of components measured in the more weathered oil samples was 

compared to those predicted by the model. The extent of evaporative loss was based on the 

correlation between the measured and modeled ratios. These two weathered oil samples exhibit 

an average mass loss of 36%. Since the age of these samples is not known, this provides a lower 

bound on the possible evaporative loss of the surface oil. 
19

SINTEF analyzed three samples of floating oil collected on June 4th and 5th for fraction 

evaporated (Leirvik, Daling et al. 2010). They analyzed the samples by GC/MS and related 

the fraction evaporated to the depletion of alkanes in the C14 to C16 range. They correlated 

the degree of evaporation with the depletion of these peaks using past results. Their 

measured data indicated a mass loss to evaporation of 44%, 47%, and 50%. They estimate 

that the time at sea for these three samples was 1-2 days, 2-3 days, and 4-5 days, respectively.

Laboratory measurements performed by S.L. Ross Environmental Research Ltd. (S.L. Ross 

Environmental Research Ltd., 2010) on unweathered Deepwater Horizon oil were consistent 

with measurements of the above-mentioned surface oil samples. They artificially weathered 

a 2cm thick slick of Deepwater Horizon oil in a wind tunnel for 2 weeks. They measured 

that approximately 35% had evaporated after 2 days, and 45% after 2 weeks. Their 

measurements exhibit a cumulative evaporation, which is essentially a logarithmic function 

of time with the majority of the evaporation occurring in the first few days.

Camili et al. (Camili, Reddy et al. 2010) from Woods Hole Oceanographic Institute 

measured the composition of oil collected from the top 30 m of the water column. They 

made no attempt to quantify the fractional loss but did attribute the loss of the more volatile 

compounds to evaporation. Their measurements can be viewed as an additional observation 

suggesting substantial evaporative loss.

As mentioned above, dissolution also affects oil composition and so losses to dissolution 

must be considered as contributing to the measured values. Measurements reported here 

constitute lower bounds to the possible combined losses to evaporation and dissolution of oil 

that reached the surface. Loss fractions range from 36% to 50% and are self-consistent. The 

authors of this report believe that these data provide a close lower bound to the fraction of 

surface oil lost to the combination of evaporation and dissolution. 

The oil budget calculator based the evaporation/dissolution estimates on the compositional 

change observed in samples of surface oil. It approximates the evaporation/dissolution 

process as occurring over 2 days; the majority of the loss occurring in the first day. To 

simplify data entry, requirements to enter variable wind speeds were eliminated. Cumulative 

evaporation is sensitive to oil composition and temperature but is relatively insensitive to 

wind speed. The original approximation was 37% lost during the first day (potentially as 

high as 44% and as low as 33%) with a second day loss of 4% (potentially as high as 6% and 

as low as 0%). Hence the range of total evaporation of the oil that surfaced was between 

37% and 50%. Currently, there is no plan to revise these values.
20

7. WEATHERING ESTIMATION BY EMULSIFICATION

An alternative approach to calculating mass loss has been suggested by Fingas (2010) that 

uses the fact that the surfacing oil formed emulsions. One important factor to the formation 

of emulsions is that oils often must weather to a certain percentage before a certain type of 

emulsion occurs. This is because the asphaltenes and resins must be at a sufficient content 

to stabilize the oil and also the viscosity must be high enough to retain water droplets long 

enough for asphaltene/resins stabilization to take place. 

Oils from similar oil fields have a tendency to form similar emulsions with similar weathering 

tendencies. Gulf oils that formed stable emulsions had weathering percentages of 37.7, 26.2, 

16.4, 25.5, 22.6, 24, and 35.2%. These average 26.8%. If the Deepwater Horizon oil shows 

similar tendencies, the weathering that took place near the release of the Deepwater Horizon 

was about 16 to 38% with the likelihood that it was near 27%. However, experimental 

studies by SL Ross Ltd. (see pp. 3 in Appendix 8) and studies at SINTEF showed that an 

evaporative loss of at least 40-45 wt %, (representing a 200 - 250C+ residue) is needed for 

this crude oil to start form a significant and stable emulsion. Some additional evaporation 

would occur as the surface oil moved away from the area, transported by winds and surface 

currents. Observations of large amounts of emulsified oil indicate that such emulsified oil, at 

least, had already weathered sufficiently to emulsify.
8. NATURAL DISPERSION 

While the oil released in this spill was buoyant, not all of the oil rose to the surface. Small 

droplets of oil stayed suspended in the water column just as small dust particles will stay 

suspended in the atmosphere due to the underlying fluid turbulence. Such droplets will 

eventually be assimilated through dissolution and biodegradation. They also may become 

attached to suspended sediment in the water.

Some limited data exists on dispersed oil from the results of RV Brook McCall Survey LISST 

(Laser In Situ Scattering and Transmissometry) measurements performed by the Bedford 

Institute of Oceanography (see Appendix 7). It lends support to the hypothesis that much 

of the oil released was dispersed into the water column. However, since the samples were 

subsurface, they may be preferentially sampling the droplet distribution with the larger 

droplets having risen to the surface. Payne’s study reported plumes of oil droplets at depths 

greater than 2 km. away from the source with larger droplets on the top of the plume 

and smaller below. This would be consistent with a large amount of dispersion and weak 

buoyancy. One should be careful about over-interpretation of particle size distribution data 

that were collected for this specific purpose during the Gulf of Mexico oil spill emergency 

response operations. Under this emergency response effort, rapid, less than perfect actions 

had to be taken to support the requirement for immediate action to monitor the fate and 

transport of the oil. 

The NOAA oil fate and behavior model, ADIOS2, uses the dispersion formulas developed 

by Delvigne and Sweeney (1988). These formulas estimate the distribution of droplets sizes 

based upon the dissipation energy rate e. For most surface spills, the turbulent energy comes 

from breaking waves. Li and Garret (1998) estimate that typical dissipation rates in breaking 

waves range from 0.1 to 10 m2 /sec3. Others have reported different values. For example, 

measurements in a wave tank gave values of 0.01 to 0.1 m2 /sec3at the surface (WickleyOlsen et al., 2007). Field measurements showed lower values varying between about 0.1 and 

1 m2/sec3 (Gemmrich and Farmer, 2004, Gemmrich, 2010). Additional information on the 

value of ε can be found in Kaku et al. (2006). 

ADIOS2 suggests that if this spill occurred at the surface under average conditions, only 

a few percent of the oil would disperse because the oil would emulsify. If emulsification is 

prevented, natural dispersion could be as much as 30%.

However, it is not breaking waves but the turbulence at the leak that formed these oil 

21
22

droplets. In this case, a new method had to be developed to estimate dispersion rate. 

ADIOS2, following standard guidelines (NRC 2003), assumes that droplets must be smaller 

than 70 microns in diameter to be considered permanently dispersed. While appropriate for 

surface spills, it is probably too restrictive for spills happening a mile deep. The Calculator 

uses 100 microns as a cutoff. Based upon the earlier discussion of surfacing time, this was 

also too restrictive but was consistent with the conservative approach used to calculate oil 

fate.

Following DS, Q(δ), the entrained droplet mass density for droplets of diameter δ is given by 

the equation

Q(δ) = Cε0.57δ0.7 (9)

where C is a constant that depends upon the properties of the oil slick.

For a surface spill, ADIOS2 would expect that the dispersed oil would be

 (10)

where the bw subscript indicates that this applies to dispersion caused by breaking waves.

If we apply this method to the subsurface leaking pipe (subscript lp) then the same equation 

would be

(11)

and the ratio, R, between the two would be

(12)

Thus the estimate of the increase in natural dispersion becomes a problem of estimating 

εlp and εbw . Lasheras (2010) has suggested that, to first approximation (Friehe et al. 1972; 

Gibson, 1963), the maximum theoretical dissipation rate of turbulent kinetic energy can be 

estimated by

 (13)

€ 

R = Mdisp(lp )

Mdisp(bw)

=1.8

elp

ebw

⎛ 

⎝ ⎜ 

⎞ 

⎠ ⎟ 

0.57

ε

ε

elp ≈ Ulp

3 / Dlp ≈ 7m2 /sec3

ε
23

where Ulp is the exit velocity of the flow jet and Dlp is the pipe diameter. He points out that 

experiments by Martinez –Bazan et al (1999) show that, for distances less than 15 pipe 

diameters from the orifice, the dissipation rate is much smaller, perhaps as little as 0.02

m2 /sec3. If we take a mid-range value from the Li and Garret suggested bounds for breaking 

wave dissipation rates compared to the theoretical maximum for εlp we get virtually all of the 

oil naturally dispersing. This obviously does not match observation. If, instead, we use the 

experimental results of Martinez-Bazan et al., we get approximately 20% subsurface natural 

dispersion. This more conservative value was used by the Oil Budget Calculator with an 

assumed maximum of 30% and a minimum of 10%. The minimum value is considered to 

be the lowest credible estimate consistent with Camilli et al. (2010) and the maximum the 

largest estimate consistent with the AVIRIS and LISST results.

Lasheras also pointed out that the characteristic size Dchar of droplets small enough not to be 

broken by the turbulent shear forces can be estimated by a simple function of the oil-water 

surface tension:

(14)

This matches the North Sea experiments discussed earlier. While the Oil Budget Calculator 

conservatively assumes that oil that is not dispersed near the bottom would come to the 

surface, this is not the case. For example, marine snow present in Gulf waters would scavenge 

some of the oil droplets on their journey to the surface. The extent of this affect for this spill 

is uncertain at this time.

Oil droplet size distribution is greatly affected by viscosity and surface tension. Since some 

of the lighter ends are lost through dissolution on the oil journey to the surface and since the 

surface oil emulsifies, the viscosity of the surface oil was quite high compared to the oil at the 

leaking riser. The seas were usually relatively calm, although there were periods of high winds. 

The original Budget Calculator assumption was that the surface oil would have negligible 

natural dispersion. This was consistent with the standard to conservatively estimate any 

reduction in surface oil through either natural or cleanup actions, although certain experts 

disagreed with this assumption. They pointed out that much of the initial surface oil was 

not on the surface after storm events. Also, the studies by SINTEF indicate that the weakly 

emulsified oil is dispersible with enough energy input. Therefore the Calculator was modified 

to include 5 % natural dispersion of surface oil, based upon ADIOS2 model results. Low 

value was no dispersion and high value was 10%. As in other dispersion estimations, there 

was not a uniform consensus by the experts on this revised estimate.

€ 

Dchar =

1.5s o−w

r oil

⎛ 

⎝ ⎜ 

⎞ 

⎠ ⎟ 

3 / 5

e2 / 5 ≈10mm

ε

ρ

σ
9. CHEMICAL DISPERSION 

A typical commercial dispersant is a mixture of three types of chemicals; solvents, additives 

and surfactants. The surfactants are the active ingredient and contain both hydrophobic 

and hydrophilic groups. This allows them, when coating the oil surface, to reduce its 

surface tension by as much as a factor of 20 or more, reducing mean droplet size in droplet 

formation caused by turbulent shearing (Li and Garrett, 1998). Caneveri et al. (1989) 

measured declines in oil-water interfacial tensions from 18 mN/m without dispersant to 

0.1 mN/m with dispersant. More recently Khelifa and So (2009) used a spinning drop 

instrument to measure declines of oil-brine interfacial tension for three different oils. 

Declines from 18.3 mN/m without dispersant to 6.5 10-4 mN/m with Corexit 9500 at 1:20 

dispersant to oil ratio (DOR) were measured. The same study showed that corresponding 

droplet size decreases from about 220 mm to 25 mm when the DOR increases from 1:500 to 

1:10. (DOR is often expressed with the 1: omitted. Therefore, a DOR of 20 and a DOR of 

1:20 refer to the same ratio).

Clayton et al. (1993) noted that successful dispersion of oil in actual dispersant applications 

only occurs if five requirements are met.

(1) The dispersant must reach the oil surface

(2) The dispersant must penetrate the oil surface

(3) The surfactant must orient at the oil-water interface

(4) The surface tension must be reduced

(5) Sufficient mixing energy must be applied

A total of 43,884 barrels of dispersant were used at the DWH incident:

• 18,379 barrels of dispersant were used by sub-sea addition to the discharging

 and gas stream. 

- Initially this was at 12 gallons/minute, later reduced to 7 gallons per minute.

- The EPA imposed maximum sub-sea use of 15,000 gallons/day (357 bbl/day), 

equivalent to 10.4 gallons/min

• 25,505 barrels of dispersant were applied to oil on the sea surface (see Figure

24
Estimating effectiveness of these dispersant operations was the most difficult challenge in 

constructing the Budget Calculator, given the present state of oil response technology. As 

Lewis (2010) points out, the effectiveness of dispersant use at real oil spill incidents cannot 

be determined by measuring the dispersed-oil-in-water concentration in the water column 

with sufficient resolution in time or space to produce an accurate mass balance. Nor are there 

currently commercially available remote sensing techniques capable of measuring oil layer 

thickness with sufficient accuracy and with sufficient resolution to enable the volume of an 

oil slick to be determined at any point in time, or how the volume changes with time. 

Some surface dispersant applications employed the SMART (2006) protocols. SMART 

(Special Monitoring of Applied Response Technologies) is a Federal cooperatively designed 

and regionally approved monitoring program for dispersant operations and in situ burning. 

However, SMART provides mostly qualitative, not quantitative, estimates of dispersant 

effectives. SMART teams could at best say that there was some dispersion caused by the 

dispersant applications.

25

Figure 8: Cumulative dispersant use at the Deepwater Horizon Spill
Ocean Imaging, using a multi-spectral scanner to estimate slick coverage at the water surface 

directly over the spill site, observed the oil appearance both during dispersant operations and 

no dispersant operations. Figure 11 shows a time period from May 10 through May 12. The 

individual images represent relative thickness measurements of surface oil at specific times. 

The larger the red area, the more thick oil is present. Surface conditions were reasonably 

similar during this time period. Subsurface injection began at 0430 hrs on May 10 and 

continued with an average injection rate of 8 gallons per minute. If the dispersant was as 

highly effective as suggested by certain experts, then more than half of the released oil should 

have been chemically dispersed. The May 10th A.M. image may be too early to show these 

results and May 10th P.M. seems to show a drop in surface oil. However, May 11th and May 

12th show similar surface expressions although the latter should be displaying more oil. 

Therefore, these observations provide inconclusive results.

26

5/10 A.M. 5/10 P.M.

5/11 A.M. 5/12 A.M.

0 0.5 1 2 Km

Oil Classification

Water

Sheen (<0.008mm)

Thin Oil (0.008-0.015mm)

Thicker Oil (0.016-0.08mm)

Thickest Fresh Oil (>0.09mm)

Emulsions

Figure 9: Multi-spectral images of the surface oil above leak (Ocean Imaging)
27

The R/V Brooks McCall field study (Appendix 7) gave some droplet size distribution 

information but not with sufficient clarity to separate the amount of oil chemically dispersed 

from the amount of oil naturally dispersed. Analysis was ongoing at the time of preparation 

of this report and may yet provide useful guidance on this matter.

Given the limited nature of the field data, the best that could be done was to estimate 

effectiveness using the experience and knowledge of dispersant experts. Unfortunately, 

reaching consensus among the experts was not possible.

9A. CHEMICAL DISPERSION: SURFACE APPLICATION

Surface application of dispersant for this spill was extensive. At over 25 thousand bbls, were it 

a spill by itself, it would be one of the larger spills in U.S. waters. Dispersants were normally 

applied by aircraft at a dosage of 5 gallons/acre (USGPA) (5000 liter/sq. km). The dosage 

(DOR) needed to disperse surface emulsions depends highly on the weathering degree 

(e.g., water content, viscosity) of the emulsion. In Figure 10, controlled laboratory testing 

shows (and is further discussed in Appendix 6, Figure 4.3) that for the low weathered dark 

brown  (pos. 4) emulsions (i.e., the type of emulsions that the aerial applications was focused 

on), a very low “absolute” dosage of 1:250 is sufficient in order to disperse the dark emulsion 

effectively under high mixing energy (breaking waves). This means that an aerial application 

with typically 5 USGPA is sufficient dosage in order to disperse such dark-brown emulsion 

with thicknesses around 1 mm as long as the application is efficient and high mixing energy 

is available. SMART samples similarly show significant dispersed oil in the surrounding water 

after surface operations in 3 out of 19 samples (see Table 2). When the emulsion become 

more weathered (i.e. light brown-orange-reddish in color), with a typical water content of 

>50%, and a significantly higher viscosity, the emulsion was still dispersible under high 

mixing energy, but a more “traditional” dosage of 1:50 - 1:25 was needed in order to disperse 

the emulsion effectively using COREXIT 9500. It is probable that some sprayed dispersant 

missed the oil or was deposited on oil layers that are thinner (such as sheen) or much thicker 

(emulsified oil) than the nominal 0.1 mm thick oil layer that most dispersant spray systems 

are designed to treat. Also, the most heavily weathered state of the emulsified oil would 

impede dispersant success.
28

Alternative View 1: We believe the Oil Budget Calculator overestimates the effectiveness 

of chemical dispersants because:

• The WHOI survey and the Ocean Imaging pictures do not suppor

• The surface oil was emulsified and there was insufficient mixing in the subsur

 injection.

Alternate View 2: We believe that the Oil Budget Calculator underestimates the 

effectiveness of chemical dispersants because:

• Lab studies show that both the fresh and emulsified oil are dispers

• There was sufficient energy both subsurface and at the surface to disperse the 

Figure 10: Results from the MNS testing with Corexit 9500 at different dispersant dosages.

Position 3 Light brown / reddish emulsion: Viscosity 7200 cP, water content 50%; Position 4: Dark 

Brown emulsion: Viscosity 1250 cP, water content 33 %.
For the reasons stated in the subsurface dispersant application estimation, it was difficult to 

assess the efficacy of this effort, with a wide variation in the opinions of the experts. As in 

the case of subsea dispersant operations a compromise value is used in the new version of the 

Oil Budget Calculator in lieu of a consensus number.  The expected DOR is 1:10 with a low 

value of 1:5 and a high value of 1:20

Table 2: USCG Surface Water Dispersant Study Sample Results.

USCG ID# Concentration (mg/ml)

USCG-062 1.7

USCG-063 3.7

NSF-036-10-GB-064 4.1

NSF-036-10-GB-065 3.5

NSF-036-10-GB-067 3.2

NSF-036-10-GB-068 5.7

NSF-036-10-GB-070 1380

NSF-036-10-GB-071 1.7

NSF-036-10-GB-072 <MDL

NSF-036-10-GB-073 966000

NSF-036-10-GB-075 21

NSF-036-10-GB-076 98

NSF-036-10-GB-077 <MDL

NSF-036-10-GB-078 37

NSF-036-10-GB-079 1.2

NSF-036-10-GB-080 2.1

NSF-036-10-GB-082 7602

NSF-036-10-GB-083 53

NSF-036-10-GB-084 110

NSF-036-10-GB-085 66

NSF-036-10-GB-086 23300

NSF-036-10-GB-087 1180

NSF-036-10-GB-088 630

NSF-036-10-GB-089 31

NSF-036-10-GB-090 310

NSF-036-10-GB-091 20

NSF-036-10-GB-092 19

29
30

9B. CHEMICAL DISPERSION: SUBSURFACE APPLICATION

Measurement of subsurface operation was, at best, highly indirect. The most directly applicable 

was a published study by Camilli et al. (2010), who reported results from a subsurface 

hydrocarbon survey using an autonomous underwater vehicles and a ship-cabled sampler. 

Using BTEX results as an indicator of oil concentration, they concluded that an observed 

plume of oil at approximately 1100 m. depth represented about 6-7% of the oil leaking from 

the wellhead. The plume location was consistent with the expected location of subsurface 

dispersed oil based upon the Clarkson well blowout model (Latimer and Zheng, 2003). 

Most of the experts believed that the conditions subsurface were good for dispersant operations. It 

is likely that all five of the conditions listed by Clayton et al (1993) were generally met. However, 

the addition of dispersant at 7 to 12 gallons / minute through a narrow diameter wand held by a 

ROV into the flow of escaping oil and gas would probably not have added dispersant to all of the 

oil; some oil would have escaped into the water column untreated with any dispersant. Without 

carrying out some experimentation, it is not possible to say what proportion of the escaping 

oil would and would not have been treated with dispersant. Also, using the estimated flow rate 

shown on Figure 3 and the data available on the daily rate of dispersant applications, the dosage of 

chemical dispersant (DOR) for subsurface application varies from 1:1200 to 1:92; 19% of the time 

(13 days) the DOR was less than 1:250, 34% of the time (23 days) it was between 1:150 and 1:250 

and 43 % of the time (29 days) it was between 1:150 and 1:100. Yet, laboratory studies show that 

COREXIT 9500 is effective on this type of oil and there was more than sufficient turbulent energy.

The Oil Budget Calculator originally used a very conservative estimate for subsurface 

dispersant operations. The ITOPF Technical Information Paper for “The use of Chemical 

Dispersants to Treat Oil Spills” (http://www.itopf.com) recommends for planning purposes 

the use of 1 part dispersant for 20 parts oil as the dosage rate. They point out that spraying 

equipment is often preconfigured to achieve this. Some laboratory studies also support this 

number (Fingas et al. 1995; Khelifa and So, 2009). 

Some experts were concerned that the entrained gas would reduce the effectiveness of the dispersant 

application by preventing contact between oil and surfactant. They also thought that the time of 

contact might be insufficient to achieve optimum effect. Their concerns are captured in the choice 

for minimum effectiveness. However, other experts believe that a 1:20 effectiveness ratio greatly 

underestimated that actual amount of oil dispersed. They point to successful applications in the North 

Sea, producing a larger ratio of dispersed oil to surfactant applied. Ratios as large as DOR 1:100 were 

suggested. One of the experts was concerned about hydrate interference with dispersant application.

Failing to achieve common agreement of the experts, the new calculator uses a compromise 

estimate of a DOR of 1:40 with a low estimate of 1:20 and high estimate of 1:90.
31

10. BURNING 

Burning in-situ was extensively used during the Deepwater Horizon Spill. Environmental 

conditions, location of the incident and the nature of the oil made this possible. Oil was 

gathered into areas enclosed with special booms and set afire.

For ignition to occur with most oils, the oil film must generally be greater than 2 to 3 

mm. Ignition will depend upon the water content of the oil, emulsions of 20 to 25 % or 

more typically being very difficult to ignite.  Since free-floating oil slicks are typically on the 

order of a tenth of a mm or less, most oil spills need to be contained in special fire-resistant 

booms.  High winds and waves (typically in excess of 1 m short-period, wind waves) may 

prevent burn operations because of the difficulty of preventing entrainment and splash over 

within the fire boom, and, because of the difficulty of achieving an initial ignition.  Most 

relatively fresh crude oils (even emulsions of 15% to 20% water) may burn with a “thicknessreduction” or “regression” rate of approximately 0.05 mm/sec, slightly more than a tenth of 

an inch per minute (ASTM, 2003).  This represents an oil elimination rate of approximately 

0.07 gpm/sq.ft. Higher water contents of approximately 25% to 40%, though very difficult 

to ignite, may burn at rates of approximately 0.05 gpm/sq.ft.  During the burning of oil or 

emulsions on water, part of the oil is turned into smoke. The actual percentage depends upon 

the size of the burn and other factors, but usually is in the range of only 5% to 15% of the 

original volume of oil burned.  With typically less than 5 % of the original volume remaining 

as burn residue, the controlled elimination of spilled oil through combustion is recognized 

as a highly efficient response option. The burn rates used during the Deepwater Horizon 

incident range from 0.07 gpm/sq.ft (representing lightly emulsified oils) to 0.05 gpm/sq.ft 

for burns that may have involved more highly emulsified oils. These oil elimination rates are 

in line with ASTM standards, and most burns were carried out on emulsions with relatively 

low water content and along convergence lines where unstable emulsions and wind-herded 

films had accumulated as dark brown to black oil layers.  Daily estimates of the volume of 

oil burned were recorded as minimum and maximum values reflecting these oil elimination 

rates.  The results were calculated throughout the Deepwater Horizon incident using surface 

and aerial observations that included the size and duration of each burn.  Nearly all burns 

involved 500-ft-long booms towed in a U-configuration, while attempting to maintain a 

“gap ratio” (i.e., swath-to-boom-length ratio) of ~ 0.3.  A single fire boom could therefore 

hold 500 to 1000 bbl of oil in its apex, the downstream area only ~1/3 of the way toward the 

leading ends of the boom.  A single burn could often eliminate its contents within an hour or 

less.  Field crews concluded that some surface slicks must have been in the range of 2.5 mm 

to 5 mm to achieve some of the larger burns. This represents a very thick oil emulsion layer.
Figure 11 illustrates the minimum and maximum daily oil elimination rates based upon the 

burn rates described above. It should be noted that the percentage of burned oil reported 

by the Calculator applies to all the oil released by the well. If one computes burning share 

based only upon the volume of available surface oil, the fraction burned is much larger, 

representing a significant portion of the surface oil. The Oil Budget Calculator uses the range 

of reported burn values depicted in Figure 11 in its calculations. The uncertainty range for 

the amount of burned oil is small compared to the other processes discussed in this report.

32

Figure 11: In situ burn history for Deepwater Horizon Spill
33

11. MECHANICAL RECOVERY 

Skimmers were the most commonly used mechanical devices use to remove oil from the Gulf 

water surface. These skimmers varied greatly in size, application, and capacity, as well as in 

recovery efficiency and water pickup (Schulze, 1993; Schwartze, 1979). In the particular case 

of the Gulf oil spill the major issue was the amount of water recovered with the skimmed 

oil. Rather than estimate oil-water ratios, simple measurements of the barge oil would 

seem the rational way to determine this amount. However, to date, no such measurements 

are available. Therefore it was necessary to estimate skimmer performance based upon sea 

conditions, nature of the oil, and past experience.

A skimmer’s performance is affected by a number of factors including the thickness of the 

oil being recovered, the extent of weathering and emulsification of the oil, the presence 

of debris, and weather conditions at the time of recovery operations. A skimmer’s overall 

performance is usually determined by a combination of its recovery rate and the percentage 

of oil recovered. The maximum amount of oil that a skimmer could recover is called the 

‘Nameplate Recovery Rate’ and is typically provided by the manufacturer of a skimmer 

(Fingas, 2010; Meyer et al, 2009). A similar definition is the ‘Effective Daily Recovery 

Capacity’, which is the amount that a skimmer could recover in daylight hours under ideal 

conditions. The recovery rate is the volume of oil recovered under specific conditions. It is 

measured as volume per unit of time and is usually given as a range. If a skimmer takes in a 

lot of water, it is detrimental to the overall efficiency of an oil spill recovery operation.

Skimmer performance is chiefly the product of three parameters. The Oil Recovery Rate 

(ORR) is the quantitative rate in volume per unit time, usually m3/hour and is corrected for 

water recovery. The throughput efficiency (TE) is applicable only to advancing skimmers. 

The throughput efficiency is the percentage of oil presented to a skimmer versus that 

recovered, in percent. The recovery efficiency (RE) is the percent of oil recovered out of the 

total oil and water recovered. For the Gulf recovery effort, the RE is the most important 

factor. We know the total liquids recovered, but we do not know exactly how much oil was 

in this liquid and therefore must estimate the actual oil recovered. Past studies show that the 

average RE of the skimmers in wave conditions is 33%.

The measured water content of skimmed emulsions varied. An estimate of the actual 

recovered oil would be a product of the average RE times the oil content of the emulsion. 

The Calculator assumes that 20% of the skimmed liquid was oil, with a range between 10% 

and 40%. These are conservative estimates in line with the philosophy behind the Calculator. 

They are also considered temporary numbers, pending any measurement results. 
34

12. OTHER OIL 

The Oil Budget Calculator does not quantify the amount of oil volume that becomes tar 

balls, forms surface slicks, sinks due too sedimentation, remains in the surf zone or impacts 

the shore and is subsequently cleaned up. These are instead grouped together as ‘other oil’.

The amount of oil listed as ‘other’ is quite large. Obviously, there are no longer large surface 

slicks in the Gulf of Mexico. So where is this oil? Some obviously did impact the coastline, 

particularly the Louisiana coastline. Cleanup operations recorded the volume of oil debris 

that was recovered during shoreline cleanup. However, no estimates were made of the 

percentage of oil in this debris so it is not possible to give a reliable estimate of this amount. 

Based upon past spills, the oil content of collected debris mass is only a few percent. Some 

of the oil may have aggregated with sediment in the water column and settled to the sea 

bottom. The Oil Budget Calculator does not estimate sedimentation.

Much of this oil probably formed tarballs or millimeter scale oil droplets since the conditions 

of this spill were conducive to such tarball/droplet formation (turbulent subsurface release, 

large use of dispersant on weak emulsions, etc). The larger tarballs are very persistent in 

the marine environment and can travel hundreds of miles, sometimes re-concentrating in 

convergence zones far from the original spill site. Some limited efforts were made to collect 

and quantify the larger tar ball mass density in the nearshore areas but the available data was 

insufficient to make a global estimate. The smaller oil droplets behave in some respects almost 

as dispersed oil and are similarly subject to biodegradation, dissolution, and sedimentation.
13. LONGER-TERM PROCESSES

While not tracked by the Oil Budget Calculator, there are other processes that work to break 

down the spilled oil. Two important ones for the Gulf of Mexico are photo-oxidation and 

biodegradation.

The combination of hydrocarbons with oxygen is called oxidation. The newly formed 

oxidized compounds may affect the oil slick by increasing dissolution, dispersion or 

emulsification. While trace metals in the oil may influence the oxidation process, ultraviolet 

light significantly increases oxidation. Virtually all of the molecules that evaporate from 

the slick undergo photochemical oxidation in hours or days. Also, beached oil will show 

the effects of exposure to sunlight. Even floating oil can show chemical changes due to 

this process. Overton (1981) exposed IXTOC I crude oil to sunlight and discovered the 

formation of tarry flakes, showing the involvement of photolysis. Observers at the Mega Borg 

spill in the Gulf of Mexico noticed the formation of crusts on floating tarmats and tar balls, 

with the hypothesis that this was due to photo-oxidation. 

Hydrocarbons, including those found in oil slicks, are a food source for many 

microorganisms. The rate of such biodegradation depends upon the availability of nitrogenand phosphorus-containing nutrients in the water, as well as the surface exposure of the oil to 

the organisms. Swannel and Daniel (1999) suggest that dispersant use on a slick may speed 

up biodegradation by promoting the growth of indigenous, hydrocarbon-degrading bacteria 

as well as increasing the surface area of the oil available for microbial colonization.

Bacteria capable of degrading various groups of petroleum hydrocarbons have been found 

in all oceans and in deep as well as shallow water. Research on biodegradation has occurred 

with many types of oils and fuels, at temperatures ranging from -1C (polar) to over 30 C 

(hot, tropical), and at natural oil seeps. Tools for documenting biodegradation rates include 

microbial taxonomy and abundance, nutrient uptake, oxygen utilization and mass loss.

The Deepwater Horizon blowout resulted in petroleum hydrocarbon contamination on sand 

beaches and marshes, at the sea surface, in the warm (30-35C) upper water column adjacent 

to dispersant operations, and in cold (4.5 to 5C) water at 1100 to 1300 m deep. Available 

results to date have been reported from surveys conducted in May and June, 2010, and 

focused on indicators of biodegradation in the contaminated deepwater layers (Camilli et al., 

2010 and Hazen et al, 2010). 

35
36

Camilli et al (2010) reported presence of sub-surface volatile hydrocarbons and also only 

slight reductions of oxygen at depth and suggested biodegradation rates were low potentially 

resulting in many months of hydrocarbon persistence. Hazen et al (2010) reported that the 

dispersed hydrocarbons in deep water originating from the Deepwater Horizon blowout 

source stimulated deep-sea indigenous γ-proteobacteria that are closely related to known 

petroleum-degraders. Hydrocarbon-degrading genes coincided with the concentration 

of various oil contaminants, namely alkanes. Changes in hydrocarbon composition 

with distance from the source and incubation experiments with environmental isolates 

demonstrated faster than expected hydrocarbon biodegradation rates at 5°C. The half-life 

degradation rates ranged from about 2 to 6 days, depending on the alkane. Based on these 

results, Hazen et al (2010) concluded that the potential exists for intrinsic bioremediation 

of the petroleum hydrocarbons in the deep-water column without substantial oxygen 

drawdown. 

Other studies unrelated to this incident have reported a wide range of hydrocarbon 

degradation rates depending on all the variables indicated above. The Hazen et al (2010) rates 

are not inconsistent with those published by Venosa and Holder (2007) for cold water using 

dispersed Alaska North slope crude oil.

Past biodegradation research has focused on marine waters of moderate to warm 

temperatures, including oil-contaminated sediment interstitial waters of beaches and 

marshes as well as dispersed/dissolved hydrocarbons. Going back into history, Walker and 

Colwell (1977) measured the loss of mass South Louisiana crude oil during replicated in 

situ incubation field experiments in a North Carolina estuary at 30C. The mean mass of 

365 mg of oil was reduced to 235 mg in one week (35% reduction), 178.5 mg in 2 weeks 

51.1 percent reduction) , 11.2 mg in 3 weeks (97.0 per cent reduction and and 8.8 mg 

in 4 weeks (97.6 per cent), yielding a half life of about 2 weeks (14 days). Bacteria counts 

reached maximum in 3 weeks then stared to decline. Presumably, the initial loss of mass was 

due to evaporation of more volatile components whereas the later stages were the result of 

biodegradation. These simple rate results fall somewhere between the predictions of Camilli 

et al (2010) and Hazen et al (2010). 

Since the 1970’s, there has been a considerable amount of field, mesocosm, and laboratory 

work done. The rates resulting from these studies also range widely depending on oil types, 

oil weathering, dispersion effectiveness, dispersant composition, nutrient loadings, and 

especially which target petroleum compounds or groups of compounds each research team 

analyzed. The National Research Council (NRC, 2005) reviewed much of the work done on 

biodegradation of dispersed oil concluding that most of the work has not been consistently 
37

standardized sufficient to confidently extrapolate from the laboratory to the field. Further, 

there is a paucity of work on degradation of higher molecular weight polycyclic aromatic 

hydrocarbons, the most toxic compounds. Field studies in contaminated marshes and beach 

sediments, have demonstrated a wide range of biodegradation rates; oil biodegraded almost 

completely within a few weeks at some locations while it persisted for tens of years at other 

locations (Reddy et al., 2002; Short et al, 2004). Boufadel et al., (2010) noted low oxygen 

and nutrient concentration that might have prevented effective biodegradation of the Exxon 

Valdez oil spill. The study of Li and Boufadel (2010) highlighted that understanding beach 

hydraulics is a critical step in predicting the fate of oil within beaches. Therefore, we would 

expect this oil to undergo biodegradation at different rates in different compartments of the 

Gulf ecosystem
38

14. ASSESSMENT AND FUTURE PLANS

While pure scientific research can be done on oil spills, most oil spill science is applied 

science. Tools such as the Oil Budget Calculator are only useful if they provide information 

that will assist response decisions. Conversely, they need only be as accurate as the precision 

needed for these decisions. Improved accuracy beyond those levels may be important in the 

academic sense but not in the response area. Figures 12 and 13, below, show the output from 

the model, given the revisions described in this document. They vary somewhat from earlier 

calculations but probably not significantly according to the above criteria.

The estimates of the Calculator were admittedly rough for the reasons discussed in this 

report. However, the estimates were probably sufficient to meet the needs of the Response. 

Ultimately, of course, that judgment will be made by the NIC. The experience in developing 

the Calculator points to areas of needed future research and planning:

(1) Protocols for surface and subsurface sampling: While oil samples were 

collected for impact assessment, few samples were properly collected and 

categorized for response. For example, samples often came from skimming 

barges, where oil in different states of degradation was blended together. Future 

response plans should specify methods for gathering proper representative 

samples.

(2) Dispersed oil droplet size: A major improvement in estimating dispersant 

efficiency would be possible if practical operational tools and methods existed 

to characterize droplet size distribution of subsurface oil.

(3) Basic Models for Longer-term processes: While longer-term processes such 

as biodegradation often happen outside the time frames of the response, 

understanding and being able to predict such longer-term changes may be 

useful in making response decisions.

(4) Estimation of collected shoreline oil: For a complete mass balance, procedures 

should be implemented that estimate the fraction that is oil or oiled debris 

gathered from shoreline cleanup.

(5) Expanded modeling capabilities: Many of the team members that assisted 

with the Oil Budget Calculator are also part of a working group of spill experts 

developing the specifications for the next generation of oil spill model. These 

specifications need to be translated into real code.

(6) Revised interface: A better interface is necessary to more properly display the 

intrinsic uncertainty in the Calculator.
39

Figure 12: Response estimates produced by the Oil Budget Calculator showing best case, expected, and 

worst case volumes of the seven different portions that the calculator tracks individually, of the cumulative 

volume of oil discharged through July 14, 2010. These estimates served solely as a guide for the national 

response to the Deepwater Horizon MC252 Gulf Incident. The best and worst cases are defined in Appendix 

1: they are the combinations of values of the seven variables depicted in each stack that correspond to the 

lower and upper endpoints of a 95% confidence interval for the volume of “Other Oil”.
40

Figure 13: Response estimates expressed as percentages of the cumulative volume of oil discharged 

through July 14, 2010, in the best, expected, and worst cases. These estimates served solely as a

guide for the national response to the Deepwater Horizon MC252 Gulf Incident.
41

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of petroleum hydrocarbons in marsh sediments”. Environmental Science Technology. Vol 

36, 4754–4760. 

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OSCAR2000”. In Proceedings of the 2001 International Oil Spill Conference 

(IOSC). Tampa, Florida.

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20th Century: Overview of the State of the Art”. Spill Science & Technology Bulletin, 

Vol 5. Issue1. April 1999, pp 3-16.
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hydrate stability field from open ocean field experiments and numerical modeling, 

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Oil, Sample ENT-052210-178.

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Marine Oil Spill Program (AMOP) Technical Seminar. Vol 493.

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12 years after the Exxon Valdez oil spill”. Environmental Science Technology, Vol. 38, 

pp. 19–25. 

Spaulding, M.; Bishnoi, P.; Anderson, E.; Isaji, T. (2000). “An Integrated Model for 

Prediction of Oil Transport from a Deep Water Blowout”. Twenty-third Arctic Marine 

Oil Spill Program (AMOP) Technical Seminar , 611-635, Environment Canada, 

Ottawa.

Swannel, R.P.J.; Daniel, F. (1999). “Effect of dispersants on oil biodegradation under 

simulated marine conditions”. In Proceedings, 1999 International Oil Spill 

Conference (IOSC). American Petroleum Institute, Wash. DC. 

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Project, Boulder, Colorado. 202 pp.

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(NOAA), United States Environmental Protection Agency (USEPA), Centers for 

Disease Control and Prevention (CDC), United States Minerals Management Service 

(USMMS), SMART - Special Monitoring of Applied Response Technologies, Report 

http://response.restoration.noaa.gov/book_shelf/648_SMART.pdf, 2006, 46 p.

Venosa, A.D; Holder, E.L. (2007). “Biodegradability of dispersed crude oil at two different 

temperatures”. Marine Pollution Bulletin. Vol. 54, 545 (May, 2007).
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of oil and other pollutants in aquatic environments”. Environmental Science and 

Technology, Vol. 11(1), pp. 93-95.

Wickley-Olsen E.; Boufadel, M.C.; King, T.; et al. (2007). “Evaluation of the energy 

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of Hydraulic Research, IAHR. (accepted for publication)

15B. ADDITIONAL KEY REFERENCES

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Miami Beach, Florida, pp 65-70.

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47
48

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12, 1998, West Edmonton Mall Hotel, Edmonton, Alberta, Canada. Ottawa, Ont.: 

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1407-1418.
49

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and water.” Canadian Journal of Chemical Engineering, Vol. 51, pp. 434-439.
50

APPENDICES
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Appendix 1

A1 Uncertainty Assessment of Mass Balance Estimates

A1.1 Introduction

We have developed and applied statistical methods to gauge the uncertainty associated with

the estimates of the volumes of the principal fractions of the oil spilled from the Deepwater

(Macondo) well that the Oil Budget Calculator tracks individually, in addition to the volume of

oil that has been recovered via the Riser Insert Tube Tool (RITT) or via the Top Hat: (i) oil that

has been naturally or chemically dispersed; (ii) oil that has evaporated or dissolved; (iii) oil in

skimmed oily seawater; (iv) oil that has been burned; and (v) residual (or, other) oil that remains

either on or below the sea surface, in other forms.

The uncertainty associated with the volume of each of these fractions includes contributions

from the uncertainty of the eight rate constants in the mass balance equations that describe

where the spilled oil will have gone.

Figure A1.1 on page A1.7 shows the result of the uncertainty analysis for the volume of residual

oil, depicted as an uncertainty envelope, throughout the 86-day period from April 20 (explosion

and fire on Deepwater Horizon platform) to July 14, 2010 (last day with discharge from the

well). The lower bound of this envelope corresponds to a best-case scenario, and the upper

bound to a worst-case scenario. Section A1.8 describes the methods used to characterize these

scenarios quantitatively, and Appendix 2 shows the corresponding results.

A1.2 Input and Output Quantities

The quantities that we are primarily interested in, and whose values we track, are referred to

as output quantities; and the quantities that these depend on (which are measured directly or

indirectly, or that we have other prior knowledge of), are referred to as input quantities.

For example, the volumeVDC(t) of oil that was dispersed on day t by underwater application of a

volume VCB(t) of a chemical dispersant can be modeled as VDC(t)=(1−k7) min

90k2VCB(t),

VR(t) −VDT(t)



, where VR(t) denotes the volume of oil discharged from the well, of which

VDT(t) will have been recovered via RITT/TopHat, and k2 and k7 denotes rate constants.

In this example, VDC(t) plays the role of output quantity, and VCB(t), VR(t), VDT(t), k2, and

k7 play the role of input quantities. Of the latter, only VCB(t) and VDT(t) were measured directly; VR(t) was estimated by other teams in the Flow Rate Technical Group equation A1.1 on

page A1.2; k2 and k7 are the results of assessments supplied by subject-matter experts.

The mass balance equations A1.1 on page A1.2– A1.16 on page A1.2 express relations between

all the relevant quantities, and involve what we have been calling input and output quantities,

as well as other quantities that we call intermediate quantities because they are used in the

calculations but are not of primary interest for the purposes of the Oil Budget Calculator.

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VRE(t) = VR(t)−VDT(t) (A1.1)

X(t) = 90VCB(t) (A1.2)

VDC(t)=(1−k7)min

k2X(t),VRE(t)

 (A1.3)

Y(t) = VRE(t)− VDC(t)

1−k7

(A1.4)

VDN(t)=(1−k7)max

0, k1Y(t)

 (A1.5)

VDB(t) = VDC(t) +VDN(t) (A1.6)

VNW(t) = k6VOW(t) (A1.7)

Z(t) = VRE(t)− VDB(t)

1−k7

(A1.8)

W(t) = max0,(1−k4)Z(t)−VBU(t)

 (A1.9)

VE(t) = k4Z(t) +k5W(t −1) + k7

1−k7

VDB(t) (A1.10)

VNS(t) = max

0, k8W(t)

 (A1.11)

VDS(t) = min

20k3VCS(t),VS(t −1)

 (A1.12)

VD(t) = VDB(t) +VDS(t) +VNS(t) (A1.13)

VSD(t) = VRE(t)−

VE(t) +VNW(t) +VBU(t) +VD(t)

 (A1.14)

VS(t) = VSD(1) +···+VSD(t) (A1.15)

VC(t) = VDS(t) +VDC(t) (A1.16)

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The input, intermediate, and output quantities are listed in Table A1.1 on page A1.4. Typically,

all of these quantities vary from day to day, and this dependence will be indicated explicitly

when necessary, as in VR(t), for the volume of oil discharged on day number t (day 1 being

April 20, 2010).

All volumes are assumed to be expressed in barrels of oil (bbl), with 1 bbl = 42 U.S. (liquid)

gallons ≈ 159L. The policy of the National Institute of Standards and Technology (NIST) is

to use the International System of Units (metric units) in all publications. In this document,

however, the units used traditionally in the oil industry are also used.

All of the output quantities have cumulative counterparts, except VS(t), which, by definition,

already includes contributions from oil released on day t as well as residues of oil that will have

been released on prior days but have not yet been recovered, evaporated or dissolved, burned,

or dispersed (which in practice means dispersion into droplets of less than 70 μm to 100 μm

in diameter). VSD(t) is the contribution to the residual oil originating from oil spilled on day

number t, and VS(t) is the corresponding cumulative sum (from day number 1 through day

number t), as defined in equation A1.15 on page A1.2.

A1.3 Uncertainty

The Guide to the expression of uncertainty in measurement (GUM) [Joint Committee for Guides

in Metrology, 2008a], and its companion International vocabulary of metrology (VIM) [Joint

Committee for Guides in Metrology, 2008c], are international standards that codify the meaning

of “uncertainty” in the context of measurement science, and provide the technical basis whereon

it may be gauged quantitatively, and interpreted in practice.

The VIM defines measurement uncertainty as a “non-negative parameter characterizing the

dispersion of the quantity values being attributed to a measurand, based on the information

used.” Measurand is the “quantity intended to be measured.” In our case, the volumes of

the different fractions of the spilled oil, aforementioned, that the Oil Budget Calculator tracks

separately, all are measurands.

Here, and in many other studies, the measurands are not accessible for direct measurement, and

the corresponding measured values, or estimates of their values, are obtained by applying measurement functions [Joint Committee for Guides in Metrology, 2008c, 2.49] to values of other

quantities that are either measured directly, or themselves are functions of yet other quantities

that will have been measured directly, or about which there exists some prior information. In

our case, these measurement functions are the mass balance equations.

The uncertainty analysis we describe in Section A1.7 on page A1.11, serves to propagate the

measurement uncertainty associated with the input quantities to the output quantities of interest.

Since the methods used to model the uncertainty of the input quantities are probabilistic, and

the methods used to propagate their uncertainties to the output quantities are statistical, the endproduct of such analysis typically is an interval that, with some specified probability (typically

95 %) includes the true, albeit unknown value, of the output quantity.

For example, we will conclude that the volume of residual oil on day number 86 (July 14,

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INPUT QUANTITIES

VBU Oil volume burned

VCB Dispersant volume sprayed, subsurface

VCS Dispersant volume sprayed, surface

VDT Oil volume recovered via RITT/TopHat

VOW Oily water volume recovered

VR Oil volume discharged

INTERMEDIATE QUANTITIES

VD Oil volume dispersed, total

VDB Oil volume dispersed, subsurface

VDC Oil volume dispersed chemically, subsurface

VDS Oil volume dispersed chemically, surface

VRE Oil volume effectively discharged

VSD Oil volume contributed to residual, daily

W Auxiliary variable

X Auxiliary variable

Y Auxiliary variable

Z Auxiliary variable

OUTPUT QUANTITIES

VDN Oil volume dispersed naturally, subsurface

VNS Oil volume dispersed naturally, surface

VC Oil volume dispersed chemically

VE Oil volume evaporated or dissolved

VNW Oil volume skimmed

VS Oil volume residual (or, other oil)

Table A1.1: Input, intermediate, and output quantities.

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2010), will have been between 0.53 and 1.5 million barrels (Mbbl) of oil, with 95% probability

(Appendix 2). This means that one is prepared to bet at odds of 19:1 in favor of the true value of

such volume indeed lying in this interval. Noting that 1 bbl = 42U.S. (liquid) gallons ≈ 159L,

that interval may equivalently be stated as 22×106 U.S. gallons to 63×106 U.S. gallons, or as

84×106 L to 240×106 L.

All the quantities in play here are affected by uncertainty. In the example above, it is not only

the rate constants k2 and k7 that have associated uncertainty reflecting the imperfect knowledge

that experts have about their values. The values of VCB(t), VR(t), and VDT(t) all include some

measurement error that we express as uncertainty about their true values.

However, the only quantities whose associated measurement uncertainty has been characterized

are VR(t) (oil volume discharged) and VBU (oil volume burned), and the rate constants k1,..., k8.

The subject-matter experts believe that these indeed are the major sources of uncertainty, and

that the contributions made by measurement error affecting VDT, VCB, VCS, and VOW (all defined

in Table A1.1 on page A1.4) are minor by comparison.

The mass balance equations that, among other outputs, produce an estimate of residual oil VS,

describe a simplified model for the partition of the oil among the several fractions that we

track, shortly after release, and reflect a particular understanding of the physical and chemical

processes that drive that partition.

Other models that could conceivably be entertained, or other choices that could be made about

the values of the rate constants, which some oil spill experts might regard as equally reasonable,

most likely would produce estimates for VS different from those this model produces, even when

applied to the same data. The corresponding dispersion of values of such alternative estimates

would reflect model uncertainty. Although our uncertainty analysis does not reflect this, it

is widely recognized that, in many studies, model uncertainty contributes substantially to the

overall uncertainty.

A1.4 Approach

The uncertainties associated with the volume of oil discharged and with the rate constants in

the mass balance equations are modeled probabilistically and then propagated to the output

quantities using the Monte Carlo simulation method that, in one form or another, has been in

use for many years in many different disciplines [Metropolis and Ulam, 1949], and that has

been codified for use in measurement science in the form of an international standard [Joint

Committee for Guides in Metrology, 2008b].

The probabilistic models used for this purpose serve to describe the imperfect or incomplete

knowledge about the values of a quantity. For example, in Section A1.5 on page A1.7, we

model the uncertainty about the volume discharged on day t as (1+Q)VR(t), where Q denotes

a Gaussian (or “normal”) random variable with mean 0 and standard deviation 0.05. This is one

way of saying that, with high confidence (95 %), the actual volume is within 10 % (= 2×0.05)

of the value estimated for the volume discharged. The same can be said in other ways, too:

for example, by modeling Q as a random variable with a uniform (or rectangular) distribution

2010-OCT-14 — REV. J A1.5
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between −0.1 and +0.1.

The same limitation applies to all the models we shall employ to model uncertainty with. Even

though we believe that they are reasonable for the situations at hand, ultimately they all reflect

arbitrary choices, because the science regulating these matters is not yet sufficiently developed

to identify a single model as necessarily better or more adequate than all the others. In Section A1.9 on page A1.14 we will assess the sensitivity of our conclusions to this, and to other

modeling assumptions.

As noted already in Section A1.3 on page A1.3, and except for VR, VBU, and for the rate constants, the uncertainty associated with the input quantities (VDT, VCB, VCS, and VOW) has not

been quantified. We proceed on the assumption that the measurement uncertainty of these input quantities is negligible by comparison with the uncertainty components attributable to the

imperfect knowledge of the “true” values of the volume discharged and of the rate constants.

Also as noted towards the end of Section A1.3 on page A1.3, model uncertainty is not captured

either, even though we cannot claim that it is negligible.

The Monte Carlo simulation method that we have used to propagate uncertainty from input to

output quantities comprises two steps: first, the generation of multiple scenarios defined by

combinations of conceivable values of all the input quantities; second, the summarization of

the values of the output quantities corresponding to these scenarios. The results reported in

Appendix 2 are based on 100 000 scenarios.

More precisely: for each scenario, we drew (or simulated) values from the probability distributions that model the uncertainty associated with the volume discharged, with the volume burned,

and with the rate constants, and then used these simulated values in the calculations that produce

time series of daily values of the output quantities. The values simulated for the rate constants

vary from scenario to scenario, not from day to day.

ConsiderVS(t), the volume of residual oil on day t, for example. Its counterparts that correspond

to m simulated scenarios are denoted V∗

1,S(t),..., V∗

m,S(t), and we refer to them as replicates of

VS(t).

These m replicates may be summarized in a histogram, or by their average and standard deviation, or, as we do in Figure A1.1 on page A1.7, by means of an interval, ranging from VS,L(t) to

VS,U (t), that brackets a specified proportion of them, say 95 %, which we then regard as a 95 %

confidence interval for the “true” value of VS(t), and interpret as explained in Section A1.3 on

page A1.3. This is the so-called percentile method of constructing bootstrap confidence intervals [Efron and Tibshirani, 1993, §13.3].

The lower and upper envelopes depicted in Figure A1.1 on page A1.7 were obtained by joining

with red lines the points corresponding to VS,L(1), VS,L(2), . . . (for the lower envelope), and to

VS,U (1), VS,U (2), . . . (for the upper envelope).

In addition to these envelopes, we also compute the average of the simulated values of VS(t),

over all m scenarios, for each day t. Owing to the non-linear way in which some of the participating quantities (rate constants or input variables) enter in the mass balance equations, and

given that the probability distributions of some of the rate constants are markedly asymmetrical,

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with one tail much longer than the other, those averages need not coincide with the values that

one obtains by solving the mass balance equations using the expected values of the rate constants, and the measured or estimated (but otherwise undisturbed) values of the input quantities.

Day (Day 1 = April 20th, 2010 / Day 86 = July 14th, 2010)

Residual Oil Volume 

VS (million bbl)

1 30 60 90

0 0.5 1 1.5

Figure A1.1: Volume of residual oil (VS), and approximate 95 % confidence band. The daily

increments VSD(1), ..., VSD(t) that VS(t) is the cumulative sum of, occasionally are negative,

which explains the behavior of the curves around day 61 (June 18, 2010), when a large volume

of oil was burned on the surface, indeed far more than the residual increment for that day. Since

there was no additional discharge after day 86 (July 14, 2010), the curves flatten beyond this

date (not shown).

A1.5 Volume Discharged VR and Volume Burned VBU

The time series VR(1),VR(2),... of volume of oil discharged daily from the well, depicted in

Figure A1.2 on page A1.8, is taken as an input to the mass balance calculations (VR(t) denotes

the volume discharged on day t, with day 1 being April 20th, 2010): in fact, these daily volumes

reflect consensus estimates produced by the Flow Rate Technical Group (an explanation of how

these estimates were derived will be published separately from this report).

The 10 % relative uncertainty that has been associated with the volume discharged is interpreted

as follows: the simulated time series V∗

R(1),V∗R(2),... of the daily discharge is modeled as the

product

V∗

R(t)=(1+Q)VR(t), for t = 1,2,..., (A1.17)

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Day (Day 1 = April 20th, 2010 / Day 86 = July 14th, 2010)

Estimated Daily Discharge (bbl)

1 30 60 90

0 20000 40000 60000

Figure A1.2: Daily volume of oil discharged from the Deepwater (Macondo) well. The best

estimate is represented by the blue line, while the red envelope defines a range of ±10% around

the best estimate. Since there was no additional discharge after day 86 (July 14, 2010), the

curves drop to zero beyond this date (not shown).

where VR(t) denotes the nominal discharge on day t, and Q is a Gaussian random variable with

mean 0 and standard deviation 0.05.

In these circumstances, and with high probability (95 %), the actual discharge should be within

10 % (= 2×0.05) of the nominal discharge; however, the model entertains a small chance (5 %)

that it could deviate by more than ±10% from nominal.

Whatever deviation from nominal is selected for one particular scenario, it is assumed to apply

to all the days of that scenario. For example, if we were 3 % too low in one scenario (meaning

that Q = −0.03), then we would be 3 % too low in every day of that scenario; and if, in another

scenario, we were 7 % too high (Q = 0.07), then we would be 7 % too high in every day of this

scenario.

Over a two and half-month period, about 400 controlled burns of oil on the surface were conducted, which eliminated about 5 % of the oil that was discharged from the well. For the several

burns that typically occurred each day, estimates of the minimum and maximum volume burned

were provided.

We model the uncertainty associated with the total volume burned on a particular day as uniformly distributed between the given minimum and maximum. The corresponding standard

deviation equals the difference between the maximum and the minimum divided by √

12. The

relative standard deviation equals the standard deviation divided by the average of the minimum

and the maximum.

For the purpose of generating stochastic scenarios, these daily relative standard deviations were

summarized by their geometric average, which turned out equal to 9.65 %. Similarly to how we

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treat the volume discharged, we have modeled the uncertainty associated with the daily volume

of oil burned VBU(t) as described in equation A1.18, where the {B(t)} denote independent

Gaussian random variables with mean 0 and standard deviation 0.0965.

V∗

BU(t) = 

1+B(t)



VBU(t), for t = 1,2,.... (A1.18)

Note that, differently from how we modeled the uncertainty of the volume discharged, here we

perturb the estimates of the daily volumes of oil burned, independently from day to day.

A1.6 Probability Models for Rate Constants

The mass balance equations listed above include rate constants k1, ..., k8. Subject-matter

experts have described their state of knowledge about the values of these constants by regarding

them as outcomes of random variables, and by providing location and spread information about

the corresponding probability distributions, listed in Table A1.2. This is a standard statistical

device to express uncertainty assessments, and should not be interpreted as suggesting that

these rate constants are intrinsically random, according to any of the common meanings of

randomness.

RATE CONSTANT DEFINITION ζ0.025 μ ζ0.975

k1 Natural dispersion (subsurface) 0.10 0.20 0.30

k2 Chemical dispersion (subsurface) 2/9 4/9 1.00

k3 Chemical dispersion (surface) 0.05 0.10 0.20

k4 1st day evaporation 0.33 0.37 0.44

k5 2nd day evaporation 0.00 0.04 0.06

k6 Net oil fraction in skimmed oil 0.10 0.20 0.40

k7 Dissolution of dispersed oil 0.05 0.075 0.10

k8 Natural dispersion (surface) 0.00 0.05 0.10

Table A1.2: Rate Constants. Expected values (μ) and 2.5th (ζ0.025) and 97.5th (ζ0.975) percentiles of the probability distributions that model the associated uncertainties.

We have interpreted the information in Table A1.2 as follows: the probability is approximately

95 % that the true value of a rate constant lies in the interval from ζ0.025 to ζ0.975, and has

expected value μ. In addition, we also assume that the possible values for the rate constants are

non-negative, and also that they are no larger than 1, except possibly for k2 and k3.

Once the probability model described below was fitted to the information in Table A1.2, these

assumptions were satisfied (with probability just about equal to 1) for all but k5 and k8. Indeed,

as modeled, both k5 and k8 could be negative with probability about 2.5 %: for this reason, their

distributions were truncated at 0.

For k2, k3, k4, and k6, it so happens that the distance from the expected value (μ) to the 97.5th

percentile (ζ0.975) is greater than or equal to the distance from the expected value (μ) to the

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2.5th percentile (ζ0.025): that is, their probability distributions have the right tail longer than

the left tail (in other words, they are skewed to the right). For k5 the opposite happens, and

its distribution is skewed to the left. The distributions for k1, k7, and k8 are approximately

symmetrical.

Many different probability models are available that describe right skewness, and a few can

describe skewness either to the left or to the right, and still involve no more than three adjustable parameters, which is the number of pieces of information listed for each rate constant

in Table A1.2 on page A1.9.

One of these, which includes the normal distribution as a special case, is the skew normal

distribution described by Azzalini [1985], and implemented by Azzalini [2010] in package sn

for the R environment for statistical programming and graphics [R Development Core Team,

2010].

Another model that we have considered, given that the rate constants have values essentially

between 0 and 1, was the beta distribution. Since the skew normal distribution managed to

reproduce the information available about the rate constants much more accurately than the beta

distribution, we have used the skew normal distribution as a model for all of the rate constants.

The adjustable parameters of the skew normal distribution are the location ξ , the scale ω, and

a shape parameter α that controls skewness. The values we selected for these parameters that

reproduce the entries in Table A1.2 on page A1.9 are listed in Table A1.3 on page A1.11.

We obtained them by taking the following steps (Figure A1.3 depicts the resulting probability

densities):

(a) If ζ0.975 and ζ0.025 denote the 97.5th and 2.5th percentiles of a skew normal distribution,

the ratio (ζ0.975 − μ)/(μ −ζ0.025) (which depends neither on μ nor on ω) is an effective

proxy for the distribution’s skewness. Since the shape parameter α is a monotonically

increasing function of the skewness of the skew normal distribution, we built an interpolating spline [Venables and Ripley, 2002] ψ such that α ≈ ψ



(ζ0.975 − μ)/(μ −ζ0.025)



with negligibly small error for values of α over a suitably wide range.

(b) For each rate constant, first we estimated α using the function ψ just described, by applying it to the values in Table A1.2, and then found values of ξ and ω that minimize



ζ0.025 −F−1

ξ ,ω,α(0.025)

2

+ζ0.975 −F−1

ξ ,ω,α(0.975)

2

+μ −ξ +ω

α

√

2

√

π(1+α2)

2

,

where F−1

ξ ,ω,α denotes the inverse of the cumulative probability distribution function of

the skew normal distribution with location ξ , scale ω, and shape α. The idea here is to

choose values for the adjustable parameters that best reproduce the mean and percentiles

given for each rate constant. The last term in the foregoing expression is determined by

the fact that the expected value of a skew normal distribution with location ξ , scale ω,

and shape α, is ξ +ωα√

2/

√

π(1+α2).

2010-OCT-14 — REV. J A1.10
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RATE CONSTANT ξω α

k1 0.200 0.0500 0.00

k2 0.191 0.356 760

k3 0.0470 0.0681 176

k4 0.332 0.0483 8.44

k5 0.0612 0.0272 −167

k6 0.0940 0.136 176

k7 0.075 0.0128 0.00

k8 0.0500 0.0255 0.00

Table A1.3: Parameters of Probability Distributions for Rate Constants. Values of the location ξ , scale ω, and shape α of the skew normal distributions used to represent the information

available about the values of the rate constants.

A1.7 Uncertainty Analysis

The uncertainty analysis is based on statistics of multiple scenarios, generated by Monte Carlo

simulation, as outlined in Section A1.4 on page A1.5. Each scenario is defined by a value

of the random variable Q introduced in equation A1.17 on page A1.7, by values of the random

variables {B(t)} in equation A1.18 on page A1.9, and by a set of values of the rate constants obtained by sampling the probability distributions fitted as described in Section A1.6 on page A1.9.

The scenario proper consists of the time series of values of the output variables that correspond

to the simulated values of Q and of the rate constants.

The fact that the same sampled value of each rate constant is used for every day of each scenario

effectively induces statistical dependencies between the quantities that are functions of the rate

constants. The uncertainty analysis does take these dependencies into account.

More precisely, we have taken the following steps:

(a) We selected a value for m, the number of scenarios (m = 100 000 for the results in Appendix 2).

(b) For i = 1,...,m:

(b1) We drew a sample of size one from the probability distribution of Q, and used it

to generate a replicate of the time series of the values of oil volume discharged, by

application of equation A1.17 on page A1.7.

(b2) We drew a sample of size one from the probability distribution of each of the random

variables {B(t)}, and used them to generate a replicate of the time series of the

values of oil volume burned, by application of equation A1.18 on page A1.9.

(b3) We drew a sample of size one from the probability distribution fitted to each rate

constant.

2010-OCT-14 — REV. J A1.11
POSSOLO — NIST

0.0 0.1 0.2 0.3 0.4

0 2 4 6 8

k1

Probability density

0.0 0.4 0.8

0.0 0.5 1.0 1.5 2.0

k2

Probability density

0.00 0.10 0.20 0.30

0 2 4 6 8 10 12

k3

Probability density

0.30 0.40 0.50

0 5 10 15

k4

Probability density

0.00 0.04 0.08

0 5 10 15 20 25 30

k5

Probability density

0.0 0.2 0.4

0 1 2 3 4 5 6

k6

Probability density

0.02 0.06 0.10

0 5 10 15 20 25 30

k7

Probability density

0.00 0.05 0.10 0.15

0 5 10 15

k8

Probability density

Figure A1.3: Skew normal probability densities for the rate constants. The vertical red lines

mark the locations of the 2.5th percentile ζ0.025, the expected value μ, and the 97.5th percentile

ζ0.975, which reproduce the values specified by the subject-matter experts, listed in Table A1.2

on page A1.9.

(b4) Using the time series obtained in step b1 on page A1.11, the values for the rate

constants obtained in step b3 on page A1.11, and the values of the input quantities, we used the mass balance equations (equations A1.1 on page A1.2– A1.16 on

page A1.2), to compute the time series of daily values of the output quantities, and

of their cumulative sums (where applicable).

(c) Step b on page A1.11 produced m simulated values of each output quantity on each day,

whose 2.5th and 97.5th percentiles define the lower and upper 95 % probability envelopes

for the value of the output quantity.

2010-OCT-14 — REV. J A1.12
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A1.8 Best-Case and Worst-Case Scenarios

For any particular day t, the corresponding points on the lower and upper bounds (red envelope)

for VS(t), depicted in Figure A1.1 on page A1.7 and whose ordinates are VS,L and VS,U (t),

bracket the true volume of residual oil on that day, with 95 % probability.

Given their fairly extreme nature, we take these endpoints to represent best-case and worst-case

scenarios, even though there are scenarios that are better than that best, and worse than this

worst because the interval from VS,L(t) to VS,U (t) encompasses only the middlemost 95 % of the

m simulated replicates V∗

1,S(t),..., V∗m,S(t) that will have been generated for VS(t).

This approximate, practical characterization of what the “best” and “worst” situations may be

is motivated by the desire to focus the management of the crisis on scenarios that, although

fairly extreme, still are “buffered” by non-negligible tail probabilities that lend them statistical

credence, rather than on speculatively extreme cases.

Now, given VS,L(t) for day t, we wish to find the combinations of values of all the other variables

that correspond to this best-case scenario, and that also satisfy the mass balance equations.

Similarly for VS,U (t), and for the worst-case scenario.

It is not immediately obvious what these combinations of values should be because VS,L(t) does

not necessarily correspond to the case where all the rate constants, and the variables Q and

{B(t)}, simultaneously have their “best” values. This is so for two reasons: first, these variables

are assumed to vary independently of one another from scenario to scenario (an assumption that

is discussed and probed in Section A1.9 on page A1.14); second, such “best” values may yield

a far more extreme, and practically irrelevant, value for the volume of residual oil, than what

we have defined VS,L(t) to be.

Since the volume of residual oil depends on the values of several other quantities, and does

so in the complicated way that the mass balance equations describe precisely, we will write

VS(t) = h(VR(t),...,VNW(t)) to denote this dependence summarily, omitting reference to Q, to

the {B(t)}, and to the rate constants. The function h subsumes all the mathematical manipulations that these quantities undergo finally to produce VS(t).

Our goal is to find the most likely values of VR(t), ..., VNW(t) that, once processed through h,

yield VS,L(t) as the volume of residual oil left on day number t, and that satisfy the mass balance

equations. (Recall that VS,L(t) denotes the 2.5th percentile of the set of m simulated values of

the volume of residual oil, as defined in Step c on page A1.12.) And then to do similarly for

VS,U (t), the 97.5th percentile.

Taken together, those most likely values will then define the best-case scenario. (And similarly

the worst-case scenario.) Since this amounts to “inverting” the function h, our goal can be fairly

described as scenario inversion. The problem, of course, is that h is not invertible in the strict

sense of mathematics because there are many combinations of values of VR(t),..., VNW(t) that

yield the same value of VS(t).

To perform a satisfactory scenario inversion that yields the best-case scenario, we start from

the realization that such scenario will correspond to some combination of values of VR(t), ...,

VNW(t) that, once processed through h, produces a value for VS(t) that is close to VS,L(t).

2010-OCT-14 — REV. J A1.13
POSSOLO — NIST

To find such combinations of values of the arguments of the function h, we selected a suitably

small positive value ε, and searched through the set of simulated values of VR(t), ..., VNW(t)

for those that satisfy |h(V∗

i,R(t), ..., V∗i,NW(t)) −VS,L(t)| < ε. Let SL denote the resulting subset

of combinations of values of VR(t),...,VNW(t).

Since SL comprises many such combinations, the problem remains of selecting one that is

representative of the best-case scenario. We did this by finding, for each output variable of

interest that the volume of residual oil is a function of, its average over those of its values that

are present in SL.

We also verified that these averages indeed are representative of the values they subsume, by

inspecting their locations in histograms of these values. This process was repeated, using a

comparably defined set SU for the worst-case scenario. These best-case and worst-case scenarios for July 14, 2010 (day t = 86) are characterized quantitatively, and depicted graphically, in

Appendix 2.

A1.9 Sensitivity Analysis

As described in Section A1.7 on page A1.11, the uncertainty analysis is based on simulated

scenarios, and each of these scenarios is defined by a time series of simulated values of discharged oil, by a time series of volumes of burned oil, and by a set of simulated values of the

rate constants.

These simulations are based on particular models (probability distributions) for all the participating quantities: although reasonable, other models could also reasonably be entertained. For

example, we mentioned already, in Section A1.4 on page A1.5, that the variable Q that appears

in equation A1.17 on page A1.7, and which we assumed had a particular Gaussian distribution,

conceivably might also have been modeled as having a particular uniform distribution instead.

The same applies to the variables {B(t)} relating to the volume of oil burned.

One assumption that we have made but that we have not yet discussed is that the random variables modeling the rate constants are stochastically independent. Although this represents an

approximation suggested by the subject-matter experts, it is not only possible but indeed likely

that some of them should be interrelated.

For example, it is reasonable to expect that the rates of evaporation, k4 and k5, on the first

and second days after discharge of a fresh batch of oil into the sea should vary together over

time, because they may be driven by common factors. Similarly, k2 and k3, the rate constants

that describe the effectiveness of chemical dispersion under and on the sea surface, may be

interrelated for similar reasons.

To ascertain the sensitivity of our results to our modeling assumptions, we have focused on the

volumes of residual oil VS, and performed an alternative uncertainty analysis assuming that the

correlation coefficient between k2 and k3, and between k4 and k5, was √

0.5, rather than 0. This

assumption about the correlations means that the potential variability in k2 over time would

“explain” about 50% of the corresponding variability in k3 (and similarly for k4 and k5).

2010-OCT-14 — REV. J A1.14
POSSOLO — NIST

To impose correlations between these two pairs of rate constants, we used a Gaussian copula

[Nelsen, 2006, Possolo, 2010], which is a standard technique for this purpose. This creates a

multivariate probability distribution with the correlations specified (√

0.5 between k2 and k3 and

also between k4 and k5, and 0 between all the other possible pairs), and such that the individual

rate constants, when taken each one by itself, still has the same skew normal distribution that

was fitted as described in Section A1.6 on page A1.9.

We have also studied the sensitivity of the results to the assumptions we made about the random

variable Q, introduced in equation A1.17 on page A1.7, and about the random variables {B(t)}

introduced in equation A1.18 on page A1.9. The former drives the variability between simulated

scenarios, of the time series of volume of oil discharged from the well. The latter models the

measurement uncertainty associated with the volume of oil that has been burned. Originally we

assumed that Q has a Gaussian distribution with mean 0 and standard deviation 0.05. For the

sensitivity analysis we have assumed instead that Q is uniformly distributed between −0.1 and

+0.1. Similarly for the {B(t)}.

The results of this sensitivity analysis, depicted in Figure A1.4 on page A1.16, show that the

presence of such statistical dependence as we have entertained for selected pairs of rate constants, possibly in conjunction with a different model for Q and for the {B(t)}, widens the

uncertainty envelope for the time series of values of VS. However, it changes the upper bound

hardly at all, and depresses the lower confidence envelope, thus suggesting that the assumptions

we made originally are the more conservative ones among the alternatives we have considered.

2010-OCT-14 — REV. J A1.15
POSSOLO — NIST

Day (Day 1 = April 20th, 2010 / Day 86 = July 14th, 2010)

Residual Oil Volume 

VS (million bbl)

1 30 60 90

0 0.5 1 1.5

Figure A1.4: Sensitivity Analysis. Volume of residual oil (VS) and variants of the approximate 95 % confidence band, corresponding to assuming that k2 and k3, as well as k4 and k5,

are correlated (dashed, green line), or that, in addition to this, the random variable Q and the

random variables {B(t)}, introduced in equation A1.17 on page A1.7 and in equation A1.18 on

page A1.9, respectively, are uniformly distributed, rather than Gaussian (dotted, brown line).

These alternatives mostly reduce the lower confidence envelope, thus suggesting that the assumptions we made originally are the more conservative ones among the alternatives we have

considered. Since there was no additional discharge after day 86 (July 14, 2010), the curves

flatten beyond this date (not shown).

2010-OCT-14 — REV. J A1.16
POSSOLO — NIST

References

A. Azzalini. A class of distributions which includes the normal ones. Scandinavian Journal of

Statistics, 12:171–178, 1985.

A. Azzalini. R package sn: The skew-normal and skew-t distributions (version 0.4-15). Università di Padova, Italia, 2010. URL http://azzalini.stat.unipd.it/SN.

B. Efron and R. J. Tibshirani. An Introduction to the Bootstrap. Chapman & Hall, London, UK,

1993.

Joint Committee for Guides in Metrology. Evaluation of measurement data —

Guide to the expression of uncertainty in measurement. International Bureau

of Weights and Measures (BIPM), Sèvres, France, September 2008a. URL

http://www.bipm.org/en/publications/guides/gum.html. BIPM, IEC, IFCC,

ILAC, ISO, IUPAC, IUPAP and OIML, JCGM 100:2008, GUM 1995 with minor corrections.

Joint Committee for Guides in Metrology. Evaluation of measurement data — Supplement 1 to

the “Guide to the expression of uncertainty in measurement” — Propagation of distributions

using a Monte Carlo method. International Bureau of Weights and Measures (BIPM), Sèvres,

France, 2008b. URL http://www.bipm.org/en/publications/guides/gum.html.

BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP and OIML, JCGM 101:2008.

Joint Committee for Guides in Metrology. International vocabulary of metrology — Basic and general concepts and associated terms (VIM). International Bureau of Weights and Measures (BIPM), Sèvres, France, 2008c. URL

http://www.bipm.org/en/publications/guides/vim.html. BIPM, IEC, IFCC,

ILAC, ISO, IUPAC, IUPAP and OIML, JCGM 200:2008.

N. Metropolis and S. Ulam. The Monte Carlo Method. Journal of the American Statistical

Association, 44:335–341, September 1949.

R. B. Nelsen. An Introduction to Copulas. Springer, New York, NY, 2nd edition, 2006. Corrected 2nd. printing, 2007.

A. Possolo. Copulas for uncertainty analysis. Metrologia, 47:262–271, 2010.

R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2010. URL

http://www.R-project.org. ISBN 3-900051-07-0.

W. N. Venables and B. D. Ripley. Modern Applied Statistics with S. Springer, New York, Fourth

edition, 2002. URL http://www.stats.ox.ac.uk/pub/MASS4. ISBN 0-387-95457-0.

2010-OCT-14 — REV. J A1.17
Appendix 2

A2 Oil Budget Calculator Web-based Tool

Section Authors: R. Sky Bristol1, David Mack2, Tim Kern3, Jeff Allen2

 1. U.S. Geological Survey, Geospatial Information Office, Denver, Colorado

 2. Arctic Slope Regional Corporation (contracted to USGS), Fort Collins, Colorado

 3. U.S. Geological Survey, Fort Collins Science Center, Fort Collins, Colorado

Background

One of the most visible aspects of the Oil Budget Calculator is the technological 

application that was created as a response tool for personnel with National Incident 

Command (NIC). The first iteration of an oil budget calculator was built in a Microsoft 

Excel spreadsheet by U.S. Coast Guard (USCG) staff with the National Response Center. 

This tool captured simple daily variables from the response effort (e.g., amount of oil 

skimmed from the surface) and instantiated basic formulas for a best- and worst-case 

scenario based on calculations used in previous spill events. The USCG requested help 

from the Interagency Solutions Group established from NIC for help with the spreadsheet 

tool in terms of reporting and characterizing the results along with help from a science 

team in the underlying calculations and assumptions.

To address this need, the Interagency Solutions Group consulted a U.S. Geological 

Survey (USGS) computer science team on the tool itself and National Oceanic and 

Atmospheric Administration (NOAA) scientific support personnel to engage a team 

of oil fate and behavior scientists. After discussing the basic requirements for the final 

tool needed by NIC staff, in particular the need for ease of access without new software 

requirements, the USGS team opted for the creation of a Web-based application. 

This section of the report discusses the engineering of the Web-based system and its 

relationship to the scientific model developed by partners in NOAA and the National 

Institute of Standards and Technology.

Requirements for the Calculator

Initial requirements for the purpose and use of the calculator were received in phone 

conversation with NIC staff on June 17, 2010 (National Incident Command Personnel, 

2010). Further requirements and requests for features in the application continued to 

evolve through the course of the project as NIC staff, scientific support personnel, and 

others began using reports from the tool in response activities. All technical requirements, 

features, and associated tasks for the tool were captured in an online project management 

system used by the USGS team as a way to document the evolution of the application and 

a record for any future activities.

 BRISTOL — MACK — KERN — ALLEN ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A2.1 
A high-level discussion of requirements is summarized below:

• The tool needed to be easily accessible by NIC staff and NOAA scientific support 

personnel providing input to response activities, not requiring any additional 

desktop software or a major learning period.

• The tool needed to be secure so that only authorized personnel could access the 

system, and specific NIC staff needed the ability to specify who could perform 

specific functions in the system.

• Only certain personnel could be authorized to enter data into the Daily Values 

Database, and all data needed to be logged so that individual entries could be 

tracked to the user who entered them and when they were entered.

• Only certain personnel could be authorized to access daily and summary reports 

from the system, and those accesses needed to be logged.

• Only certain personnel involved with scientific support could be authorized to 

change the underlying calculations and assumptions used to calculate the oil 

budget.

• The tool needed to include the ability to view the daily and cumulative total figures 

and associated charts for any day in the incident.

• The tool needed to provide a print feature so that authorized report users could 

output a daily executive summary used by response and communications 

personnel.

• The tool needed a feature to output all daily values entered into the system in a 

spreadsheet table so that the figures could be shared and viewed independently.

In total, 98 separate features or improvements were captured and tracked in the project 

management application used for the work during the course of the engineering and 

development effort.

Scenarios

The oil budget calculation needed to incorporate the concept of scenarios to include 

at least a theoretical best and worst case scenario in terms of the amount of oil being 

discharged and the relative effectiveness of response activities. A third scenario used 

mean values to provide a probable case, or at least a baseline set of numbers predicated 

on the best available knowledge of oil fate and behavior. The requirement to essentially 

bound the problem and effectiveness of the response played a critical role in the design of 

the application and reports.

During the period when the well was still discharging oil, the Oil Budget Calculator used 

a simplistic approach to calculating and presenting scenarios that used only the difference 

in flow rate estimates as the variable between scenarios. After continued examination of 

the tool with the statistical experts working on the oil budget model, the final iteration 

of the calculator (version 2.0) included a more rigorous presentation of the scientific 

uncertainty in the theoretical best- and worst-case scenarios. This version of the tool 

 BRISTOL — MACK — KERN — ALLEN ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A2.2 
 BRISTOL — MACK — KERN — ALLEN ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A2.3 

incorporated the upper and lower bounds of uncertainty for the effectiveness of individual 

response activities in addition to the uncertainty bounds for the flow rate.

Components of the Calculator

The tool consisted of the following basic elements:

• Daily Variables – daily variables entered by USCG staff with the NIC

• Global Variables – global flow rate values based on government estimates and 

formulas stored as variables to produce cumulative totals from the oil budget 

calculating model

• Modeling Application – technological application to pass daily variables to a 

scientific model that encapsulated assumptions and statistical calculations to 

produce daily oil budget figures

• Reporting Application – executive summary reporting component that presented 

daily oil budget numbers and charts

These components were brought together as a Web-based tool within a Java application 

framework called Grails (Grails Community, 2010) and several existing infrastructural 

elements already in place within the USGS network and able to be tuned toward 

the application. Front-end Web services were provided on an Apache Web server 

platform (Apache Project, 2010). Database elements were stored within a PostgreSQL

(PostgreSQL, 2010) environment using Hibernate (JBoss Community, 2010) for data 

object and relational mapping and persistence. Authentication services were provided by 

a USGS framework founded on a technology called Java Open Single Sign-On (JOSSO 

Community, 2010). The Oil Budget Calculator model was written in the R scientific 

programming language (R Project, 2010) using a dedicated virtual machine set up as an 

“R Application Server.” All application components were configured to run on servers 

using the Red Hat (Red Hat, 2010) version of the Linux operating system.

Daily Variables

A simple database table managed through the Web application housed variables of 

the response effort entered by NIC personnel. In some cases, the values were direct 

measurements, and in others the values were the results of established standard 

measurement protocols used by the USCG for incident response (National Incident 

Command Personnel, 2010).

• Oil Collected via RITT/Top Hat (barrels) – Values for the amount of oil recovered 

by the vessels Helix Producer, Discoverer Enterprise and the Q4000 and reported 

by British Petroleum.

• Oil Burned (barrels) – Total oil burned using standard rates from the American 

Society for Testing and Materials (American Society for Testing and Materials, 

2006).

• Oily Water Collected (barrels) – Total of estimated measurements from multiple 

collection methods and contractors.
 BRISTOL — MACK — KERN — ALLEN ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A2.4 

• Subsurface Dispersants Used (gallons) – Direct report from dispersant operations.

• Surface Dispersants Used (gallons) – Direct report from dispersant operations.

Later in the response effort, once inland recovery of oil reached a point where total 

numbers could be captured, a value of tons of oily debris was added to the daily variables 

database as a reporting value. This figure was not used in the oil budget calculation but 

was included in the reporting tool. Based on the requirement to have an output of daily 

variables in a spreadsheet format, a feature was added to export all daily values as an 

Excel spreadsheet file.

Global Variables

The Oil Budget Calculator went through three distinct iterations based on the best 

government estimates of flow/discharge rate from the leaking well. All values were 

provided to the Oil Budget Calculator team from the Deepwater Horizon Flow Rate 

Technical Group and were refined over time as more and better information became 

available. The need to include multiple scenarios resulted in flow estimates of high, low, 

and mean being stored in the global variables portion of the database and used in the 

calculations.

The global variables data also contained several formulas stored as a simple formula 

language and used in the final calculations of cumulative totals on the output from the oil 

budget model. Global variables were considered the domain of the oil fate and behavior 

science team building the model and were only editable by application administrators.

Modeling Application

The initial iteration of the Oil Budget Calculator employed a very simple model based 

directly on the individual calculations provided in an interim report. These formulas 

were stored in the application as global variables and acted directly on daily variables, 

triggered whenever daily variables were modified in any way. Initial reports from this 

method shared amongst the development and science teams prior to production release of 

the application indicated a need for an increased level of statistical sophistication in the 

model.

Following engagement of personnel from the National Institute of Standards and 

Technology (NIST), the oil budget calculations were refactored into a statistical model 

and codified using the R scientific programming language (R Project, 2010). The 

development team engineered an application server environment to house and run the 

R processing engine, receive values from the application maintaining daily variables, 

trigger the model to run calculations, and ingest calculated output variables from the 

model back into the final database.
 BRISTOL — MACK — KERN — ALLEN ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A2.5 

Reporting Application

The reporting toolset was built within the same Grails application framework (Grails 

Community, 2010). The executive summary report presented two to three scenarios over 

the course of the application. Initially, the application presented only a high flow scenario, 

based on the government’s maximum discharge estimate, and a low flow scenario, based 

on the government’s minimum discharge estimate. Personnel from NIC later requested 

the addition of a scenario based on mean discharge as a single value report that could be 

used in certain planning and communication processes. For each scenario, the reporting 

tool provided a table of the calculator output of daily and cumulative totals.

The report also included charting generated using the JFreeChart Java library (JFree.org, 

2010). The Oil Budget pie chart provided a “response estimate” representation of the total 

amount of oil released over time for the actual government estimates of discharge as well 

as a higher flow and lower flow estimate from the ±10% uncertainty factor, the relative 

amounts of oil recovered or dispersed by both natural and response methods, and the total 

remaining oil calculated by the oil budget model. A stylized stacked bar chart was used in 

place of the pie chart in earlier iterations of the tool.

The reporting application included a feature to select any day in the incident and view 

the full report for the day, showing cumulative totals at that point. The reports could 

be viewed online via a Web browser and exported in Adobe Acrobat (PDF) format. 

Charts could also be viewed individually as image files for incorporation into alternate 

presentations.

Evolution of the Oil Budget Calculator

The Oil Budget Calculator online tool went through several distinct iterations recorded 

as versions of the tool throughout its lifecycle. The iterations of the tool used for the 

incident response activity were recorded as 1.x versions through the final version 1.3.1. 

This first generation of the tool was used for the public release of information about the 

Oil Budget Calculator (NOAA, 2010).

Based on input from the science team responsible for the final Oil Budget Calculator 

report to National Incident Command and a significant reworking of the scientific 

model, a second generation (version 2.0) of the tool was prepared. The major difference 

between the two generations of the tool was the calculation and characterization of 

scientific uncertainty for the variable elements in each of three scenarios. Version 1.x of 

the tool used only a variable discharge rate based on the ±10% uncertainty in the official 

government estimates of discharge produced by the Deepwater Horizon MC252 Flow 

Rate Technical Group - 62,200 bbl on April 22, 2010 to 52,700 bbl on July 14, 2010 

(Lehr and others, 2010) – to produce reports for three scenarios (official government 

estimates, high flow scenario, and low flow scenario). Version 2 of the tool used 

calculations of scientific uncertainty for individual elements of the report to produce 
 BRISTOL — MACK — KERN — ALLEN ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A2.6 

expected, best case, and worst case scenarios as discussed in Appendix 1 of the Oil 

Budget Calculator report.

Version 2 of the online tool was not used in the actual response effort. Rather it was 

prepared as the next generation of the tool and presented to National Incident Command 

as an improvement on the calculations based on increased information and knowledge 

about the Deepwater Horizon incident provided by the science team. It was meant to help 

inform future development of the response tool for other incidents.

The following sections present report elements from the two generations of the online 

tool captured for July 14, 2010 – the last day in the incident where reported values for 

skimming and burning oil were recorded.

Oil Budget Calculator – Version 2.0

Table A2.1 comes directly from version 2.0 of the online tool using the calculations 

described in Appendix 1 of the report. The colors next to the individual calculated values 

correspond to colors in the charts that are part of the generated oil budget report. Two of 

the charts showing cumulative total oil and percentage of total are provided in the body 

of the report for the same day, July 14, shown in the table.

Table A2.1. Oil Budget Calculator cumulative totals for the official estimated discharge rate and 

expected scenario (62,200 bbl on April 22, 2010 to 52,700 bbl on July 14, 2010) through July 14, 2010 

(from version 2.0 of the Oil Budget Calculator)

* All unlabeled values in barrels; rounded to 2 significant digits
 BRISTOL — MACK — KERN — ALLEN ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A2.7 

Oil Budget Calculator – Version 1.x

Table A2.2 and figure A2.1 show the cumulative oil budget from the version of the online 

tool used in the response effort. The response tool used a numbered versioning scheme 

of 1.x, ending with 1.3.1. This version of the tool and the table and figure included here 

provide background documentation on the evolution of the tool. The previous section on 

version 2.0 represents a significant improvement in the Oil Budget Calculator based on 

the substance of this report.

Table A2.2. Oil Budget Calculator cumulative totals for the official estimated discharge rate (62,200 

bbl on April 22, 2010 to 52,700 bbl on July 14, 2010) through July 14, 2010 (from version 1.3.1 of the 

Oil Budget Calculator)

* All unlabeled values in barrels.

Table A2.2 comes directly from the online tool. The colors next to the individual 

calculated values correspond to colors in the charts that were part of the generated oil 

budget report. Significant refinements occurred between the version of the tool used 

during the response the new version presented in the final report. Most notably for the 

table of values:

• The term “Remaining” was changed to “Other Oil” to help alleviate an impression 

that the other calculated values in the report might not remain in the environment 

at all.

• Numeric figures were rounded to two significant digits to better represent the level 

of scientific uncertainty inherent in the calculations.
 BRISTOL — MACK — KERN — ALLEN ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A2.8 

Figure A2.1. Cumulative percentage of oil from the calculator for natural processes and response 

activities using the official estimated discharge rate (62,200 bbl on April 22, 2010 to 52,700 bbl on 

July 14, 2010) through July 14, 2010 (from version 1.3.1 of the Oil Budget Calculator)

Figure A2.1 comes directly from the online tool. Significant refinements occurred 

between the charts included in the version of the tool used during the response and the 

new version presented in the final report. Most notably, the single pie chart that presented 

only one possible scenario at a time in three separate images was replaced with a bar 

chart that presented best case, expected, and worst case scenarios in one graphic to help 

show the scientific uncertainty inherent in the model. These charts are included in the 

body of the report.

Application Security and Logging

Authentication for the application was provided using an existing framework in the 

USGS based on JOSSO technology (JOSSO Community, 2010) and a directory service of 

user accounts. Users were placed into one of three roles within the application:

• Report Readers – Access to view and print reports for any day in the incident

• Data Entry Personnel – Access to view and print reports and to enter and maintain 

daily variables

• Administrators – Each of the above rights plus access to enter and maintain global 

variables
 BRISTOL — MACK — KERN — ALLEN ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A2.9 

All accesses to the online application were logged via the authentication system. Changes 

to daily and global variables were logged separately and available for viewing within the 

application. Printed reports, generated as a PDF download, were stamped with a user ID 

and date in the footnote of every page.

All versions of the Web application and R statistical model were tracked as they were 

deployed onto the technological framework using a versioning system called Subversion 

(Apache.org, 2010). Subversion logs and underlying Web site access logs are stored in a 

secure data storage framework and available as necessary.

Future of the Oil Budget Calculator Web Application

The overall utility of the online application proved a valuable tool for the response and 

communication efforts as reported by NIC and NOAA staff involved directly in response 

activities. The first generation of the tool was still in use for the Deepwater Horizon 

incident at the time of writing, and USCG staff indicated a desire to continue discussions 

about either ongoing partnership with the USGS or technology transfer to a USCG 

facility. 

Following the production of this report and major input by the oil fate and behavior 

science team, version 2.0 of the Oil Budget Calculator was prepared with modifications to 

the calculator model and major changes to the presentation of scientific uncertainty in the 

output reports. The scientific reasoning behind the calculations is discussed extensively in 

the report, and the new model is laid out within Appendix 2. The new model resulted in 

significant differences in the final cumulative totals and associated visualizations with a 

higher overall degree of confidence based on the extensive review of the model and input 

by the science team preparing the Oil Budget Calculator report.

The technological framework developed for the calculator, including the separation 

between data entry, model calculations, and reporting was created to be extensible 

and scalable to other incidents using variations on the model and to entirely different 

applications of similar concepts. Further discussions with NIC personnel will determine 

the eventual fate of the tool beyond the Deepwater Horizon incident.

References

American Society for Testing and Materials. 2006. ASTM D635 - 10 Standard Test 

Method for Rate of Burning and/or Extent and Time of Burning of Plastics in a 

Horizontal Position. ASTM.

Apache Project. 2010. Retrieved August 20, 2010 from Apache Web Server Web Site: 

http://httpd.apache.org/

Apache.org. 2010. Retrieved August 20, 2010 from Apache Subversion: 

http://subversion.apache.org/
 BRISTOL — MACK — KERN — ALLEN ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A2.10

Grails Community. 2010. Retrieved August 20, 2010 from Grails Web Site: 

http://www.grails.org/

JBoss Community. 2010. Retrieved August 20, 2010 from Hibernate Web Site: 

http://www.hibernate.org/

JFree.org. 2010. JFreeChart. Retrieved August 22, 2010 from 

http://www.jfree.org/jfreechart/

JOSSO Community. 2010. Retrieved August 20, 2010 from JOSSO - Java Open Single 

Sign-On Project Home: http://www.josso.org/

Lehr, Bill and members of the Deepwater Horizon MC252 Gulf Incident Flow Rate 

Technical Group. July 31, 2010. Email correspondence to the Oil Budget Calculator 

engineering team with official government estimates for Deepwater Horizon MC252 

discharge rate. (Official report from the Flow Rate Technical Group on the calculations 

and assumptions for discharge rates being prepared at time of writing.)

National Incident Command Personnel. 2010. Personal communication with National 

Incident Command staff on the requirements for the Deepwater Horizon Oil Budget 

Calculator; notes captured in USGS Jira project management system. (R. S. Bristol, 

Interviewer)

National Oceanic and Atmospheric Administration (NOAA). August 4, 2010. Federal 

Science Report Details Fate of Oil from BP Spill. 

http://www.noaanews.noaa.gov/stories2010/20100804_oil.html

PostgreSQL. 2010. Retrieved August 20, 2010 from PostgreSQL Web Site: 

http://www.postgresql.org/

R Project. 20-August). Retrieved August 20, 2010 from The R Project for Statistical 

Computing: http://www.r-project.org/

Red Hat. 2010. Retrieved August 20, 2010 from Red Hat Linux Web Site: 

http://www.redhat.com/

Disclaimer: Any use of trade, product, or form names is for descriptive purposes only 

and does not imply endorsement by the U.S. Government.
 _______________________________________________________________________________________________________________

Appendix 3

A3 Data Input Used by Calculator

 _____________________________________________________________________________________________________________________

Daily Variables A3.1 Daily Variables Page 1

Date

Government 

Estimate of 

Discharge

Inland 

Recovery

Oil 

Burned

Oil Collected 

via 

RITT/TopHat

Oily Water 

Collected

Subsurface 

Dispersants

Surface 

Dispersants

VRG IR VBU VDT VOW VCBg VCSg

bbls tons bbls bbls bbls gallons gallons

04/20/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

04/21/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

04/22/2010 62200.00 0.00 0.00 0.00 0.00 0.00 1701.00

04/23/2010 62100.00 0.00 0.00 0.00 1630.00 0.00 0.00

04/24/2010 61900.00 0.00 0.00 0.00 155.00 0.00 0.00

04/25/2010 61800.00 0.00 0.00 0.00 0.00 0.00 9818.00

04/26/2010 61700.00 0.00 0.00 0.00 7832.00 0.00 14486.00

04/27/2010 61600.00 0.00 0.00 0.00 18557.00 0.00 27078.00

04/28/2010 61500.00 0.00 95.00 0.00 3306.00 0.00 42143.00

04/29/2010 61400.00 0.00 0.00 0.00 3245.00 0.00 40913.00

04/30/2010 61300.00 0.00 0.00 0.00 1427.00 2196.00 4900.00

05/01/2010 61200.00 0.00 0.00 0.00 992.00 0.00 11653.00

05/02/2010 61000.00 0.00 0.00 0.00 0.00 3399.00 0.00

05/03/2010 60900.00 0.00 0.00 0.00 0.00 5812.00 0.00

05/04/2010 60800.00 0.00 0.00 0.00 9996.00 7580.00 34273.00

05/05/2010 60700.00 0.00 880.00 0.00 0.00 0.00 49575.00

05/06/2010 60600.00 0.00 6155.00 0.00 21452.00 0.00 28770.00

05/07/2010 60500.00 0.00 2010.00 0.00 13788.00 0.00 7270.00

05/08/2010 60400.00 0.00 0.00 0.00 26189.00 0.00 41690.00

05/09/2010 60200.00 0.00 0.00 0.00 33865.00 0.00 55932.00

05/10/2010 60100.00 0.00 0.00 0.00 8957.00 9460.00 56220.00

05/11/2010 60000.00 0.00 0.00 0.00 0.00 2100.00 7940.00

05/12/2010 59900.00 0.00 0.00 0.00 0.00 0.00 39710.00

05/13/2010 59800.00 0.00 0.00 0.00 0.00 0.00 41620.00

05/14/2010 59700.00 0.00 0.00 0.00 0.00 0.00 44031.00

05/15/2010 59600.00 0.00 0.00 500.00 0.00 7222.00 14208.00

05/16/2010 59400.00 0.00 0.00 900.00 6987.00 6300.00 670.00

05/17/2010 59300.00 0.00 3175.00 1030.00 23873.00 7980.00 13213.00

05/18/2010 59200.00 0.00 785.00 1200.00 13903.00 3450.00 12386.00

05/19/2010 59100.00 0.00 27350.00 3000.00 11849.00 4879.00 3352.00

05/20/2010 59000.00 0.00 11150.00 2200.00 13600.00 14151.00 1.00

05/21/2010 58900.00 0.00 0.00 2175.00 13126.00 14400.00 29892.00

05/22/2010 58800.00 0.00 0.00 1361.00 12025.00 14130.00 52946.00

05/23/2010 58700.00 0.00 950.00 1120.00 14108.00 14738.00 18104.00

05/24/2010 58500.00 0.00 2450.00 0.00 4135.00 14494.00 630.00

05/25/2010 58400.00 0.00 350.00 6078.00 3943.00 12925.00 200.00

05/26/2010 58300.00 0.00 450.00 2596.00 7681.00 11529.00 7752.00

05/27/2010 58200.00 0.00 700.00 0.00 9936.00 14347.00 1029.00

05/28/2010 58100.00 0.00 0.00 0.00 10110.00 13670.00 18445.00

05/29/2010 58000.00 0.00 1200.00 0.00 27319.00 14588.00 2900.00

05/30/2010 57900.00 0.00 2550.00 0.00 10471.00 13073.00 17631.00

05/31/2010 57700.00 0.00 16550.00 0.00 11845.00 13936.00 11686.00

06/01/2010 57600.00 0.00 7900.00 0.00 11719.00 12201.00 0.00

06/02/2010 57500.00 0.00 700.00 0.00 18241.00 8073.00 3375.00

Appendix 3: Data Input Used by 

Calculator
Daily Variables Page 2

Date

Government 

Estimate of 

Discharge

Inland 

Recovery

Oil 

Burned

Oil Collected 

via 

RITT/TopHat

Oily Water 

Collected

Subsurface 

Dispersants

Surface 

Dispersants

VRG IR VBU VDT VOW VCBg VCSg

bbls tons bbls bbls bbls gallons gallons

06/03/2010 60000.00 0.00 0.00 0.00 4833.00 17753.00 6200.00

06/04/2010 59900.00 0.00 0.00 6087.00 8912.00 20655.00 13701.00

06/05/2010 59800.00 0.00 0.00 10496.00 8568.00 20306.00 125.00

06/06/2010 59700.00 0.00 0.00 11119.00 3123.00 13937.00 0.00

06/07/2010 59500.00 0.00 850.00 14842.00 11754.00 14732.00 10744.00

06/08/2010 59400.00 0.00 5450.00 15006.00 5350.00 13763.00 8324.00

06/09/2010 59300.00 0.00 3500.00 15816.00 12802.00 12112.00 2100.00

06/10/2010 59200.00 0.00 350.00 15402.00 9658.00 10163.00 5872.00

06/11/2010 59100.00 0.00 0.00 15554.00 18510.00 8447.00 14305.00

06/12/2010 59000.00 0.00 7550.00 15039.00 14459.00 4852.00 10356.00

06/13/2010 58900.00 0.00 16600.00 15208.00 12383.00 9916.00 36012.00

06/14/2010 58800.00 0.00 4300.00 15421.00 6083.00 9800.00 10741.00

06/15/2010 58600.00 0.00 1000.00 10448.00 11891.00 11726.00 2768.00

06/16/2010 58500.00 0.00 20150.00 18227.00 16995.00 8777.00 13593.00

06/17/2010 58400.00 0.00 13300.00 25295.00 9185.00 5763.00 12423.00

06/18/2010 58300.00 0.00 59550.00 24552.00 16436.00 9148.00 15711.00

06/19/2010 58200.00 0.00 2300.00 21041.00 12713.00 16911.00 8510.00

06/20/2010 58100.00 0.00 600.00 23291.00 11335.00 14070.00 19576.00

06/21/2010 58000.00 0.00 17050.00 25836.00 25583.00 14233.00 11217.00

06/22/2010 57800.00 0.00 0.00 27097.00 8828.00 9793.00 2008.00

06/23/2010 57700.00 0.00 0.00 16866.00 4660.00 9891.00 5099.00

06/24/2010 57600.00 0.00 0.00 23735.00 9875.00 12871.00 21088.00

06/25/2010 57500.00 0.00 0.00 24548.00 14530.00 12540.00 4633.00

06/26/2010 57400.00 0.00 0.00 22758.00 17462.00 12654.00 23022.00

06/27/2010 57300.00 0.00 0.00 24455.00 18641.00 11558.00 6626.00

06/28/2010 57200.00 0.00 0.00 23400.00 0.00 13174.00 0.00

06/29/2010 57100.00 0.00 0.00 25223.00 50.00 11560.00 324.00

06/30/2010 56900.00 0.00 0.00 23079.00 290.00 13609.00 0.00

07/01/2010 56800.00 0.00 0.00 25154.00 284.00 10558.00 17852.00

07/02/2010 56700.00 0.00 0.00 25291.00 74.00 11065.00 14210.00

07/03/2010 56600.00 0.00 0.00 25198.00 1966.00 11698.00 432.00

07/04/2010 56500.00 0.00 0.00 24960.00 854.00 10429.00 3079.00

07/05/2010 56400.00 0.00 0.00 24982.00 7417.00 11688.00 803.00

07/06/2010 56300.00 0.00 0.00 24761.00 218.00 11655.00 473.00

07/07/2010 56100.00 0.00 0.00 24578.00 156.00 11770.00 1245.00

07/08/2010 56000.00 0.00 0.00 24379.00 26.00 11512.00 0.00

07/09/2010 55900.00 0.00 8300.00 24792.00 12140.00 10748.00 0.00

07/10/2010 55800.00 0.00 9850.00 15199.00 25924.00 13210.00 0.00

07/11/2010 55700.00 0.00 5300.00 8235.00 18296.00 15420.00 0.00

07/12/2010 53000.00 0.00 0.00 8302.00 9397.00 14038.00 0.00

07/13/2010 52900.00 0.00 1400.00 17064.00 9998.00 13997.00 999.00

07/14/2010 52700.00 0.00 1100.00 12843.00 25551.00 13746.00 0.00

07/15/2010 0.00 0.00 350.00 9307.00 10793.00 8391.00 0.00

07/16/2010 0.00 0.00 1100.00 0.00 14135.00 0.00 0.00

07/17/2010 0.00 0.00 0.00 0.00 7601.00 0.00 0.00

07/18/2010 0.00 0.00 0.00 0.00 5122.00 0.00 0.00

07/19/2010 0.00 0.00 100.00 0.00 3113.00 0.00 200.00

 ____________________________________________________________________________________________________________________

 _____________________________________________________________________________________________________________________

Daily Variables A3.2 
Daily Variables Page 3

Date

Government 

Estimate of 

Discharge

Inland 

Recovery

Oil 

Burned

Oil Collected 

via 

RITT/TopHat

Oily Water 

Collected

Subsurface 

Dispersants

Surface 

Dispersants

VRG IR VBU VDT VOW VCBg VCSg

bbls tons bbls bbls bbls gallons gallons

07/20/2010 0.00 0.00 0.00 0.00 930.00 0.00 0.00

07/21/2010 0.00 0.00 0.00 0.00 56.00 0.00 0.00

07/22/2010 0.00 0.00 0.00 0.00 142.00 0.00 0.00

07/23/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

07/24/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

07/25/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

07/26/2010 0.00 0.00 0.00 0.00 1.00 0.00 0.00

07/27/2010 0.00 0.00 0.00 0.00 226.00 0.00 0.00

07/28/2010 0.00 0.00 0.00 0.00 390.00 0.00 0.00

07/29/2010 0.00 0.00 0.00 0.00 264.00 0.00 0.00

07/30/2010 0.00 0.00 0.00 0.00 35.00 0.00 0.00

07/31/2010 0.00 0.00 0.00 0.00 79.00 0.00 0.00

08/01/2010 0.00 0.00 0.00 0.00 105.00 0.00 0.00

08/02/2010 0.00 0.00 0.00 0.00 51.00 0.00 0.00

08/03/2010 0.00 0.00 0.00 0.00 15.00 0.00 0.00

08/04/2010 0.00 0.00 0.00 0.00 11.00 0.00 0.00

08/05/2010 0.00 0.00 0.00 0.00 2.00 0.00 0.00

08/06/2010 0.00 0.00 0.00 0.00 98.00 0.00 0.00

08/07/2010 0.00 0.00 0.00 0.00 2.00 0.00 0.00

08/08/2010 0.00 0.00 0.00 0.00 179.00 0.00 0.00

08/09/2010 0.00 0.00 0.00 0.00 2.00 0.00 0.00

08/10/2010 0.00 41693.00 0.00 0.00 170.00 0.00 0.00

08/11/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/12/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/13/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/14/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/15/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/16/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/17/2010 0.00 4222.00 0.00 0.00 0.00 0.00 0.00

08/18/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/19/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/20/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/21/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/22/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/23/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/24/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/25/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/26/2010 0.00 2424.00 0.00 0.00 0.00 0.00 0.00

08/27/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/28/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/29/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/30/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

08/31/2010 0.00 2134.00 0.00 0.00 0.00 0.00 0.00

09/01/2010 0.00 126.00 0.00 0.00 0.00 0.00 0.00

09/02/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

09/03/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

09/04/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

 ____________________________________________________________________________________________________________________

 _____________________________________________________________________________________________________________________

Daily Variables A3.3 
Daily Variables Page 4

Date

Government 

Estimate of 

Discharge

Inland 

Recovery

Oil 

Burned

Oil Collected 

via 

RITT/TopHat

Oily Water 

Collected

Subsurface 

Dispersants

Surface 

Dispersants

VRG IR VBU VDT VOW VCBg VCSg

bbls tons bbls bbls bbls gallons gallons

09/05/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

09/06/2010 0.00 427.00 0.00 0.00 0.00 0.00 0.00

09/07/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

09/08/2010 0.00 429.00 0.00 0.00 0.00 0.00 0.00

09/09/2010 0.00 405.00 0.00 0.00 0.00 0.00 0.00

09/10/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

09/11/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

09/12/2010 0.00 562.00 0.00 0.00 0.00 0.00 0.00

09/13/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

09/14/2010 0.00 0.00 0.00 0.00 0.00 0.00 0.00

09/15/2010 0.00 493.00 0.00 0.00 0.00 0.00 0.00

 ____________________________________________________________________________________________________________________

 _____________________________________________________________________________________________________________________

Daily Variables A3.4 
1. Incident Name 2. Operational Period (Date / Time)

From: To: Time of Report

| 

ICS 209-CG OIL/HAZMAT 

ATTACHMENT

3. HAZMAT/Oil Spill Status (Estimated, in gallons)

Common Name(s): 

UN Number: Secured Unsecured

CAS Number: Remaining Potential (bbl): 

Rate of Spillage (bbl/hr): 

Adjustments To Previous 

Operational Period

Since Last Report Total

Volume Spilled/Released

Mass Balance - HAZMAT/Oil Budget

Recovered HAZMAT/Oil

Evaporation/Airborne

Natural Dispersion

Chemical Dispersion

Burned

Floating, Contained

Floating, Uncontained

Onshore

Total HAZMAT/Oil accounted for: N/A N/A

Comments: 

4. HAZMAT/Oil Waste Management (Estimated, Since Last Report)

Recovered Disposed Stored

HAZMAT/Oil (bbl)

Oily Liquids (bbl)

Liquids (bbl)

Oily Solids (tons)

Solids (tons)

Comments: 

5. HAZMAT/Oil Shoreline Impacts (Estimated in miles)

Degree of Impact Affected Cleaned To Be Cleaned

Light

Medium

Heavy

Total

Comments: 

A4 ICS 209 Form (Oil Budget Component)

Appendix 4 

 ____________________________________________________________________________________________________________ 

Appendix 4

 A4 ICS 209 Form (Oil Budget Component)

 _______________________________________________________________________________________________________________

 A4
 ____________________________________________________________________________________________________________________

Appendix 5

 A5 Oil Properties

 _____________________________________________________________________________________________________________________

 A5.1

Appendix 5

A5 Oil Properties

Table A5.1: Macondo reservoir fluid composition, based upon Pencor data provided 

by BP.

Gas-Liquid Ratio 2.89 scf/stb Vapor Gravity 0.807 (Air = 1.00)

API Gravity 35.2

°API at 60 °F 

(Water Free)

Water 

Content 0.02 weight %

Component

Atmospheric

Liquid

Atmospheric

Liquid

(Symbol / Name) (mole %) (weight %)

N2 Nitrogen 0.000 0.000

CO2 Carbon Dioxide 0.000 0.000

H2S Hydrogen Sulfide 0.000 0.000

C1 Methane 0.003 0.000

C2 Ethane 0.148 0.021

C3 Propane 0.456 0.097

iC4 i-Butane 0.263 0.073

nC4 n-Butane 0.958 0.268

iC5 i-Pentane 0.943 0.327

nC5 n-Pentane 1.536 0.533

C6 Hexanes 3.977 1.648

C7 Heptanes 8.318 3.747

C8 Octanes 11.541 5.960

C9 Nonanes 9.103 5.250

C10 Decanes 7.837 5.048

C11 Undecanes 5.965 4.215

C12 Dodecanes 4.982 3.855

C13 Tridecanes 4.754 4.000

C14 Tetradecanes 4.254 3.886

C15 Pentadecanes 3.563 3.528

C16 Hexadecanes 3.455 3.688

C17 Heptadecanes 2.755 3.139

C18 Octadecanes 2.685 3.240

C19 Nonadecanes 2.274 2.874

C20 Eicosanes 1.963 2.594
C21 Heneicosanes 1.599 2.237

C22 Docosanes 1.421 2.083

C23 Triacosanes 1.281 1.959

C24 Tetracosanes 1.149 1.827

C25 Pentacosanes 0.938 1.555

C26 Hexacosanes 0.850 1.467

C27 Heptacosanes 0.892 1.603

C28 Octacosanes 0.791 1.474

C29 Nonacosanes 0.704 1.361

C30 Triacontanes 0.642 1.283

C31 Hentriacontanes 0.607 1.255

C32 Dotriacontanes 0.543 1.159

C33 Tritriacontanes 0.470 1.035

C34 Tetratriacontanes 0.458 1.039

C35

Pentatriacontane

s 0.379 0.885

C36 Hexatriacontanes 0.346 0.832

C37

Heptatriacontane

s 0.333 0.823

C38 Octatriacontanes 0.316 0.802

C39 Nonatriacontanes 0.273 0.712

C40 Tetracontanes 0.268 0.717

C41 Hentetracontanes 0.195 0.534

C42 Dotetracontanes 0.217 0.610

C43 Tritetracontanes 0.194 0.557

C44

Tetratetracontane

s 0.186 0.548

C45

Pentatetracontan

es 0.169 0.508

C46

Hexatetracontane

s 0.146 0.450

C47

Heptatetracontan

es 0.160 0.503

C48

Octactetracontane

s 0.135 0.434

C49

Nonatetracontane

s 0.123 0.402

C50+

Pentacontanes 

Plus 2.482 11.355

Total 100.000 100.000

 Calculated Mole Weight 208.03

 Measured Mole Weight 208.03

 ____________________________________________________________________________________________________________________

 _____________________________________________________________________________________________________________________

 A5.2
Table A5.2: Analysis by Louisiana State University of fresh oil samples compared to 

reference oil.

LSU ID#: Lab Ref Oil

Source Oil, Pre-spill South Louisiana Crude

Sample Weight: 310 mg Sample Weight: 500 mg

Final Extracted Volume: 30 mL Final Extracted Volume: 20 mL

Alkane Analyte:

Concentration 

(ng/mg) Alkane Analyte:

Concentration 

(ng/mg)

nC-10 Decane 2600 nC-10 Decane 2600

nC-11 Undecane 2600 nC-11 Undecane 2700

nC-12 Dodecane 2600 nC-12 Dodecane 2600

nC-13 Tridecane 2500 nC-13 Tridecane 2600

nC-14 Tetradecane 2400 nC-14 Tetradecane 2300

nC-15 Pentadecane 2000 nC-15 Pentadecane 2200

nC-16 Hexadecane 1800 nC-16 Hexadecane 2000

nC-17 Heptadecane 1700 nC-17 Heptadecane 1900

Pristane 960 Pristane 970

nC-18 Octadecane 1500 nC-18 Octadecane 1700

Phytane 770 Phytane 910

nC-19 Nonadecane 1300 nC-19 Nonadecane 1500

nC-20 Eicosane 1300 nC-20 Eicosane 1400

nC-21 Heneicosane 1100 nC-21 Heneicosane 1300

nC-22 Docosane 1000 nC-22 Docosane 1200

nC-23 Tricosane 940 nC-23 Tricosane 1100

nC-24 Tetracosane 890 nC-24 Tetracosane 1000

nC-25 Pentacosane 600 nC-25 Pentacosane 620

nC-26 Hexacosane 510 nC-26 Hexacosane 510

nC-27 Heptacosane 350 nC-27 Heptacosane 360

nC-28 Octacosane 300 nC-28 Octacosane 310

nC-29 Nonacosane 250 nC-29 Nonacosane 260

nC-30 Triacontane 230 nC-30 Triacontane 230

nC-31 Hentriacontane 150 nC-31 Hentriacontane 190

nC-32 Dotriacontane 120 nC-32 Dotriacontane 150

nC-33 Tritriacontane 100 nC-33 Tritriacontane 110

nC-34 Tetratriacontane 90 nC-34 Tetratriacontane 110

nC-35 Pentatriacontane 92 nC-35 Pentatriacontane 110

Total Alkanes 30752 Total Alkanes 32940

LSU ID#: 2010133-02 LSU ID#: Lab Ref Oil

Source Oil South Louisiana Crude

Sample Weight: 310 mg Sample Weight: 500 mg

 ____________________________________________________________________________________________________________________

 _____________________________________________________________________________________________________________________

 A5.3
Final Extracted Volume: 30 mL Final Extracted Volume: 20 mL

Aromatic Analyte:

Concentration 

(ng/mg) Aromatic Analyte:

Concentration 

(ng/mg)

Naphthalene 750 Naphthalene 710

C1-Naphthalenes 1600 C1-Naphthalenes 1300

C2-Naphthalenes 2000 C2-Naphthalenes 1500

C3-Naphthalenes 1400 C3-Naphthalenes 1100

C4-Naphthalenes 690 C4-Naphthalenes 590

Fluorene 130 Fluorene 100

C1-Fluorenes 340 C1-Fluorenes 270

C2-Fluorenes 390 C2-Fluorenes 270

C3- Fluorenes 300 C3- Fluorenes 240

Dibenzothiophene 53 Dibenzothiophene 56

C1-Dibenzothiophenes 170 C1-Dibenzothiophenes 210

C2-Dibenzothiophenes 220 C2-Dibenzothiophenes 280

C3- Dibenzothiophenes 160 C3- Dibenzothiophenes 240

Phenanthrene 290 Phenanthrene 200

C1-Phenanthrenes 680 C1-Phenanthrenes 360

C2-Phenanthrenes 660 C2-Phenanthrenes 340

C3-Phenanthrenes 400 C3-Phenanthrenes 200

C4-Phenanthrenes 200 C4-Phenanthrenes 84

Anthracene 6.1 Anthracene 6.2

Fluoranthene 4.2 Fluoranthene 4.5

Pyrene 8.9 Pyrene 7.1

C1- Pyrenes 68 C1- Pyrenes 43

C2- Pyrenes 84 C2- Pyrenes 31

C3- Pyrenes 96 C3- Pyrenes 31

C4- Pyrenes 54 C4- Pyrenes 20

Naphthobenzothiophene 11 Naphthobenzothiophene 7.8

C-1 

Naphthobenzothiophenes 48

C-1 

Naphthobenzothiophenes 30

C-2 

Naphthobenzothiophenes 37

C-2 

Naphthobenzothiophenes 30

C-3 

Naphthobenzothiophenes 22

C-3 

Naphthobenzothiophenes 25

Benzo (a) Anthracene 5.5 Benzo (a) Anthracene 5.4

Chrysene 36 Chrysene 14

C1- Chrysenes 100 C1- Chrysenes 28

C2- Chrysenes 100 C2- Chrysenes 27

C3- Chrysenes 54 C3- Chrysenes 18

C4- Chrysenes 19 C4- Chrysenes 5.6

Benzo (b) Fluoranthene 2.3 Benzo (b) Fluoranthene 1.7

 ____________________________________________________________________________________________________________________

 _____________________________________________________________________________________________________________________

 A5.4
Benzo (k) Fluoranthene 1.8 Benzo (k) Fluoranthene 1.5

Benzo (e) Pyrene 6.6 Benzo (e) Pyrene 2.9

Benzo (a) Pyrene 1.0 Benzo (a) Pyrene 1.0

Perylene 0.92 Perylene 0.89

Indeno (1,2,3 - cd) Pyrene 0.20 Indeno (1,2,3 - cd) Pyrene 0.22

Dibenzo (a,h) anthracene 1.3 Dibenzo (a,h) anthracene 0.92

Benzo (g,h,i) perylene 1.2 Benzo (g,h,i) perylene 1.1

Total Aromatics 11203 Total Aromatics 8394

 ____________________________________________________________________________________________________________________

 _____________________________________________________________________________________________________________________

 A5.5
 ____________________________________________________________________________________________________________________ Appendix 6: SINTEF Report

 _____________________________________________________________________________________________________________________

 A6.1

SINTEF REPORT 

TITLE

Laboratory study of the dispersibility of DWH surface 

emulsions. 

AUTHOR(S) 

Frode Leirvik, Kjersti Almås and Per S. Daling 

CLIENT(S) 

SINTEF Materials and Chemistry

P.O.Box: 4760 Sluppen 

Address: NO-7465 Trondheim, 

 NORWAY 

Location: Brattørkaia 17C, 

4. etg. 

Telephone: +47 4000 3730 

Fax: +47 930 70730 

Enterprise No.: NO 948 007 029 MVA 

BP 

REPORT NO. CLASSIFICATION CLIENTS REF.

SINTEF A16134 Unrestricted David Fritz 

CLASS. THIS PAGE ISBN PROJECT NO. NO. OF PAGES/APPENDICES 

Unrestricted 978-82-14-05008-0 801599 17 

ELECTRONIC FILE CODE PROJECT MANAGER (NAME, SIGN.) CHECKED BY (NAME, SIGN.) 

Report dispersibility testing DWH_final.docx Per Daling Ivar Singsaas 

FILE CODE DATE APPROVED BY (NAME, POSITION, SIGN.) 

2010-07-13 Tore Aunaas, Research Director 

ABSTRACT

A study using the MNS and IFP dispersibility tests has been performed at SINTEF on three emulsions from the 

DWH spill.

Difference in effectiveness of different dispersant products: The products Corexit 9500, Corexit 9527 and OSR 52 

were tested on two emulsions with different degrees of weatering (Viscosities of 2700 and 7200mPas) . Corexit 

9500 show good efficiency for both emulsions, Corexit 9527 showed reduced effectiveness for the heavily 

weatered emulsion, while OSR 52 showed reduced dispersibility for both tested emulsions 

Dispersant dosage:Tests were performed on two emulsions with a range of dispersant dosages. Results show that a 

relatively high dosage (DER = 1:25 or higher) was required to obtain good dispersant efficiency for the heavily 

weathered emulsion. For less weathered emulsions a lower dosage was sufficient..

-Aerial application of dispersants at a low dosage (5USGPA) can be recommended for moderately weathered 

emulsion (dark brown colour)

-Boat application is recommended for highly weathered emulsions (light brown/orange).A high dosage should be 

used (25USGPA), and reapplication should be considered if necessary.

Mixing energy requirements: Results from tests with different energy input were compared to assess the 

requirement for mixing energy on the sea surface to disperse the emulsions. Results show that as long as mixing 

energy is sufficiently high (i.e. breaking waves) even the most weathered sample showed good dispersibility (given 

sufficient dosage of dispersants). At low sea states artificial mixing energy may be a recommended option 0.5-1 

hour after dispersant application.

 Viscosity Limit for use of dispersants: Testing indicate reduced dispersibility for emulsions with viscosity >9000 

mPas, and poor dispersibility >25000mPas. These limits are valid for DER=1:25.

KEYWORDS ENGLISH NORWEGIAN 

GROUP 1

GROUP 2

SELECTED BY AUTHOR
2

TABLE OF CONTENTS 

1 Background .......................................................................................................................... |3

2 Experiemental Setup..............................................................................................................4

2.1 The MNS Test ..................................................................................................................4

2.2 The IFP Test .....................................................................................................................5

3 Sampling and Physical Characterisation of the Test Emulsions.......................................6

4 Experimental Results.............................................................................................................8

4.1 Testing with Various Dispersants.....................................................................................8

4.2 Testing with Various Dispersant Emulsion Ratios (DER)...............................................9

4.3 Testing with Different Energy Input ..............................................................................10

4.4 Chemical Dispersibility vs Viscosity .............................................................................11

4.5 Visual Observations During Testing ..............................................................................13

5 Conclusions and Operational Recommendations .............................................................15

5.1 Testing with different dispersant products .....................................................................15

5.2 Dispersant dosage requirement ......................................................................................15

5.3 Sea state dependency......................................................................................................16

5.4 Viscosity limit for use of dispersants .............................................................................16

6 References .........................................................................................................................17

 ____________________________________________________________________________________________________________________ 

 _____________________________________________________________________________________________________________________

 A6.2
3

1 Background 

A sampling campaign were conducted in the vicinity of the DWH release point cruise in the 

period June 2-5. Three samples were taken of weathered DWH-oil and the physical properties of 

the samples have been characterised. The sampled emulsions had a span in weathering time 

estimated to 1 to 5 days on the sea surface. The span in weathering gives the emulsions very 

different physical properties. Sampling and physical characterisation of the emulsions are 

described in the cruise report (Leirvik,et.al.2010). 

As the physical properties change the dispersibility of the emulsions will change. From an 

operational point of view this would mean that different dispersant application strategies may be 

needed for emulsions at different stages of weathering. 

A dispersibiltiy study has been performed at SINTEF on the sampled emulsions. The following 

operational aspects have been studied: 

 Dosage of dispersant at different stages of emulsion weathering. 

 Effectiveness of three dispersant products at different stages of emulsion weathering 

 Mixing energy required to efficiently disperse the DWH emulsions. 

 Viscosity Limit for the dispersibility of DWH emulsions. 

The IFP and MNS dispersibility tests are described in Chapter 2. Sampling positions and the 

physical properties of the emulsions are summarised in Chapter 3. Results from the dispersibility 

testing are given in Chapter 4. Conclusions and operational recommendations are given in Chapter 

5.

 ____________________________________________________________________________________________________________________ 

 _____________________________________________________________________________________________________________________

 A6.3
4

2 Experiemental Setup 

There are several different tests for evaluating the effectiveness of chemical dispersants. Energy 

input will differ in different tests, and the obtained effectiveness will be representative for 

different wave energies. Most tests in this study is performed using the medium-to-high energy 

MNS test (representing breaking wave conditions). The MNS test is described in chapter 2.1. To 

assess the energy requirement for dispersing emulsions at different stages of weathering. Tests 

have also been performed with the low energy IFP test. The IFP test is described in chapter 2.2 . 

2.1 The MNS Test 

 The MNS test (Mackay-Nadeau-Szeto, Mackay and Szeto, 1980) is estimated to correspond to a 

medium to high sea-state condition with breaking waves. The energy input in this system, applied 

by streaming air across the oil/water surface, produces a circular wave motion. The sample of the 

oily water is taken under dynamic conditions after a mixing period of 5 min. The test apparatus is 

shown in Figure 2.1. 

Oil containment ring

Air inlet

Cooling coil

Air blower

Manometer

Air flow

Thermometer

Water sampling tube

Air outlet

ik22206200\wp\tegner\mns-c.eps

MNS Test

Figure 2.1 MNS test apparatus. 

When the test results in the MNS test shows an effectiveness > 70 – 80%, the emulsion is 

considered to be easily (good) dispersible. In the range down to 5% effectiveness, the emulsion is 

still dispersible, however, the dispersion process may need some more time. Effectiveness < 5% 

means that the emulsions is poorly dispersible when using dispersant. These laboratory-derived 

dispersibility borders have been established based on correlations to field studies (Daling and 

Strøm, 1999). 

 ____________________________________________________________________________________________________________________ 

 _____________________________________________________________________________________________________________________

 A6.4
5

2.2 The IFP Test 

The IFP Test (Institute Francais du Petrole test, Bocard et al, 1984) is a low energy test estimated 

to represent low wave energies (2 - 5 m/s wind speed). A ring beating up and down in the test 

vessel at a given frequency, gives energy input to the seawater column. The water column is 

continuously diluted, which gives a more realistic approach to field conditions compared to other 

tests. The test apparatus is shown in Figure 2.2. 

1. Experimental beaker

2. Peristaltic pump

3. Storage water

1 4

5

3

6 7

2

7. Timer

8. Oil containment ring

4. Sampling bottle

5. Surge beater

6. Electro-magnet

8

ik22206200\wp\tegner\ifp-c.eps

IFP Test

Figure 2.2 IFP test apparatus. 

When the test results in the IFP test shows an effectiveness ~ 50%, the emulsion is considered to 

be easily (good) dispersible- even at low sea conditions. If the effectiveness is below 40 -50% 

effectiveness, the emulsion may still be dispersible. During a response operation under calm 

conditions in the field, additional mixing energy may be required. This extra turbulence can be 

supplied by e.g. propel-washing from vessels or by using high delivery FI-FI monitors 1-2 hours 

after a dispersant application in order to fulfil the dispersion process. 

 ____________________________________________________________________________________________________________________ 

 _____________________________________________________________________________________________________________________

 A6.5
6

3 Sampling and Physical Characterisation of the Test Emulsions 

The physico-chemical properties of the sampled emulsions were characterised both on site, and in 

analysis at SINTEF laboratories. The results from the measurements are summarised in Table 3.1. 

The sampling positions are shown in Figure 3.1, and the samples are described in brief below. 

Sampling and analysis is described in detail in the cruise report (Leirvik et.al.,2010). 

Table 3.1 Summary of physical and chemical properties of the sampled emulsion 

Position 2 Position 3 Position 4 

Evaporative loss (wt%) 47 50 44 

Estimated time on sea surface (days) 1-1.5 4-5 2-3 

Emulsion thickness (mm) 1.3 2.6-3.7 0.9-1.4 

Water content (vol%) 67 50 33 

Density (g/ml) 0.961 0.975 0.956 

Viscosity (mPas)10 s-1 at 32°C 2770 7230 1250 

Viscosity (mPas)10 s-1 at 27°C 3540 12500 2030 

Viscosity (mPas)10 s-1 at 25°C 17900 

Viscosity (mPas)10 s-1 at 22°C 24700 

Viscosity (mPas)10 s-1 at 20°C 32300 

5 nm restrictive zone

Figure 3.1 Sample positions compared to the DWH source 

 ____________________________________________________________________________________________________________________ 

 _____________________________________________________________________________________________________________________

 A6.6
7

Position 2 

Samples were taken 12 nm miles NE 

(downwind) from the DWH source. The 

slick was only 100–200 m long and 2–

10 m wide, and the oil was readily 

spreading on the sea surface. The 

emulsion was light brown in color,

indicating significant emulsification. 

Figure 3.2 Emulsion in Position 2 

Position 3 

Samples were taken 17nm NE 

(downwind) from the DWH source. The 

slick was approximately 100 x 30 

meters. The emulsion was light brown 

/orange /reddish in color and appeared 

more elastic and less prone to spreading 

on the sea surface, which indicates that 

this slick had been heavily weathered 

(evaporative loss, emulsification and 

photo-oxidation).

Figure 3.3 Emulsion in Position 3 

Position 4 

Samples were taken 10 nm miles NE 

(downwind) from the DWH source. The 

sampled slick was approximately 50 x 

30 meters, and was part of a continuous 

belt of slicks aligned downwind from 

the DWH source. The emulsion was 

dark brown, and darker than the 

emulsions in Positions 2 and 3. This 

dark color indicates a lower degree of 

weathering than the emulsion in 

Positions 2 and 3. 

Figure 3.4 Emulsion in Position 4 

 ____________________________________________________________________________________________________________________ 

 _____________________________________________________________________________________________________________________

 A6.7
8

4 Experimental Results 

Laboratory tests have been performed to study different operational aspects. Comparative testing 

between different dispersant products is described in chapter 4.1. Results from testing with 

different dispersant dosages are shown in chapter 4.2. The requirement for energy is studied by 

testing with a low energy test representing sea states without breaking waves (IFP), and a 

Medium/high energy test (MNS) representing sea states with breaking waves. The results are 

shown in chapter 4.3. Viscosity limits for the dispersibility of DWH emulsions have been 

established by testing at increasing viscosities. This work is presented in Chapter 4.4. 

4.1 Testing with Various Dispersants 

Tests have been performed with different dispersant products for samples from position 2 and 

position 3. The three tested products were Corexit 9500, Corexit 9527 and OSR52. The 

comapartive tests were performed with a dispersant/emulsion-ratio (DER) of 1:25. 

Table 4.1 Results from the MNS test with different dispersant products. DER=1:25 in all tests. 

Position 3 

(7200 mPas) 

Position 2 

(2770 mPas)

Corexit 9500 86 91 

Corexit 9527 55 90 

OSR 52 71 62 

blank 2 44 

0

10

20

30

40

50

60

70

80

90

100

Corexit 9500 Corexit 9527 OSR 52 blank

Efficiency in the MNS test (wt%)

Position 3

Position 2

Figure 4.1 Results from the MNS test with different dispersant products. The dosage is 1:25 in all 

tests. 

Reduced effectiveness in the MNS test is defined as <75% (Daling and Strøm,1999), while poor 

dispersibility is defined as <5%. The two Corexit products show good efficency for the moderatly 

weathered emulsion from position 2, while OSR 52 have a somehow reduced dispersibility. For 

the heavily weathered emulsion sampled in position 3, only Corexit 9500 show good 

dispersibility, while Corexit 9527 and OSR 52 showed reduced dispersibility. 

 ____________________________________________________________________________________________________________________ 

A6.8
9

4.2 Testing with Various Dispersant Emulsion Ratios (DER) 

Tests have been performed to study the dispersant effectiveness as a function of dispersant 

dosage. The tests have been done on the emulsions from position 3 and position 4. The 

medium/high energy MNS test has been used in the study. Results are shown in Table 4.2 and in 

Figure 4.2. 

Table 4.2 Results from the MNS test with Corexit 9500 at different dispersant dosages 

%Effectiveness in the MNS test 

DER

Position 3 

(7200 mPas) 

Position 4 

(1250mPas) 

1:10 81 

1:25 86 99 

1:50 44 99 

1:100 31 96 

1:250 15 99 

no dispersant 2 48 

0

10

20

30

40

50

60

70

80

90

100

1:101:251:50

1:1001:250

no dispersant

Dispersantefficiency (wt%)

Position 3

Position 4

Figure 4.2 Results from the MNS testing with Corexit 9500 at different dispersant dosages. 

Results show that at a dosage of 1:25 and higher, the dispersant efficiency is high for the highly 

weathered emulsion sampled in position 3. At lower dosages the efficiency will gradually 

decrease. Tests performed on the least weathered emulsion (position 4) show a good efficiency 

for all the tested dosages. 

 ____________________________________________________________________________________________________________________ 

 _____________________________________________________________________________________________________________________

 A6.9
10

4.3 Testing with Different Energy Input 

To study the effect of energy input on the dispersibility testing has been performed with both the 

MNS and IFP tests. The MNS is a high energy test representative to high sea states. The IFP test 

supply a relatively low energy input and is thought to be representative for low sea states without 

breaking waves. Results from testing with the two methods are shown in Table 4.3 and in Figure 

4.3.

Table 4.3 Results from the MNS and IFP tests with samples from the different positions using 

Corexit 9500 and DER=25. 

Test effectiveness (wt%) w/ Corexit 9500 and DER=1:25

MNS IFP

Position 4 99 46 

Position 2 91 48 

Position 3 86 34 

0

10

20

30

40

50

60

70

80

90

100

Position 4 Position 2 Position 3

Efficiency in the MNS / IFP tests

Increasing degree of weathering

MNS

MNS

MNS

IFP

IFP

IFP

Figure 4.3 Results from the MNS and IFP tests with samples from the different postions using 

Corexit 9500 and DER=1:25. 

Results show that whith a dosage of 1:25 of Corexit 9500 all the samples show a relative good 

dispersibility for the MNS test. This is in accordance to the conclusions for the tests performed 

with the Field Effectiveness Test onboard Mr.Joe (Leirvik,et.al.2010). For the low energy IFP 

samples from positions 2 and 4 show a slightly reduced dispersibility. The heavily weathered 

sample from position 3 show a significant reduction in dispersibility.

 ____________________________________________________________________________________________________________________ 

 _____________________________________________________________________________________________________________________

 A6.10
11

4.4 Chemical Dispersibility vs Viscosity 

In systematic weathering studies performed over the past 20 years at SINTEF (Daling and 

Strøm,1999) an upper viscosity limit for an oils dispersibility is determined. The viscosity limit is 

strongly related to the specific oil that is investigated. In this study with a limited amount of 

emulsions even the most weathered emulsion had a good dispersibility (at32°C), a viscosity limit 

could not be established. Therefore additional testing where performed at lower temperatures to 

yield dispersibility data on higher viscosities. The results from all tests done with the MNS test is 

compared with the emulsion viscosities in Table 4.4. 

Table 4.4 Results from the MNS test and the Viscosity of the emulsions. The table includes the 

additional tests performed at lower temperatures. Tests are performed with Corexit 9500 and 

DER=1:25. 

Position Temperature 

(°C)

Temperature 

(°F) 

Viscosity at shear rate 

10s-1(mPas) 

MNS dispersant 

efficiency (wt%) 

4 32 90 1250 99 

2 32 90 3700 91 

3 32 90 7230 86 

3 28 82 12500 66 

3 25 77 17000 44 

3 22 72 24700 16 

3 20 68 32300 0 

The dispersant effectiveness from the MNS test is plotted against the emulsion viscosity in Figure 

4.4.

0

10

20

30

40

50

60

70

80

90

100

1000 10000 100000

MNS Dispersantefficiency (wt%)

Viscosity (mPas)

Reduced dispersibility

Poordispersibility

Figure 4.4 Dispersant efficiency in the MNS tests plotted against viscosity. Viscosity is reported at 

shear rate 10s-1.

 ____________________________________________________________________________________________________________________ 

 A6.11
12

As described in chapter 2.1, in the MNS test reduced dispersibility is defined as below 75%, while 

poor dispersibility is defined below 5%. Based on the curve drawn in Figure 4.4 reduced 

dispersibility will occur for viscosites above 10000mPas, while poor dispersibility can be 

expected for viscosities exceeding 25000 mPas. The drawn limits is based on studies using a 

dispersant/emulsion-ratio of 1:25. 

The time it take for emulsions to reach the defined viscosity limits will depend on the wind speed 

and temperatures. The weatering time for the tested emulsions where estimated based on the 

evaporative loss of the samples in the cruise report (Leirvik,et.al.,2010). The estimated time on 

the sea surface for the emulsions is shown in Table 4.5. 

Table 4.5 - Tentative time at sea based on evaporative loss and use of the SINTEF Oil Weathering 

Model.

Evaporative loss 

(wt%) 

Viscosity 

(mPas) 

Tentative time 

at sea 

Position 2 47% 3700 2-3 days 

Position 3 50% 7200 4-5 days 

Position 4 44% 1250 1-2 days 

 ____________________________________________________________________________________________________________________ 

 _____________________________________________________________________________________________________________________

 A6.12
13

4.5 Visual Observations During Testing 

The moderately weathered emulsions sampled in position 2 and 4, generally dispersed well. For 

the emuslion from position 4 small droplets were formed within the first minute of the test as 

shown in Figure 4.5

Figure 4.5 Gradual formation of small droplets with time in the MNS test. The image is from 

testing with Emulsion 4 and Corexit 9500 at DER=1:25 

In tests performed with the heavily weathered emulsion from position 3, the formation of small 

droplets was slower. After five minutes (the test duration) a significant amount of small droplets 

were formed, but strings of emulsion were still present in the water. This is demonstrated in 

Figure 4.6. 

Figure 4.6 Gradual formation of small droplets with time in the MNS test. The image is from 

testing with Emulsion 3 and Corexit 9500 at DER=1:25 

1 minute 

5 minutes 

20 seconds 1 minute 

 ____________________________________________________________________________________________________________________ 

 _____________________________________________________________________________________________________________________

 A6.13
14

In the IFP test the same effects could be observed. For the less weathered emulsions (Position 2 

and 4) small droplets were formed to make a cafe au lait coloured suspension. In the tests with the 

heavily weathered emulsion from position 3 the particles in suspension were non-spherical and 

larger in size. This is examplified in Figure 4.7. 

Figure 4.7 Droplet formation in the IFP test with the emulsion from position 3. The test is 

performed with Corexit 9500 and DER=1:25. 

Even though not all dispersed particles are within the optimal particle size range, the dispersant 

will contribute to breaking up the viscous emulsion and significantly reduce the lifetime of oil on 

the sea surface. 

Emulsions were also tested without addition of dispersants. Images from the tests are shown in 

Figure 4.8. The natural dispersion in the tests with emulsions from position 2 and 4 were 

relatively high. The emulsion from position 2 even formed quite small droplets. The emulsion 

from position 3 did not spread on the surface of the test vessel, and few droplets formed at all. 

Figure 4.8 Droplet formation in the MNS tests without addition of dispersant in the different 

positions.

Position 2 Position 3 Position 4 

 ____________________________________________________________________________________________________________________ 

 _____________________________________________________________________________________________________________________

 A6.14
15

5 Conclusions and Operational Recommendations 

5.1 Testing with different dispersant products 

Tests have been performed with different dispersant products for one moderately weathered 

emulsion (position 2 / 2770mPas) and one heavily weathered emulsion (position 3 / 7250mPas). 

The three tested products were Corexit 9500, Corexit 9527 and OSR52. The comapartive tests 

were performed with a DER of 1:25. 

The two Corexit products show good effectiveness for the moderatly weathered emulsion from 

position 2, while OSR 52 showed a somehow reduced dispersibility. For the heavily weathered 

emulsion sampled in position 3, only Corexit 9500 show good dispersibility, while Corexit 9527 

and OSR 52 showed reduced dispersibility (<75% effectiveness in the MSN test). 

5.2 Dispersant dosage requirement 

A minimum DER is required to yield efficient dispersion of a slick. The required dosage usually 

increases as the oil weathers on the sea surface. As the physical properties of the emulsion change 

the thickness of the slick will also increase and the required dosage will increase accordingly. In 

dispersant application operations the dosage is often given in US Gallons Per Acre (USGPA). 

Dispersant/Emulsion-ratio at different dosages is given at differing slick thicknesses in Table 5.1. 

Table 5.1 Dispersant/Emulsion-ratio at different dosage and slick thickness 

DER at varying Slick thickness

USGPA 1 mm 2 mm 4mm

5 1:200 1:400 1:800

25 1:50 1:100 1:200

2x25 1:25 1:50 1:100

Low/Moderately weathered emulsions (dark brown appearance) 

The results show good dispersibility in the MNS test for the least weathered emulsion (Position 

4). The emulsion disperses even at DER as low as 1:250 in the MNS test. The slick sampled in 

position 4 had a thickness of ~1mm. To achieve a dispersant/emulsion-ratio of 1:250 for a slick of 

this thickness an application dosage of 5 USGPA is required (Table 5.1). This mean that the low 

dosage used in standard aerial application will be sufficient for emulsions at a such low degree of 

weathering. 

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16

Highly weathered emulsions (light brown/orange appearance)

The results show good dispersibility in the MNS test for the most weathered emulsion (Position 3) 

at DER of 1:25 and above. At ratios under 1:50 the efficiency in the MNS test were gradually 

decreasing. The highly weathered slick sampled in position 3 had a documented thickness of up to 

4 mm. A dosage of 5 USGPA would correspond to a dispersant/emulsion-ratio of 1:800 for a slick 

with this thickness (Table 5.1). According to the test results, this is a too low dosage to disperse 

the emulsion. A dispersant/emulsion-ratio above 1:50 is recommended as the minimum dosage for 

heavily weathered emulsions such as the sample from position 3. According to Table 5.1 a 

minimum dosage of 25 USGPA is required to efficiently disperse heavily weathered emulsion 

similar to the tested emulsion from position 3. 

The slick should be monitored after the dispersant treatment, and if emulsion is still on the surface 

a re-treatment of the slick should be considered in order to achieve sufficient dosage. 

5.3 Sea state dependency 

Tests were conducted with the high energy MNS test and with the low energy IFP test. The MNS 

is thought to be representative for energy at high/medium sea states with presence of breaking 

waves (typically >5m/s). The IFP test is representative to calmer sea states with no breaking 

waves. The comparative tests were carried out on all the sampled emulsions and with a 

dispersant/emulsion-rate of 1:25. At this dosage all emulsions dispersed readily in the MNS test. 

The dispersibility was slightly reduced in the IFP test for the moderately weathered emulsions 

from position 2 and 4, while dispersibility was significantly reduced for the heavily weathered 

sample from position 3. This means that the emulsions are dispersible given sufficient wave 

energy. In calm sea conditions, introduction of additional mixing-energy/turbulence 0.5-1 hour 

after dispersant treatment, could be a rational operational strategy. Such mixing energy could be 

supplied to the treated slick e.g. by prop-washing or by spraying the slick with the vessels FI-FI 

system. 

5.4 Viscosity limit for use of dispersants 

As an emulsion weather on the sea surface the physical properties will change, and the 

dispersibility will gradually decrease. The change in physical properties and thus the changes in 

dispersibility are highly dependent on the wind/wave conditions. In the systematic weathering 

studies performed in general at SINTEF, dispersant effectiveness is linked to the viscosity of the 

emulsion. The viscosity is predicted by use of the SINTEF Oil Weathering Model (Daling and 

Strøm,1999) and a time window for effective use of dispersants can be estimated. The weathering 

properties of the DWH oil are not studied and predictions of the change in physical properties can 

not be done. A defininite time window for use of dispersants can therefore not be established. In 

this study only the relationship between dispersibility and viscosity has been established. 

The most weathered sample tested in this study had a viscosity of 7200 mPas after an estimated 

weathering time of 4-5 days on the sea surface under relative calm weather conditions. This 

emulsion still showed good dispersibility in the high energy MNS test at a dispersant/emulsionratio of 1:25. In lack of more viscous emulsions the emulsion from position 3 is tested at lower 

temperatures to gain higher viscosities. The tests indicate that at a dispersant/emulsion-ratio of 

1:25 the dispersibility will be reduced at a viscosity of 10000 mPas. Poor dispersibility will occur 

as the emulsion reaches a viscosity of approximately 25000mPas. 

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6 References 

Bocard C. Castaing, C. G. and Gatillier, C. 1984: "Chemical oil dispersion in trials at sea and in 

laboratory tests". In: Oil Spill Dispersants, ASTM STP 840 (T.E. Allen ed.) Philadelphia, 

USA, pp 125- 142 

Daling, P.S., T. Strøm, 1999. Weathering of Oils at Sea: Model/Field Data Comparisons. Spill 

Science and Technology Bulletin, Vol. 5, no. 1, pp. 63-74, 1999 

Leirvik,F., Daling,P.S.,Trudel,K.,Parschal,B.2010. ”Cruise report – Assessment of dispersibility 

of DWH oil at different stages of weathering” 

Mackay, D.and Szeto, F. 1980. Effectiveness of oil spill dispersants - development of a 

laboratory method and results for selected commercial products. Institute of Environmental 

Studies, University of Toronto, Publ. no. EE-16. 

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Appendix 7

A7 “Bench Top” LISST Particle Size Analysis

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Appendix 7

 A7 “Bench Top” LISST Particle Size Analysis 

K. Lee, Z. Li and H. Niu – Centre for Offshore Oil, Gas and Energy Research, 

Fisheries and Oceans Canada 

The standard operational procedure (SOP) for LISST-100X particle size analysis used in 

the Gulf of Mexico oil spill monitoring program (i.e., Deepwater Horizon Spill) was 

developed for the measurement of particle-size distributions under two scenarios. The 

first was bench top measurement of small particles, and the second was continuous in-situ 

monitoring with the instrument deployed over the side of the vessel at specific depths. 

Laboratory “bench-top” measurements with the LISST-100X instrument on board the 

vessel were specifically targeted at measuring small particles (d < 70 μm) suspended in 

the water column. For this purpose, discrete samples were collected from both the 

surface (with bucket), and from different depths in the water column using CTD casts 

with a Niskin rosette sampler. On average, the total length of time between the recovery 

of sample and the bench-top LISST-100X analysis was more than half an hour including 

casting of rosette Niskin bottles, sub-sampling, and data acquisition by LISST-100X. 

Due to the buoyant nature of dispersed oil droplets in the water column, larger particles 

(Lunel, 1993; Lunel, 1995) would have risen at a speed that is beyond the limit of the 

time period for handling discrete seawater samples (Table 1). 

The LISST-100X particle size analyzer (Type C) is an optical device that measures light 

intensity over a series of detector rings (numbered 1 through 32). After the acquisition of 

light intensity for the 32 discrete rings and eight other auxiliary parameters, the raw data 

are subsequently processed with the manufacturer provided inversion algorithm to 

automatically calculate volume concentrations (in μl/l) for particle size bin number 1 

through 32 (corresponding with the detector ring numbers), along with output of 10 other 

parameters including laser transmission sensor power, laser reference sensor in calibrated 
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A7.2 

2

units, pressure, temperature, computed optical transmission over path, and beamattenuation, etc. 

Under ideal conditions, the data acquired using the bench-top measurement SOP would 

have recorded discrete particle size volume concentrations over the first 20 size bins (bins 

1-20 or 2.5 - 68.8 μm) only, and would have shown zero or close to zero readings for the 

large-sized bins (#21 – 32, or 68.8 to 500 μm). However, the recorded data do not always 

show the low readings expected. Instead, extremely high values over the last several bins 

were recorded. A number of conditions may exist that lead to high apparent values of 

large particles: 

(1) Variation of the seawater temperature of the samples collected from different depths 

in the water column and the ambient air temperature. The water temperatures vary 

widely from close to freezing (4o

C) at maximum depths to very warm water at the 

surface (30o

C). Stratification of the water inside the small chamber may cause laser 

beam reflection and a false signal of the presence of larger particles (Mikkelsen et al., 

2008; Styles, 2006). Corrective action was taken in late June, 2010 to overcome the 

effect of temperature variation by introducing a full-path mixing chamber. 

(2) Slight miss-alignment of the LISST-100X (#1215 and #1174) that may impact the 

inner ring light intensity reading. This will subsequently propagate through inversion 

process to affect several numbers of upper-end particle size bins, but negligible 

impact on medium and small particle size data (communication with the manufacturer) 

(3) The presence of actual particles larger than the upper limit of our targeted small 

particles (68.6 μm). This is not unexpected, for a number of reasons such as the 

retention of larger oil droplets within the counting cell of the instrument due to (a) the 

relatively short time between sample recovery and analysis for the samples that were 

collected from the surface or near the surface (0-50m depth), (b) the potential 

presence oil droplets with a density close to the seawater because of the dissolution of 

light components, and (c) potential coalescence of small particles into larger ones. 

Furthermore, large particles other than oil (e.g., biogenic material) may also exist. 

Considering the high uncertainties involved in the analysis of large particles, analysis of 

“bench top” data and interpretation should be focused on the small sized particles (d < 70 

μm) which have been recognized in the oil spill community as permanently dispersed oil 

droplets. We discourage over-interpretation of particle size distribution data that were 

collected for this specific purpose during the Gulf of Mexico oil spill emergency response 

operations. Under this emergency response effort, rapid, less than perfect actions had to 

be taken to support the requirement for immediate action to monitor the fate and transport 

of the oil following subsurface injection of dispersant. 

Preliminary data analysis was performed with particle size bins 1 through 25, 

corresponding to particle size ranges of 2.5 to 157 μm (Table 2). Figures 1 and 2 display 

discrete particle size distribution of the surface samples for all stations, and Figures 3 and 

4 the peak total particle concentrations in sub-surface samples. The peaks that were used 

are defined as the maximum small particle concentration at depth for each station. These 

particle size distribution histograms clearly demonstrate the presence of a large amount of 
3

very small particles (d < 10 μm), suggesting the presence of chemically dispersed oil 

droplets (Li et al., 2008; Li et al., 2009; Lunel, 1993; Lunel, 1995). The strong signal of 

the chemically dispersed oil particles is also indicated by the observed multimodal 

distribution profiles rather than a mono-modal size distribution that is often generated by 

natural dispersion (Li et al., 2009). 

Figures 5 to 8 summarize the fraction of small particles (d < 68.8 μm) within the 

complete range of particle sizes (2.5 – 157 μm). These data clearly indicate that the vast 

majority of volume fractions of the measured particles are in the small particles range. 

However, exceptions do exist for a number of stations where large fractions of particles 

appear to fall in the range above 70 μm (e.g. the surface samples of stations 101-104 and 

subsurface samples of stations 101 and 102 of R/V Brooks McCall, surface samples of 

stations 60-80 and subsurface samples of stations 60-80 of R/V Ocean Veritas). This 

needs to be further investigated. 

Figures 9-12 present the cumulative particle size distribution of all measured particles 

from all the surface stations and all the peak sub-surface stations. These graphs show that 

nearly 80-90% of the measured surface and subsurface particles are  70 μm (bin 1 to 21), 

and the median diameters of the measured particles of both surface and subsurface 

particles are nearly 20 to 30 μm. These size data are in good agreement with previous 

observations at sea (Lunel, 1993; Lunel, 1995). 

Due to the restriction of time, more detailed data analysis and interpretation are certainly 

yet to be done. The association with field operational and oceanographic parameters 

needs to be further investigated. Nevertheless, the preliminary results and elementary 

data analysis suggest high effectiveness of chemical dispersants in oil dispersion from 

subsurface application. An estimation of the amount of oil dispersed based on the 

operational parameter, namely dispersant to oil ratio, in calculating the amount of 

chemically dispersed oil may not be the most accurate approach. A thorough inspection 

of all the field collected data, including discrete samples and continuous in-situ (over-theside) vessel deployment data (to be addresses in future reports), and numerous other field 

sampling data should be synthesized and digested to provide a more scientifically sound 

estimation of dispersant effectiveness, the amount of oil naturally or chemically dispersed 

from the subsurface and surface dispersant application, and the oil mass balance on the 

whole. The possible impact of dispersant-containing oil in rising and after rising to the 

water-air interface should not be neglected. Effective chemical dispersion of oil after 

adding dispersant in calm sea proves still effective after prolonged standing time in static 

and flowing waters before increased wave energy becomes available (Lewis et al., 2010). 

Such a scenario may happen to the subsurface dispersant injection situation, in which an 

excellent mixture of oil and dispersant in situ at depth can facilitate continued dispersion 

of oil wherever turbulent mixing energy is encountered – regardless of whether it is at the 

surface or subsurface. 

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Table 2: The lower, medium and upper limit of each size bin in microns for the LISST-100X particle 

counter 

Size bin # Lower Medium Upper 

1 2.50 2.72 2.95 

2 2.95 3.20 3.48 

3 3.48 3.78 4.11 

4 4.11 4.46 4.85 

5 4.85 5.27 5.72 

6 5.72 6.21 6.75 

7 6.75 7.33 7.97 

8 7.97 8.65 9.40 

9 9.40 10.2 11.1 

10 11.1 12.1 13.1 

11 13.1 14.2 15.4 

12 15.4 16.8 18.2 

13 18.2 19.8 21.5 

14 21.5 23.4 25.4 

15 25.4 27.6 30.0 

16 30.0 32.5 35.4 

17 35.4 38.4 41.7 

18 41.7 45.3 49.2 

19 49.2 53.5 58.1 

20 58.1 63.1 68.6 

21 68.6 74.5 80.9 

22 80.9 87.9 95.5 

23 95.5 104 113 

24 113 122 133 

25 133 144 157 

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Figure 1: Particle-size distributions (see Table 1 for size range of corresponding Bin No.) for the 

surface samples of all stations of the R/V Brooks McCall. 

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Figure 2: Particle-size distributions for the surface samples of all stations of the R/V Ocean Veritus. 

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Figure 3: Particle-size distributions for the peak total concentration in sub-surface samples of all 

stations of the R/V Brooks McCall. 

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Figure 2: Particle-size distributions for the peak sub-surface samples of all stations of the R/V Ocean 

Veritus. 

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Figure 3: Small particle (d < 68.6 um) fraction of the total measured particles (2.5 – 157 um) for the 

surface samples of all stations of the R/V Brooks McCall. 

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Figure 6: Small particle (d < 68.6 um) fraction of the total measured particles (2.5 – 157 um) for the 

surface samples of all stations of the R/V Ocean Veritas. 

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Figure 7: Small particle (d < 68.6 um) fraction of the total measured particles (2.5 – 157 um) for the 

peak sub-surface samples of all stations of the R/V Brooks McCall. 

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Figure 4: Small particle (d < 68.6 um) fraction of the total measured particles (2.5 – 157 um) for the 

peak sub-surface samples of all stations of the R/V Ocean Veritas. 

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Figure 5: Cumulative particle-size distribution for all the surface samples of all stations of the R/V 

Brooks McCall. 

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Figure 10: Cumulative particle-size distribution for all the surface samples of all stations of the R/V 

Ocean Veritas. 

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Figure 11: Cumulative particle-size distribution for all the peak sub-surface samples of all stations of 

the R/V Brooks McCall. 

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Figure 6: Cumulative particle-size distribution all the peak sub-surface samples of all stations of the 

R/V Ocean Veritas. 

References: 

Lewis, A., K. Trudel, B., Belore, R.C. and Mullin, J.V., 2010. Large-scale dispersant 

leaching and effectiveness experiments with oils on calm water. Marine Pollution 

Bulletin, 60(2): 244-254. 

Li, Z., Lee, K., King, T., Boufadel, M.C. and Venosa, A.D., 2008. Oil droplet size 

distribution as a function of energy dissipation rate in an experimental wave tank 

2008 International Oil Spill Conference. Americal Petroleum Institute, 

Washington D.C., Savannah, GA, pp. 621-626. 

Li, Z., Lee, K., King, T., Boufadel, M.C. and Venosa, A.D., 2009. Evaluating Chemical 

Dispersant Efficacy in an Experimental Wave Tank: 2, Significant Factors 

Determining In Situ Oil Droplet Size Distribution. Environmental Engineering 

Science, 26(9): 1407-1418. 

Lunel, T., 1993. Dispersion: Oil droplet size measurement at sea. In: Proceedings of the 

Sixteeth Arctic and Marine Oil Spill Program (AMOP) Technical Seminar, 

Calgary, Alberta, Canada. Environment Canada, Ottawa, Ontario, Canada. Pp. 

1023-1056 

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Lunel, T., 1995. Understanding the mechanism of dispersion through oil droplet size 

measurements at sea. In: P. Lane (Editor), The Use of Chemicals in Oil Spill 

Response, ASTM STP 1252. American Society for Testing and Materials, 

Philadelphia, PA, pp. 240-270. 

Mikkelsen, O.A. et al., 2008. The influence of schlieren on in situ optical measurements 

used for particle characterization. Limnology and Oceanography-Methods, 6: 

133-143. 

Styles, R., 2006. Laboratory evaluation of the LISST in a stratified fluid. Marine Geology, 

227(1-2): 151-162. 

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A8 

Appendix 8 

Spill Related Properties of 

MC 252 Crude Oil 

Sample ENT-052210-178 

For 

 

BP 

by 

SL Ross Environmental Research Ltd. 

July, 2010 

—————————————————————————————————- 

Table of Contents 

1. Introduction ..................................................................................................................................1 

2. Physical Property Tests: Methods and Results ............................................................................1 

2.1 Results ................................................................................................................................... 2 

2.1.1 Evaporation ....................................................................................................................... 2 

2.1.2 Density ............................................................................................................................... 6 

2.1.3 Viscosity ............................................................................................................................ 6 

2.1.4 Interfacial Tension ............................................................................................................. 6 

2.1.4 Pour Point .......................................................................................................................... 6 

2.1.5 Flash Point ......................................................................................................................... 6 

2.1.6 Emulsification Tendency and Stability ............................................................................. 6 

3. References ....................................................................................................................................7 

Appendix A. Oil Property Test Methodology and Relationship to Spill Behavior .........................8 

A.1 Evaporation ..................................................................................................................... 9 

A.2 Physical properties .......................................................................................................... 9 

A.2.1 Density ...................................................................................................................... 10 

A.2.2 Viscosity ................................................................................................................... 10 

A.2.3 Interfacial Tension .................................................................................................... 10 

A.2.4 Pour Point.................................................................................................................. 11 

A.2.5 Flash Point ................................................................................................................ 11 

A.2.6 Emulsification Tendency and Stability ..................................................................... 11 

Appendix B. Oil Property Analysis Results for MC 252 ENT-052210-178 Crude Oil ................13 

———————————————————————————

Appendix 8

A8 Spill Related Properties of MC 252 Crude Oil

Sample ENT-052210-178
-1-

1. INTRODUCTION

When oil is spilled in the marine environment its physical and chemical properties will change 

over time through processes such as evaporation and emulsification. These changes will affect 

both the fate and behavior of the spill and the opportunities for using countermeasures 

effectively. For example, an oil may be relatively fluid and non-viscous when initially spilled, 

but may become viscous within a short time. It is important to know whether this will happen 

and how long it will take, defining the so-called Window of Opportunity for countermeasures. 

The objective of this study was to conduct simulated oil spill weathering experiments on MC 252 

ENT-052210-178 crude oil. The quantitative results of the tests (involving both fresh and 

weathered oil) can be used as input to most oil spill models that are used internationally to 

predict the fate and behavior of spills of specific oils. 

2. PHYSICAL PROPERTY TESTS: METHODS AND RESULTS

The laboratory testing described here involved 2.7 L of the crude oil. The oil was subjected to 

the analyses outlined in Table 2-1. Test temperatures were chosen to cover the typical range of 

seasonal variation for the open water season in the target region. Temperature of 15°C and 35°C 

were chosen. 

A discussion of the methodology of each of these tests is presented in Appendix A, along with an 

explanation of the effect that each oil property has on spill behavior. 

The results of the weathering and analyses of the crude oil are presented separately in the 

following section. Complete test results can be found in Appendix B.

Table 2-1 Test Procedures for Spill-Related Analysis of MC 252 ENT-052210-178 Crude Oil 

Property Test 

Temperature(s)

Equipment Procedure 

Evaporation Ambient Wind TunnelASTM 

Distillation Apparatus 

ASTM D86 

Density 15° and 35° Anton Paar Densitometer ASTM D4052 

Viscosity 15° and 35° Brookfield DV III+ Digital 

Rheometer c/w Cone and 

Plate 

Brookfield M/98-

211 

Interfacial Tension Room 

Temperature

CSC DuNouy Ring 

Tensiometer 

ASTM D971 

Pour Point N/A ASTM Test Jars and 

Thermometers 

ASTM D97 

Flash Point N/A Pensky-Martens Closed Cup 

Flash Tester 

ASTM D93 

Emulsification 

Tendency/Stability 

15° and 35° Rotating Flask Apparatus (Mackay and 

Zagorski 1982; 

Hokstad and Daling 

1993) 

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2.1 RESULTS

The results of the property analysis of MC 252 ENT-052210-178 are summarized in Table 2-2. The 

complete test results can be found in Appendix B. The two levels of evaporation noted in the 

table represent the amounts evaporated from a 2 cm-thick slick in the wind tunnel after two days 

and two weeks, respectively. 

2.1.1 Evaporation 

MC 252 ENT-052210-178 is a light crude with an API gravity of 37.2°. Approximately 35% of 

the oil evaporated after two days in the wind tunnel, and about 45% evaporated after two weeks 

of exposure. 

Figure 2-1 is a predicted evaporation curve for a spill involving a 10-mm thick slick in a 5 knot 

wind at 25°

C (77°F). Please note that the curve only applies at a water temperature of 25°C. If 

other temperatures (or slick thicknesses and wind speeds) are of interest, these curves can be 

generated using the equations in Appendix A and data in Appendix B1. Computerized oil spill 

models automatically do these calculations. 

Figures 2-2, 2-3 and 2-4 show the effect of evaporation on the properties of oil viscosity, density 

and pour point. 

 

1

 The evaporation curve of the oil in the wind tunnel is shown in Appendix B, plotting the volume fraction of oil 

evaporated, Fv, on the y-axis versus evaporative exposure, 2, on the x-axis, where 2 is the unit of time expressed in 

dimensionless form. Equations described in Appendix A and data in Table 2-2 of Appendix B can be used to convert 

this curve into a more usable form for estimating oil evaporation under various spill conditions of temperature, 

elapsed time and wind speed. 

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Table 2-2 Spill-Related Properties of MC252 Crude Oil 

Spill-related properties BP MC252 ENT-052210-178 API ° = 37.2

Evaporation (Volume %) 0 34.50 44.66

Density (g/cm3

)

15 °C 0.839 0.882 0.897

35 °C 0.825 0.868 0.883

Dynamic Viscosity (mPa.s) at approx 460 s-1

15 °C 4.1 43 85

35 °C 1.4 10 23

Kinematic Viscosity (mm2

/s)

15 °C 4.8 49 95

35 °C 1.7 12 26

Interfacial Tension (dyne/cm)

Oil/ Air 23.5 26.8 30.1

Oil/ Seawater 23.3 22.6 22.5

Pour Point (°C)

<-9 6 6

Flash Point (°C)

<-8 54 100

Emulsion Formation-Tendency and Stability @ 22.5 °C

 Tendency Unlikely Unlikely Unlikely

 Stability Unstable Unstable Unstable

 Water Content 0% 0% 0%

Emulsion Formation-Tendency and Stability @ 34 °C

 Tendency Unlikely Unlikely Unlikely

 Stability Unstable Unstable Unstable

 Water Content 0% 0% 0%

ASTM Modified Distillation

 Liquid Vapour

Evaporation Temperature Temperature

(% volume) (°C) (°C)

IBP 84 39.8

5 111.6 77.4

10 124.4 91.7

15 137 102.4

20 151.2 115.8

25 168.8 116

30 188.2 126.4

35 208 150

40 227 129.7

45 248 142.5

Weathering Model

Fv =

where: Fv is volume fraction of oil evaporated

 is evaporative exposure

Tk is environmental temperature (K)

C1 = 5472

C2 = 12.90

C3 = 5739

ln[1 + (C1/Tk)exp(C2-C3/Tk)]

(C1/Tk)

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Figure 2-2 Effect of Evaporation on Oil Viscosity

1.0

10.0

100.0

0 10 20 30 40 50

Percent Loss to Evaporation (volume)

Dynamic Viscosity 

(at approximately 60 s-1)

Viscosity @ 15°C

Viscosity @ 35°C

Figure 2-1 Evaporation of MC252 Ent-052210-178

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0 12 24 36 48 60 72 84 96 108 120 132 144

Elapsed Time (hr)

Fv (Volume Fraction 

Evaporated)

 Water Temp (°C ): 25

 Wind Speed (knots): 5

 Thickness (mm): 10

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Figure 2-3 Effect of Evaporation on Oil Density

0.820

0.830

0.840

0.850

0.860

0.870

0.880

0.890

0.900

0.910

0 10 20 30 40 50

Percent Loss to Evaporation (volume)

Density (g/cm3)

Density @ 15°C

Density @ 35°C

Figure 2-4

 Effect of Evaporation on Pour Point

-15

-10

-5

0

5

10

0 20 40 60

Percent Loss to Evaporation (volume)

Pour Point (°C)

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2.1.2 Density 

MC 252 oil is a light crude oil, with a density of 0.839 g/cm3

 at 15°C (API gravity of 37.2°). 

2.1.3 Viscosity 

The oil has a very low viscosity that is typical of light oils. At 15°C the viscosity of the fresh oil 

is about 4.1 cP (mPa.s). The viscosity increases to 42.9 cP after 35% evaporation and to 85.1 cP 

after 45% evaporation. The crude oil exhibits minor non-Newtonian behavior (slightly pseudoplastic, or shear-thinning, characteristics) at 15°C. It is a Newtonian fluid at 35°C. 

2.1.4 Interfacial Tension 

The oil/water interfacial tension of MC 252 ENT-052210-178 crude was measured using 

standard laboratory water with 35 ppt of salt. The value measured was 23.7 dynes/cm, which is 

in the range of most crude oils. 

2.1.4 Pour Point 

MC 252 ENT-052210-178 crude has a pour point of less than -9°

C when fresh. This increases to 

6°

C at 35 and 45 percent evaporation. 

2.1.5 Flash Point 

MC 252 ENT-052210-178 has a low flash point (below -8°

C) when fresh. This rises after 45% 

evaporation to 100°

C. 

2.1.6 Emulsification Tendency and Stability 

From the viewpoint of spill countermeasures and slick persistence, emulsification is a very 

negative process because strongly emulsified oils are highly viscous — they can have ten to 100 

times the viscosity of the parent oil. It is general believed that oils that have relatively high 

concentrations of asphaltenes are the most likely to form stable water-in-oil emulsions. Some oil 

spills do not form emulsion immediately, but once evaporation occurs and the asphaltene 

concentration increases, the emulsification process begins and usually proceeds quickly 

thereafter. 

The MC 252 ENT-052210-178 crude oil has no tendency to form stable water-in oil emulsions 

when mixed with seawater. At sea, it is observed that MC 252 crude does eventually form stable 

emulsions. The reason that the ENT-052210-178 sample does not could be due to several factors: 

 The ENT-052210-178 sample evaporated in the wind tunnel for two weeks is equivalent 

to only about 10 hours at sea for a 1-mm thick slick or 100 hours for a 10-mm thick slick 

and the onset of emulsification may not occur until greater degrees of evaporative 

exposure that this are reached. 

 The sample may have been exposed to an anti-foaming agent and/or methanol during it’s 

collection from the damaged riser by the RITT and this exposure may inhibit 

emulsification. 

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 Exposure to sunlight (not a part of the SL Ross weathering protocol) can produce photooxidation products that promote emulsification. 

Enough of the two-week weathered sample from the wind tunnel remains to place a thinner slick 

back into the wind tunnel and further expose it to the equivalent of one week at sea for a 1-mm 

slick. The emulsification of this sample will then be measured. As well, during the earlier 

alternative field-testing program, surface samples of the slick were collected and shipped to the 

SL Ross lab. Aliquots of these will be subjected to the laboratory emulsification test to determine 

their emulsification characteristics. 

3. REFERENCES

Fingas, M., B. Fieldhouse and J. Mullin. 1998. Studies of Water-in-Oil Emulsions: Stability and 

Oil Properties. Proceedings of the 21st Arctic and Marine Oilspill Technical Seminar.

Environment Canada, Ottawa. pp 1-26 

Hokstad, J. and P. Daling. 1993. Methodology for Testing Water-in-Oil Emulsions and 

Demulsifiers. Description of Laboratory Procedures. In Formation and Breaking of Water-inOil Emulsions: Workshop Proceedings Marine Spill Response Corporation, Washington DC, 

MSRC Technical Report Series 93-108, pp 239-254 

Mackay, D., W. Stiver and P.A. Tebeau. 1983. Testing of crude oils and petroleum products for 

environmental purposes. In Proceedings of the 1983 Oil Spill Conference, American 

Petroleum Institute, Washington, D.C., pp 331-337. 

Zagorski, W. and D. Mackay. 1982. Water in oil emulsions: a stability hypothesis, in 

Proceedings of the 5th Arctic and Marine Oilspill Program Technical Seminar, Environment 

Canada, Ottawa, ON, pp 61-74. 

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APPENDIX A. OIL PROPERTY TEST METHODOLOGY AND 

RELATIONSHIP TO SPILL BEHAVIOR 

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A.1 E VAPORATION

The oil was divided into three aliquots. Two aliquots were weathered in a wind tunnel: one for 

two days and one for two weeks. Depending on the conditions at a spill site, this is typically 

equivalent to a few hours and a few days at sea. In addition, the fresh oil was subjected to a 

modified ASTM distillation (ASTM D86-90, modified in that both liquid and vapor temperature 

are measured) in order to obtain two oil-specific constants for evaporation prediction purposes. 

Evaporation is correlated using Evaporative Exposure (θ), a dimensionless time unit calculated 

by: 

θ = kt/x 

where: k = a mass transfer coefficient [m/s] (determined

experimentally in the laboratory wind tunnel or by an 

equation related to wind speed for spills at sea)

t = elapsed time [s] 

 x = oil thickness [m] 

The distillation information is used in conjunction with the wind tunnel data to predict 

evaporation rates for oil spills at sea. 

A.2 P HYSICAL PROPERTIES

The oils were subjected to the analyses outlined in Table 1. Test temperatures are chosen to 

represent typical values for the region for those tests that are temperature-sensitive, such as 

density and viscosity. 

Table 1: Test procedures for oil analysis 

Property Test Temperature(s)

Equipment 

Procedure

Evaporation Ambient Wind Tunnel 

ASTM Distillation Apparatus ASTM D86 

Density 15°

 and 35 °C Anton Paar Densitometer ASTM D4052 

Viscosity 15°

 and 35 °C Brookfield DV III+ Digital Rheometer 

c/w Cone and Plate 

Brookfield 

M/98-211 

Interfacial Tension Room Temperature CSC DuNouy Ring Tensiometer ASTM D971 

Pour Point N/A ASTM Test Jars and Thermometers ASTM D97 

Flash Point N/A Pensky-Martens Closed Cup Flash Tester ASTM D93 

Emulsification 

Tendency/Stability 15°

 and 35 °C Rotating Flask Apparatus 

(Mackay and 

Zagorski 1982; 

Hokstad and 

Daling 1993) 

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A.2.1 Densit y 

Density, the mass per unit volume of the oil (or emulsion), determines how buoyant the oil is in 

water. The common unit of density is grams per millilitre or cubic centimetre (g/mL or g/cm3

); 

the SI unit is kg/m3

, which is numerically 1000 times the value in g/mL. The density of spilled 

crude oil increases with weathering and decreases with increasing temperature. Density affects 

the following spill processes: 

 Sinking - if the density of the oil exceeds that of the water it will sink; 

 Spreading - in the early stages of a spill, more dense oils spread faster; 

 Natural dispersion - more dense oils stay dispersed more easily; and, 

 Emulsification stability - dense oils form more stable emulsions. 

A.2.2 Viscosit y 

Viscosity is a measure of the resistance of oil to flowing, once it is in motion. The common unit 

of dynamic viscosity is the centi-Poise (cP); the SI unit is the milli-Pascal second (mPas), which 

is numerically equivalent to the centi-Poise. The common unit of kinematic viscosity (calculated 

by multiplying the dynamic viscosity by the density) is the centi-Stoke (cSt) the SI unit is the 

square millimetre/second (mm2/s), which is numerically equivalent to the centi-Stoke. The 

viscosity of spilled crude oil increases as weathering progresses and decreases with increasing 

temperature. Viscosity is one of the most important properties from the perspective of spill 

behavior and affects the following processes: 

 Spreading - viscous oils spread more slowly; 

 Natural and chemical dispersion - highly viscous oils are difficult to disperse; 

 Emulsification tendency and stability - viscous oils form more stable emulsions; and, 

 Recovery and transfer operations - more viscous oils are generally harder to skim and 

more difficult to pump. 

A.2.3 Interfacial Tension 

Interfacial tension is a measure of the surface forces that exist between the interfaces of the oil 

and water, and the oil and air. The common unit of interfacial tension is the dyne/cm; the SI unit 

is the milli-Newton/metre (mN/m), which is numerically equivalent to the dyne/cm. Chemical 

dispersants work by reducing the oil/water interfacial tension to allow a given mixing energy 

(i.e., sea state) to produce smaller oil droplets. Emulsion breakers also work by lowering the 

oil/water interfacial tension; this weakens the continuous layer of oil surrounding the suspended 

water droplets and allows them to coalesce and drop out of the emulsion. Interfacial tensions 

(oil/air and oil/water) are fairly insensitive to temperature, but are affected by evaporation. 

Interfacial tension affects the following processes: 

 Spreading - interfacial tensions determine how fast an oil will spread and whether the oil 

will form a sheen; 

 Natural and chemical dispersion - oils with high interfacial tensions are more difficult to 

disperse naturally, chemical dispersant work by temporarily reducing the oil/water 

interfacial tension; 

 Emulsification rates and stability; and, 

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 Mechanical recovery - oleophilic skimmers (e.g., rope-mop and belt skimmers) work best 

on oils with moderate to high interfacial tensions. 

A.2.4 Pour Point 

The pour point is the lowest temperature (to the nearest multiple of 3 °C) at which crude oil will 

still flow in a small test jar tipped on its side. Near, and below this temperature, the oil develops 

a yield stress and, in essence, gels. The pour point of an oil increases with weathering. Pour point 

affects the following processes: 

 Spreading - oils at temperatures below their pour points will not spread on water; 

 Viscosity - an oil’s viscosity at low shear rates increases dramatically at temperatures 

below its pour point; 

 Dispersion - an oil at a temperature below its pour point may be difficult to disperse; and, 

 Recovery - crude oil below its pour point may not flow towards skimmers or down 

inclined surfaces in skimmers 

A.2.5 Flash Point 

The flash point of crude oil is the temperature at which the oil produces sufficient vapors to 

ignite when exposed to an open flame or other ignition source. Flash point increases with 

increasing evaporation. It is an important safety-related spill property. 

A.2.6 Emulsifica tion Tendency and Stability 

The tendency of crude oil to form water-in-oil emulsions (or “mousse”) and the stability of the 

emulsion formed are measured by two numbers: the Emulsification Tendency Index (Zagorski 

and Mackay 1982, Hokstad and Daling 1993) and the Emulsion Stability (adapted from Fingas et

al. 1998). The Emulsification Tendency Index is a measure of the oil’s propensity to form an 

emulsion, quantified by extrapolating back to time = 0 the fraction of the parent oil that remains 

(i.e., does not cream out) in the emulsion formed in a rotating flask apparatus over several hours. 

If a crude oil has an Emulsification Tendency Index between 0 and 0.25 it is unlikely to form an 

emulsion; if it has a Tendency Index between 0.25 and 0.75 it has a moderate tendency to form 

emulsions. A value of 0.75 to 1.0 indicates a high tendency to form emulsions. Recently the 

Emulsion Stability assessment has been changed to reflect the four categories suggested by 

Fingas et al. 1998. Emulsion types are selected based on water content, emulsion rheology and 

the visual appearance of the emulsion after 24 hours settling. The four categories, and their 

defining characteristics, are: 

1. Unstable – looks like original oil; water contents after 24 hours of 1% to 23% averaging 

5%; viscosity same as oil on average 

2. Entrained Water – looks black, with large water droplets; water contents after 24 hours of 

26% to 62% averaging 42%; emulsion viscosity 13 times greater than oil on average 

3. Meso-stable – brown viscous liquid; water contents after 24 hours of 35% to 83% 

averaging 62%; emulsion viscosity 45 times greater than oil on average 

4. Stable – the classic “mousse”, a brown gel/solid; water contents after 24 hours of 65% to 

93% averaging 80%; emulsion viscosity 1100 times greater than oil on average 

Under the old emulsion stability assessment scheme, the stability was determined by the fraction 

of the original oil that remained in the emulsion after 24-hours settling (0 to 0.25 = unstable, 0.25 

to 0.75 = fairly stable, 0.75 to 1 = very stable). 

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Both the Tendency Index and Stability generally increase with increased degree of evaporation. 

Colder temperatures generally increase both the Tendency Index and Stability (i.e., promote 

emulsification) unless the oil gels as the temperature drops below its pour point and it becomes 

too viscous to form an emulsion. Emulsion formation results in large increases in the spill's 

volume, enormous viscosity increases (which can reduce dispersant effectiveness), and increased 

water content (which can prevent ignition of the slicks and in situ burning). 

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APPENDIX B. OIL PROPERTY ANALYSIS RESULTS FOR MC 252 ENT052210-178 CRUDE OIL

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Oil Weathering BP MC252 ENT-052210-178 Volume Weathered(ml) 970

Volume for 2cm thick 969.50

Tray 7 Tray 8 Average Air Temp Tray thickness (m) 0.02001031

Tray Mass (g) 240.4 240.8 24.7 °C

Fv vs. Theta Modeling

Date/Time Fm Oil Fv Evaporative Model

Tray 7 Tray 8 Tray 7 Tray 8 Tray 7 Tray 8 Density Tray 7 Tray 8 Exposure Evaporate

(g) (g) (g) (g) (g/cm3

) (Corrected) (Fv)

07/06/2010 17:17 1032.7 1040.2 792.3 799.4 0.000 0.000 0.832 0.000 0.000 0.0 0.00007/06/2010 17:50 1001.2 1009.5 760.8 768.7 0.040 0.038 0.837 0.046 0.045 199.0 0.10807/06/2010 19:00 963.1 971.6 722.7 730.8 0.088 0.086 0.844 0.101 0.099 621.1 0.16507/06/2010 19:30 949.0 957.7 708.6 716.9 0.106 0.103 0.847 0.121 0.119 802.0 0.17807/06/2010 22:35 927.1 936.0 686.7 695.2 0.133 0.130 0.850 0.152 0.149 1917.5 0.22408/06/2010 10:30 833.6 839.6 593.2 598.8 0.251 0.251 0.868 0.282 0.282 6228.9 0.28808/06/2010 19:30 815.1 822.3 574.7 581.5 0.275 0.273 0.871 0.307 0.305 9485.1 0.31109/06/2010 10:22 795.8 800.7 555.4 559.9 0.299 0.300 0.874 0.333 0.334 14863.8 0.33509/06/2010 17:10 786.8 791.8 546.4 551.0 0.310 0.311 0.876 0.345 0.345 17324.0 0.34310/06/2010 10:13 776.3 535.5 0.330 0.879 0.366 23492.6 0.36011/06/2010 13:45 763.3 522.5 0.346 0.881 0.383 33454.1 0.37914/06/2010 13:16 742.2 501.4 0.373 0.885 0.410 59328.6 0.410

16/06/2010 9:48 732.0 491.2 0.386 0.887 0.423 75440.6 0.423

17/06/2010 11:15 728.4 487.6 0.390 0.887 0.428 84648.3 0.42921/06/2010 16:45 713.9 473.1 0.408 0.890 0.447 121370.6 0.449

2-day 2-week 2-day 2-week

Fm 0.310 0.408 Fv 0.345 0.447

Mass of Oil + Tray Mass of Oil

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Density BP MC252 ENT-052210-178

Measurements Density Constants for SL Ross Model

API Gravity

Mass Density Temperature Density Constant 1 (slope, kg/m3

) 128.770 Standard Density Temperature, To (K) 288.72

Evaporated (g/cm3

) (°C) Density Constant 2 (kg/K.m3

) 0.705 Standard Density (kg/m3

) 838.736

(Fm) API Gravity @ 15.5°C 37.21

0 0.838 16.6 Calculations0 0.823 37.8 Temperature Density Density Volume T-To

0.31 0.880 17.3 (°C) (g/cm3) (kg/m3

) Evaporated (K)

0.31 0.866 37.4 (Fv)0.41 0.896 17.1 15 0.839 839 0 -0.560.41 0.881 38.2 15 0.882 882 0.345 -0.56

0 0.832 24.69 15 0.897 897 0.447 -0.56

0.31 0.875 24.69 35 0.825 825 0 19.440.41 0.891 24.69 35 0.868 868 0.345 19.44

35 0.883 883 0.447 19.44

slope 0.142 15.5 0.897 897 0.000

intercept 0.832

r

2 0.999

910900890880870860850840830820

0.00 0.10 0.20 0.30 0.40 0.50

Fv

Density (kg/m^3)

910900890880870860850840830820

-5 0 5 10 15 20 25

T-To

Density (kg/m^3)

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Wind Tunnel Calibration Toluene

Tray Mass (g)

Elapsed Mass Toluene

Time Tray 9 Tray 6

(s) (g)

0 825.0 829.2

1980 765.3 776.1

6180 673.2 686.7

7980 630.7 645.7

19080 549.1 567.0

Tray 9 Tray 6 Average

slope -0.01384046 -0.013238 -0.013539

E (kg/s) -1.3539E-05

Wind Tunnel Temperature, T (K) 297.8499 24.69 °C

Toluene Vapor Pressure, P (kPa) 3.733

Ideal Gas Constant (R, kPa.m^3/kg.mol.K) 8.314

Molecular Weight of Toluene (W, kg/kg.mol) 92.13

Tray Area (A, m^2) 0.048475

K = ERT/APW (m/s) -0.002011016

Mackay Constants BP MC252 ENT-052210-178

(automated)

Point Fv Tb/T H ln(H)

1 0.022 1.238 2.244E-04 -8.402

2 0.072 1.295 1.287E-04 -8.958

3 0.109 1.338 1.089E-04 -9.125

4 0.134 1.367 2.737E-05 -10.506

5 0.215 1.461 3.071E-05 -10.391

6 0.293 1.551 7.128E-06 -11.851

7 0.319 1.581 5.345E-06 -12.139

8 0.339 1.604 4.785E-06 -12.250

9 0.356 1.623 3.308E-06 -12.619

10 0.374 1.644 1.708E-06 -13.280

11 0.396 1.670 1.060E-06 -13.757

12 0.417 1.693 8.178E-07 -14.017

13 0.426 1.704 5.036E-07 -14.501

calculated adjusted

Fv vs. Theta B (-slope) 12.30515 15.9

Fv vs. Theta A (intercept) 7.032316 12.9

Wind Tunnel Calibration

300

400

500

600

700

800

900

0 2000 4000 6000 8000 10000

Elapsed Time (s)

Toluene Remaining 

(g)

ASTM Distillation BP MC252 ENT-052210-178

200 ml Fresh oil

Volume Fraction Temperature

Distilled Distilled Liquid Vapor

(mL) (Fv) (°C) (°C)

IBP 0.00 84.0 39.8

10 0.05 111.6 77.4

20 0.10 124.4 91.7

30 0.15 137.0 102.4

40 0.20 151.2 115.8

50 0.25 168.8 116.0

60 0.30 188.2 126.4

70 0.35 208.0 150.0

80 0.40 227.0 129.7

90 0.45 248.0 142.5

slope 344.1

intercept 87.8

Distillation Constant A (slope, K) 344.1

Distillation Constant B (intercept, K) 360.9

Used original data set

Water Subtracted

ASTM Distillation

y = 344.13x + 87.767

0.0

50.0

100.0

150.0

200.0

250.0

300.0

0.00 0.10 0.20 0.30 0.40 0.50

Volume Fraction Evaporated (Fv)

Liquid Temperature (°C)

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MC252 Crude Ent-052210-178 - Fv vs Theta

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

0.500

0.0 50000.0 100000.0 150000.0

Evaporative Exposure

Fv

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Viscosity BP MC252 ENT-052210-178

Viscosity Constants for SL Ross Model

Mass Shear Standard Viscosity Temperature (K) 273.16

Evaporated Viscosity Temperature rpm Spindle # Rate ln(Viscosity) Standard Viscosity (cP) 9.03

(Fm) (cP) (°C) (s-1) Viscosity Constant 1 6.49

0 4.1 15.0 120.0 CP-42 461.0 1.399 Viscosity Constant 2 (K-1) 5646.99

0 1.4 35.0 120.0 CP-42 461.0 0.329

0.31 42.9 15.0 120.0 CP-42 461.0 3.7590.31 10.3 35.0 120.0 CP-42 461.0 2.3320.41 85.1 15.0 120.0 CP-42 461.0 4.4440.41 22.8 35.0 120.0 CP-42 461.0 3.127

Volume

Evaporated Viscosity Temperature ln(Viscosity) 1/T-1/To

(Fv) (cP) (°C) (K-1)

0 4.1 15.0 1.399 -0.0001905640 1.4 35.0 0.329 -0.000415685

0.34 42.9 15.0 3.759 -0.0001904430.34 10.3 35.0 2.332 -0.0004156850.45 85.1 15.0 4.444 -0.0001904430.45 22.8 35.0 3.127 -0.000415685

Volume Viscosity Viscosity

Evaporated 1°C 15°C

(Fv) (cP) (cP)

0 4.1 1.4

0.34 42.9 10.30.45 85.1 22.8

5.0004.0003.0002.0001.0000.000

-0.0005 -0.0004 -0.0003 -0.0002 -0.0001 0

1/T-1/To

ln(Viscosity)

5.0004.0003.0002.0001.0000.000

0 0.1 0.2 0.3 0.4 0.5

Fv

ln(Viscosity)

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A8.20 
Pour Point BP MC252 ENT-052210-178

Test Results Pour Point Constants for SLR Spill Model

Fv Initial Pour Point (K) 264.6211

Measured Reported Pour Point Constant 0.136472

(°C) (°C)

0.000 <-10 -9 less than0.345 5 60.447 5 6

slope 36.11327

intercept -8.528878

Pour Point

-10

-5

50

10

0.000 0.100 0.200 0.300 0.400 0.500

Fv

Pour Point (°C)

Interfacial Tension BP MC252 ENT-052210-178

Interfacial Tension Constants for SL Ross Model

Fv Interfacial Tension Dial Reading Correction Factor (F) Oil/Water Interfacial Tension (dyne/cm) 23.273

Oil/Water Oil/Air Oil/Water Oil/Air Oil/Water Oil/Air Oil/Water Interfacial Tension Constant -0.079(dyne/cm) (dyne/cm) (dyne/cm) (dyne/cm) Oil/Air Interfacial Tension (dyne/cm) 23.300

0.000 23.3 23.5 23.7 26.4 0.985 0.893 Oil/Air Interfacial Tension Constant 0.5740.345 22.6 26.8 22.4 30.0 1.007 0.8960.447 22.5 30.1 22.1 33.4 1.019 0.900

slope -1.841 13.365

intercept 23.273 23.300

5.00.0

35.030.025.020.015.010.0

0.000 0.100 0.200 0.300 0.400 0.500

Fv

Interfacial Tension 

(dyne/cm)Oil/Water Oil/Air Linear (Oil/Air) Linear (Oil/Water)

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A8.21 
Flash Point BP MC252 ENT-052210-178

Test Results Flash Point Constants for SLR Spill Model

Initial Flash Point (K) 170.989

Fv Flash Point Flash Point Constant 2.647

(°C)

0.000 <-80.345 540.447 100

slope 452.664693

intercept 170.989269

0

80604020

120100

0.000 0.100 0.200 0.300 0.400 0.500

Fv

Flash Point (°C)

SL Ross Model BP MC252 ENT-052210-178

Modeling Constants

Standard Density 838.736 kg/m3

Standard Density Temperature 288.720 K

Density Constant 1 128.770 kg/m3Density Constant 2 0.70499 kg/K.m3

Standard Viscosity 9.03203 cP

Standard Viscosity Temperature 273.160 K

Viscosity Constant 1 6.4856Viscosity Constant 2 5646.99 K-1

Oil/Water Interfacial Tension 23.2729 dyne/cm

Air/Oil Interfacial Tension 23.3002 dyne/cm

Oil/Water Interfacial Tension Constant -0.07910

Air/Oil Interfacial Tension Constant 0.57362

Initial Pour Point 264.621 K

Pour Point Constant 0.13647

ASTM Distillation Constant A (slope) 344.133 K

ASTM Distillation Constant B (intercept) 360.927 K

Emulsification Delay 9999999999

Initial Flash Point 170.989 K

Flash Point Constant 2.64733

Fv vs. Theta A 12.90000Fv vs. Theta B 15.90000

B.Tg 5471.72B.To 5738.73

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Emulsification Formation - Tendency and Stability BP MC252 ENT-052210-178Emulsification Tendency and Stability - 23°C

Test Results 300ml H2

O 22.5 °C

oil @ 39.0 °Cmixing don 22.7 °Csettling don 22.7 °CFinal 24 hr 22.7 °Ctwo replicates of each oil

Fresh Oil Weathered Two Days Weathered Two Weeks

All measurements in mm Emulsion Free Oil Emulsion Free Oil Emulsion Free Oil Emulsion Free Oil Emulsion Free Oil Emulsion Free Oil

Start 0 10 0 10 0 10 0 10 0 10 0 10

After first hour mixing 10 0 10 0 11 0 11 0 10 0 10 0

plus 10 minutes 0 9 0 9 10 0 0 9 0 9 10 0

plus 20 minutes 0 9 0 9 0 9 0 9 0909

plus 30 minutes 0 9 0 9 0 9 0 9 0909

After second hour mixing 0 9 0 9 10 0 10 0 10 0 10 0

plus 10 minutes 0 9 0 9 0 9 10 0 10 0 10 0plus 20 minutes 0 9 0 9 0 9 10 0 10 0 10 0plus 30 minutes 0 9 0 9 0 9 10 0 0 9 10 0

After third hour mixing 0 8 0 9 10 0 10 0 10 0 10 0

plus 10 minutes 0 9 0 9 10 0 10 0 0909

plus 20 minutes 0 9 0 9 0 9 0 9 0909

plus 30 minutes 0 9 0 9 0 9 0 9 0909

After fourth hour mixing 0 9 0 9 10 0 10 0 10 0 10 0

plus 10 minutes 0 9 0 9 0 9 0 9 10 0 10 0

plus 20 minutes 0 9 0 9 0 9 0 9 0909

plus 30 minutes 0 9 0 9 0 9 0 9 0909

Appearance Brown solid 0 0 0 0 0 0

Brown viscous

liquid XX X X XX

Black with

large droplets 00 0 0 00

Looks like oil

X

X

X

X

X

X

plus 24 hour 0 9 0 9 0 9 0 9 0 9 0 9

Conclusions: Fresh Oil Weathered Two Days Weathered Two Weeks

 Tendency Unlikely Unlikely Unlikely Stability Unstable Unstable Unstable Water Content 0% 0% 0% (after 24 hr)

 SL ROSS ENVIRONMENTAL RESEARCH LTD.

 ___________________________________________________________________________________________________________________

 _______________________________________________________________________________________________________________________ 

A8.23 
-24-

Emulsification Tendency and Stability - 35°C

Test Results 300ml H2

O 34.0 °C

oil @ 40.0 °Cmixing don 36.0 °Csettling don 22.0 °CFinal 24 hr 36.0 °Ctwo replicates of each oil

Fresh Oil Weathered Two Days Weathered Two Weeks

All measurements in mm Emulsion Free Oil Emulsion Free Oil Emulsion Free Oil Emulsion Free Oil Emulsion Free Oil Emulsion Free Oil

Start 0 0 0 10 0 10 0 10 0 10 0 10

After first hour mixing 0 9 0 9 0 9 0 9 0 9 0 9

plus 10 minutes 0 9 0 9 0 9 0 9 0909

plus 20 minutes 0 9 0 9 0 9 0 9 0909plus 30 minutes 0 9 0 9 0 9 0 9 0909

After second hour mixing 0 9 0 9 0 9 0 9 0909

plus 10 minutes 0 9 0 9 0 9 0 9 0909plus 20 minutes 0 9 0 9 0 9 0 9 0909plus 30 minutes 0 9 0 9 0 9 0 9 0909

After third hour mixing 0 9 0 9 0 9 0 9 0 9 0 9

plus 10 minutes 0 9 0 9 0 9 0 9 0909

plus 20 minutes 0 9 0 9 0 9 0 9 0909plus 30 minutes 0 9 0 9 0 9 0 9 0909

After fourth hour mixing 0 9 0 9 0 9 0 9 0909

plus 10 minutes 0 9 0 9 0 9 0 9 0909plus 20 minutes 0 9 0 9 0 9 0 9 0909plus 30 minutes 0 9 0 9 0 9 0 9 0909

Appearance Brown solid 0 0 0 0 0 0

Brown viscous

liquid 00 0 0 00

Black with

large droplets 00 0 0 00

Looks like oil

X

X

X

X

X

X

plus 24 hour 0909 0 9 0 9 0909

note: 0

Conclusions: Fresh Oil Weathered Two Days Weathered Two Weeks

 Tendency Unlikely Unlikely Unlikely Stability Unstable Unstable Unstable Water Content 0% 0% 0% (after 24 hr)

 SL ROSS ENVIRONMENTAL RESEARCH LTD.

 ___________________________________________________________________________________________________________________

 _______________________________________________________________________________________________________________________ 

A8.24 
-25-

Viscosity Measurements with Brookfield DV-III+ Rheometer 

Nominal Test Temperature 15.0 35.0

Viscosity RPM Spindle Shear Rate Viscosity RPM Spindle Shear Rate

Fresh 4.1 120.0 CP-42 461.0 1.4 120.0 CP-42 461.0

2 Day Weathered 42.9 120.0 CP-42 461.0 10.3 120.0 CP-42 461.0

2 Week Weathered 85.1 120.0 CP-42 461.0 22.8 120.0 CP-42 461.0

Spindle RPM % Torque Viscosity Shear Rate Temp

cP °C

Fresh CP-42 15 0.3 2.6 57.6 14.9

30 0.8 3.4 115.0 14.9

45 1.5 3.7 173.0 14.9

60 1.8 3.8 230.0 14.9

90 2.8 4.0 346.0 14.9

120 3.8 4.1 461.0 14.9 <===

180 5.8 4.1 691.0 14.9

250 8.1 4.2 960.0 14.9

2 Day Weathered CP-42 15 6.8 58.0 57.6 14.9

30 12.3 52.5 17.4 14.9

45 17.4 49.5 173.0 14.9

60 22.3 47.6 230.0 14.9

90 31.5 44.8 346.0 14.9

120 40.2 42.9 461.0 14.9 <===

180 57.0 40.5 691.0 14.9

250 75.6 38.7 960.0 14.9

2 Week Weathered CP-42 15 15.1 128.9 57.6 15.1

30 26.9 114.8 115.0 15.1

45 37.1 105.5 173.0 15.0

60 46.3 98.8 230.0 15.0

90 63.7 90.6 346.0 15.0

120 79.8 85.1 461.0 15.0 <===

180 -over- -over- 691.0 15.1

Fresh CP-42 15 0.1 0.9 57.6 35.0

30 0.2 0.9 115.0 35.0

45 0.5 1.4 173.0 35.0

60 0.7 1.5 230.0 35.0

90 1.0 1.4 346.0 35.0

120 1.3 1.4 461.0 35.0 <===

180 2.2 1.6 691.0 35.0

250 3.0 1.5 960.0 35.0

2 Day Weathered CP-42 15 1.2 10.2 57.6 35.0

30 2.4 10.2 115.0 35.0

45 3.7 10.5 173.0 35.0

60 4.9 10.5 230.0 35.0

90 7.3 10.4 346.0 35.0

120 9.7 10.3 461.0 35.0 <===

180 14.4 10.2 691.0 35.0

250 19.9 10.2 960.0 35.0

2 Week Weathered CP-42 15 2.8 23.9 57.6 35.0

30 5.5 23.5 115.0 35.0

45 8.3 23.6 173.0 35.0

60 10.9 23.3 230.0 35.0

90 16.1 22.9 346.0 35.0

120 21.4 22.8 461.0 35.0 <===

180 31.6 22.5 691.0 35.0

250 43.4 22.2 960.0 35.0

 SL ROSS ENVIRONMENTAL RESEARCH LTD.

 ___________________________________________________________________________________________________________________

 _______________________________________________________________________________________________________________________ 

A8.25 
 ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A9.1 

Appendix 9

A9 Expert Resumes

Mr. Alan A. Allen has over 43 years experience as a technical advisor and field 

supervisor involving hundreds of oil spills around the world. He has developed 

specialized strategies and equipment for the prevention, surveillance and control 

of oil spills; and he has conducted many hundreds of oil spill training courses 

under arctic, temperate and tropical conditions. Mr. Allen is listed as a technical 

advisor for emergency response to oil spills in numerous emergency response 

plans worldwide. A few of the spills on which he has worked include the Santa 

Barbara blowout (1969), the Mizushima tank failure in Japan (1974), the Ixtoc 

blowout off Mexico (1979), the Exxon Valdez grounding (1989), the Gulf of 

Arabia war-related spills (1991), the Sea Prince grounding off South Korea (1995), 

the Deepwater Horizon blowout in the Gulf of Mexico (2010), and a number of 

pipeline spills onshore and in wetlands.

Dr. Michel Boufadel is Professor and Chair of the Department of Civil and 

Environmental Engineering at Temple University, Philadelphia, Pennsylvania, 

USA.  He is a Professional Engineer (Environmental Engineering) in the 

Commonwealth of Pennsylvania (USA).  He is also a Professional Hydrologist 

(hydrogeology) as accredited by the American Institute of Hydrology (USA).  

His area of expertise is Environmental Hydrology, where he develops methods to 

account for changes in the environment due to natural and anthropogenic stressors.  

He has been the lead researcher on various projects funded by the Oil Spill Research 

program within the United States Environmental Protection Agency (USEPA).  

He is currently investigating the lingering of the Exxon Valdez oil (1989) in the 

beaches of Prince William Sound, where he and his team conducted field studies 

on various beaches and are exploring remediation technologies for dealing with 

the 20-year old spill.  Dr. Boufadel is author of numerous articles in publications 

such as NATURE geoscience, Environmental Science and Technology, the 

Journal of Environmental Engineering, Environmental Geology, Journal of the 

American Water Resources Association, Journal of Contaminant Hydrology, and 

Journal of Stochastic Environmental Research and Risk Assessment.  The work 

of Dr. Boufadel has been reported in various media outlets such as the New 

York Times, the Washington Post, and the BBC World News and numerous 

international media outlets. 
 ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A9.2 

Mr. Sky Bristol leads a computer and information science research and 

development effort with the U.S. Geological Survey (USGS), pursuing a concept 

called the Integrated Information Environment. Before coming to the USGS 

in 2004, Sky held various positions with the U.S. Fish and Wildlife Service 

(USFWS) as a biologist in the Environmental Contaminants Program and an 

engineer and developer with the Information Resource Management Program. 

Sky was educated as a wildlife biologist and criminologist at the University of 

New Mexico, learning computer and information science as a secondary vocation 

in the course of creating program in these areas for the USFWS and USGS. 

Dr. Thomas S. Coolbaugh is a Distinguished Scientific Associate with 

ExxonMobil Research & Engineering. Dr. Coolbaugh has extensive experience 

in a variety of research settings as a research scientist and research leader and 

currently leads ExxonMobil’s downstream oil spill response technology group. 

Dr. Coolbaugh earned a B.A., magna cum laude in Chemistry from Amherst 

College, and a Ph.D. in Chemistry from the California Institute of Technology 

with Prof. Robert Grubbs, 2005 Nobel laureate in Chemistry. He also earned 

an M.S. in Management of Technology from Polytechnic University (New 

York).

Mr. Per Daling is a Senior Research Scientist at Marine Environmental 

Technology Dept. at SINTEF. He has 30 years of experience within the field 

of oil pollution, and has been project manager and scientist responsible for 

many research programs involving weathering and behaviour of oil at sea, field 

and laboratory testing of oil spill dispersants and countermeasure techniques 

and oil spill planning. Mr. Daling has authored or co-authored 40 refereed 

publications, and more than 150 non-refereed publications, papers and invited 

presentations. He has a MSc in Organic Analytical Chemistry from Institute of 

Chemistry, University of Trondheim.

Dr. Merv Fingas was Chief of the Emergencies Science Division of 

Environment Canada for over 30 years and is currently working privately in 

Western Canada. His specialities include; spill dynamics and behaviour, spill 

treating agent studies, remote sensing and detection, and in-situ burning. 

He has over 750 papers and publications in the field. Dr. Fingas has been an 

editor of the Journal of Hazardous Materials for 6 years. He has served on 

two committees on the National Academy of Sciences of the United States 

on oil spills including the recent ‘Oil in the Sea’. He is chairman of several 
 ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A9.3 

ASTM and inter-governmental committees on spill matters. Dr. Fingas has a 

PhD in environmental physics from McGill University, three masters degrees; 

chemistry, business and mathematics, all from University of Ottawa.

Dr. Deborah French-McCay is a Principal at Applied Science Associates 

(ASA, Narragansett, RI, USA), where she specializes in quantitative assessments 

and modeling of oil and chemical releases: transport and fates, exposure, 

bioaccumulation of pollutants by biota, toxicity, and effects on populations 

and aquatic ecosystems. Dr. French McCay was principal investigator in the 

development of NRDA models, established in the US Federal regulations for 

simplified assessments under CERCLA and the Oil Pollution Act. She has been 

principal investigator and primary author of more than one hundred technical 

reports and papers, and is an internationally recognized expert in oil spill fate 

and effects modeling. Dr. French-McCay received her bachelor’s degree in 

Zoology from Rutgers in 1974 and her Ph.D. in Biological Oceanography from 

the University of Rhode Island in 1984

Dr. Ron Goodman attended the University of Saskatchewan and graduated 

with a Bachelor of Arts in Science with specialization in physics. He undertook 

graduate studies at McMaster University in Hamilton and obtained a PhD. in 

nuclear physics in 1964. Innovative Ventures Ltd. (IVL) was started by Dr. 

Goodman in 1971 to consult on Arctic Environmental issues. Joining Imperial 

Oil in 1978, he developed the only industry-based oil-spill research program in 

Canada. He served as the manager of surveillance and tracking for the Exxon 

Valdez response during 1989. Dr. Goodman retired from Imperial Oil in 2000, 

and reactivated Innovative Ventures Ltd. IVL has been involved in the JESSICA 

spill in the Galapagos Islands and the rail spill at Wabamun, Alberta Canada. He 

has been in the oil-spill technology business for thirty-five years, specializing in 

remote sensing, arctic oil-spill response and oil-spill models. Dr. Goodman has 

served on a number of national and international committees including IMO/

MEPC and ASTM. He has published over 200 papers. 

Dr. Ali Khelifa is Head of the Spill Modelling Laboratory at the Emergencies 

Science and Technology Section of Environment Canada. He received his 

Master in 1992 and PhD in 1998 in Environmental Hydraulics from Laval 

University in Québec in Canada. Dr. Khelifa has conducted extensive research 

on transport and fate of pollutants spilled in the environment using computer 

and laboratory modelling for the last 20 years. The last nine years of his 
 ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A9.4 

research were devoted to developing quantitative understanding of the fate 

of physically and chemically dispersed oils in the aquatic environment. He 

has authored over a hundred of scientific papers and technical reports and his 

extensive contribution related to oil dispersion and oil-sediment interaction is 

internationally recognized. 

Dr. Robert Jones is a Chemist in NOAA’s Office of Response and Restoration, 

Emergency Response Division, He is extensively involved in the development 

of computer models used to predict the fate and transport of chemicals in the 

environment and is a member of the spill response team.

Dr. Jones received his B.S. in Chemistry from Xavier University, and his Ph.D. 

in Physical Chemistry from Indiana University. After graduation he worked 

as a research associate at the National Institute of Standards and Technology in 

Washington D.C. Prior to joining NOAA in 1988, Dr. Jones was an assistant 

professor in the chemistry department at Western Washington University. 

Mr. Patrick Lambert is a chemist with twenty years experience in the 

environmental field of chemical and oil spill research and development, and 

twenty-three years of laboratory experience in both the private and public 

sectors. He is Head of Field Work and Response, Emergencies Science and 

Technology Section, Environment Canada. He has was part of the science 

team in numerous oil spill projects such as the Newfoundland Offshore Burn 

Experiment and has extensive operational experience with contaminated sites, 

environmental emergency response, contaminated site and counter-terrorism 

related.

Dr. Kenneth Lee is a senior research scientist and Director of the Centre for 

Offshore Oil and Gas Environmental Research, Fisheries and Oceans Canada. 

The centre is responsible for the coordination of a national strategic program 

in environmental and oceanographic research relating to oil and gas activities. 

Dr. Lee is the recipient of Fisheries and Oceans Canada’s Prix d’Excellence 

(Science) for his research contributions on environmental issues associated with 

offshore oil and gas activities; and the Government of Canada’s Federal Partners 

on Technology Transfer (FPTT) Leadership Award for the development of oil 

spill countermeasures. He currently serves as a Committee Member of NATO’s 

Science for Peace and Security Program. 

Dr. Ira Leifer has a Ph.D. in bubble-mediated air-sea interaction from the 
 ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A9.5 

Georgia Institute of Technology, Earth and Atmospheric Sciences Department. 

Over the last decade, he has been a leader in research related to natural seepage, 

including numerical bubble modeling, video observations of bubbles including 

oily bubbles, multibeam sonar bubble plume observations, remote sensing of 

atmospheric methane and of oil slicks by imaging spectroscopy, weathering of 

oil slicks from natural seepage, and in situ measurements of light n-alkanes in 

the air and water. Research sites have spanned the Gulf of Mexico, to Santa 

Barbara Channel, to Norwegian and Russian Arctic waters, from submarines to 

research vessels, to airplanes.

Dr. William (Bill) J. Lehr is Senior Scientist at the Office of Response 

and Restoration of the National Oceanic and Atmospheric Administration 

(NOAA).  He was previously Spill Response Group Leader for the same 

organization. Dr. Lehr has also served as an adjunct professor for the World 

Maritime University and oil spill consultant for UNESCO. Dr. Lehr is a 

world-recognized expert in the field of hazardous chemical spill modeling and 

remote sensing of oil spills. He has served as guest editor for the journal, Spill 

Science and Technology and the Journal of Hazardous Materials and as CoChair of the International Oil Weathering Committee.  NOAA and the United 

States Coast Guard have awarded him several medals for his spill response 

efforts at major spill incidents of national or international significance. He has 

numerous publications in the field. Dr. Lehr holds a Ph.D. in Physics from 

Washington State University.

Dr. Alan Mearns is Senior Staff Scientist, Ecology, with NOAA’s Emergency 

Response Division (ERD), Office of Response and Restoration.  Dr. Mearns 

holds a PhD in Fisheries from the University of Washington, and Master 

of Science and Bachelor of Science degrees from California State University 

at Long Beach. He is past leader of the Biology Division of the Southern 

California Coastal Water Research Project and served as Ecologist for the 

NOAA Puget Sound MESA Program. He has provided support to the US 

Coast Guard and the NOAA Scientific Support Coordinators (SSC’s) on 

numerous oil spills throughout the US and internationally. He supports 

numerous oil spill science training programs, is a member of several technical 

advisory committees in Alaska and has recently started a third decade of annual 

shoreline monitoring in Prince William Sound. He has received various awards 

from California State University and NOAA.
 ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A9.6 

Dr. Edward B. Overton is Professor Emeritus in the Department of 

Environmental Sciences, School of Coast and the Environment at LSU

He received his B.S.(1965) and Ph.D.(1970) degrees from the University of 

Alabama, Tuscaloosa. His research interests include understanding the fates 

and distributions of hydrocarbons following an oil spill, the environmental 

chemistry of hazardous chemicals, and the detection of environmental 

pollutants at the site of sample collection. Dr. Overton held the Clairborne 

Chair in Environmental Toxicology and Air Quality prior to his retirement, 

and was honored as an LSU Distinguished Faculty in 2008, and was the 1996 

Louisiana Technologist of the Year.

Dr. James Payne has been involved in oil-spill research for over 36 years, including 

work at the Woods Hole Oceanographic Institution, the University of California 

Bodega Marine Laboratory, Science Applications International Corporation, 

and the last 12 years as President of Payne Environmental Consultants, 

Incorporated. He has conducted oil fate and behavior research and studies on 

oil-spill dispersants since 1979, and he has served on four National Academy 

of Sciences/National Research Council Committees dealing with petroleum in 

the marine environment. Most recently he served on the NRC Committee on 

Understanding Oil Spill Dispersants. 

Dr. Antonio Possolo is a statistician and Chief of the Statistical Engineering 

Division at the National Institute of Standards and Technology (NIST, U.S. 

Dept. of Commerce). He has been a statistician at General Electric and at 

Boeing, and has taught at the University of Washington in Seattle and at 

Princeton University. His areas of specialty include statistical modeling of 

spatio-temporal data and uncertainty analysis. Dr. Possolo holds a Ph.D in 

Statistics from Yale University.
 ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A10.1 

Appendix 10

A10 Reviewer Comments and Replies

Sixteen reviewers (see Table 1) with diverse background were selected by 

the Coastal Response Research Center to review all or part of the report. 

Fourteen responded within the allotted time frame. We would like to thank 

all the reviewers for their efforts under conditions that required expeditious 

action on their part. While the list of names is provided in this Appendix, 

individual comments were provided to the team anonymously. One should 

not, for example, identify Reviewer 1 with the first name in Table 1. Page 

numbers refer to the unedited draft report and may not correspond to those 

in the final report.

First Last Affiliation

 Eric Adams Massachusetts Institute of Technology (MIT) Dept. 

of Mechanical Engineering

 Kathryn Burns Austrailian Institute of Marine Sciences, Organic 

Geochemist, Water Quality Team

 Robert Chen University of Massachusetts Boston, Environmental 

 Earth and Oceans Sciences

 Kurt Hansen U.S. Coast Guard, Research & Development Center

 Terry Hazen Lawrence Berkeley National Laboratory - 

 Ecology Dept.

 Wolfgang Konkel ExxonMobil

 Buzz Martin Texas General Land Office -

Oil Spill Prevention & Response

 Joseph Mullin Bureau of Ocean Energy Management, Regulation &

Enforcement (BOEMRE)

 Michael O’Brien Internation Tanker Owners Pollution Federation Ltd.

(ITOPF)

 Rob Swift University of New Hampshire,

Mechanical Engineering/Ocean Engineering

 Terry Wade Texas A & M

 Xiaomong Wang Florida State University, Dept. of Mathematics

 Tom Weber Center for Coastal & Ocean Mapping

 Steven Werely Purdue University
 ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A10.2 

Reviewers’ comments are in italics and replies are in plain text.

Reviewer 1:

This section focuses on estimating the amount of oil recovered by surface 

skimmers. The total amount of oil and water mixture held in tanks on the 

skimmers is measurable, but the percent that is actually oil, referred to as the 

recovery efficiency (RE), is needed to calculate the amount of oil collected. The 

report states that a RE of 33% was used based on “prior studies” (no reference). 

To assess whether this is a reasonable value, the types of skimmers employed 

would have to be taken into account. Oliophilic mechanisms such as disk and 

drum skimmers can have RE’s of 90% or better (see, for example, Schulze, 

1998). Suction or weir skimmers, on the other hand, typically have RE values 

of a few percent unless the slick is unnaturally thick and the surface is without 

waves. Thus assuming an average RE for the skimming fleet should be based 

on the distribution of skimmer types (as well, of course, sea state and slick 

thickness).

It should also be pointed out that determining the water content in recovered 

oil and water mixtures is a standard laboratory analysis done at the 

OHMSETT oil spill test facility. It would seem that their protocol could be 

adapted to either on-board determination of RE or representative samples 

could be transported to a shore laboratory for RE analysis.

Skimmers were the most commonly-used mechanical devices used to remove 

oil from the Gulf water surface. These skimmers varied greatly in size, 

application, and capacity, as well as in recovery efficiency and water pickup. 

(Schulze, 1998; Schwartz, 1979). In the particular case of the Gulf oil spill 

the major issue is the amount of water recovered. A skimmer’s performance 

is affected by a number of factors including the thickness of the oil being 

recovered, the extent of weathering and emulsification of the oil, the presence 

of debris, and weather conditions at the time of recovery operations. A 

skimmer’s overall performance is usually determined by a combination of 

its recovery rate and the percentage of oil recovered. The maximum amount 

of oil that a skimmer could recover is called the ‘Nameplate Recovery Rate’ 

and is typically provided by the manufacturer of a skimmer (Fingas, 2010; 

Meyer et al, 2009). A similar definition is the ‘Effective Daily Recovery 

Capacity’, which is the amount that a skimmer could recover in daylight 

hours under ideal conditions. The recovery rate is the volume of oil recovered 

under specific conditions. It is measured as volume per unit of time, e.g., 

m3/h, and is usually given as a range. If a skimmer takes in a lot of water, it 

is detrimental to the overall efficiency of an oil spill recovery operation. The 
 ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A10.3 

summary results of performance testing on various types of skimmers are 

given in Table 1 (Schulze, 1998;Fingas, 2010). 

Table 2 shows the three most important values of skimmer performance, 

ORR, TE, and RE. This is a sample table showing a fraction of the skimmers 

tests reported in the reference (Fingas, 2010). The Oil Recovery Rate(ORR) 

is the quantitative rate in volume per unit time, usually m3/hour and is 

corrected for water recovery. The throughput efficiency (TE) is applicable 

only to advancing skimmers. The throughput efficiency is the percentage of 

oil presented to a skimmer versus that recovered, in percent. The recovery 

efficiency (RE) is the percent of oil recovered out of the total oil and water 

recovered. For the Gulf recovery effort, the RE is the most important factor. 

We know the total liquids recovered, but we do not know exactly how much 

oil was in this liquid and therefore must estimate the actual oil recovered. 

Table 2 shows that the average RE of the skimmers in wave conditions is 

33%.

The emulsion recovered typically contained 30- 60% water (Appendix 6). 

Using 40% as an average water fraction, the typical amount of oil in the 

recovered mixture is 0.6 X 0.33 or 0.20 (20%). The amount of liquid recovered 

was 735,000 barrels and thus the amount of oil recovered was about 20% of 

this or 147,000 barrels.

1 Schulze, R., Oil Spill Response Performance Review of Skimmers, 

ASTM Manual Series, ASTM, 1998

2 Schwartz, S.H., Performance Tests of Four Selected Oil Spill 

Skimmers, AMOP, 493, 1979

3 Fingas, M.F., Weather Effects on Oil Spill Countermeasures, Chapter 

13 in Oil Spill Science and Technology, p. 339-426,2010

4 Meyer P., W. Schmidt, J.-E. Delgado, D. DeVitis, S. Potter, E. 

Haugstad and M. Crickard, Application of the American Society of 

Testing and Materials’ (ASTM) New Skimmer Test Protocol, AMOP, 

323, 2009

5 SINTEF, Laboratory study of the dispersibility of DWH surface 

emulsion, 2010
 ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A10.4 

Table 1 Performance of Typical Skimmers

 Recovery Rate (m3/hr) for given oil type* Percent

Skimmer Type Light Crude Heavy Crude Bunker C Oil**

Oleophilic Skimmers

small disc 0.2 to 2 80 to 95

large disc 10 to 20 10 to 50 80 to 95

brush 0.5 to 20 0.5 to 2 0.5 to 2 80 to 95

large drum 10 to 30 80 to 95

small drum 0.5 to 5 80 to 95

large belt 1 to 20 3 to 20 3 to 10 75 to 95

inverted belt 10 to 30 85 to 95

rope 2 to 20 2 to 10

Weir Skimmers

small weir 0.5 to 5 2 to 20 20 to 80

large weir 30 to 100 5 to 10 3 to 5 50 to 90

advancing weir 5 to 30 5 to 25 30 to 70

Elevating Skimmers

paddle conveyer 1 to 10 1 to 20 1 to 5 10 to 40

Submersion Skimmers

large 1 to 80 1 to 20 70 to 95

Suction Skimmers

small 0.3 to 2 3 to 10

large trawl unit 2 to 40 20 to 90

large vacuum 

unit 3 to 20 3 to 10 10 to 80

Vortex/Centrifugal Skimmers

centrifugal unit 0.2 to 10 2 to 20

* Recovery rate depends very much on the thickness of the oil, type of oil, sea state,

 

 and many other factors

** This is the percentage of oil in the recovered product or recovery rate. The higher the value,

 the less the amount of water and thus the better the skimmers’ performance
 ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A10.5 

Oil Slick Wave

Skimmer Year Oil Viscosity Thick. #of Speed height Wave ORR TE RE

of 

Test Type mPa. S mm Tests m/s m Conditions m3

/h % %

Harbour/small 

skimmers

Skimming Barrier 1977 200 120 3 0.25 calm 58.2 56

Skimming Barrier 1977 200 120 5 0.25 0.3 harbour chop 47.4 34.5

Skimming Barrier 1977 200 120 4 0.5 0.3 regular 71.7 48.9

Sirene Skimming 

Barrier 1979 545 3 1 0.75 calm 15.8 11 26

Sirene Skimming 

Barrier 1979 545 3.2 1 0.38 0.6 harbour chop 18.6 99 31

Sirene Skimming 

Barrier 1979 545 3.2 1 0.38 0.5 regular 16.4 99 27

Lori Brush Skimmer 1979 med. oil 600 ns 1 1.5 calm 0.96 78

Lori Brush Skimmer 1979 med. oil 600 ns 1 0.75 0.16 regular 0.35 81

Disc skim. - flat -CCG 

tests 1993 lt. crude 5 to 50 10 1 0 0.4 regular 65

Disc skim. - flat -CCG 

tests 1993 lt. crude 5 to 50 10 1 0 0.8 harbour chop 48

Disc skim. - flat -CCG 

tests 1993 lt. crude 5 to 50 25 1 0 calm 96

Disc skim. -T-disk 

-CCG tests 1993 lt. crude 5 to 50 10 1 0 0.4 regular 46

Disc skim. -T-disk 

-CCG tests 1993 lt. crude 5 to 50 10 1 0 0.8 harbour chop 24

Paddle skimmer 1977 heav. oil 1900 26 1 0 calm 9.4 91 84

Paddle skimmer 1977 heav. oil 1900 26 1 0 0.2 regular 4.8 70 18

Rope Mop towed 

single 1978 med. oil 793 5 1 1.3 0.6 harbour chop 5.7 49

Rope Mop towed 

single 1978 med. oil 793 5 1 1.5 0.6 harbour chop 5 46

Oil Mop ZRV 1976 lt. crude 65 4 ave 1 1.25 calm 7 36 23

Oil Mop ZRV 1976 lt. crude 65 4 ave 1 1.5 0.6 harbour chop 4.8 21 10

Marco Belt skimmer 1976 heav. oil 837

8 to 

11 6 0.5 calm 11.5 85 57

Marco Belt skimmer 1976 heav. oil 837

8 to 

11 4 1.5 calm harbour chop 20.6 62 76

DIP 2001 1973 Alberta crude 8 .7 ave 1 1.3 calm 2.7 88 30

DIP 2001 1975 Arab crude 24 0.5 1 1 calm 0.9 77 94

DIP 2001 1975 Arab crude 24 1 1 0.5 0.4 natural 0.9 81 95

Stationary skim. - 

Manta Ray 1975 DOP 79 20 6 calm 20.1 27

Stationary skim. - 

Manta Ray 1975 DOP 79 20 1 0.6 harbour chop 15.2 22

Table 2 Skimmer Performance (Sample cases)*
 ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A10.6 

Stationary skim. - 

Skim pak 1980 medium 200 7 3 calm 2.5 8

Stationary skim. - 

Skim pak 1980 medium 200 7 1 0.26 regular 2 7

Destroil weir 

skimmer 1979 heavy 810 5 5 calm 16.2 69

Destroil weir 

skimmer 1979 heavy 810 5 2 0.47 harbour chop 11.5 59

GT-185 1988 Bunker c 11700 1 0.4 regular 15 50

GT-185 1988 Terra Nova 100-600 1 calm 30 100

Walosep 1988 Bunker c >100k 1 calm 38 2

Walosep 1988 Bunker c >100k 1 0.4 regular 10 2

Veegarm towed weir 1980 1 1 0.25 calm 11 100 8

Veegarm towed weir 1980 1 0.25 1.9 regular 10 40 5

Veegarm towed weir 1980 light 9 2 1 0.25 0.19 harbour chop 5 60 5

Averages (taken from whole Table) 476 mixed wave conditions 15 58 44

under test 

conditions

* See reference 3 for the full 

table

under 

typical 

wave 

conditions 33

Reviewer 2:

Page 11, paragraph 2: This paragraph is not based on data and only media 

reports from some dramatized news conferences. I would not include this 

speculation.

Paragraph is modified to note that extensive DHS oil on the bottom has not 

been confirmed in the ongoing sampling programs.

Page 39, paragraph 1: 

The factors affecting biodegradation rates are as follows:

1.Type of oil

2. limiting nutrients, P, N, O, and in the ocean Fe

3. Concentration of the oil
 ________________________________________________________________________________________________

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A10.7 

4. how weathered the oil is

5. oil droplet size

6. adaption of microbial community to long-term episodic events like natural 

seeps.

The purpose of the report is to describe the oil budget calculator, not provide 

an in-depth study of long-term weathering processes. Therefore, we feel 

it would be inappropriate to expand on the admittedly brief discussion of 

biodegradation.

Reviewer 3:

P 7, line 2 remove: if slightly dull

P 8, Item 4, Line 2-3: replace bottom with lower water column

P 9, Line 3-4, rephrase: Hence, this oil is assumed to have already lost all oil 

that might evaporate.

P 10, Para 3, Line 4: Formed an emulsion when surfacing.... on

P 11, Para 1 Line 6, formed emulsion on the water surface within Km’s of 

wellhead. This needs to be consistent.

P11, Para 1, last line: this sentence is unclear 

Revisions made for the above. 

P 11, Para 2, Line 3: Basing anything here on News Reports would seem to be 

a dramatic flaw. The news reports were abysmally incorrect with respect to all 

aspects of the science of the DWH spill.

Paragraph is modified to note that the claim of extensive DWH oil on the 

bottom has not been confirmed in the ongoing sampling programs.

P 12, Para 2, Line 6: Can “44% oil percentage” be replaced with a unitless 

GOR (Gas Oil Ratio) for accuracy and understanding here and throughout the 

document? (e.g. P14, para 1, Line13)

We have kept the units of the cited report for consistency.

P 14, Para 1, Line 13: Should high not be low?

P 14, Para 2, Line 9: Replace globs with accumulations of droplets or 

particles 

Section text revised 
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A10.8 

P 15, Para 2, Line 1: Replace old saying with proverb.

P 15, Para 2, Line 3: Replace unimportant with less important 

P 17, Para 2, Line 5: Is this naturally or chemically dispersed oil?

P 19, Para3, Last line: to the wind speed and temperature

P 20, Para 2 , Line 4: from the sea 

P 22, Figure 8 and all additional similar figures: Please make a table, these 

are indecipherable 

Figure 8 and similar figures have been removed

P 22, Para 4, Line 5: I am not familiar with cubic seconds. My utilization of 

horizontal and vertical spreading rates have been as m2/sec.Check here and 

following

The unit of m2/sec3 relates to the energy dissipation rate and not to spreading. 

It is equivalent to energy per unit mass per unit time.

P 27, Last line: remove =

P 28, Para 1, Line 4: Less oil, not more oil

P 28, Figure 12, Move under 9a Header.

P 29, Section 9a, Location of tables and figures is awkward and needs to be 

revised.

Revisions made

P 30, Table 2 Concentration as what units, also please provide detail 

regarding these samples, e.g. depth sampled, time after dispersants applied, 

measured how, etc. If this is a scattershot of nonspecific sampling, please say 

so.

New table 2 Heading inserted

P 31, Last line: for

P 36, Para 2, Line 2: to

P 36, Para 3, Line 8: What evidence is there that oil settled to bottom in 

offshore areas?

The text was revised to say some of the oil has combined with sediment in the 

water column and settled to the bottom in the nearshore or, perhaps, offshore 

areas.

P 39, Does this paper not deserve conclusions? We’ve spent 39 pages getting to 

this point, provide the DWH Mass Balance.
 ________________________________________________________________________________________________

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A10.9 

New mass balance figures included in Section 14

Reviewer 4:

Generating a modeling tool such as the Oil Budget Calculator is no simple 

task, not least because there is a paucity of information – both theoretical and 

empirical - on which to base it. The need for better information on which 

to base the model is apparent throughout much of the document, with a few 

exceptions (e.g., section 6 on Evaporation). Still, assembling the Calculator 

seems to have been a worthwhile exercise for two reasons: 1) it provides a 

framework for assembling actionable data; 2) it highlights areas where the 

information gaps are the largest (e.g., direct observations of subsea dispersant 

effectiveness). I would encourage the further development of this tool both 

through efforts to better model the processes (e.g., chemical and natural 

subsurface dispersion) and by adding more capability to the model. If the 

latter could be done in such a way that the model could adaptively tune itself 

to new scenarios using field data (see comment 4. below), it could become very 

powerful. 

There are several technical issues which should be addressed. These are 

listed below. In addition, the writing of this document should be tightened up 

considerably. There are better editors than myself for doing this, so I won’t 

provide an exhaustive list of corrections, but will say that there are many 

ambiguous comments or phrases (e.g., a “good” fraction; “very” rapid;) and 

several unnecessary comments and colloquialisms (e.g., filling out an ICS209 

is “slightly dull”; “the old saying that oil and water do not mix...’). Physical 

interpretations and definitions for the rate constants do not belong in an 

appendix – this makes following the body of the text too onerous. The figures 

that take the form of Figure 8 are totally non-intuitive – this information 

should be provided in a much clearer way. Clearly, this document is in draft 

form – a thorough polishing and reworking is warranted.

Corrections to the language have been implemented. Unfortunately, multiple 

authors and strict publication deadlines prohibit extensive wordsmithing or 

style uniformity. Figure 8 and similar figures have been removed. 

1. The amount of subsurface dispersed oil is assumed to be high 

(seemingly, greater than 25% of the oil based on the combination of 

rate constants), although the data presented here do not seem to either 

support or refute that assessment. If the goal is to bring a conservative 
 ________________________________________________________________________________________________

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A10.10 

assessment (from the perspective of not underestimating the response 

resources required, such as skimmers, burners, surface dispersant, 

amount of subsurface chemical dispersant – the “lesser of two evils” - 

to be used,etc.) to the Unified Command, as was stated, than I would 

have to conclude that the analysis of the dispersed oil at the bottom was 

incorrect (too high). I recognize that there have been many observations 

of subsurface oil, and would not be surprised to learn that enough oil 

made it into the subsurface to have a large and long term impact (in 

fact, I would be more surprised to learn the opposite). But given that 

4.9 million barrels were released, even this finding would not suggest 

that a large fraction of the oil was dispersed into the subsurface . That 

is, it is conceivable that even 5% of 4.9 million barrels of oil could 

have a large, long term impact (clearly, better methods for empirically 

determining the amount of subsurface oil are needed). Specific 

comments along the lines of this theme follow.

I do not see how the LISST data support the hypothesis that “much” of the 

oil released was dispersed into the water column, as stated on page 22, or 

that they “suggest the high effectiveness of the chemical dispersants...from 

subsurface application” as stated in appendix 6. Assuming that equations 

(13) and (14) are correct (and they need to be better supported than a personal 

communication with Lasheras), it does seem reasonable to expect that the only 

way to generate these small droplets is through the use of chemical dispersant 

(otherwise, it would seem that the smallest characteristic size of a droplet 

would be about 1 mm). However, the LISST measurements only provide 

estimates of these very small droplets – they don’t appear to say anything 

about the relative quantity of the small droplets compared to larger droplets. 

Based on these measurements, it appears just as likely that the application of 

subsea dispersant was ineffective, and that only a small fraction of chemically 

dispersed bubbles was created, some of which eventually had the eventual fate 

of being sampled. From the perspective of figuring out how much surface oil 

can be responded to, it would seem more conservative (and just as likely based 

on these measurements) to say that only a small amount of oil was dispersed 

and that the chemical dispersant was ineffective.

Equation 13 is supported by the two preceding references to peer reviewed 

publications. Equation 14 has been proposed by Hinze (1955). It gives an 

estimate of the characteristic droplet size in a dispersion process based on the 

assumption that the dynamic pressure forces due to turbulent fluctuations 

control droplet formation due to breakup. It is provided in this report for 

background only (it is not used in the Calculator). The natural dispersion 

model itself is a variation of the Delvigne model, commonly used to estimate 

natural dispersion.

The LISST data found oil droplets in the locations consistent with dispersed 
 ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A10.11 

oil and subsurface current patterns. The lack of such droplets would be a 

strong indicator of little dispersion. Therefore, the Report states that these 

findings lend support to the hypothesis of significant dispersion, while citing 

other findings that do not support this hypothesis. As noted in the report, the 

experts were unable to reach a consensus on the amount of dispersion and a 

compromise value was used for the Calculator. This is obviously an area that 

requires further research.

Hinze, J.O., (1955). Fundamentals of the hydrodynamic mechanism of 

splitting in dispersion processes. AIChE Journal, Vol. 8 (4), 289-295.

Due to the limitations of available technologies in droplet size measurement 

(for example, current available LISST can only go as deep as 300 m, LOPC 

has detection limit of > 100 um and resolution of 106 counts/m3), the present 

LISST method has been used to monitor small particles generated in the 

water column by retrieving water column samples using Niskin bottles and 

surface bucket sampler. It is unfortunately that the current method did 

not measure the full spectrum of droplet size distributions in subsurface, 

and in particular, there were large uncertainties and measurement gaps for 

large particles. However, it is not true to say that “they don’t appear to say 

anything about the relative quantity of the small droplets compared to larger 

droplets”. As a matter of fact, the data presented in Figures A7-5~8 are the 

fractions of small particles (d < 68.8um) to the total particles in an expanded 

range between 2.5 and 157 um. Similarly, Figures A7-9~12 present 

cumulative particle size fractions between 2.5 and 157 um, too. These data 

all indicate that the vast majority of the measured particles are within the 

defined small particle frame (i.e., d < 68.8 um), and therefore support our 

conclusion that dispersion of oil was effective at subsurface. However, we 

do emphasize there are tremendous amount of unknowns in terms of efficacy 

evaluation of the subsurface dispersant injection, and the knowledge gaps in 

this regard must be bridged in the forthcoming research activities now that 

the importance of this issue has come into light. 

Are Payne’s reports of oil droplets at depths greater than 2 km (page 22)

supported by any other measurements (e.g., CDOM fluorometer, hydrocarbon 

fluorometer, DO, etc.)? What is the mechanism by which the oil droplets sank 

through the water column into a stable deeper plume? Most importantly, 

how can these type of observations be translated into the stated assessment 

that there was a “large amount of dispersion” ? That is, why couldn’t Payne 

have been observing the result of a small amount of dispersion? If Payne 

had a way to extrapolate a few observations to a general behavior, it should 

be clearly stated here. Otherwise, the conservative approach (from the 

perspective of figuring out how much surface oil can be responded to) would 

be to assume that Payne’s observations support a small amount of dispersion.
 ________________________________________________________________________________________________

 _________________________________________________________________________________________________

A10.12 

The report of dispersed oil droplets between 1000 and 1200 m was based 

on: 1) visual (video and digital photographic) observations of droplets (0.5-

3 mm) in the water column and impinging on a down-ward facing grid 

mounted on the ROV; 2) video and digital recording of said droplets with 

white (tungsten) and “black light” illumination: 3) CDOM fluorometry; 4) DO 

anomalies or “sags” in the water-column within that depth range; and 5) GC/

MS measurements of PAH in dissolved (filtered) seawater samples and SHC 

plus PAH associated with particulate-phase components (trapped on 0.7 um 

glass fiber filters) from finite water samples collected at these depths.In addition, the dispersed droplet concentrations were high enough to be observe

by sonar (670 kHz) on the ROV, and we photographed droplets of different 

sizes (between 100 and 500 micrometers) at different depths using the Holographic Camera provided by Cabell Davis of WHOI. 

These plumes were observed consistently within this depth range on two 

cruises on the M/V Jack Fitz in the May-June time-frame out to 4 km from 

the well head, and other vessels observed similar DO anomalies and fluorometer signals elsewhere at distances up to 18 km from the well head during that period. These other vessels did not have the ROV and video/digital 

photography to document the droplet sizes, but the chemistry data on whole 

water samples collected between 1000 and 1300 m yielded similar PAH and 

SHC patterns documenting the presence of dissolved components and whole 

oil droplets. More recently (in August and September 2010), PAH components associated with finite oil droplets in this depth range have been measured by UV/fluorescence using AquaTracka fluorometers and confirmed by 

GC/MS well beyond 200 km from the well head. Measurements above and 

below these depths do not show the oil signal. In more recent cruises, the 

Aqua Tracks signals also corresponded with DO anomalies at the same depth.

With regard to the mechanism of how these droplets reached this depth, it 

is important to stress that we are not implying that they were dispersed at 

the surface and somehow «sank» or were entrained to 1000 m. Rather, we 

believe that the physically (and chemically) dispersed oil released from the 

well head at depth broke up to yield finite droplets that advected away from 

the well as they slowly rose toward the surface, and that those droplets removed from the rapidly rising gas plume and within a particular size range 

became entrained within a density or stratification layer at this depth. Particle size measurements clearly indicated that the larger droplets were still 

slowly rising (between 1300 and 1000 m), and that smaller droplets were left 

behind over time. This entrainment of smaller oil droplets in the 0.2 – 3 mm 

size range follows the expected behavior described by Socolofsky and Adams 

(2002, 2003), where oil is detrained (along with seawater) from the plume 

as it ascends through a density-stratified ocean and passes a level of neutral 

buoyancy. Such detrainment events are clearly evident in laboratory mea-
 ________________________________________________________________________________________________

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A10.13 

surements of gas and water plumes and in field experiments of oil and gas 

mixtures released at depth (Masutani and Adams 2000; Adams and Socolofsky 2005), and evidence for such detrained oil droplets was observed in subsurface plumes between 1300 and 1000 m from numerous vessels and over 

4-5 months.

Masutani, S., and E. Adams. 2000. Experimental study of multiphase plumes 

with application to deep ocean oil spills. Final report to U.S. Dept of Interior, 

Minerals Management Service. Contract No. 1435-01-98-CT-30946. 

http://www.mms.gov/tarprojects/377.htm.

Socolofsky, S. and E. Adams. 2002. Multiphase plumes in uniform and stratified crossflow. J. Hydr. Res., 40: 661-672.

Socolofsky, S. and E. Adams. 2003. Liquid volume fluxes in stratified multiphase plumes. J. Hydr. Engrg. 129: 906-914.

Adams, E.E. and S. A. Socolofsky. 2005. Review of Deep Oil Spill Modeling 

Activity Supported by the Deep Spill JIP and Offshore Operator’s Committee. 

Final Report.

It seems odd, and not very conservative, to assume that 1 gallon of dispersant 

generates 20 gallons of dispersed oil at the surface and 90 gallons of dispersed 

oil subsurface. Multiplying these values by k3 and k2 respectively, it appears 

that 1 gallon of dispersant would generate 2 gallons of dispersed surface oil, 

or 40 gallons of dispersed subsurface oil if deployed at depth. This disparity 

should be clearly addressed.

The expected subsurface DOR is 40 and the expected surface DOR is 10. 

The reason for the disparity is explained in sections 9a and 9b. In many 

respects, the subsurface operations were ideal dispersant applications. The 

oil was fresh and in direct contact with the dispersant. There was significant 

turbulent energy. This was not always the case for the surface operations.

The report states on page 30 that the best (most) direct evidence collected 

during the spill suggested that 6-7% of the oil – in a plume where models 

expected the subsurface plume to be – was dispersed near the bottom 

(apparently negating the remote sensing estimate on page 13). Presumably 

this was both chemically and naturally dispersed. So why not use 6-7% 

rather than the approximately 5 times this amount suggested by the rate 

constants?

The Camilli study concluded that 6-7% of the BTEX leaking from the well 

was required to support the plume that they found. This defines a lower 

bound to the dispersed oil, not an upper bound since one cannot presume 
 ________________________________________________________________________________________________

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A10.14 

that they detected all of the subsurface oil. The Camilli study is not in 

contradiction to the AVIRIS findings since the latter looked at the cumulative 

amount of oil that both made it to the surface and did not evaporate. Also, 

it must be noted that the remote sensing estimate has a high degree of 

uncertainty.

Even if the chemical dispersant was effective, just because a droplet is small 

doesn’t necessarily mean it takes a long time to get to the surface. If the 

volume fraction of the dropletsis high enough, there can be a net transport 

upward that is faster than the individual rise speed of the droplets. If 

bubbles are added to the mix – and they apparently constitute 56% of the mix 

as stated on page 12 of this report – the upward net transport can be even 

greater (this type of group behavior can be demonstrated in the extreme by 

going to a bar and ordering a pint of Guiness and watching the individually 

buoyant bubbles flow downward). A conservative assessment of the amount of 

surfacing oil should either explain why this net transport isn’t happening, or 

conversely it should take it into account. 

The reviewer is correct. In the dynamic zone above the riser (the first tens of 

meters or more, depending on the intensity of jet), the vertical transport of 

small droplets is mostly controlled by the jet flow and its bouncy, and later 

on by the flow induced by the movement of large oil droplets and gas bubbles 

rising to surface. Above this dynamic zone there is abundant evidence that 

gas bubbles will separate from the plume of oil-gas mixture (explained in 

Section 3) and the small droplets will continue to rise to the surface according 

to their terminal rising velocity controlled by their size and effective density. 

In deep waters, the last process is dominant (because of the long distance the 

droplets have to travel to reach the sea surface), while in relatively shallow 

water (water depth of few tens of meters or possibly hundred meters) the 

first process (induced upward transport) becomes dominant and cannot be 

neglected in accessing oil movement to the sea surface. The depth at the 

DWH oil spill is 1.5 km, which is large enough to assume that oil droplets will 

travel the major part of this depth according to their terminal rising velocity 

as explained above and shown from previous well blowout incidents, field 

experiences and modeling (Johansen, 2000, 2003; Johansen et al., 2001, 2010; 

Chen and Yapa, 2007; Yapa and Chen, 2004).”

Chen F., and Yapa, P.D., (2004). Estimating the Oil Droplet Size 

Distributions in Deepwater Oil Spills. Journal of Hydraulic Engineering, 

133(2), pp. 197-207.

Johansen, O., (2003). Development and verification of deep-water blowout 

models. Marine Pollution Bulletin. 47, pp. 360–368.

Johansen, O., (2000). DeepBlow - a Lagrangian Plume Model for Deep Water 

Blowouts. Spill Science & Technology Bulletin, 6 (2), pp. 103-111.
 ________________________________________________________________________________________________

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A10.15 

Johansen, O., Rye, H., and Durgut I., (2010). Generalized integral plume 

model for simulation of marine discharges. In AMOP proceeedings, 

Environment Canada, Ottawa, 1, pp. 863-878.

Johansen, O., H. Rye, A.G. Melbye, H.V. Jensen, B.Serigstad, T. Knutsen 

(2001). Deep Spill Jip Experimental Discharges of Gas and Oil at Helland 

Hansen, Technical Report STF66 F01082, 159p.

Yapa, P.D., and Chen F., (2004). Behavior of Oil and Gas from Deepwater 

Blowouts. Journal of Hydraulic Engineering, 130(6), pp. 540-553

The report (p13) assumes that gas associated with the plume dissolved before 

reaching the surface. Without evidence to the contrary, I find it hard to believe 

that the surface flow around the bubble would push enough oil on a bubble 

surface to the downstream hemisphere so that the gas transfer rate out of the 

bubble or methane-seawater contact was not appreciably changed. Although 

the oil budget does not keep track of gas, adsorption of oil onto gas bubbles is 

a mechanism by which the oil can surface more quickly and by which smaller 

droplets can reach the surface. Therefore, it is not sufficient (or conservative, 

from the calculator perspective) to simply neglect the gas, especially when it is 

estimated to be more than 50% of the discharge volume (page 12).

Although the mechanism suggested by the reviewer is perhaps possible 

and worthy of further study, it does not reflect existing understanding of 

subsurface well blowouts, as discussed in the references cited. As stated on 

page 8, the Calculator is not a research tool.

The model inherently appears to assume that the oil that is skimmed, 

naturally dispersed, or evaporated at the surface is negligible compared to 

the oil that is burned (see calculation of W). This may have been accurate for 

DWH (I couldn’t say), but it doesn’t seem to be appropriate to assume that this 

will always be correct.

This is not correct. The model does not neglect skimmed, naturally dispersed 

or evaporated oils. These are important in the oil budget calculations. 

Skimmed oil is counted as shown in equation A1.7), naturally dispersed in 

equation A1.11, and evaporated oil is counted by the terms k4Z(t) + k5W(t-1) 

in equation A1.10. The note “(neglecting natural surface dispersion and 

skimming)” mentioned in the report applies to calculation of evaporation 

from the previous day and still available on day t, as mentioned in the report 

“The oil that rose to the surface on day t-1 and is still left”. This “old” oil is 

estimated by the intermediate variable W.

The report states that some 18,000 barrels of subsea dispersant were used, 

and multiplying this by (1-k7)*90*k2 (from equation 2) gives 700,000 barrels 

of chemically dispersed oil in the subsurface. Figures 1 and 2 in Appendix 

2 (example outputs from day 86, the last day dispersant was used, I believe) 
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A10.16 

gives an answer of only about 400,000 barrels. Why is this different? Note 

that 90*k2Vcb should always have been smaller than the government estimate 

of the discharge even when 12 gallons/minute of subsurface dispersant was 

being used. 

Figures 1 and 2 in Appendix 2 refer to the earlier version of the Oil Budget 

Calculator (version 1.3) while the formulas listed in the Report are those for 

the present version (2.0). See Appendix 2, Figure 3 for the equivalent tables 

using the new formulas.

2. One of the stated goals is to provide the UC with actionable data 

even when there is missing or incomplete data. I’m wondering what 

happens when the estimate of the discharge is either not available 

or has a very high level of uncertainty. If stated too low, this would 

presumably result in a response (dispersant, skimmers, etc.) that was 

too low, if state too high, this would result in too much dispersant 

deployed and inefficiency (too many skimmers floating around). If 

either of these scenarios occur, I don’t see any built-in mechanism (e.g., 

feedback) that allows the calculator to adjust, which could render the 

calculator ineffective. Given the uncertainty surrounding the flow 

rate for a significant portion of the DWH spill timeframe, this seems 

likely to occur. I would suggest that future work include exploration 

of feedback mechanisms. One that readily comes to mind is tracking 

the daily amount of oil estimated to be reaching the surface, which is 

VRE – VDC/(1-k7) – VDN/(1-k7), and comparing it with the daily amount 

of oil skimmed and burned (normalized by effort) . One could imagine 

a scenario in which such a tracking mechanism is used to tune the 

basic model inputs (e.g. the leak discharge rate), which in turn gives 

the response the indirect ability to tune decisions for which the result 

is difficult to monitor (e.g., amount of subsurface dispersant). Place 

holders for new feedback mechanisms (advances in technologies to 

assess surfaced or subsurface oil amounts or characteristics) could also 

be added.

Mass balance was used by another group to estimate flow rate during 

DWH. Certainly having a corrective feedback mechanism in future model 

applications would be useful.

3. Considering that VDC cannot be greater than VRE by definition, the 

“max” in equation (3) does not seem to be necessary.

True, but it does not affect the calculations. Equation was written this way 

for consistency .
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A10.17 

4. It seems misleading to state that a similarity between DWH and Ixtoc is 

that relief wells were used to stem the flow in both cases. Didn’t DWH 

utilize a cap to almost completely stem the flow? In any case, what is 

the relevance of the method of stemming the flow to the assessment of oil 

fate and transport before the flow is stemmed?

The use of relief wells is mentioned because in both cases this was the final 

method to assure no future leakage and because it was believed during both 

responses that this was possibly the ultimate method to stop the flow. For 

IXTOC, this proved to be the case. As noted by the Reviewer, DWH was 

successfully capped at the top.

5. In describing the difference between DWH and Ixtoc, it is stated that 

Ixtoc I oil formed a “chocolate mousse.” Wasn’t this also observed in 

DWH? I recall seeing something that could be described as a “mousse”, 

at any rate.

Both oils emulsified although the Deepwater Horizon spill formed a less 

stable emulsion and took longer to emulsify.

6. On page 11, there is a statement suggesting that “current news reports” 

provide evidence that a scientific hypothesis (that a “good” fraction of 

the oil is settling to the bottom) is true. Given the inaccuracies often 

associated with press reports, and the lack of peer review associated 

with news media report, I would suggest that the report only state 

that such a hypothesis exists but has not yet been evaluated, and that 

references to the news media be deleted. Further, what is a “good” 

fraction? These kind of ambiguous statements found throughout the 

document.

Sentence discussing bottom oil is modified to note that these reports have not 

been confirmed

7. What is a “glob” of oil (page 14)?

Corrected

I don’t see how the 5-10% dissolution rate is supported. It would seem to me 

that in order to assess this, you would need to know a) the oil composition; b) 

the dissolution rates as a function of droplet size (not solubility – presumably 

the Gulf of Mexico is still under-saturated; c) the droplet size distribution. I 

don’t see that any of this is sufficiently known to estimate a dissolution rate. 

The text makes the case that some oil will dissolve, but does not go further. 

This should be addressed; if it is a guess, than this should be clearly stated
 ________________________________________________________________________________________________

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A10.18 

Dissolution was only considered as a separate process for dispersed 

subsurface oil (less than 100 micron droplet size). As stated in the Report, the 

dissolution rate depends directly upon surface area, which was proportionally 

larger per volume spilled for this spill incident compared to a normal surface 

spill by factors of 100 to 1000. Mackay and Leinonen (1977) concluded that, 

for droplets less than 100 microns in diameter, dissolution is very rapid for 

any component that will dissolve at all. Using the known composition of the 

oil, we arrived at the 5-10% number.

Reviewer 5:

This document needs a more straightforward description of what the major 

processes are that need to be considered. Once the simple formula is provided 

then they can describe their more sophisticated formulas for deriving the 

estimate. Here is a simplified example of how they should proceed. The fate 

of oil in the marine environment has been of concern for many years and the 

major processes determining its fate are known in relative terms. A budget can 

be constructed which would contain these parameters:

Total oil discharged = TD

Oil Recovered before entering the ocean = REC

Oil Dissolved/Dispersed = DD

Oil on sediments/shore = SED

Oil on the ocean surface = SURF

Oil evaporated = EV

Oil Biodegraded = BD

Oil Burned =BUR

Oil Skimmed = SK

Then the following equation can be used:

TD = REC + DD + SED + SURF + EV + BD + BUR + SK

The National Incident Command (NIC) is most interested in the surface oil 

(SURF) as that is where they can attempt to burn skim or disperse the oil 

or set up booms to keep the oil from sensitive ecosystems. The equation is 

therefore solved to estimate this term:

SURF = TD – (REC + DD + SED + EV + BD + BUR + SK)

Since the spill occurred offshore where the suspended sediment load is small 

sedimentation near the spill site is not likely to be a major term in this budget 
 ________________________________________________________________________________________________

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A10.19 

for this spill. This term for the offshore areas was assumed to be comparatively 

small. The NIC was trying to keep the oil of the shore. Once on the shore it is 

difficult to estimate the amount of oil stranded and how much of that oil is 

recovered. This oil budget calculator does not attempt to estimate this process, 

but includes this oil with the surface oil. The equation for the calculater is 

therefore:

SURF + SED = Other Oil = TD – (REC + DD + EV + BD + BUR + SK)

The budget then uses the physical properties of the oil, estimated rates and 

developed a model to calculate and estimate the above parameters. Modelers 

and other experts will need to comment on how well they were able to estimate 

these parameters. Based on my review of the parameters that the oil budget 

calculator used it has captured the most important aspects. While SED can be 

assumed small it does not mean it is not important especially for selected near 

shore areas and perhaps some off shore areas.

Spilled oil undergoes a number of processes. Dispersion and evaporation 

are just two examples. Collectively they are called weathering. The 

circumstances surrounding the Deepwater Horizon incident resulted in 

interdependence of these processes. To address this the approach taken 

was to follow a logic structure beginning at the wellhead and the released 

oil. Unfortunately, interdependencies of the weathering/cleanup processes 

made the simple approach proposed by the Reviewer not possible. For 

example, evaporation depends upon surface oil but surface oil depends upon 

the amount of oil that disperses subsurface. The formulas chosen by the 

development team were as simple as we believed possible while still retaining 

these basic dependencies. However, a descriptive narrative has been added to 

the section before the formal equations are presented.

My conclusion is that they have oil budget calculator used it has 

captured the most important aspects of the fate of the oil.

The Oil Budget Calculator is designed to capture short-term oil behavior as 

it affects response. Environmental Impact Assessment studies are needed to 

track the ultimate fate of the oil.

Specific sections Comments

Dissolution and Evaporation:

I discuss these sections together as it make since for the calculator to 

talk about these processes together. The discussion in these sections is very 

good. They explains how the unique aspects of this spill led to enhanced 
 ________________________________________________________________________________________________

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A10.20

dissolution even of sparingly soluble components. Based on the paucity of 

studies they have done a good job of estimating dissolution process. And since 

the same components of the oil are more water soluble and more volatile it 

makes since to combine as a process called “weathering” as it is difficult to 

establish which was the dominate process. Separating the two processes is also 

of limited value for the NIC. Some editorial comments:

Page 17 Figure 6. The open and filled circles are not labeled.

We are reluctant to modify a figure produced by someone outside the 

development group. The original figure appeared in the proceedings of the 

1987 Oil Spill Conference.

Page 17 ...lose between 5-10% of its volume. Is this also 10 % of the mass since 

mass in used in the calculator.

The Calculator uses volume in its calculations and output. However, oil 

density changes little so that mass and volume percentages are quite similar.

Page 18 “The Oil Budget Calculator groups these terms” change “terms” to 

“dissolution and evaporation”.

Page 19 Figure caption change to ...alkanes below C-14 have been removed...

Change implemented

Residual Oil (in my letter) or Other Oil (in draft document) plus Program 

Structure & Interface (Appendix 2 Oil Budget Calculator-Web-based 

Tool)).

In the equations I provided above the Other Oil is equivalent to my 

term SURF + SED. This is the most important parameter for the NIC. This 

is the oil that they have the oil that they have to attempt to mitigate (skim, 

disperse, burn) or contain (booms) and the proportions that they were unable 

to keep from the shore. In short it is the number they wanted calculated. The 

final test of this Oil Budget Calculator is was it of value to the NIC to make 

management decisions. It would be good to include in this report a statement 

by the NIC that this Oil Budget Calculator was useful or that they think it 

would be helpful in the event of future spills. They are the ultimate primary 

users. It is also important to attempt to improve these estimates as the results 

contradict some of the original unrealistic predictions. 

We agree that the ultimate judge of the usefulness of the Calculator was/is the 

unified command. The development team was encouraged by the support given 

to its development by the NIC who are reviewing future plans for the tool. 
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A10.21 

Other Comments:

Figures 8, 9, 13, 15 and 16 appear to be taken from appendix 2 figure 4. It 

would be better to just use that figure in the main text and delete the other 

figures as they are hard to understand until you see figure 4 Appendix 3.

Figure 8 and other related figures have been removed.

Appendix 2 Figure 1 and 3 (which are actually tables unless the colors have 

some significance) appear to be the same accept rounding. One should be 

eliminated or the reason to have both explained. 

Specific Comments:

Page 7 A copy of ICS 209 should be available as an appendix.

A copy of the ICS 209 form has been added as an Appendix

Page 8 The model uses not assumes. This whole page should use the same 

terminology found in Figure 1 Appendix 2. For example “(1) Subtract off direct 

recovery from total amount escaping from the reservoir”, just say recovered. 

This section is extremely had to follow and should be edited by a non-technical 

person.

A challenge in a document of this type is to reconcile precise technical 

terminology with more common descriptions. ‘Direct recovery’ is used to 

distinguish this oil from other oil recovered by, for example, skimming 

operations. We have simplified the equation expressions, added an 

introductory narrative to them and added a table of acronyms to the final 

version to assist the reader.

Page 12 Par 3 add surface after water.

Page 14 ...converted to hydrates at a much higher level... do you mean 

shallower depth or deeper depth?

Shallower depth

Page 22 (representing a 200 -250C+ residue) is this a boiling point? Need to 

explain.

50degC+ residue is that the residue has lost all components with boiling point 

below 250degC

Page 25 Par 1. change “disappeared during” to was not on the surface after 
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A10.22

Page 27 last line half=of ?

Page 32 Par 2. 2 to 3 mm thick.... Oil with emulsions...delete typically. ...mm 

or less thick.

Page 35 Simple measurement of barge oil!!! This should be made a 

requirement!!

We agree when practical.

Page 39 (5) this is self advertisement and should be deleted. What is real code? 

Is there fake code!!

We disagree with this conclusion. A weakness of oil spill science in the past 

was translating new ideas into practical tools.

Page 39 (6) Jargon. Explain what the “Interface” you are talking about is. 

Consider deleting this as it seems to serve no purpose. 

One of the key challenges for the team was to provide to the unified command 

output that is simple to understand yet captures the uncertainty in the 

estimates. We believe this is a topic that requires more research.

Conclusion

My conclusion is that this model appears to provide a valid estimation 

of the source and fate of the oil from the DWH incident. It generally agrees 

with what has been found by previous oil spill research and currently 

available research on this specific incident. 

Unfortunately, the ultimate fate of the oil is more complicated than can be 

calculated by this tool. Environmental impact assessment is ongoing and will 

provide a more complete picture than the Oil Budget Calculator.

Reviewer 6:

The OBC as an NRDA tool?

The introduction in particular and the paper overall set the scene well and 

describe the operational needs that led to the creation of the OBC, the limitations 

that this development faced, and the authors’ overall (and reasonable) conclusion 

that the OBC outputs “only need to be accurate to the extent that they correctly 

inform cleanup decisions and do not lead to errors in response actions.” (p. 

5) Explicit warnings are also made that the OBC tool is not appropriate for 

damage assessment. While there is no protection against misuse, perhaps the 
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A10.23

paper could describe more explicitly just why the OBC is not appropriate as an 

NRDA tool.

The Oil Budget Calculator is unsuitable for NRDA because it does not track 

the final fate and environmental impact of the spilled oil. This is explicitly 

stated on page 3 of the report.

The problem with DOR 

The model is a nest of part-weathering, part-response/fate equations describing 

processes that are partly simultaneous, partly consecutive. For example, 

while subsurface dissolution is nicely modelled as occurring after subsurface 

dispersion (i.e. consecutive processes), chemical dispersion, skimming and insitu burning on the sea surface are all allowed to happen at the same time 

(simultaneous processes).

One key problem we have with the model is that a great part of the results 

(in fact, all the consecutive elements, such as evaporation, dispersion and 

dissolution) are based on the theoretical volume of oil that can be dispersed by 

a given quantity of dispersant (i.e. DOR = dispersant-to-oil ratios). These are 

the 90VCB and 20 VCS elements of the equations. The issue is that outputs are 

measured solely in terms of inputs, albeit with the correction factors k2 and k3

(which must capture not only the ‘encounter rate’ of the dispersant application, 

but also all other technical and environmental factors that may influence the 

dispersant application). The Appendix (in particular) explains that outputs 

are measured on the basis of inputs, but we do not believe that this makes it 

acceptable. 

In the real world, DORs are meant as a planning guideline, an indication as 

to the quantity of dispersant that should be made available (i.e. what should 

be put in), not the end result (i.e. what comes out). Further, they refer to the 

dispersibility of surface slicks of a certain thickness (0.1mm according to the 

ITOPF Technical Paper quoted). 

The matter is made worse by the fact that the DOR figures come up so many 

times in the same equation/ model. For example, if the model equations are 

solved simultaneously for total evaporation/ dissolution (not even dispersion!), 

the 90VCB component comes up numerous times in relation to:

• Day t evaporation of non-chemically dispersed at bottom oil (that just rose)

• Day t evaporation of non-naturally dispersed at bottom oil (that just rose)

• Day t evaporation of non-chemically dispersed at bottom oil from the previous 

day(s)

• Day t evaporation of non-naturally dispersed at bottom oil from the previous 

day(s)

• Day t dissolution of chemically dispersed at bottom oil
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A10.24

• Day t dissolution of naturally dispersed at bottom oil

The use of DOR to define effectiveness was based upon industry familiarity with 

this approach. Similar results would have been produced if the development 

team had used fraction dispersed as a function of dispersant applied to available 

oil with an upper limit on the fraction allowed. What is really needed is solid 

research to empirically determine effectiveness.

Multiple expert coefficients.

Although the model makes logical sense when analysed part by part, when 

the equations are solved simultaneously there are numerous places where the 

educated guesses of experts are multiplied by a series of other such educated 

guesses. For example, the 2nd day surface evaporation (with coefficient k5) is a 

function of the first day evaporation (with coefficient k4) which is a function of 

sub-surface natural dispersion (with coefficient k1) which is a function of subsurface chemical dispersion (with coefficient k2

) which is a function of theoretical 

sub-surface chemical dispersibility (i.e. DOR = 90). In other words, that part of 

the overall evaporation function which looks like:

really takes the following form when solved for “W” which includes “Z” which 

includes “Y” which includes “X”:

As can be seen, if the equation is solved, the results include such elements as: 

k5*k4*k1*k2*90*VCB which is the multiplication of 5 guesses by the observed 

number of drums of dispersant applied. It is very, very hard to accept this as a 

meaningful result.

Naturally, the paper includes a number of various variance histograms for 

each of the main fate/ recovery alternatives, and the Appendix includes a long 

and detailed statistical model based on repeated computer simulations (i.e. 

the Monte Carlo analysis),. These do not, however, in our opinion, get over the 

fact that there are very few variables in the overall set of theoretical equations 

which are actually populated by real measurements, observations, or even 

observation-based estimates.

While an argument could be made that having an inaccurate result, even if far 

from reality, may be more useful than having no result at all when faced with 

emergency-phase decision making, it cannot be stressed enough that it is not 

appropriate to use the results in post-spill evaluations of impact or response 

actions. 

The Reviewer is correct in assessing that these numbers chiefly rest upon 

the experienced judgment of the dispersant experts consulted for this project. 

However, using expert judgment when hard data is not available is often 

employed in many fields.
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A10.25

We agree that there are many alternative ways of re-writing the equations 

that describe the simplified model that we have adopted for the fate of the 

oil. Some of these may appear counter-intuitive. The formulation we have 

settled on represents our particular way of viewing the chronology, chemical 

reactions, and phase changes affecting the spilled oil, which is based on our 

own, varied experience with oil spills into the ocean. One of our colleagues, 

who has no expertise in this specific area, and merely as an exercise, did use 

our description of these processes to formulate an alternative set of equations, 

independently of our formulation. This alternative had shortcomings that 

were obvious to those with expertise in the subject. However, it produced 

estimates for the different volumes that we track that were within the 

uncertainty range of the estimates that the equations we did use had 

produced. This suggests that the simplified model we have adopted is fairly 

robust to varied interpretations, and that rather different sets of equations 

that may be used to represent our understanding may end-up bringing about 

no more than relatively minor changes to the bottom line.

Regarding the comment on the 2nd day surface evaporation, this is evaluated 

by the following expression after development and assuming that VDC=(1-k7) 

k290VCB and VDN=(1-k7) k1 [VRE- VDC/(1-k7)]:

k5 (1-k4) (1-k1)[VRE- k290VCB] – k5VBU

This expression includes terms that are function of one rate constant such 

as k5 VRE and k5 VBU, terms that are function of two rate constants such as k5 

k1 VRE and k5 k4 VRE, terms that are function of three rate constants such as 

k5 k4 k1 VRE and k5 k2 90 VRE, and terms that are function of four and five rate 

constants such as the one mentioned in the reviewer comment. Of course, the 

terms that include less rate constants have more influence on the end results 

than the one multiplied with four and five rate constants, given that all the 

rate constants are less than or equal to 1. The other terms were kept in the 

equations of the Calculator for logical consistency and for transparency of the 

method used.

 The uncertainty analysis certainly was done assuming that the rate 

constants were like realized values of independent random variables, whose 

probability distributions expressed the knowledge that the subject-matter 

experts had about their values. This assumption, of statistical independence, 

clearly is questionable for at least some of them. For this reason, Appendix 1 

describes a study of the sensitivity of the conclusions to this assumption, by 

considering the effects that substantial dependence between several of them 

would bring about. As it turned out, this lowered the lower endpoint of the 

confidence interval for the volume of residual oil (or, “other oil”), but hardly 

changed the upper endpoint, meaning that the assumption of independence 
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A10.26

actually was conservative, at least for the variables considered in the 

sensitivity study.

Re-coalescence

The model has no explicit mechanism by which some part of the initiallydispersed (sub-surface or surface) oil re-coalesces and rises to join the surface 

oil. One could, of course, say that the oil that disperses in the model is, by 

definition, only that which is permanently dispersed. We do not believe that this 

is a particularly neat solution, given the important discussion in the spill world 

on the fact that naturally-dispersed oil tends to be less permanently dispersed 

than chemically-dispersed oil. 

Near the riser, it is possible for dispersed oil droplets to collide and form larger 

droplets due to the abundance of oil droplets and sufficient mixing energy. 

However, due to the relatively low concentration of oil away from the source, 

the collision frequency of droplets is very small when one moves away from 

the riser. Thus, the conditions there are not favorable for coalescence. Any 

coalescence would compete with shear forces breaking up the larger droplets. 

There may be an intermediate area where coalescence dominates. The impact 

of coalescence near the riser might affect the overall effectiveness of dispersant 

but would be included in the wide uncertainty range selected for oil dispersion.

Dissolution from chemically- vs. naturally-dispersed oil

Related to the point just above, it would be expected that the (mean) droplet 

size for chemically- and naturally-dispersed oil would differ. This would surely 

have an important impact on the sub-surface dissolution potential (i.e. k7), 

even disregarding the question of effect of droplet size on dispersion stability 

(as discussed above). In other words, k7 might be best split into two separate 

coefficients (e.g. k7C and k7N).

Mean droplet size would be considerably smaller for chemically dispersed oil. 

Mean droplet size for natural dispersion would be expected to be much larger 

than our cutoff value of 100 micron diameter. However, some droplets would 

be 100 microns or smaller. These are the ones considered naturally dispersed. 

Mackay and Leinonen (1977) concluded that, for droplets less than 100 microns 

in diameter, dissolution is very rapid for any component that will dissolve at 

all.

Emulsion & Emulsion effects for surface dispersant effectiveness

There is no explicit modelling of emulsion formation in spite of the fact that this 

was an important process in this incident. Likewise, there is no discussion of the 

effect of emulsification on surface dispersant effectiveness. This is a key factor, 

considering the practical experience that shows that emulsions, if dispersible at 

all, require greater quantities of dispersant than non-emulsified (surface) slicks. 
 ________________________________________________________________________________________________

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A10.27

Emulsification is not a process that directly contributes to mass balance in 

the way that, for example, does evaporation. The observation that the oil did 

emulsify is reflected in the burn estimates and in the effectiveness of surface 

dispersion. Both were reduced to account for the characteristic behavior 

change of oil that has emulsified. Certain experts thought that the reduction 

was insufficient, a judgment summarized by Alternate View 1.

Weather & response activity

We find no explicit variables to account for variations in the weather and how 

these might have affected the response success, for example surface dispersion 

operations.

Several of the report authors have developed much more complex oil fate 

and behavior models than the Oil Budget Calculator. However, these models 

required more detailed input than the design specifications allowed for the 

Calculator. Sea state, in particular, affects surface processes but, for this spill, 

varied both temporally and spatially. Also, the subsurface release and extended 

duration of the leak were not compatible with the standard fate and behavior 

model used by NOAA and USCG for routine spills. Recommendation 5 suggests 

expanding existing modeling capabilities so that perhaps this difficulty will 

not arise in future incidents.

Equations

While we find no mathematical fault in the equations, there are places where 

slightly different forms would leave the equations more easily interpretable. 

For example:

Equation 

number

As presented More easily followed form

A1.4

or

Interpretation: Oil volume available to natural dispersion (and 

subsequent rising to the surface for that part that does not 

disperse), once chemical dispersion at the well head has run its 

course.
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A10.28

A1.8

or 

Interpretation: Oil that rises to the surface after chemical and 

natural dispersion has run its course.

Part of A1.10

Interpretation: That part of the subsurface chemically-dispersed 

and naturally dispersed oil that dissolves into the water column

While the proposed forms to represent the three equations appear to be simpler 

than what is proposed in Appendix 1, these simplifications do not respect logical 

consistency in the formulation and the solution of the problem. The “min” and 

“max” functions were introduced in equations A1.3, A1.5, A1.9, A1.11, and 

A1.12 to ensure that the oil volume is conserved and no non-realistic results 

are generated in the Calculator.

Conclusions

The equations on which the OBC is based offer an interesting theoretical study 

of many of the fate and weathering processes relevant for a sub-sea blowout 

incident such as the DWH. There are some elements which could be added to the 

model such as explicit weather (e.g. wind, waves), emulsification, re-coalescence 

of dispersed oil, or refined such as droplet-size-based dissolution.

As a practical tool, however, the OBC suffers the same shortcomings of most 

mass-balance efforts: lack of real-world observations. In this case, lack of 

actual observations of dispersion effectiveness in particular render the overall 

result practically meaningless. The only true observations in the model appear 

to have been the volume of oil/water mix collected by skimming operations 

(traditionally a very suspect measure of actual oil volume collected), the volume 

of oil dispersant used (probably reasonably well counted but utterly meaningless 

in terms of estimating actually-dispersed oil), and the number and approximate 

size of in situ burns (a very rough value for a very rare activity).

One could argue that having the OBC result, although an inaccurate number, 

was better than having no number. The truth is, however, that the overall 

result could have been no better or no worse without it. In fact, the response 

operations were guided primarily by 1) the visible presence of oil, whether it 

was escaping from the wellhead, on the surface in the form of slicks that could 

be burned, skimmed, or dispersed, 2) the availability of resources, and 3) non-
 ________________________________________________________________________________________________

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A10.29

technical concerns (such as the maximum daily-dosage of subsurface dispersant 

application). 

The Oil Budget Calculator was only one tool among many that the unified 

command used to make response decisions. As with any tool, it has its 

strengths and limitations. The skill of the decision maker is to utilize all the 

tools available in an appropriate manner to optimize the response. Based upon 

the request by the NIC for this tool, and its continued usage during the spill, 

we must conclude that they found some value in it.

Reviewer 7:

General;

I thought the report was informative and as precise as was possible under the 

constraints of the available data. Errors and uncertainties were identified. 

I was specifically asked to review Oil properties, Dissolution and long-term 

processes. I think each section was adequately presented under the terms of 

reference. 

I suggest you provide a list of acronyms for all the various agencies, etc.

“in situ” or “in situ” is not consistently presented in the report

A list of acronyms will be included.

I have the flowing specific suggestions to the text.

P12 p 2. L 6. “an average”...

P13 p1 L4 “considerably larger”

Fig 3. Define bopd in the figure caption.

Fig. 7 The blue series is not described. Presumably it is a surface oil sample 

taken near the source. Thus the title should be amended to read “..., floating 

oil samples immediately adjacent to the source (red,blue).....

The blue series is not a sample collected at the surface, but came from a 

sample from dispersant efficiency testing.

P. 20 p2. L10 “data provided (to, by???) BP”

Provided by BP. Correction will be made in the text.

Fig. 8 should be moved up 2 paragraphs to appear under its description.
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A10.30

P27 under Fig 11. L3 delete “when” L 7. delete “=”

P27 under Fig 11. I could not see a conclusion from this paragraph. The 

issue of dispersant efficacy is left hanging. A statement that says there is no 

conclusion possible from the sattelite data would be useful. 

Sentence added

However, May 11th and May 12th show similar surface expressions although 

the latter should be displaying more oil. Therefore, these observations 

provide inconclusive results.

P 29-30. Delete reference to Fig 13 here as the same sentence appears on P 13 

just above the figure.

P31 last L “from”

P38 p1 L8 What is the basis for concluding hydrocarbon degradation in the 

water column occurs without oxygen drawdown? This needs to be expanded to 

clarify and justify this statement.

Hydrocarbon degradation doesn’t occur without oxygen utilization. There 

was draw down, as discussed in the JAG 2 report. The fact that the DO2 

drawdown was not as high as expected is most likely do to mixing as 

described in Appendix 2 of the JAG 2 report.

References: 

P. 12 Yapa 2010 should be et.al. 

P 19 Kirsten et al 1984 is not in the list

There are several citations in this section where multiple citations are not 

properly presented

Eg: P. 20 (Payne, Kirsten et al., 1984) should be (Payne et al, 1984; Kirsten et 

al., 1984)

There are 3 such citations on Page 20. Other sections of the report do it 

properly.

References in the list but not cited in text or figures

These should be cited, deleted or added as “additional useful references” or 

some such. 

Belore, R. (2003). “Large wave tank dispersant effectiveness testing in 

cold water”. International Oil Spill Conference; Prevention, Preparedness, 

Response and Restoration, Perspectives for a Cleaner Environment: April 

6-11, 2003, Vancouver, British Columbia, Canada, pp 381-385.
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A10.31

Belore, R.C.; Trudel, B.K.; Lee, K. (2005). “Correlating wave tank dispersant 

effectiveness tests with at-sea trials”. International Oil Spill Conference; 

Prevention, Preparedness, Response, and Restoration: May 15-19, 2005, 

Miami Beach Convention Center, Miami Beach, Florida, pp 65-70.

Brandvik, P.; Daling, P.S.; Lewis, A.; Lunel, T. (1995). “Measurements of 

dispersed oil concentrations by in-situ UV fluorescence during the Norwegian 

experimental oil spill with Sture blend”. In Proceedings, Eighteenth 

Arctic Marine Oil Spill Program Technical Seminar, June 14-16, 1995, 

West Edmonton Mall Hotel, Edmonton, Alberta, Canada. Ottawa, Ont.: 

Environment Canada. pp. 519-535.

Brutsaert, W. (1982). Evaporation into the Atmosphere: Theory, History, and 

Applications. Dordrecht, D. Reidel Publishing Company.

Colcomb, K.; Salt, D.; Peddar, M.; Lewis, A. (2005). “Determination of the 

limiting oil viscosity for chemical dispersion at sea”. In 2005 International 

Oil Spill Conference; Prevention, Preparedness, Response, and Restoration: 

May 15-19, 2005, Miami Beach Convention Center, Miami Beach, Florida. 

Washington, D.C.: American Petroleum Institute. pp. 53-58.

Daling, P.S.; Brandvik, P.J.; Reed, M. (1998). “Dispersant experience in 

Norway: dispersant effectiveness, monitoring, and fate of dispersed oil”. In 

Dispersant Application in Alaska: A Technical Update, Anchorage Hilton 

Hotel, Anchorage, Alaska, March 18 and 19, 1998. Cordova, Ak.: Prince 

William Sound Oil Spill Recovery Institute. pp. 111-146.

Fiocco, R.J.; Daling, P.S.; DeMarco, G.; Lessard, R.R. (1999). “Advancing 

laboratory/field dispersant effectiveness testing”. In Beyond 2000, Balancing 

Perspectives: Proceedings: 1999 International Oil Spill Conference: March 

8-11, 1999, Seattle, Washington. Washington, D.C.: American Petroleum 

Institute. pp. 177-185.

Lehr, W.; Jones, R.; Evans, M.; et al. (2002). «Revisions of the ADIOS Oil Spill 

Model”, Environmental Modeling & Software, Vol 17, Issue 2, pp. 191-199.

Lewis A.; Crosbie, A.; Davies, L.; Lunel, T. (1998b). “Dispersion of emulsified 

oil at sea”. AEA Technology report. AEAT-3475. AEA Technology, National 

Environmental Technology Centre (NETCEN, Didcot, Oxfordshire, England. 

Lewis A.; Crosbie, A.; Davies, L.; Lunel, T. (1998). “Large scale field 

experiments into oil weathering at sea and aerial application of dispersants”. 

In Proceedings: Twenty-First Arctic and Marine Oil Spill Program (AMOP)

Technical Seminar, June 10 to 12, 1998, West Edmonton Mall Hotel, 

Edmonton, Alberta, Canada. Ottawa, Ont.: Environment Canada. pp. 319-

344.

Lunel, T.; Rusin, J.; Bailey, N.; Halliwell, C.; Davies, L. (1997). “The net 

environmental benefit of a successful dispersant operation at the Sea Empress 

incident”. In Proceedings: 1997 International Oil Spill Conference: Improving 

Environmental Protection: Progress, Challenges, Responsibilities: April 7-10, 

1997, Fort Lauderdale, Florida. Washington, D.C.: American Petroleum 

Institute. pp. 185-194.
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A10.32

Lunel, T.; Lewis, A. (1999). “Optimization of oil spill dispersant use”. In 

Beyond 2000, Balancing Perspectives: Proceedings: 1999 International Oil 

Spill Conference: --‐44--‐March 8-11, 1999, Seattle, Washington. Washington, 

D.C.: American Petroleum Institute. pp. 187-193.

Mackay, D.; Matsugu, R.M. (1973). “Evaporation rates of liquid hydrocarbon 

spills on land and water.” Canadian Journal of Chemical Engineering, Vol. 

51, pp. 434- 439. 

National Research Council (1989) Using Oil Spill Dispersants in the Sea, 

National Academy Press, Washington DC, 335 pp.

Reviewer 8:

General comment. In the report use laboratory not lab. 

Page 6, Top Paragraph, Third sentence – “Other longer-term processes” Name 

the processes are the authors referring to.

Sentence revised 

Other longer-term processes such as biodegradation, photo-oxidation, and 

sedimentation may have an impact on the environment but are less amenable 

to response decisions

Page 7, Top paragraph, second sentence. Suggest changing “..usually a 

simple, if slightly dull, process” to “... usually a straightforward process.” Do 

not minimize the task of correctly filling out Form ICS 209.

Page 8, (2) Determine the bottom chemical dispersion amount. The equation 

uses a term 90k2. Why was 90 used? I reviewed Appendix 1. I see that k2 is 

the constant for subsurface chemical dispersion Table A1.2. on pp. A1.9. Why 

was 90 added to the constant?

 All the rate constants are defined in such a manner that they vary between 

0 and 1. In the model, the DOR for subsurface dispersants has a maximum 

possible of 90. Therefore, the 90 appears as a scaling factor.

Page 10. The Ixtox Spill. First paragraph, last sentence refers to depth 

of the well “about 50 m”. Last paragraph, First sentence has depth as 

“approximately 165 feet (51 m). Be consistent.

Comment for Section 3.0 Ixtoc spill - I suggest when comparing Ixtoc to DWH 

add the fact that that distance to shore was a factor in differences. 
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A10.33

Comment for Section 3.0 Ixtoc spill – I suggest mentioning that dispersant 

application was more closely targeted for DWH than Ixtoc. During Ixtoc, 

dispersants were applied very close to shore, in the surf zone, and at the 

entrances to tidal creeks and other waterways. 

Page 13, Top paragraph, Start of Third sentence. Is something missing at the 

start? The sentence starts out with “estimated” 

Or does the sentence start out with the citation (Lehr , 2001....)? Not clear to 

the reviewer.

Page 27. Last paragraph. The multi-spectral scanner measures the thickness 

and distribution of the oil slick in coastal and offshore waters in real-time. I 

suggest the explanation of the 4-picture sequence be explained in better detail. 

Explanation expanded

Found a number of references in Section 15 not in the report.

Belore (3002)

Belore et al (2005)

Brandvick et at (1995)

Brutsaert (1982)

Colcomb (2005)

Daling (1998)

Fiocco (1999)

Lehr (2002)

Lewis (1998b)

Lewis (1998)

Lunel (1997)

Lunel (1999)

Mackay (1973)

Mackay (1975)

NRC (1989)

The IXTOC I Oil Spill 

Appendix 1 – Reference Section. No reference for W.N. Venables (2002) in the 

appendix.

The reference to Venables & Ripley (2002) is made on page A1.10 (Appendix 

1), and the reference itself is specified on page A1.17.
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A10.34

Reviewer 9:

Overall: There were problems in following the calculations as the descriptions 

were in the front and the equations in the appendix. It is not clear if the 

equations had any background or references. Did the authors develop them 

independently or was there a background or previous papers on each? I also 

had difficulties following the Appendixes due to lack of consistent numbering.

A challenge in constructing a report of this nature is the diversity of 

the audience. We anticipate readers who will gravitate to the intricate 

mathematics of Appendix 1 while others will wish to see only the descriptive 

explanation of the model. As an imperfect compromise, the authors have 

chosen to provide the basic equations with a descriptive narrative at the 

beginning of the report, leaving the more complex mathematics to the 

Appendix. The equations themselves were constructed specifically for the 

calculator, based upon mass balance preservation and the spill experiences of 

the development team. Previous algorithms were used or modified for specific 

processes and are referenced in the main text.

Consider putting in Resumes for the other contributors so that when they 

are referenced through “Personal Communication”, readers know their 

background to add credibility. (especially Yappa and Galt)

There are important differences between the major report contributors, who 

have all read the report and determined that it was acceptable for external 

review and the other contributors who may have not read the report in its 

entirety. We have chosen to acknowledge the latter’s help but do not believe 

it would be appropriate to put them in an Appendix of major contributor 

resumes.

Check all figures and move so that they are placed conveniently. (For example, 

Figure 7 is on page 19 but referenced on page 20 and figure 8 should be placed 

before Section 7 On page 21. 

Individual Comments.

1) Table of Contents: appendix names not consistent with titles in Appendix 

themselves.

2) page 8, report goes right into equations without enough explanation. Try 

adding a couple of sentences at top of page 8 after straightforward: Something 

like “There are eight general categories for identifying the mass balance 

and...”

Additional introductory material is added, including a narrative explanation 
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A10.35

of the equations

3) page 8-9, any specific references to these equations? If not, consider stating 

“These equations are based on...”. Also consider putting a copy of Table A1.1 

near beginning of report.

See the new narrative.

4) page 22, the range of volume figures used throughout is confusing. Consider 

placing the left-hand line under the rectangle and labeling the individual 

dotted/shaded areas. It is not clear what these are showing unless somehow 

they are combined at the end to show range.

Figure 8 and similar figures in the report have been deleted.

5) page 23, again are equations from NRC 2003 or ADIOS2?

Equations are adapted from the Delvigne-Sweeney dispersion model 

(Delvigne and Sweeney, 1988)

6) move figure 12 to page 29.

7) page 39. Why are results given in Appendix? 

 Model results will be added to Section 14.

8) Appendix 1 is confusing due to multiple page numbers. 

This has been improved pursuant to the reviewer’s observation.

9) Need a better link from main section of report to Appendix 1. Note that there 

are 16 equations with limited explanations.

Appendix 1 provides the mathematical underpinning of the model. The 

purpose of the main section is to provide the science behind it. Section 

2 provides an explanation of the important equations that control the 

processes. Deleted in this explanation, are auxiliary equations that sum 

results for graphing.

10) Table A1.2 – Not completely clear if and how rate constants are related to 

estimates given in main section.

Toward the end of each section is a discussion of the range of values that can 

be expected for that process. For example, the section on natural dispersion 

defines 20 % as the expected fraction of oil that will naturally disperse with a 
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A10.36

range between 10% and 30%. This translates into an expected value for of 

0. 2 with a range between 0.1 and 0.3. 

11) Appendix 3, consider wrapping text in title cells so that all number can fit 

on single page for easier reading.

12) Appendix 4, need better explanation of how samples were collected and 

analyzed. Just a brief paragraph that provides more than the Figure titles.

13) Appendix 6, need to state in first paragraph that “this Appendix does not 

provide information on the in-situ monitoring.

14) Consider describing test apparatus/process. For example, is the water 

sample poured through a column that the LISST monitors? 

15) Consider adding more information about sampling depths, etc.

16) Appendix 7 – Label and reference all tables and figures in Appendix B. 

Had much difficulty in correlating with explanations.

Appendices 4 through 7 reproduce already published reports reprinted here 

with the owners’ permission to provide completeness to the report. We cannot 

modify them.

Reviewer 10:

Overall, this is a well-written document, with a clear objective of advising 

response options rather than being a comprehensive scientific study. The 

document references the literature of spill science well, and appropriate 

experts have contributed to various aspects of the report. It appears that the 

calculator was useful to inform response options over the summer. Below are 

several detailed comments:

The Executive Summary summarized the report well for the most part. Extra 

emphasis on the differences between a deep water spill such as this one and 

much more common surface spills should be made to overcome misconceptions 

that most managers will hold having only experienced shallow waters spills. 

Introductory paragraph added to the Executive Summary

An estimate of the oil budget should be included in the Executive Summary. 

Maybe even just the pie chart (Figure 2 in the web-based tool section)

While there is much to be said for including the results in the executive 

summary, it was also recognized that this could lead to some readers 
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A10.37

proceeding no further. The development team believes that it is essential to 

understand the purpose and limitations of the calculator before considering 

its results. As a compromise, the results are now included in Section 14 of the 

report where the Calculator itself is assessed.

Degradation rate of the oil components should be evaluated quantitatively if 

possible. Over the timescales relevant to the response, degradation rates are 

seen to be significant. Degraded oil should be a fate in Figure 2 pie chart. 

The development team believes that this would lead the use of the Calculator 

away from response toward damage assessment, something that it is illsuited to assist.

The changing composition of the oil should be part of the analysis. While 

this may seem to be too much detail for response, some fractions of oil are 

more toxic (will have bigger impacts) than others, so response options should 

consider the composition (e.g. toxicity) of the remaining oil in addition to just 

the total amount.

A good point but it exceeds the purpose of the Oil Budget Calculator since it 

is only about the oil budget. We have tried to stay as close as possible to the 

standard ICS 209 output categories. See new Appendix that includes that 

part of the ICS 209 form that deals with the oil budget.

The pie chart seems to be a good instantaneous visual for making decisions. If 

uncertainty could be built into the graphic, that would be very effective. 

I think this is a point where you would find universal agreement among the 

Calculator developers. The challenge has been to provide that uncertainty 

explanation and yet keep the output graphic clear and understandable. We 

are still working on it. See recommendation number 6 at the end of Section 14.

My biggest criticism of the report is the handling of dissolution vs. 

evaporation of the oil. The discussion in Section 5 is good. It is most likely 

that dissolution of lower molecular weight hydrocarbons will occur at a much 

higher proportion for a deep water spill compared with a shallow water spill, 

especially as oil was dispersed. The processes of evaporation and dissolution, 

while similar, are not identical, and should not be treated as such in the sense 

that they will affect the composition of remaining oil differently. Of larger 

concern is that the composition of oil will be altered in sub-surface oil as well 

as surface samples, so the sampling protocols and expected compositions 

of oil would be different than for a shallow water spill. The ultimate fate 

of dissolved oil will not be considered with this model, and in some senses, 

dissolved oil is the most toxic as it is readily bioavailable. I would suggest the 
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A10.38

dissolved quantities be separated from the evaporated quantities. Evaporative 

losses require no response as atmospheric processes act on wholly different 

spatial and temporal scales. The dissolved “losses” means that there is a 

significant amount of oil still in the ocean that needs consideration if not some 

hydrophobic recovery option. 

Your points are well taken here and the group had considerable discussion 

about the merits of separating the two processes into two categories. 

Certainly, any environmental impact assessment calculation would be 

required to do that. However, this is strictly a response tool, and from the 

responder’s point of view, there is no further cleanup applicable to evaporated 

or dissolved oil. Plus, we did not believe we had sufficient sample data to 

distinguish between the two overlapping processes. Therefore, we reluctantly 

kept them together. As more data become available, we may wish to revisit 

this decision.

The Longer Term Processes section was indeed needed in this report, even 

though it does not affect short-term response efforts. This section describes the 

processes of photodegradation and biodegradation. The literature review and 

discussion were good. A simple table illustrating the quantitative removal 

of various components of oil would be useful in communicating the fact that 

photo- and biodegradation are important processes that can remove (or at 

least change composition) of a significant amount of oil in the environment. 

Conditions (mainly temperature and depth) need to be included. This 

understanding of longer term impacts should affect decisions that are made in 

the short term, in that the ultimate fate and impact of the oil must be weighed 

against costs of response.

 We included the section on longer-term processes to provide background to 

those who are new to spill science. We are wary of expanding this section 

because there are others preparing much more complete and detailed 

analysis on these phenomena that should be the true reference sources on the 

subject.

In the assessment and future plans section, #2 states that practical 

operational tools are needed to characterize droplet size. Has the LISST-100 

(Sequoia) particle size analyzer been considered? It measures particle size in 

32 size class bins from 1-55 um by laser, Mie scattering. An Optical Plankton 

Counter (e.g. Brooke Ocean Technologies)uses laser shadow graphs to measure 

particles from 100 um to over 1.5 mm. If these instruments cannot be used, it 

would be useful to know why not. If they were not tested, maybe they could be 

mentioned in the future plans section.

As noted in Appendix 7, the LISST-100X was used for particle size analysis 
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A10.39

on the R/V Brooks McCall and the R/V Ocean Veritas.The accompanyin

documentation outlined the limitations of using the LISST in the benchtop mode for the provision of data for the calculation of total dispersed 

oil for calculation of mass balance. However, the mission was to verify if 

the chemical oil dispersants were effective by quantifying the formation 

of smaller oil droplets and to identify the presence of the subsurface oil 

plume. This was conducted successfully. However, in the bench top mode 

utilized, explained in Appendix 7, the team could not trust the data in the 

larger size class bins. At the time of the spill, Sequoia had discontinued the 

manufacture of of a LISST for deepwater deployment (>300m for the LISST100X) due to the lack of sales (and apparent problems with leaks).

A LISS

DEEP (depth range of 2500-6000m) is now in production as direct result of 

demand generated by the Gulf of Mexico spill that will resolve some of the 

limitations of using the LISST in bench top mode.

A Laser Optical Plankton Counter (LOPC), developed by Dr. Alex Herman 

at Department of Bedford Institute of Oceanography, Fisheries and Oceans 

Canada, measures particles >100um. NOAA is currently deploying a 

LOPC with a Moving Vessel Profiler (MVPTM) in the Gulf of Mexico. A new 

prototype for the monitoring of plankton in ballast water with a lower size 

limit of > 20 um is being evaluated. For the sampling work in the Gulf 

of Mexico, the sampling team chose to use the LISST-100X as chemically 

dispersed oil typically results in an increase in oil particles in the <10um 

size range. It is important to note that both the LISST and LOPC cannot 

distinguish oil droplets from other particles.

Regarding future plans, there is a need to evaluate the various other in situ 

instruments that may be capable of measuring of oil droplet size. Years ago, 

Dr. Jim Bonner at Texas A&M evaluated field instruments including the 

LISST for real time in-situ crude oil concentration measurements but not oil 

droplet size distributions.

For work in the Gulf of Mexico, the sampling team chose to use the LISST100X as chemically dispersed oil typically results in an increase in oil 

particles in the <10um size range. It is important to note that both the 

LISST and LOPC cannot distinguish oil droplets from other particles.

 

Figures 4&5 of the Web-based tool section were informative in that the 

estimates were fairly close from worst case to best case so that the inherent 

uncertainty was not very large. I would agree with the report that some visual 

representation of the uncertainty in the calculator would help decision makers 

dramatically.

We agree.
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A10.40

Reviewer 11:

The report contains a lot of good information, and the approach to the budget 

calculations is sound. However, the draft could be improved in several 

ways. Firstly, it is somewhat uneven in its coverage (e.g., stronger coverage 

of surface processes such as evaporation and surface dispersibility than on 

corresponding sub-surface processes). Secondly, it would benefit the reader to 

include results of at least the base case budget, obtained using the calculator, 

in the main text and the Executive Summary, instead of just the appendix. 

Most importantly, I wonder if the motivation for the report should be 

expanded. The stated objective was for “response advice only and...not... any 

other purpose.” This may be literally true but it seems very limited. Everyone 

understands that there was at least some oil in each of the “pieces of the pie”, 

and there was a tacit assumption that it was worth responding to any oil that 

was amenable to clean-up. (A lot of effort was put into skimming surface 

oil even though it only amounted to 3% of the total release.) Do we really 

need a budget calculator for this purpose? Given the enormity of the spill, 

its environmental consequences, the fact that the opportunity for most cleanup procedures has passed, the threat of future spills, and our uncertainty 

regarding much of the science, a far more important motivation would to 

better understand the underlying processes affecting the oil’s fate. And this 

calculator makes a good place to start. More specific comments (many minor) 

are provided below by section.

The Oil Budget Calculator was developed to meet response needs only and 

the group would strongly counsel against using it for any other purpose. 

The report was written to document for the external scientific community 

the inner workings of the Calculator and provide recommendation for future 

research.

Page 8 (and elsewhere). In several places the report refers to dispersed oil “at” 

the bottom. “Near” the bottom would be more descriptive.

Page 8. In calculating the chemically dispersed “bottom” oil (eq 2), the factor 

90 is not explained. Nor is the factor 20 in eq 7 in the subsequent discussion 

of chemically dispersed surface oil. (I assume these relate to the respective 

DORs?)

Your assumption is correct. The two values relate to the respective maximum 

DOR and are used in the equations as scaling factors so that the rate 

constants will have a maximum range of 0-1.

Page 9. Since chemically dispersed surface oil is time dependent (efficiency 
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A10.41

decreases with weathering, like evaporation), was consideration given to 

introducing time dependent (day 1, day 2...) values for k3 as is done for 

evaporation (k4 and k5)?

This was considered. However, surface dispersant operations were applied 

to oil slicks at various states of weathering. Therefore, it would have been 

logistically impossible to assign a t-value to the sprayed surface oil.

Page 11, paragraph 2 mentions “Current news reports...” and Page 12, 

paragraph 1 mentions “At the time of this document preparation...”. Better to 

include the date (or at least month) since the information may not be current 

when it is read.

Page 13, paragraph 2. The Clark et al (2010) study estimates a minimum 

amount of surface oil on May 17 as 66,000 to 120,000 bbl, but implies the 

value could have been as high as 500,000 bbl (a little less than 1/3 of the oil 

spilled at that point) which is the value used in the calculator. This fraction 

(~1/3) seems quite high, especially since, subsequent to May 17, sub-surface 

recovery and dispersant use would dramatically reduce the oil reaching the 

surface, and ongoing evaporation and natural surface dispersion would 

reduce the equilibrium fraction of oil on the surface.

The NASA/AVIRIS system was measuring cumulative oil remaining on the 

surface by mid-May. Through April and early May, burning, direct capture, 

and subsurface dispersant usage were limited, leaving more surface oil. 

Some of this early oil, probably emulsified, would still be on the surface 

during the NASA ER/2 flights. However, we agree that this would be at the 

high end of our estimates.

Pages 13 (bottom) and 14 (top) discuss the gas portion of the release and 

mention that a large portion of the gas dissolved and hence may never have 

made it to the surface. Is there observational evidence (or modeling) that 

suggests that ANY significant fraction of the gas made it to the surface? The 

paragraph mentions the potential for hydrate formation. Clearly hydrates 

were formed within the confining spaces of some of the earlier containment 

vessels, but was there evidence of their formation in the free plume? (If so, 

please indicate.) Since the principle components of natural gas (methane, 

ethane and propane) have different hydrate stability zones, it would be 

helpful to know the composition of the natural gas. Concentrations are given 

in Appendix 4, but I gather this is just for the oil. Steve Masutani from U. 

Hawaii performed some nice experiments demonstrating the effect that an 

oil film has in inhibiting hydrate formation on a gas bubble (Masutani and 

Adams, 2000).
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A10.42

See revised text. Leifer (2010) modeled plume behavior based on observations 

presented in Camilli et al. (2010) and concluded that the gas phase of the 

plume dissolved in the mid-water column and deeper and was transported by 

oil plume processes to the thermocline. Only gas components dissolved in oil 

reached the sea surface to exsolve and evaporate. As a result, atmospheric 

measurements (Leifer et al., 2010, in progress) do not show enhancement in 

atmospheric methane (except associated with flaring). In contrast, hexane 

and large organic compounds were enhanced. Tow sled video data (Asper, 

unpublished, 2010), showed extensive plumes of orange-white material 

uncommon to the deep sea (i.e., not marine snow), which was interpreted as 

oily hydrate flakes in the depth where others have reported enhanced CDOM 

(a fluorometric indicator of oil, albeit with very poor sensitivity).

 Leifer, I., et al. (2010 in progress), Air quality implications of the Great 

Macondo Oil Spill.

Page 14, last paragraph. It is mentioned that the tiny oil droplets were 

continually exposed to uncontaminated Gulf waters and hence their 

dissolution was not controlled by equilibrium factors. It seems likely that 

much of the sub-surface oil was contained in thick (hundreds of meters in the 

horizontal and at least tens of meters in the vertical) intrusions with fluxes of 

thousands of cubic meters per second. So even without exposure to ambient 

GoM water, dissolution would not likely have been constrained by equilibrium 

build-up.

Point noted. The development team is cautious about reaching extensive 

conclusions on dissolved hydrocarbons while studies are ongoing.

Page 15 (Fig 4 on droplet rise velocity). It would be helpful to give the density 

of the oil that was assumed, and to expand the graph to the physically more 

interesting lower diameter range.

The graph was meant to illustrate the fact that the rise velocity for even 

larger droplets was small enough that the transit time to the surface was 

long. See the discussion and reference by Spaulding. The fresh oil density 

was 0.85 gm/cm3 (API 35).

Page 15 (and elsewhere). Much of the most useful information in the report 

has been provided by the experts, and cited as personal communication (Yapa 

on this page; also Galt, Lasheras elsewhere) or unpublished reports (Lewis). 

Can additional information be provided when there is no formal reference?

Common in spill response is research that is used before it is formally 

documented. The Oil Budget Calculator is one example. We hope that these
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A10.43

experts will formally publish their findings but are reluctant to release their 

private communications beyond what is necessary in the report. In all cases 

we have tried to cite published references to supplement their unpublished 

comments.

Page 17, paragraph 2. It was estimated that any oil exposed to conditions that 

could lead to dissolution would lose 5-10% of its volume... Presumably this 

explains the mean value of 0.075 for k7? (It would help reinforce the readers’ 

understanding if this was mentioned.) Can the authors relate Fig 6 showing 

the dissolution potential vs C-number to the oil composition in Appendix 4 to 

show the reader where the 5-10% comes from?

A dissolution potential of 5-10 % is a rough estimate based on earlier 

experiences from dissolution studies of various oils (referred to Melbye et 

al 1999 in the report) and with the present available chemistry of the DWH 

crude oil. Unfortunately, the detailed chemistry of the BTEX’es are not 

specified in the appendix 4 data. (which will be a significant contribution to 

the dissolution potential). That is why we ended up in this relatively wide 

span of 5-10 %. It is therefore difficult to directly relate the dissolution from 

the chemistry data in appendix 4 to Fig. 6.

More specific estimate of the dissolution potential will be calculated when the 

more detailed data of the crude oil chemistry are available.

Page 22 (Figure 8, and others like it). The range of volumes (mean and with 

uncertainty) relative to the total spill (4.9 Mbbl) is clear, but what does the 

placement of the colored bar mean? Is there a horizontal bar chart invisioned 

that aims to add all of these up? (I know there is a pie chart in Appendix 2.) 

Figure 8 and similar figures have been removed

Pages 21 (bottom) and 22 (top). The discussion of emulsification is useful but 

brief. Where do the percentages (16 to 38) come from?

Most crude oils and petroleum products require weathering (evaporation) 

before they will form emulsions (NAS, 2000) This weathering is necessary to 

increase the viscosity and the asphaltene/resin content to the point where the 

next water-in-oil type is possible. Oils from similar oil fields have a tendency 

to form similar emulsions with similar weathering tendencies. It should also 

be noted that there are some exceptions to this as well. Table 2 shows the 

emulsification tendencies of Gulf of Mexico oils (Fingas, 2009). Table 2 shows 

that oils that formed stable emulsions had weathering percentages of 37.7, 

26.2, 16.4, 25.5, 22.6, 24, and 35.2%. These averaged 26.8%. 
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A10.44

1 NAS, Oil in the Sea III, Inputs, Fates and Effects, National Research 

Council, National Academies Press, Washington, D.C., 2002

2 Fingas, M. and B. Fieldhouse, Studies on Crude Oil and Petroleum 

Product Emulsions: Water Resolution and Rheology, Colloids Surf. A, 67, 

2009

Were there no direct measurements of emulsification in oil samples from the 

DWH spill?

Only very limited emulsion analysis was available at the time of the 

Calculator construction. It indicated a weak and easily broken emulsion.

How has percent emulsification been used in the budget? Was it used? (Since 

emulsified oil was skimmed and presumably could no longer be evaporated, 

does it matter to the budget calculator what its percentage was?) Where does 

the analysis from Appendix 7 enter the discussion?

The observed tendency of the oil to emulsify was used in reducing the 

effectiveness for burning and surface dispersion.

Page 22, Section 8, paragraph 2. Is there a reference for “Payne reports 

plumes of oil droplets...”?

See reply to the Fourth Reviewer of a similar question.

Page 23, paragraph 2 mentions that using 100 microns as an upper bound 

diameter for dispersible droplets is conservative (presumably in view of 

the fact that somewhat larger droplets are also slow to rise). It should be 

mentioned that this is conservative in that it may overestimate the amount of 

oil reaching the surface, but is NON conservative in estimating the oil retained 

sub-surface. Aren’t both fractions important?

Table 2 Stable Emulsions from Gulf Oils

Weathering to form emulsions Complex Water

Oil % Visual Density Modulus Content

evap. Stability (@15C) Viscosity Saturates Aromatics Resins A s phal ten es Stability (mPa) (%w/w)

Garden Banks 426 37.7 Stable 0.8993 136 56 32 10 3 588 8.00E+04 64.7

Mississippi Canyon 72 26.2 Stable 0.9095 195 52 34 11 3 1128 2.20E+05 74.4

Mississippi Canyon 807 16.4 Stable 0.9375 491 39 41 13 7 111 5.45E+04 67.5

Mississippi Canyon 807 25.5 Stable 0.9582 3454 31 43 18 8 46 1.60E+05 65.4

Neptune Spar (Viosca Knoll 826) 22.6 Stable 0.8986 187 61 29 8 2 4947 9.25E+05 62.5

Viosca Knoll 826 24 Stable 0.9067 325 59 29 8 3 1046 3.40E+05 64.1

Viosca Knoll 990 35.2 Stable 0.8905 91 62 28 8 2 1075 9.8E+04 63.9

average 26.8

of weathering of Louisiana

crudes to yield Stable emulsions
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A10.45

By overestimating surface oil, and hence oil that potentially could be subject 

to further recovery, we meet the Calculator definition of conservative. It may 

not be conservative in regard to environmental impact.

Page 23, eq 12. Is the ratio reversed? As written R appears to be the dispersed 

mass due to leaking pipe divided by dispersed mass due to breaking waves

Corrected expression inserted

Page 24, top. There is a huge (2.5 order of magnitude) difference in the 

predicted “theoretical” dissipation rate (Eq 13) of 7 m2/s3 and the MartinezBazen et al. (1999) value of 0.02 m2

/s3 (rate = 0.003 Uo

3

/Do). The latter rate 

is based on actual measurements, but the measurements are for a pure water 

jet near the orifice, while the dissipation rate in a real oil/gas leak would be 

higher due to the turbulence introduced by the bubbles and droplets. Thus 

the real dissipation rate is likely to be in between as the authors state. (It 

would help to give the exit velocity and pipe diameter so that the reader could 

verify these computed dissipation rates.) Compounding the uncertainty 

in dissipation rates are the differences between a sub-surface oil plume 

and a surface oil slick. A sub-surface oil plume is reasonably steady and 

uniform (in an Eulerian framework), whereas surface wave conditions are 

quite patchy over space and time scales of tens of meters and hours. On the 

other hand, surface oil has a time window of a few days during which it is 

dispersible, while an oil parcel traveling through a plume has a window 

of only a few tens of seconds, since dissipation rate drops off rapidly with 

distance. (The dissipation rate is proportional to the plume velocity (which 

decreases) cubed divided by the plume diameter (which increases).) In view 

of the above, quantitative calculation of the rates of subsurface and surface 

natural dispersion are quite uncertain. Also, could the authors provide more 

explanation as to where the 10 to 30% figures come from?

Your observations are correct and illustrate the challenges faced in 

estimating subsurface dispersion, both natural and chemical. One of the 

unsettled points among the experts, for example, was whether there was 

sufficient mixing time between the surfactant and the oil for effective 

dispersion. Following the rule to be conservative, the lower dissipation rate 

of Martinez-Bazen is used, minimizing natural dispersion and increasing 

oil on the surface. The minimum natural dispersion value is considered to 

be the lowest credible estimate consistent with Camilli et al. (2010) and 

the maximum the largest estimate consistent with the AVIRIS and LISST 

results.

Information on exit velocity and pipe dimensions can be found in the FRTG 

report, Deepwater Horizon Release, Estimate by PIV, 215 pp. (2010), 
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A10.46

available on the USGS Deepwater Horizon website. 

Page 31, second paragraph. It would be helpful to have more information 

about how the dispersants were added sub-surface. For example, how close 

to the plume was the narrow diameter wand held by a ROV? The high plume 

velocity would have introduced an entrainment (radially inward towards the 

plume) velocity of about 10% of the local plume centerline velocity, and this 

entrainment velocity should have carried dispersant into the plume if it were 

released anywhere near the plume edge.

The team did not have access to videos of dispersant injection, only data on 

the volume of dispersant used.

Page 31, bottom. There appears to be little consensus among the report 

authors as to the effectiveness of sub-surface dispersant use, making this a 

prime topic for further research. My feeling is that it was probably quite 

effective. In addition to the arguments presented by “Alternative View 2” 

on page 29, we know that vertical profiles of fluorescence (indicating oil), 

typically showed a major intrusion a few hundred meters above the seafloor, 

with much smaller intrusions similarly spaced above the first intrusion. 

(See figure from one of the R/V Brooks McCall surveys below.) I believe that 

the intrusions contain oil that is detrained (along with seawater) from the 

plume as it ascends through a density-stratified ocean and passes a level of 

neutral buoyancy. Such detrainment events are clearly evident in laboratory 

measurements of gas and water plumes (Socolofsky and Adams, 2002, 2003). 

Oil droplets would follow the seawater into the intrusion only if they were 

small, with insignificant rise velocity, as if chemically dispersed. (A small 

amount of dissolved oil could also be included in the intrusions.)

Many of our experts would agree with your conclusions. Unfortunately, 

others, including another reviewer, disagree. All would agree that there is 

little consensus on the subject and further research is required.

 

Appendix 1 (Uncertainty). I did not have time to do this section justice, but 

it appears to be solid. I do question if the k-values in Table A1.2 should be 

called “rate” constants. Rate constants usually have dimensions of reciprocal 

time, whereas the values of k are dimensionless.

We have used the expression “rate constant” by force of analogy with 

chemical kinetics, and agree with the reviewer that this may not have 

been the most accurate characterization for these constants: alternatives 

that came to mind, including “efficiency constants” and “yield factors”, also 

suggest analogies that could be deemed deficient. Since we believe that the 

risk of misinterpretation is minor, we have chosen to retain this expression. 
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A10.47

Appendix 2 (Oil budget calculator). The appendix should have a number (2) 

and its title should match that listed in the TOC. (Appendix 5 also needs a 

number.) These are small points, but “Figures” 1 and 3 are actually tables. 

It would also be nice to make the figure and table numbering consistent with 

the rest of the report, e.g., Figure A2.1, etc. Similar comments apply to other 

appendices.

Appendix 5 (SINTEF dispersibility study). The MNS tests were conducted 

for 5 minutes; for how long were the IFP tests conducted? Was there any 

thought given to similar studies with sub-surface oil like that in the plume 

(unweathered but subjected to relatively high energy for short periods of time)?

Appendix 6 (LISST measurements). Paragraph 1 of page 1 refers to both 

bench top LISST measurements (of collected samples brought onboard) and 

continuous in-situ measurements with the instrument deployed at specific 

depths. The text only describes the former. Were the in-situ measurements 

not useful? It seems they would have avoided some of the issues described on 

pages 2 and 3 concerning the finite handling times and corresponding loss 

of large droplets. I gather that the droplet size distributions (both discrete 

and cumulative) are number distributions, but it is not clear. For example, 

the captions for Figures 5-8 indicate that the figures plot the FRACTIONS 

of the total measured particles that are small, while the text that refers to 

these figures (third paragraph of page 3) suggests that the vast majority 

of VOLUME fractions ...are ...small. Assuming the plotted distributions 

are by number, it would help to also include cumulative mass (or volume) 

distributions so the reader could determine the droplet diameters below which 

a give portion of the dispersed oil was contained. The text mentions (last 

paragraph of page 3) that the median diameters are nearly 20-30 microns; 

however my reading of Figures 9-12 suggest that these ranges are closer to 9 to 

16%. Figures 4, 5, 8, 9 and 12 are mis-numbered. It would also help if figure 

numbers were consistent with the whole oil budget calculator report instead 

of (or in addition to) starting over with each new appendix. (This comment 

refers to several appendices.)

The in situ measurements are very useful. The data analysis is currently

ongoing and cannot be included here due to the deadline of this report. 

All the LISST data presented in this appendix are based on volume

concentration and are expressed in ul/l. This is stated on the first page of this

Appendix.

Figures 4,5,8,9 and 12 were mislabelled. Their corrections are suggested in

this response (see 2 above).
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A10.48

Note the x-axis of Figures A7-9~12 (Cumulative particle-size distribution

is expressed as bin number, see Table 2 of this appendix for corresponding

lower, medium, and upper size of each bin. For example, bin # 9 has medium

size of 10.2um, and bin # 16 has medium size of 32.5 um.

Appendix 7 (Ross oil properties report). Table 2-2 gives MC252 oil properties 

at temperatures of 15 and 35 oC. What was the temperature of the oil as it 

emerged from the pipe? Much of the oil resided/resides in intrusions a few 

hundred meters above the seabed where the temperature is ~5 oC. It would 

help to know oil properties (especially density) at these temperatures. Can the 

authors suggest a reasonable way to extrapolate from the data at 15 and 35 

o

C? It is too bad that the emulsification tests were conducted with a 2 cm thick 

oil which is mentioned in the text as a possible reason the oil in the lab did not 

emulsify, while the oil in the field did. It makes sense to repeat the tests with 

a 1 mm thick sample as suggested. Has this been done already? Also why 

would a 20 mm thick sample weathered for two weeks (336 hr) correspond to a 

1 mm field sample exposed for 10 hrs? Wouldn’t it be 336/20 = 17 hrs?

Appendices 5 through 8 reproduce already published reports reprinted here 

with the owners’ permission to provide completeness to the report. We cannot 

modify them. However, the author of Appendix 6 has offered the following 

reply. The IFT test is a dilution test conducted over a test periode of 1 hour. 

Both the medium/high energy MNS method and low-energy IFP method are 

both standard laboratory methods designed to test the relative effectiveness 

of dispersnt applied on surface oil/emulsions. In order to simulate and study 

the effectiveness of dispersant injection into a sub-surface oil plume, special 

designed laboratory test systems have to be constructed.

Appendix 8 (Expert resumes). Michel Boufadel is not represented.

He is now included

References

Masutani, S., and E. Adams. 2000. Experimental study of multiphase plumes 

with application to deep ocean oil spills. Final report to U.S. Dept of Interior, 

Minerals Management Service. Contract No. 1435-01-98-CT-30946. 

http://www.mms.gov/tarprojects/377.htm.

Socolofsky, S. and E. Adams. 2002. Multiphase plumes in uniform and 

stratified crossflow. J. Hydr. Res., 40: 661-672.

Socolofsky, S. and E. Adams. 2003. Liquid volume fluxes in stratified 

multiphase plumes. J. Hydr. Engrg. 129: 906-914.
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A10.49

Reviewer 12:

Overall, a very good document. I have only minor suggestions to offer.

Page: 3, Executive Summary

I really like the line “It is important to remember that the Deepwater Horizon 

incident was an emergency, not an experiment” from your introduction on 

page 5. You should consider incorporating it somewhere in your Executive 

Summary.

We also thought it was an important point but concluded that it would 

require too much additional explanatory material if it was added to the 

Executive Summary.

Page: 20, 1st paragraph

Since you mention the temperature of the oil here, it might be helpful to state 

the relationship between the oil’s temperature and water temperature if it has 

not been previously stated.

Oil temperature is assumed to be the same as the water temperature. Report 

is revised to clarify this point.

Page: 21, 2nd full paragraph

In the sentence beginning with “The stronger color ...” , replace the word 

“stronger” with “darker”.

Page: 22

Figure 8 (and the other, similar figures) is a confusing graphic. I understand 

what the writer is trying to do, but this is unconventional to the point that it 

may cause confusion for some readers. It contains too many elements that are 

not important: height of the box and position of the shaded portion within the 

box.

Figure 8 and similar figures have been removed.

In Section 8, 2nd paragraph

The acronym LISST should be defined (Laser In-Situ Scattering and 

Transmissometry) at first use. 

Page: 23

First paragraph

I can’t tell if the sentence beginning with “ADIOS2 suggests that ...” is part of 

the previous paragraph or if it’s supposed to be the start of a new paragraph.
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A10.50

Second paragraph, 

For the sentence beginning with “Also, ADIOS2, following ...” delete the word 

“Also” so that the sentence reads “ADIOS2, following standard guidelines ...”.

Page: 25, 2nd paragraph

In the sentence beginning with “As before, the stronger color ...” , replace the 

word “stronger” with “darker”.

Page: 26

There’s something wrong with the color assignments for the bars in this graph. 

There’s some strange optical effect that makes it difficult to view.

Adjusted.

Page: 27

Second paragraph, second sentence

Add the phrase “and regionally approved” so that it reads “... is a Federal 

cooperatively designed and regionally approved ...”.

Figure 11

What is the blue rectangle that overlaps the 5/10 A.M. circular image?

Also, the legend for this figure is not legible.

Figure replaced

Last paragraph

There’s a typo in the sentence that begins with “If the dispersant ...” after the 

word “half” there’s an “=” sign that shouldn’t be there.

Page: 28

Second paragraph

The sentence that reads “Analysis was undergoing at the time ...” should read 

“Analysis was ongoing at the time ...”.

Third paragraph

The sentence that reads “Given this limited nature ...” should read “Given the 

limited nature ...”.

Reviewer 13:

The document is longer than I expected with 156 pages and the content goes 

well beyond my own expertise.
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A10.51

The document addresses an urgent and difficult problem of estimating 

oil budget in the gulf due to the Deepwater Horizon Spill. Moreover, the 

authors tried to simplify the process so that the output (Calculator) can be 

used by non‐specialized staff. The various kind of simplification leads to 

huge uncertainties. The authors have done a very good job in addressing 

many of these uncertainties. However, I’m not fully convinced that all these 

uncertainties are properly quantified (this is due partly to the limited time and 

the length of the document). There are also a few other minor issues:

1. Low resolution of some of the mathematical symbols and equations;

2. A few misconception (typo) such as the relationship between the kinematic 

viscosity and dynamic viscosity in A.2.2.

Reviewer 14:

General thoughts on each section:

1. Introduction: no comments

2. Purpose and brief description: seems well thought through. The 

formulae seem to be well constructed and shouldn’t lead to absurdities 

like more oil evaporating than leaving the well head. However, it seems 

that something like this should have appeared in the literature already 

from previous disasters yet there are no citations in this section.

3. Previous experience Ixtoc: Good discussion of most relevant previous 

case.

4. Leak Rate and Subsurface Oil Behavior: good discussion, no criticisms.

5. Dissolution: well referenced but out of my area of expertise

6. Evaporation: “To simplify data entry, requirements to enter variable 

wind speeds were eliminated. Cumulative evaporation is sensitive 

to oil composition and temperature but is relatively insensitive to 

wind speed.” This statement should be supported, preferably with 

a citation to the literature. Evaporation rates depend strongly on 

temperature and also on the relative saturation of the air with the 

compound evaporating (for example, relative humidity in the case 

of water evaporating). It may be that keeping track of wind speed is 

prohibitively complicated and the model needs to be simplified but that 

argument is not well made.

While the evaporation rate is sensitive to wind speed, cumulative evaporation 

depends upon the mole fractions of the hydrocarbons that make up the oil. 

If high wind on day 1 cause large amounts of evaporation, there will be less 

volatile hydrocarbons the next day, slowing evaporation rate. Calm winds 

on the first day lowers evaporation, making more of the volatile factions able 

to evaporate later. The cumulative fraction remains much the same after a 
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A10.52

few days in both cases, although second order effects such as evaporation 

reducing oil available for dispersion exist, but are not included in the 

calculator.

7. Weathering Estimation by Emulsification: no comment

8. Natural Dispersion: good discussion, good references to the appropriate 

literature

9. Chemical Dispersion: there seems to be quite a diversity of opinion 

on the effectiveness of dispersants. In keeping with the nature of this 

report, a judgment needed to be made to estimate the amount of oil 

remaining in the sea. However, it is clear that more scientific research 

needs to be conducted into the use of dispersants, both on the surface 

and sub-surface.

10.Burning: good discussion

11.Mechanical Recovery: good discussion

12.Other Oil: this seems to be another area of considerable uncertainty.

13.Longer Term Processes: another area for future research.

14.Assessment and Future Plans: it is clear that we should learn from this 

terrible disaster. I hope that the recommendations (some of which I 

echo in my review) of this panel for future research are heard and acted 

upon so that we can more effectively respond to future accidents.

Itemized list of spelling and grammar errors

Page 9, the following sentence fragment does not make sense: “Disperse 

the surface oil that is available for surface dispersion, after subtracting 

evaporation and burning,...”

Page 11, “chocolate mouse” should be “chocolate mousse” unless the quoted 

source also made this mistake.

Page 12, “The DOE/FRTG consensus generally accepted a average value of 

44% oil percentage by ...” ‘a’ should be ‘an’.

Page 13, “This is considerable larger than the common maximum...”, should 

be ‘considerably’.

Page 13, Figure 3 has text on it saying “62 bopd” and the like. These labels 

should say “62 kbopd”.

Page 20, “the two weathered oils (green and purple) collected from th sea 

surface on or around 16 May.” The word ‘the’ is misspelled.

Page 22, “Payne reports plumes of oil droplets at depth over 2 km.” awkward 

sentence.

Page 27, “...then more than half =of the released oil...” misplaced ‘=’.

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Deepwater Horizon

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