PNNL-XXXXX

Analysis of How Environmental 

Conditions Affect Dispersant 

Performance during Deep Ocean 

Application

Final Report

August 2017

GT Bonheyo R Jeters

K Rose L-J Kuo

A Bunn J Park

A Avila J Velma

T Bays E Winder

V Cullinan P Wingo

R Duran
Disclaimer

This report has been reviewed by the Bureau of Safety and Environmental Enforcement (BSEE). 

Review does not signify that the contents necessarily reflect the views and policies of the 

Bureau, nor does mention of the trade names or commercial products constitute endorsement or 

recommendation for use.
PNNL-XXXXX

Analysis of How Environmental 

Conditions Affect Dispersant 

Performance during Deep Ocean 

Application

Final Report

August 2017

GT Bonheyo1,2 R Jeters1

K Rose3 L-J Kuo1

A Bunn4 J Park1

A Avila1 J Velma3

T Bays4 E Winder1

V Cullinan1 P Wingo3

R Duran3

Prepared for

the U.S. Department of Energy

under Contract DE-AC05-76RL01830

Pacific Northwest National Laboratory

Richland, Washington 99352

 1 Pacific Northwest National Laboratory- Marine Science Laboratory, Sequim, WA

2 Washington State University, Pullman, WA

3 National Energy Technology Laboratory, Albany, OR

4 Pacific Northwest National Laboratory, Richland, WA

iii

Preface

The study reported herein was funded by the U.S. Department of the Interior, Bureau of Safety and 

Environmental Enforcement (BSEE) through an Interagency Agreement, BSEE Contracts E14PG00043, 

“Biodegradation and Toxicity Following Dispersant Usage in a Cold, Stratified, Deep Sea Setting,” and 

E15PS00027, “Analysis of How Environmental Conditions Affect Dispersant Performance During Deep 

Ocean Application,” with the U.S. Department of Energy, Pacific Northwest National Laboratory. This 

report describes Pacific Northwest National Laboratory researchers’ experiments conducted to assess the 

behavior of oil, with and without dispersants, under pressure and the development of methods to conduct 

high-pressure studies.

Point of Contact

George Bonheyo, Ph.D. George.bonheyo@pnnl.gov

5

Executive Summary

Oil exploration in the deep waters of the Outer Continental Shelf creates a need for adequate spill 

response tools that can meet the unique conditions presented by deep ocean blowouts. This study entailed 

the design and execution of basic and applied research to 1) better understand the behavior of oil, 

dispersants, and microorganisms under relevant conditions; 2) identify tools for conducting high-pressure 

oil research; and 3) apply the data and insights to improve how models, such as the Blowout and Spill 

Occurrence Model (BLOSOM) predict the movement of oil with and without dispersant.

The ability to conduct high-pressure studies and to examine pressurized fluids (including oil) without 

depressurizing and thus physically altering the sample remains challenging. A wide range of analytical 

methods were evaluated and all were hampered by factors such as difficulty taking measurements through 

the thick steel walls of a pressure vessel, the tendency of oil to adhere to chamber or tubing surfaces; the 

often rapid movement of oil up to the surface of a fluid; and difficulties holding oil droplets within a zone 

of interrogation long enough for measurements to be completed.

Experiments conducted to examine the effect of a dispersant on the bacterial and fungal metabolism of oil 

and on the influence of oil droplet size on the rate of biodegradation yielded inconsistent results. 

Although the dispersant Corexit 9500 was not toxic to a set of model oil-degrading organisms, its use had 

very inconsistent effects on the degradation of a large panel of hydrocarbons that were tested. Droplet size 

studies were also inconsistent; no clear pattern was observed based upon initial conditions.

Another set of experiments sought to examine how pressure and/or temperature affect microbial growth, 

and the metabolism of high concentrations of oil. Cold temperatures, and nutrient or oxygen depletion 

were found to have the most significant impact. Pressure also negatively affected the growth of marine 

species collected in surface water.

Blowout simulation experiments involving the rapid depressurization of oil found the following:

• Depressurization leads to the atomization and emulsification of a fraction of the oil without the use of 

dispersants.

• The addition of a dispersant resulted in a greater volume fraction of the oil having smaller droplet 

sizes.

• Matching the temperature of the dispersant to the oil did not appear to affect the outcome.

• Premixing dispersant with the oil (optimal mixing) rather than injecting the oil into the plume did not 

appear to affect the oil droplet size distribution.

• The droplet sizes produced by the experimentally derived PNNL distributions differed in size from 

the size range used for the Johansen et al. 2013 distribution by roughly an order of magnitude.

Although the method of measurement used in this study did provide a wider dynamic range of 

measurement, it appears that the experimental design (e.g., orifice diameter, discharge rate, GOR) 

also impacted the distributions.

The presence of a sediment in the oil was found to reduce median droplet sizes by >60% compared to an 

identical oil containing little or no sediment. The effect was also observed when a dispersant was used in 

different concentrations.

The BLOSOM has been updated to include the droplet size distribution provided in Johansen et al. (2013) 

to simulate the presence of dispersant.
6

The droplet size distributions derived from the experiments have been implemented in BLOSOM. The 

newly implemented distributions produced droplet size distributions under simulated conditions that were 

consistent with those provided in literature.

Using realistic hydrodynamic conditions and either the Johansen et al. 2013 or PNNL experimentally 

derived size distributions, BLOSOM simulations resulted in a greater amount of oil remaining at depth 

when the application of dispersant was included at the blowout as part of the simulation. This is 

consistent with observations from the Deepwater Horizon accident.

A strong correlation between crude oil viscosity and dispersant effectiveness was identified through the 

literature review process, and was implemented in BLOSOM as an additional option.
vii

Acknowledgments

The authors wish to thank John Linehan, and Molly O’Hagan for their efforts testing highpressure nuclear magnetic resonance spectroscopy as a means of monitoring water + oil + 

dispersant mixtures under pressure.

ix

Acronyms and Abbreviations

3D three-dimensional

ABW UltraSeal ABW (a coating from Nanofilm Ltd)

ADC analog-to-digital converter

AFT Anti-Fouling Treatment

ANS Alaskan North Slope (crude oil)

ATCC American Type Culture Collection

atm standard atmosphere (unit of pressure)

BSEE Bureau of Safety and Environmental Enforcement

BLOSOM Blowout and Spill Occurrence Model

°C degree(s) Celsius

cm centimeter(s)

cP centipoise

CV coefficient of variation

DOF depth of field

DOSS dioctyl sodium sulfosuccinate

DOSY diffusion ordered spectroscopy

DOR dispersant oil ratio

DSD droplet size distribution

DWR dispersant water ratio

EC50 effective concentration that reduces bioluminescence to ≤50% of the control

EPA U.S. Environmental Protection Agency

FOV field of view

g gram(s)

GC-MS gas chromatography-mass spectrometry

1

H-DOSY diffusion ordered spectroscopy

1

H-NMR proton nuclear magnetic resonance

hr hour(s)

HyCOM Hybrid Coordinate Ocean Model

ID inside diameter

IFP Institute Francais du Pétrole

in. inch(es)

J2013 DSD in Johansen et al. (2013)

K degree(s) Kelvin

km kilometer(s)

μL microliter(s)

L liter(s)
x

LED light-emitting diode

μm micron(s)

m meter(s)

MΩ megaohm(s)

MC 252 Mississippi Canyon Block 252 well

MDL method detection limit

mg milligram(s)

MHz megahertz

mL milliliter(s)

mm millimeter(s)

mN/m millinewton(s) per meter

mPa millipascal

mPa-s millipascal-second

MNS Mackay-Nadau-Steelman

MSL Marine Science Laboratory

NCOM Navy Coastal Ocean Model

NETL National Energy Technology Laboratory

nm nanometer

NMR nuclear magnetic resonance (spectroscopy)

OD outside diameter

O.D. optical density (absorbance)

O.D.590 optical density of a sample measured at a wavelength of 590 nanometers

OPA oil-particle-aggregate

PAH polycyclic aromatic hydrocarbon

PCR polymerase chain reaction

PNNL Pacific Northwest National Laboratory

PNNL2017 DSD obtained experimentally by PNNL

ppm parts per million

ppt parts per thousand

psi pound(s) per square inch (unit of pressure)

PSU practical salinity unit(s)

PTFE polytetrafluoroethylene

rpm rotations per minute

s second(s)

SCFM standard cubic feet per minute

WTI West Texas Intermediate (crude oil)
xi

Contents

Preface ......................................................................................................................................................... iii

Executive Summary ...................................................................................................................................... 5

Acknowledgments....................................................................................................................................... vii

Acronyms and Abbreviations ...................................................................................................................... ix

1.0 Introduction ....................................................................................................................................... 1.1

2.0 Background........................................................................................................................................ 2.1

3.0 Study Methods................................................................................................................................... 3.1

3.1 Marine Laboratory Facilities..................................................................................................... 3.1

3.2 Droplet Size Determination....................................................................................................... 3.2

4.0 Project Tasks, Experimental Design, and Findings........................................................................... 4.1

4.1 Characterize the Physical State of Oil after Treatment with a Dispersant in a Cold, Deep, Low 

Turbulence Setting .................................................................................................................... 4.1

4.1.1 Oil and Dispersant Samples ........................................................................................... 4.2

4.1.2 Pressure Systems............................................................................................................ 4.2

4.1.3 Oleophobic Coatings...................................................................................................... 4.3

4.1.4 View Cells...................................................................................................................... 4.8

4.1.5 Interfacial Tension........................................................................................................ 4.10

4.1.6 Visualization of the Effect of Pressure on Droplet Size............................................... 4.14

4.2 Examine the Influence of Droplet Size on Biodegradation..................................................... 4.16

4.2.1 Means of Creating and Maintaining Droplet Suspensions........................................... 4.16

4.2.2 Microorganisms Used in the Experiments ................................................................... 4.16

4.2.3 Experimental Setup to Examine the Effect of Droplet Size on Biodegradation .......... 4.26

4.2.4 Results of the Droplet Study ........................................................................................ 4.28

4.3 Explore Novel, Noninvasive Approaches to Characterizing Emulsions and Oil Degradation in 

Pressure Chambers.................................................................................................................. 4.35

4.3.1 Nuclear Magnetic Resonance Spectroscopy ................................................................ 4.35

4.3.2 Investigation of Other Spectroscopic Approaches....................................................... 4.40

4.4 Examine the Effect of Deep Water Conditions on Cell Growth and the Biodegradation of High 

Concentrations of Oil .............................................................................................................. 4.42

4.4.1 Microbiology: Strain Selection .................................................................................... 4.42

4.5 Characterize the Effects of Pressure, Time, DOR, and Oil:Water Ratio on Droplet Size....... 4.44

4.5.1 Oil and Dispersant Physical Characteristics: Rheometrics .......................................... 4.44

4.6 Characterize the Effect of Pressure Drop and Temperature on Oil Droplet Size.................... 4.48

4.6.1 Sensor Array Development .......................................................................................... 4.50

4.6.2 Results from the Thermal Array................................................................................... 4.50

4.6.3 Blowout Testing: Particle Size Analysis..................................................................... 4.51

4.6.4 Dispersant Injection...................................................................................................... 4.53
xii

4.6.5 The Tests...................................................................................................................... 4.54

4.6.6 Test Results.................................................................................................................. 4.54

4.7 Explore the Effect of Sediments on Droplet Size.................................................................... 4.66

4.8 Update BLOSOM (Blowout and Spill Occurrence Model) to Incorporate Dispersant Effects on 

Droplet Size Distributions....................................................................................................... 4.72

4.8.1 Results: Numerical Simulations................................................................................... 4.78

4.8.2 Future Enhancements: BLOSOM ................................................................................ 4.85

5.0 Conclusions and Next Steps............................................................................................................ 5.86

6.0 References ......................................................................................................................................... 6.1

Appendix A – Interfacial Tension Data .................................................................................................... A.1

Appendix B – PAH Analysis Following Biodegradation ..........................................................................B.1

Appendix C – Droplet Size Distribution....................................................................................................C.1

Appendix D – Determination of Oil, Dispersant, and Seawater Fluorescent Spectra............................... D.1

Appendix E – Determination of Oil, Dispersant, and Seawater Ratios Using Fluorescence.....................E.1
xiii

Figures

3.1. Location of the PNNL-Marine Science Laboratory on Sequim Bay in northwest 

Washington ...................................................................................................................................... 3.1

3.2. Example of particle (droplet) identification in a challenging blowout test image with 

larger droplets and a large cloud of atomized droplets .................................................................... 3.2

3.3. A) The original photo with ruler for scale; B) the cropped subsection of the photo used

for particle size determination; C) a mask of the cropped image with a 10 mm scale bar; 

and D) a second mask with a 1 mm scale bar .................................................................................. 3.4

4.1. Examples of a non-mixing and mixing reactorfitted with the ethylene glycol cooling systems...... 4.3

4.2. Photo of the sampling tubes and mixer assembly with the reactor vessel removed

and the reactor head to show the arrangement of access ports......................................................... 4.3

4.3. A ramé-hart 590-U1 Advanced Automated Goniometer/Tensiometer used to measure 

contact angle of oil on surfaces to determine the oleophobicity of a surface and to measure 

the interfacial tension between fluids............................................................................................... 4.5

4.4. Left panel: Positive control test with Islands 77 showing >99% reduction in luminescence 

compared with the stainless steel negative control blank. Right panel: Aculon Glass 

Repellency coated glass coupon compared with uncoated glass negative control blank................. 4.7

4.5. Coatings used on stainless steel coatings showed no toxic effects .................................................. 4.8

4.6. View cells disconnected from the Teledyne pump and Parr reactor................................................ 4.9

4.7. Effect of dispersant concentration and temperature on the interfacial tension between oil 

and water ...................................................................................................................................... 4.12

4.8. Effect of dispersant concentration and temperature on the interfacial tension between oil 

and water. *Approximation: the droplet would not remain formed long enough to test ............... 4.13

4.9. Original and cropped images taken of 400 μL oil with 16 μL Corexit in 200 mL seawater

at 17.8°C ...................................................................................................................................... 4.15

4.10. Left to right: Hormoconis resinae, Alkanivorax borkumensis, and Pseudomonas putida

grown on agar media...................................................................................................................... 4.17

4.11. Preliminary glucose assays ............................................................................................................ 4.18

4.12. Preliminary protein assays ............................................................................................................. 4.19

4.13. Glucose assays ............................................................................................................................... 4.20

4.14. Protein assays................................................................................................................................. 4.21

4.15. Protein assays................................................................................................................................. 4.22

4.16. Glucose assays ............................................................................................................................... 4.23

4.17. Experimental setup for testing the effect of dispersant/emulsifier on cell growth......................... 4.24

4.18. Impact of dispersant/emulsifier on A. borkumensi......................................................................... 4.24

4.19. Impact of dispersant/emulsifier on seawater microorganism mix.................................................. 4.25

4.20. Microtox® assay results.................................................................................................................. 4.26

4.21. Diffusion ordered spectrum of Finasol OSR 52 in deuterium oxide.............................................. 4.36

4.22. 1H-NMR spectrum of 1 micron diameter polystyrene beads......................................................... 4.37

4.23. Three spectra aligned vertically obtained by 1

H-NMR spectroscopy for Finasol OSR 52, DOSS, and 

Corexit EC9500A........................................................................................................................... 4.37
xiv

4.24. 1

H-DOSY spectra of A) 1:50 Finasol OSR 52: D2O ratio and B) 1:1 ODR in a 1:50 analyte 

mixture:D2O ................................................................................................................................... 4.39

4.25. Absorption spectra of water. .......................................................................................................... 4.40

4.26. Non-Newtonian shear-thinning behavior exhibited by Corexit EC9500 during a 

viscosity vs. shear rate sweep......................................................................................................... 4.45

4.27. Finasol OSR 52 viscosity vs. temperature curve. Testing performed by SwRI............................. 4.46

4.28. Corexit EC9500 viscosity vs. temperature curve. Testing performed by SwRI ............................ 4.46

4.29. Alaska North Slope crude oil viscosity vs. temperature curve....................................................... 4.47

4.30. Macondo crude oil viscosity vs temperature curve ........................................................................ 4.47

4.31. Graph illustrating the increase in density of Macondo crude oil while cooling from 52.5°C 

to 19.5°C ...................................................................................................................................... 4.48

4.32. Test tank showing a 4×3 thermal probe array................................................................................ 4.49

4.33. Laboratory setup for the blowout tank........................................................................................... 4.50

4.34. Sample data from the thermal array indicating the temperature of the surrounding water............ 4.51

4.35. Image acquired at 10 min after blowout......................................................................................... 4.52

4.36. A subsection of the camera field of view imaged at 5 s post-blowout and a subsection 

of the camera field of view imaged at 10 min post-blowout.......................................................... 4.53

4.37. Time series of images during a Macondo crude oil blowoutwith coinjection of 1.0 g 

of Corexit EC9500 into raw seawater ........................................................................................... 4.54

4.38. Droplet size distributions for blowout #2 at approximately 5 s after the start of the 

blowout and again after 10 min after blowout start........................................................................ 4.57

4.39. Droplet size distributions for blowout #2-2 at approximately 5 s after the start of the 

blowout and again after 10 min after blowout start........................................................................ 4.58

4.40. Droplet size distributions for blowout #3 at approximately 5 s after the start of the 

blowout and again after 10 min after blowout start........................................................................ 4.59

4.41. Droplet size distributions for blowout #3-2 at approximately 5 s after the start of the 

blowout and again after 10 min after blowout start........................................................................ 4.60

4.42. Droplet size distributions for blowout #3-3 at approximately 5 s after the start of the 

blowout and again after 10 min after blowout start........................................................................ 4.61

4.43. Droplet size distributions for blowout #4 at approximately 5 s after the start of the 

blowout and again after 10 min after blowout start........................................................................ 4.62

4.44. Droplet size distributions for blowout #4-2 at approximately 5 s after the start of the 

blowout and again after 10 min after blowout start........................................................................ 4.63

4.45. Droplet size distributions for blowout #5 at approximately 5 s after the start of the 

blowout and again after 10 min after blowout start........................................................................ 4.64

4.46. Droplet size distributions for blowout #6 at approximately 5 s after the start of the 

blowout and again after 10 min after blowout start........................................................................ 4.65

4.47. Size distributions for samples with and without sediment and no dispersant ................................ 4.68

4.48. Size distributions for samples with and without sediment and a DOR of 1:50.............................. 4.69

4.49. Size distributions for samples with and without sediment and a DOR of 1:25.............................. 4.70

4.50. Oil suspensions without sediment and with sediment after mixing but prior to the addition of 

Corexit ...................................................................................................................................... 4.71
xv

4.51. Surface expression of oil without sediment and with sediment after the addition 

of dispersant ................................................................................................................................... 4.72

4.52. Johansen et al. (2013) distributions for oil droplet size as a function of droplet size as 

computed by BLOSOM internally ................................................................................................. 4.73

4.53. Droplet size distributions as a function of droplet sizefor experiments using oil at 16.8°C 

with application of dispersant and without dispersant .................................................................. 4.75

4.54. Droplet size distributions for blowouts with oil at 60°C as a function of droplet size................... 4.75

4.55. Normalized droplet size distribution as a function of droplet size for two experiments 

without any dispersant applied. Gray indicates the intersection of the two distributions. ............. 4.76

4.56. Normalized droplet size distribution as a function of droplet size for two experiments 

with dispersant applied................................................................................................................... 4.77

4.57. Droplet size distribution as a function of droplet size for the blowout without 

dispersantand the blowout with dispersant used for BLOSOM simulations.................................. 4.77

4.58. Droplet size distributions from Johansen et al. (2013) as implemented in BLOSOM for 

both dispersant-treated and untreated oil. ...................................................................................... 4.78

4.59. The droplet size distributions from the experimentally derived observations, 

as implemented in BLOSOM......................................................................................................... 4.79

4.60. Oil blowout as simulated with BLOSOM after a 30-day simulation using the 

distribution from Johansen et al. (2013) for untreated droplet sizes.............................................. 4.80

4.61. Oil blowout as a function of longitude, latitude, and depth, after a 30-day simulation 

with BLOSOM, using the distribution from Johansen et al. (2013) for treated droplet sizes....... 4.81

4.62. Oil blowout as simulated with BLOSOM after a 30-day simulation using the distribution 

from PNNL for untreated droplet sizes.......................................................................................... 4.82

4.63. Oil blowout as simulated with BLOSOM after a 30-day simulation using the distribution 

from PNNL for treated droplet sizes.............................................................................................. 4.83

4.64. Curve fit for dispersant effectiveness as a function of oil viscosity............................................... 4.85
xvi

Tables

3.1. Comparison of water properties: Sequim Bay, WA and Gulf of Mexico. ......................................... 3.1

3.2. Particle size determination using a polystyrene bead standard.......................................................... 3.4

3.3. Example of raw data obtained from Fuji particle size analysis.......................................................... 3.5

3.4. Sequoia LISST bins and digital floc camera bins used in particle size analysis research................ 3.6

4.1. Contact angles using crude oil. ........................................................................................................ 4.6

4.2. Contact angles using decane. ........................................................................................................... 4.6

4.3. Composition of samples used to study the effect of droplet size on biodegradation ....................... 4.28

4.4. Summary of the droplet size data for the Alcanivorax borkumensis and Pseudomonas putida

samples ...................................................................................................................................... 4.30

4.5. Summary of the droplet size data for the Hormiconis resinae samples........................................... 4.31

4.6. Percent loss of hydrocarbon compared to Sample O; bacterial exposures ...................................... 4.32

4.7. Percent loss of hydrocarbon compared to Sample O; fungal exposures. (a)...................................... 4.33

4.8. Tests performed on the 1H-NMR spectrometer for in situ particle size determination and micelle 

diffusion analysis ........................................................................................................................... 4.38

4.9. Samples and ratios used to try and force micelle formation for particle size determination and 

diffusion analysis ........................................................................................................................... 4.39

4.10. Density measurements of Finasol OSR 52, Corexit EC9500, Alaska North Slope crude oil, and 

Macondo crude oil obtained at 20°C and at 70°C.......................................................................... 4.48

4.11. Cross-sectional area of oil particles at 5 s and 10 min after a test blowout with no dispersant. .... 4.53

4.12. Test parameters used for the simulated blowouts.......................................................................... 4.56

4.13. Summary of droplet size data (mm)............................................................................................... 4.67

4.14. Experimental setup for each blowout............................................................................................. 4.76

4.15. Percent of oil mass by depth for each of the four BLOSOM simulations ..................................... 4.84

4.16. Values for parameters A, B, and C used to determine dispersant effectiveness as a function of 

viscosity values obtained using either the IFP or MNS test protocol............................................. 4.85
1.1

1.0 Introduction

This report covers two projects conducted under BSEE Contracts E14PG00043, “Biodegradation and 

Toxicity Following Dispersant Usage in a Cold, Stratified, Deep Sea Setting,” and E15PS00027, 

“Analysis of How Environmental Conditions Affect Dispersant Performance During Deep Ocean 

Application,” with the U.S. Department of Energy, Pacific Northwest National Laboratory. The primary 

objective of these projects was to examine how deep subsea conditions such as pressure, pressure drop, 

and temperature combine with other aspects, such as dispersant-to-oil ratio (DOR), degassing, and 

sediment to affect the effectiveness of dispersants. The principal means of evaluating dispersant 

effectiveness in this effort was to examine oil droplet size, including changes in droplet size over time. 

Additional studies explored noninvasive methods for measuring pressurized fluids in closed laboratory 

systems (pressure vessels) to monitor droplet size, oil and dispersant mixing, and oil chemistry. A second 

set of studies examined the potential effect of dispersant use and droplet size on microbial biodegradation 

of oil. Finally, results of the study were provided to the National Energy Technology Laboratory to 

strengthen the Blowout and Spill Occurrence Model (BLOSOM) for deep ocean scenarios.

The overall study was organized as a set of eight research tasks, which are used as the basis for organizing 

the remainder of this report. Chapter 2.0, Background, provides context for the study. Chapter 3.0, Study 

Methods, describes some of the general methods used throughout the study. Chapter 4.0, Project Tasks, 

Experimental Design, and Findings, describes each of the eight study tasks, including the rationale, 

experimental design, findings, and conclusions. The tasks in Chapter 4.0 include the following:

1. Characterize the physical state of oil after treatment with a dispersant in a cold, deep, low turbulence 

setting.

2. Examine the influence of droplet size on biodegradation.

3. Explore novel, noninvasive approaches to characterize emulsions and oil degradation in pressure 

chambers.

4. Examine the effect of deep water conditions on cell growth and the biodegradation of high 

concentrations of oil.

5. Characterize the effects of pressure, time, DOR, and oil:water ratio on droplet size.

6. Characterize the effect of pressure drop and temperature on oil droplet size.

7. Explore the effect of sediments on droplet size.

8. Update BLOSOM (Blowout and Spill Occurrence Model) to incorporate dispersant effects on droplet 

size distributions.
2.1

2.0 Background

Oil production in waters of the U.S. Outer Continental Shelf is likely to increase in the foreseeable future. 

In 2012, U.S. crude oil production was 6.5 million barrels per day, of which 20% came from federal 

offshore sources and 19% from Gulf of Mexico federal offshore sources in particular (US-EIA 2013).

After a temporary drilling moratorium (imposed after the Deepwater Horizon spill) was lifted in October 

2010, the Gulf of Mexico yielded total discoveries of 411 million barrels in 2011—a 90% increase over 

2010 discoveries—which placed it among the top three geographical regions of the U.S. for new 

discoveries (US-EIA 2011). Production in waters deeper than 1300 ft in the Gulf was forecasted to be 1.5 

million barrels per day in 2014, and some analysts are forecasting that the deep waters of the Gulf of 

Mexico will produce 1.9 million barrels per day in 2020 (Klump 2013). In 2016, there were eight new 

field starts in water between 3700 ft and 9556 ft and there are 7 anticipated starts for 2017-2018 ranging 

from depths of 1200 ft to 7128 ft (US-EIA 2017).

While deep ocean exploration represents an important resource for growing U.S. energy demands, the 

Deepwater Horizon blowout of 2010 highlights the many uncertainties about spill remediation, and in 

particular the interplay of oil leaks, dispersants, and biodegradation in the cold and high-pressure 

environment of the Outer Continental Shelf. Oil breaks down due to physical, chemical, and biological 

(metabolic) effects, and the rate of each effect is affected by the environmental conditions that are present.

Oil spill dynamics and cleanup are much better studied in the surface environment than in the deep ocean, 

because of the more recent onset of deep ocean drilling and the challenges of conducting studies in the 

deep ocean or under simulated conditions. Some of these challenges are noted below and motivated the 

studies outlined in this report. 

Dispersant use in surface and deepwater conditions. During the Deepwater Horizon incident, a total of 

771,000 gallons of Corexit 9500A was injected subsurface, representing 42% of the 1.84 million total 

gallons of dispersant used during the response (USCG 2011). This represented the first deep ocean use of 

dispersant and only six shallow subsurface tests had been previously performed (USCG 2011).

4

Dispersants are added to released crude oil to break its physical form into small droplets, thereby 

increasing its surface area to provide greater accessibility to water and microorganisms to facilitate 

biodegradation (National Research Council 1989). The resulting small droplets may disperse and spread 

throughout the water column, thereby diluting the amount of suspended oil per unit volume of seawater 

and enhancing biodegradation by enabling the natural concentration in seawater of available nitrogen, 

phosphorous, oxygen, and other essential elements for microbial growth to remain sufficiently high. 

Desired outcomes of dispersant use are to enhance biodegradation and ultimately to restore an 

environment to one in which there is no measurable toxicity. Dispersants are most effective when used on

freshly ejected, non-weathered oil, which is one motivation for applying dispersants at the source of a 

leak. However, while dispersant activity has been fairly well characterized for surface environments that 

have a relatively high degree of mixing (by waves), oxygenation (which promotes aerobic metabolism), 

light (photo-oxidation), atmospheric pressure, and warmer temperatures, few controlled studies have been 

performed to examine the effectiveness of dispersants in dark, high-pressure, low-temperature 

environments, and where mixing is greatly reduced short distances from the point of blowout and the

initial injection of dispersant (GAO 2012; National Research Council 2005).

The ability of microorganisms to biodegrade dispersants (e.g., Corexit 9500) and dispersant-treated oil 

have been the subject of a number of studies (see for example, Mulkins-Philips and Stewart 1974; 

Lindstrom and Braddock1999, 2002; Venosa and Holder 2007; Campo et al. 2013; Prince et al. 2013; 

McFarlin et al. 2014; and Kleindienst et al. 2015). While it is clear from these studies that some 

microorganisms are capable of degrading oil, dispersants, and dispersant:oil mixtures, some discrepancy 

exists about whether dispersants promote or slow down the process of biodegradation (Prince et al. 2013; 
2.2

Kleindienst et al. 2015). Conflicting test results may stem from differences in the experimental design and 

methods of analysis, the test conditions (e.g., volume, temperature, mixing, oxygenation), and the 

microorganisms, oil, and dispersant used in the tests. 

Effects of pressure and pressure drop on oil droplet size and dispersant use. Even though the 

hydrostatic pressure found at a 2 km deep wellhead is approximately 2900 psi, the pressure of oil at a 

blowout may be even higher—10,000 to 15,000 psi—resulting in a relative drop in pressure of over 7000 

psi. The pressure drop, the size of the leak orifice, and viscosity of the oil will influence the velocity of 

the escaping oil and initial mixing. As oil leaves the wellhead and drops in pressure, the liquid oil 

expands slightly, but any undissolved gas portion rapidly expands in volume and a significant portion of 

the dissolved gas will escape initially. The initial expansion of gas contributes to turbulent mixing, which 

promotes oil and water mixing, and it may support dispersant and oil mixing when a dispersant is injected 

at the leak site, or it may lead to some phase separation of the oil and dispersant. Oil will also increase in 

viscosity due to the loss of gas and the drop in temperature, which will interfere with the ability of 

dispersants to penetrate the oil (US-EIS 2013; Becker and Lindblom 1983). The dispersants may also 

affect the solubility of gas in the oil and mixing of oil with water, which in turn could affect how much 

and how quickly the released gas is dissolved into the surrounding water. Shortly after the initial ejection 

from the well, high ambient pressure will impede the release and expansion of any remaining dissolved 

gas from oil, thereby reducing the buoyancy of oil plume (Johansen et al. 2013). It is unclear how all of 

these effects balance out and ultimately affect dispersant effectiveness.

Effects of temperature on oil droplet size and dispersant use. While the ambient seawater temperature 

on the deep ocean seafloor may be less than 4°C, the temperature of the oil at the wellhead may exceed 

100°

C. Temperature directly contributes to the viscosity of oil, which increases with colder temperatures. 

Dispersants are less effective at penetrating and emulsifying viscous oil, and temperature also affects the 

solubility of the dispersant (US-EIS 2013; Becker and Lindblom 1983). Unless a means of temperature 

control is provided, upon reaching the location of a deep ocean blowout, the temperature of the 

dispersants are likely to be close to the ambient water temperature. Whether dispersants are injected down 

into the wellhead, providing some opportunity for mixing and heating prior to mixing with cold ocean 

water, or are injected at the site of the blowout as the oil meets the cold water may have an impact on 

their effectiveness. 
3.1

3.0 Study Methods 

3.1 Marine Laboratory Facilities

The majority of the tests were conducted at the Pacific Northwest National Laboratory’s Marine Science 

Laboratory located in Sequim, Washington (Figure 3.1), while the high-pressure nuclear magnetic 

resonance (NMR) spectroscopy was conducted at the Pacific Northwest National Laboratory in Richland, 

Washington. Unless otherwise described, seawater was sourced from Sequim Bay, which maintains an 

average salinity of 30.6 ± 0.8 psu (n = 1009 data points recorded over 45 months, CV = 2.7%) with 

limited seasonal variation due to the lack of significant freshwater sources (i.e., rivers or streams) entering 

the bay and limited rainfall (<14 in. annually) (Gill et al. 2016). The laboratory’s seawater has an average 

pH of 7.83 ± 0.12 (n = 988; CV = 1.5%), which fluctuates slightly on a seasonal basis due to algal growth 

in the bay (Table 1). Surface seawater in the Gulf of Mexico near the location of the Deepwater Platform 

is slightly higher at ~35.5 to 36 psu with a pH near 8.0; both salinity and pH drop slightly at greater 

depths; salinity values are around 34.9 psu and pH near 7.9 (Bianchi et al. 2014). Seawater is supplied to 

the laboratories as both unfiltered and filtered (Arkal Spin Klin filter system with a nominal pore size 40 

μm) supplies. The filtered seawater can undergo further filtration in the laboratory if needed down to a 

nominal pore size of 0.22 μm for sterile conditions.

Figure 3.1. Location of the PNNL-Marine Science Laboratory on Sequim Bay in northwest Washington.

Table 3.1. Comparison of water properties: Sequim Bay, WA and Gulf of Mexico.

Sequim Bay

Gulf of Mexico 

Surface Water

Gulf of Mexico 

Deep Water

pH 7.8 8.0 7.9

Salinity (psu) 30.6 35.5 to 36 34.9
3.2

3.2 Droplet Size Determination

Throughout the study, unless otherwise noted, droplet size was determined by digital photographic 

imaging using a Canon EOS 70D (20.2 MP) with attached 100 mm macro lens (f/16, ISO 6400) or GoPro 

Hero4 12 MP modified by replacing the original lens with an AXION 4.35 mm rectilinear lens (for use 

with the pressurized view cells). Size standards or a ruler were included in the field of view (FOV) for 

calibration, and the depth of field (DOF) was also measured to allow for volumetric calculations. Highspeed photography proved to be the best approach for imaging droplets during vigorous mixing in flasks 

or in simulated blowouts, in situations with high oil or dispersant concentrations, and was the only means 

of collecting droplet size data in the blowout tanks.

The research team also visited Sequoia Instruments, located in nearby Bellevue, Washington, to run 

several tests with their technical staff using the LISST-100 and LISST-Holo to see if either could be used 

to image fluids in cuvettes and flow cells at the speeds and concentrations necessary for this study. These 

devices have been used in many other studies reporting droplet size distributions (see for example, 

Brandvik et al. 2013), but it was not possible to generate useful data with the off-the-shelf equipment. 

Significant adaptations and further testing would have been required to use the LISST devices in this 

study.

Images with sizable particles were processed using Fiji (ImageJ software with preloaded plugins for 

particle calling/recognition) (Schindelin et al. 2012). The images were first scale calibrated to rulers 

emplaced in the FOV and then processed by cropping to remove areas outside the calibrated FOV and 

DOF. Next, background subtraction and thresholding were used to remove noise and material surface 

effects. Then particles were identified by their degree of circularity to rule out images of overlapping 

droplets, clusters of small droplets that could be mistakenly identified as very large droplets, or other 

debris. Due to shear forces experienced during mixing or in simulated blowouts, the oil particles are 

circular. Large particles also have a greater likelihood of overlapping with smaller particles (located 

elsewhere in the DOF), which, despite representing only a very small portion of the overall volume of oil, 

were typically present in large numbers. A value of 30% circularity was selected for particle identification 

(Figure 3.2).

Figure 3.2. Example of particle (droplet) identification in a challenging blowout test image with larger 

droplets (left side of original) and a large cloud of atomized droplets (right side of original).
3.3

To prevent the addition of subjectivity to the particle identification the thresholding was automatically 

performed using the Otsu thresholding method for blowouts and the Triangle method for particle 

degradation testing. The Otsu method performs well in white background, clear solution situations and 

thus was well suited to the blowouts; however, it could not identify particles in biomass dense 

degradation solutions, so the Triangle method was used instead. After thresholding, the particles were 

then selected and size analyzed. Some particle sizing methods include a “fill holes” step prior to calling 

particles, but we chose to not include this step because the pressurized oil contained a portion of gas, 

which could be seen as bubbles in the water column. By not filling holes these bubbles were not identified 

as oil particles, because they had a solid edge and translucent center and thus maintained the fidelity of 

our particle size assessments.

Images were processed using the Batch-Macro Mode in Fiji using the following macro code:

run("Set Scale...", "distance=508.0157 known=10 pixel=1 unit=mm global");

makeRectangle(816, 1308, 2424, 1482);

run("Crop");

run("Subtract Background...", "rolling=200 light");

run("8-bit");

run("Auto Threshold", "method=Otsu");

setOption("BlackBackground", false);

run("Convert to Mask");

run("Analyze Particles...", " circularity=0.30-1.00 show=Masks display exclude");

run("Scale Bar...", "width=10 height=24 font=84 color=Red background=None 

location=[Lower Right] bold overlay");

The scale numbers and rectangle coordinates noted in the code are specific to each batch of images and 

therefore variable, however the remaining code is unchanged between blowout image sets. Particle 

degradation image sets, as noted above, change the “Auto Threshold” method from “Otsu” to “Triangle.” 

Image processing with Fiji produced a text file with the image label and estimated particle cross-sectional 

area, circumference, and other descriptors for each particle detected in a given image. The diameter was 

calculated as d = 2 �area⁄ππ and the volume (v = 4

3

ππ �

d

2

�

3

) of the associated sphere represented by each 

particle. The particle size distribution and volume fraction were calculated as in Mikkelsen et al. (2005). 

All 50 logarithmically spaced diameter bin sizes (microns) covered by the LISST-100 and the digital floc 

camera used in Mikkelsen et al. (2005) were used for easy comparison. Descriptive statistics were 

calculated for each image including the particle count, minimum, maximum, mean, and standard 

deviation of the particle area and diameter for each image.

A calibration experiment was conducted using 0.75 mm size standard polystyrene beads. The beads were 

placed in a seawater tank used to simulate oil blowouts and photographed with a ruler in the FOV (Figure 

3.3). A subsection of the image was processed and 29 beads were selected at random and sized using the 

method described above, except that the beads had to be artificially darkened in the center to accurately 

calculate the cross-sectional area (the beads reflected light and each bead has a bright center that was 

misinterpreted as a hole, as seen in Figure 3.3B). Additionally, 29 beads were selected at random and the 

diameter of each was determined using a caliper. The results, provided in Table 3.2, show good 

agreement between the image processing and manual measurements: the image processing yielded a 3.8% 

difference in mean size relative to the manual measurement and a higher standard deviation.
3.4

Figure 3.3. A) The original photo with ruler for scale; B) the cropped subsection of the photo used for 

particle size determination; C) a mask of the cropped image with a 10 mm scale bar; and D) 

a second mask with a 1 mm scale bar. The vertical object in image A is part of a thermal 

probe array that sits in the camera field of view.

Table 3.2. Particle size determination using a polystyrene bead standard.

Particle Size Analysis Manual Particle Size Analysis (Caliper)

Label Diameter (mm) Bead Number Diameter (mm)

1 0.82 1 0.68

2 0.75 2 0.64

3 0.69 3 0.79

4 0.72 4 0.87

5 0.79 5 0.86

6 0.83 6 0.75

7 0.75 7 0.73

8 0.80 8 0.75

9 0.68 9 0.72

10 0.71 10 0.78

11 0.83 11 0.83

12 0.75 12 0.78

13 0.73 13 0.73

14 0.93 14 0.73

15 0.76 15 0.81

16 0.70 16 0.70

17 0.79 17 0.73

18 0.77 18 0.80

19 0.74 19 0.72

20 1.21 20 0.81
3.5

Particle Size Analysis Manual Particle Size Analysis (Caliper)

Label Diameter (mm) Bead Number Diameter (mm)

21 0.75 21 0.85

22 1.08 22 0.81

23 0.75 23 0.91

24 0.78 24 0.88

25 0.77 25 0.80

26 0.81 26 0.74

27 0.79 27 0.73

28 0.86 28 0.81

29 0.79 29 0.84

Mean 0.81 Mean 0.78

SD 0.46 SD 0.06

Min 0.68 Min 0.64

Max 1.21 Max 0.91

Example raw data obtained from Fiji particle size analysis are shown below in Table 3.3. The raw data 

include the particle identification number, circular area, perimeter, and circularity. The raw particle data 

were imported into Microsoft Excel for further modification and analysis. Equations were used to convert 

the particle area to particle diameter and particle volume. The particle volumes of each identified particle 

in each image were summed to determine the total volume of particles imaged, by which each particle 

was then divided to calculate the volume fraction of each particle in the whole. 

Table 3.3. Example of raw data obtained from Fuji particle size analysis.

Label Cross-Sectional Area Circumference Circularity

1 Blowout 6_01 4.21E-04 0.058 1

2 Blowout 6_01 0.001 0.116 1

3 Blowout 6_01 4.21E-04 0.058 1

4 Blowout 6_01 4.21E-04 0.058 1

5 Blowout 6_01 4.21E-04 0.058 1

To maintain consistency with other oil research the measured particles were binned according to their 

particle diameter and particle volume fraction percent. The bins used were those that have previously 

been used by the Sequoia LISST and digital floc camera devices (Table 3.4) (Mikkelsen et al. 2005).
3.6

Table 3.4. Sequoia LISST bins and digital floc camera bins used in particle size analysis research

(Mikkelsen et al. 2005).
4.1

4.0 Project Tasks, Experimental Design, and Findings 

The eight study tasks are described in the following sections. They describe how the physical state of oil 

was characterized after treatment with a dispersant in a cold, deep, low turbulence setting; the influence of 

droplet size on biodegradation; the exploration of novel, noninvasive approaches to characterize 

emulsions and oil degradation in pressure chambers; the effect of deep water conditions on cell growth 

and the biodegradation of high concentrations of oil; the effects of pressure, time, DOR, and oil:water 

ratio on droplet size; oil blowout experiments conducted to determine t the effect of pressure drop and 

temperature on oil droplet size; the effect of sediments on droplet size; and the BLOSOM.

4.1 Characterize the Physical State of Oil after Treatment with a 

Dispersant in a Cold, Deep, Low Turbulence Setting 

Oil droplet size is considered an important factor controlling biodegradation and a primary objective of

using dispersants is to render oil into small droplet sizes to enhance the rate of degradation. Furthermore, 

dispersants dilute a given volume of oil into a larger volume of seawater, presumably increasing the 

availability of oxygen and other nutrients to support microbial activity (Lessard and DeMarco 2000; Broje 

et al. 2014). Dispersants include surfactants, which are amphiphilic molecules having two distinct 

functional groups or sidechains: one attracted to oil (oleophilic) and one attracted to water (hydrophilic). 

In the absence of a surfactant, oil itself is hydrophobic (water “fearing”) and thus collects together into 

large droplets or slicks rather than mixing with the water. The surfactant creates an interface between the 

oil and water, effectively reducing the interfacial surface tension between the two and allowing the oil to 

be broken up into smaller droplets.

With surface slicks, a dispersant can be sprayed evenly over the slick and allowed to penetrate into the oil 

to mix with it. Wind, wave, and current action at the surface provides further mixing to break apart the 

slick and disperse the smaller droplets of oil into the water column. In a subsurface leak, the dispersant 

must be injected into the escaping plume of oil; the addition of a dispersant to oil as it leaks from a source 

requires thorough mixing energy from the blowout, the turbulent rising plume, or ocean currents to blend 

the dispersant with the oil and surrounding water. 

Oil escaping from a deep ocean leak may initially experience extreme turbulence due to the pressure 

differential when exiting from the geological reserve (as much as 10,000 to 15,000 psi) into the ocean 

environment (approximately 2900 psi of hydrostatic pressure at 2 km depth) (USNC 2011). Degassing 

also contributes to the extreme turbulence as dissolved and free gas rapidly expands out of the oil upon 

release. As the plume of oil moves several meters from the leak orifice, some turbulence continues as gas 

and the buoyancy of larger oil droplets carry the plume upwards and additional oil and gas continue to be 

released and rise from the wellhead below. This turbulence may continue to diminish as the plume 

expands, if droplet sizes decrease and buoyancy decreases (i.e., oil density increases due to gas loss and 

diffusion of low molecular weight hydrocarbons), and if the gas content within the plume also decreases. 

Subsurface ocean currents are unlikely to provide turbulent mixing energy, but will affect the movement 

of the oil and gas plume, and may separate less buoyant droplets from rising, buoyant droplets.

The effect of dispersants on surface plumes is readily observed and reasonably well-studied (National 

Research Council 1989; Fingas 2014; Zeinstra-Helfrich et al. 2015). However, much less is known about 

the effects of dispersants on subsurface leaks, and such scenarios are difficult to recreate experimentally. 

This task was created to explore the physical properties of oil and oil:dispersant mixes under deep ocean 

conditions. The interfacial tension of the sample oil in seawater in the presence of different concentrations 

of dispersant was also examined. Experiments were designed to create a well-mixed plume of oil in 
4.2

seawater with and without different concentrations of dispersant under pressure (up to 2900 psi, 197 atm). 

The initial vigorous mixing energy would then be reduced and the effect on droplet size observed. This 

section describes the experimental setup, observations, and key challenges that were addressed including 

1) replicating and maintaining pressure during the experiments and observations; 2) reducing 

experimental artifacts, particularly from surfaces introduced by the pressure systems to which oil could 

stick; and 3) overcoming the effects of gravity and oil buoyancy to maintain droplet suspensions.

4.1.1 Oil and Dispersant Samples

For the study, Mississippi Canyon Block 252 well (“MC 252”) was provided by BP Exploration & 

Production Inc. (BP), herein referred to as Macondo crude. Some preliminary studies or experiments in 

which multiple samples of oil were desired for comparison also used a West Texas Intermediate (WTI) 

crude sample purchased from Texas Raw Crude and Alaskan North Slope (ANS) crude oil, supplied by 

the sponsor. Samples of Corexit 9500 were provided by BSEE/Ohmsett and samples of Finasol OSR 52 

were purchased directly from Solvardis LLC. 

4.1.2 Pressure Systems

Large volume pressure studies were carried out using 2 L pressure reactors (Parr Instruments) fabricated 

from 2205 duplex stainless steel and outfitted with Hasteloy C inner fittings to avoid corrosion when 

exposed to seawater or concentrated brines for extended periods. Four of the reactors included impeller 

mixing blades and two of the reactors had no internal mixers (Figure 4.1). All reactors were rated for 

3000 psi at up to 300°C and included three separate sampling ports positioned to reach the bottom, 

middle, and upper portions of the cylinder (Figure 4.2). An additional port was used for a thermocouple to 

record temperature. When not operating at room temperature, the temperature of the reactors was 

maintained by immersing the reactor in an ethylene glycol-filled jacket that contained a recirculating 

copper tubing system also filled with ethylene glycol and connected to a temperature control unit (Figure 

4.2). With added outer insulation, the jackets were connected to a chiller and used to bring the 

temperature of the reactor fluids down to ~4.5°C. Pressurized samples were withdrawn from each of the 

three sampling ports in the reactors using a 30 mL, 3000 psi rated sampling syringe. The samples were

then depressurized and analyzed. The reactor systems were pressurized using a pneumatically driven 

Haskel AGD30 Double Acting Gas Booster pump connected to a standard gas cylinder filled with air (gas 

supply to pressurize the reactors) and the building’s pressurized air supply (~100 psi and ~40 SCFM) to 

drive the booster pump. The pump was also used to inject fresh seawater into the reactor (using the 

pressure sampling syringe) and to restore pressure after withdrawing a sample, and with this approach it 

was possible to collect multiple time point samples or to monitor and adjust pH. 
4.3

 

Figure 4.1. Examples of a non-mixing (left) and mixing reactor (right) fitted with the ethylene glycol 

cooling systems (with insulation removed to show the cooling coils. The wall-mounted 

manifold to the left allows for independent control of cooling to each of the six reactors. The 

image on the right shows a mixing reactor without the cooling system.

Figure 4.2. Photo of the sampling tubes and mixer assembly with the reactor vessel removed (left) and 

the reactor head (right- no mixing shaft installed) to show the arrangement of access ports.

4.1.3 Oleophobic Coatings

In studies with crude oil in an enclosed system (e.g., flask or tank), one difficulty is the tendency of the oil 

to stick to sidewalls, mixers, sampling tubes, and optical windows. This phenomenon can interfere with 

maintaining droplet suspensions, limit the ability to accurately measure droplet size and dispersion in 

water, and affect biodegradation studies because suspended oil droplets form a coating on available 

surfaces. In an attempt to minimize the effects of oil sticking to the sidewalls of the pressure systems, a 
4.4

number of commercially available coatings were explored for their oleophobicity and potential toxicity 

(toxicity would interfere with biodegradation studies). A coating that would repel oil, while not affecting 

the marine microbial community by leaching biocidal compounds into the water, was sought. A clear 

coating was desired for the sapphire glass windows in the pressurized view cells (described below), but 

no such limitation was placed on coatings for the other surfaces. Prior work on anti-fouling coatings for 

marine energy devices and sensors had identified a set of coatings with promising attributes (Bonheyo et 

al. 2015). Coatings that were tested included the following:

• Intersleek 900 (International Paint-AkzoNobel), a fluoropolymer-based paint that is very hydrophobic 

and reported to be somewhat oleophobic

• Cerakote (NIC Industries, Inc. and MAD Custom Coating, Bremerton, WA), a very durable thin-film 

polymer-ceramic composite coating; several variations were tested for oleophobicity and three were 

tested for toxicity

• UltraSeal ABW from Nanofilm Ltd., a chlorofluropolymer

• Aculon Glass Repellency (Aculon, Inc), a clear fluorinated alkane-based coating

• Aculon Anti-Fouling Treatment (AFT), an opaque fluorinated alkane-based coating

• 3M NeverWet Multisurface (2-step aerosol).

Coatings for oleophobicity testing were applied by following the manufacturers’ instructions onto 1” × 3” 

coupons of steel, 316 stainless steel, and FR4 fiberglass. Optically clear coatings were applied to glass

microscopy slides. Some surfaces were sanded to allow for good coating adhesion in accordance with the

manufacturer’s instructions. Control surfaces included Teflon, glass, stainless steel, steel, FR4 (glossy 

surface), and FR4 (sanded surface). Hydrophobicity was assessed using crude oil (ANS); some coatings 

were also tested with decane, an oleophobicity standard. Measurements were made using a ramé-hart 590-

U1 Advanced Automated Goniometer/Tensiometer (Figure 4.3). Each droplet was 10 μl and 

measurements were made using images taken from the droplets. Six droplets, 3 drops could fit per long 

edge, were used and each droplet was imaged 8 times (0.1 second between each image acquired) for a 

total of 32 images per sample set. The number of drops measured was increased if the quality of the drop 

being imaged was suspect. A summary of the data is provided in Table 4.1 and Table 4.2.
4.5

Figure 4.3. A ramé-hart 590-U1 Advanced Automated Goniometer/Tensiometer used to measure contact 

angle of oil on surfaces to determine the oleophobicity of a surface and to measure the 

interfacial tension between fluids.

The UltraSeal ABW and Aculon coatings displayed comparable oleophobic properties that were better 

than those of Cerakote, NeverWet, or Intersleek. Ultimately, the Aculon Glass Repellency coating was 

chosen because it could be applied more easily on all exposed surfaces, including the mixing blades and 

exterior of the sampling ports in the Parr reactors.

Coatings used for biocidal effects testing were prepared on 1⁄2” × 1⁄2”, 316 stainless steel coupons, except 

for the Aculon Glass Repellency, which was painted onto 3⁄4” × 3⁄4” glass microscopy coverslips. The 

biocidal effects testing was performed using a Microtox® assay, a standard method used by the U.S.

Environmental Protection Agency (EPA) and other researchers to assess the toxicity of metals and 

organic compounds in liquid or solid phase (Fulladosa et al. 2005; Tsiridis et al. 2006; Backhaus et al. 

2000; Lsi and Aoyama 2010; Park et al. 2016). The Microtox assay measures the luminescence of the 

bioluminescent marine bacterium Aliivibrio fischeri (ATCC 49387); the luminescent output is an 

indicator of cellular metabolism and the overall “health” of the organism. All tests were performed in 

triplicate to get an average and standard deviation values. Glass or 316 Stainless Steel coupons were used 

as non-toxic controls and to establish baseline luminescence values. Sea Hawk Islands 77 Plus (New 

Nautical Coatings, Inc), a copper- and tin-based biocidal coating was used as a positive (toxic) control. 

Cells were exposed to the coatings and leachate for 30 minutes in accordance with standard methods and 

luminescent output was measured using a Synergy HT microplate reader (BioTek Instruments, Inc)

(Girotti et al. 2002).
4.6

Table 4.1. Contact angles using crude oil.

Coupon Physical Prep Surface Coating

Solution 

Tested

Average 

Angle

Std. 

Dev.

Steel SandedCoupon Sanded w ABW ANS 89.15 2.11

FR4 SandedCoupon Sanded w ABW ANS 87.56 4.25

Stainless Steel SandedCoupon Sanded w Aculon AFT ANS 87.39 1.42

FR4 SandedCoupon Red Cerakote w ABW ANS 82.90 1.86

FR4 SandedCoupon Teal Cerakote w ABW ANS 79.39 3.61

FR4 GlossyCoupon Glossy w ABW ANS 78.93 1.90

Steel SandedCoupon Teal Cerakote w ABW ANS 78.28 1.53

Steel SandedCoupon Yellow Cerakote w ABW ANS 76.98 3.15

Glass Slide HT Glossy Cerakote w ABW ANS 76.63 1.85

Steel SandedCoupon Red Cerakote w ABW ANS 75.11 7.66

FR4 SandedCoupon Yellow Cerakote w ABW ANS 69.45 4.56

Teflon Slide Blank ANS 45.17 0.86

Steel SandedCoupon Red Cerakote ANS 44.40 2.01

FR4 SandedCoupon Teal Cerakote ANS 44.27 2.55

Steel SandedCoupon Teal Cerakote ANS 42.97 1.52

FR4 SandedCoupon Red Cerakote ANS 42.23 3.56

Steel SandedCoupon Yellow Cerakote ANS 41.72 1.10

FR4 GlossyCoupon NeverWet ANS 41.58 3.03

FR4 SandedCoupon Yellow Cerakote ANS 39.26 2.11

Teflon High Density Tape Blank ANS 36.18 1.47

Glass Slide HT Glossy Cerakote ANS 35.52 1.43

Teflon Coupon Coupon w ABW ANS 34.20 2.68

Glass Slide Blank ANS 28.44 0.85

FR4 GlossyCoupon Blank ANS 27.38 22.31

Steel Sanded Coupon Blank ANS 22.81 0.90

Teflon Coupon Blank ANS 20.45 2.80

FR4 SandedCoupon Blank ANS 18.01 2.49

Stainless Steel Sanded Coupon Blank WTI 16.96 2.36

ANS = Alaska North Slope crude oil; WTI = West Texas Intermediate crude oil

Table 4.2. Contact angles using decane.

Coupon Physical Prep Surface Coating Solution Tested Average Angle Std. Dev.

Teflon Matte Coupon Coupon w ABW Decane 26.82 2.19

Steel SandedCoupon Sanded Coupon w ABW Decane 48.74 1.90

Polypropylene SandedCoupon Sanded Coupon w ABW Decane 27.24 3.69

Glass Slide Slide 5 w ABW Decane 55.28 3.02

Glass Slide Slide 1 CeraKote w ABW Decane 39.80 2.95

Glass Frosted Slide Frosted Coupon w ABW Decane 61.17 0.93

Glass Coupon Coupon w ABW Decane 51.56 2.69

FR4 GlossyCoupon Glossy Coupon w ABW Decane 56.79 0.75
4.7

Coupon Physical Prep Surface Coating Solution Tested Average Angle Std. Dev.

FR4 SandedCoupon Sanded Coupon w ABW Decane 56.16 1.77

FR4 SandedCoupon Intersleek Gray w ABW Decane 40.03 3.19

FR4 SandedCoupon Intersleek Blue w ABW Decane 41.78 2.37

FR4 SandedCoupon Yellow Cerakote w ABW Decane 41.32 2.04

FR4 SandedCoupon Teal Cerakote w ABW Decane 51.49 0.36

FR4 SandedCoupon Red Cerakote w ABW Decane 53.90 1.82

Steel SandedCoupon Yellow Cerakote w ABW Decane 44.06 1.99

Steel SandedCoupon Teal Cerakote w ABW Decane 47.97 2.41

Steel SandedCoupon Red Cerakote w ABW Decane 45.77 1.85

FR4 SandedCoupon Red Cerakote Decane 14.66 4.80

FR4 SandedCoupon Yellow Cerakote Decane 16.95 0.05

*Intersleek coating exhibited swelling upon contact with decane.

The Islands 77 Plus coating exhibited a >99% decrease in luminescence compared to the negative control 

samples (Figure 4.4). None of the other coatings displayed significant toxicity in response to the coatings, 

indicating that all could be used in experiments examining microorganisms or in which microbial activity 

might affect the results (Figure 4.4 and Figure 4.5). Intersleek 900 had been previously tested and showed 

no antimicrobial activity (data not shown).

 

Figure 4.4. Left panel: Positive control test with Islands 77 showing >99% reduction in luminescence 

compared with the stainless steel negative control blank. Right panel: Aculon Glass 

Repellency coated glass coupon compared with uncoated glass negative control blank. 

Experiments were done in triplicate and average values are shown. Error bars represent the 

standard deviation values.
4.8

Figure 4.5. Coatings used on stainless steel coatings showed no toxic effects. Experiments were done in 

triplicate and average values are shown. Error bars represent the standard deviation values.

4.1.3.1 Challenges with the Parr Reactors

One challenge with the pressure chambers came from the process of pressurizing the systems. If the fluid 

level inside the chamber was high enough to submerge all of the sampling ports, the high-pressure air 

introduced into the oil and seawater during pressurization resulted in significant mixing and frothing of 

the oil. Reducing the water level created a significant headspace and less fluid for analysis. Under all fill 

levels, a gas:fluid interface existed at the top of the reactor, which allowed the oil to form a slick, even 

when droplet sizes were <10 μm in diameter and with mixing speeds fast enough to generate a vortex 

cone. Surface slicks could not be sampled through the sampling ports, and increasing the mixing energy 

to draw down the oil back into the water column affected the droplet size distribution, as was observed in 

unpressurized systems. Additionally, it appeared that with high mixing speeds, the impeller came into 

contact with the oil droplets, resulting in a mechanical reduction of the droplet sizes. 

A second and fundamental challenge with working in pressurized systems is the ability to analyze 

samples under pressure. Withdrawing samples for size analysis using dynamic light scattering or 

photographic imaging proved to be impossible because the action of collecting a sample into a highpressure sampling syringe resulted in atomization of the oil as it moved from the pressure vessel into the 

void space of the sampling syringe. Even partially pressurizing the syringe to reduce the effect of pressure 

drop could not be counted on to minimize effects. To overcome the effects of depressurization, efforts 

were made to identify noninvasive means of analyzing the pressurized fluids in the Parr reactors or in 

pressurized lines that could be used either to draw samples at pressure from the reactor or as standalone 

pressurized systems. Several methods that were investigated are described in Section 4.3 below. 

4.1.4 View Cells

One method that did allow for examination of the pressurized fluids was to design a separate highpressure reactor in which fluids could be pressurized, or into which pressurized fluids could be injected 

(while maintaining pressure) and observed. This high-pressure view cell included integrated sapphire 

glass view windows for direct observation of the pressurized fluids and oil droplets. Samples could also 

be withdrawn from the Parr reactors through high-pressure tubing into a high-pressure view cell. The 

view cells were milled from block 216 Stainless Steel with a 1⁄2 in. (1.27 mm) diameter hole drilled in one 

direction intersecting a perpendicular hole 1

/8 in. (.3175 mm) in diameter (Figure 4.6). Two Teledyne 
4.9

260D Syringe Pumps rated to 7500 psi and outfitted with cooling jackets for temperature control were 

used to control the withdrawal of samples from any of the three sampling ports into the view cell through 

the 1

/8 in. tubing and into the central chamber where droplets could be observed through a pipe affixed to 

a sapphire glass window and locked in the 1⁄2 in. hole. A second pipe and window opposite the view 

window could be used with a fiber optic light source to illuminate the interior (Figure 4.6). 

Figure 4.6. View cells disconnected from the Teledyne pump and Parr reactor. The viewing tubes are 1⁄2” 

OD and the sample line tubes are 1/8” OD. The inner chamber is 1⁄2” diameter × 3⁄4” long.

Unavoidably, samples drawn through the sample port and connecting lines experienced some sheer 

forces, which may have impacted droplet size. The small diameter sampling lines could not be treated 

effectively with any of the oleophobic coatings, and therefore, oil also coated the interior of the sampling 

lines. A third challenge was that oil within the Parr reactors that formed a surface slick could not be 

retrieved using the three sampling ports.

Alternatively, a Teledyne pump was used to pressurize a sample of oil and water injected directly in the 

view cell using the opposing sample port. After loading a sample, that port was sealed and the pump was 

used to pressurize the system through the port on the opposite side. Once pressurized, the second port was 

sealed off from the pump to maintain pressure in the view cell as a standalone system. Small magnetic stir 

bars were placed in the central view chamber and used to provide some mixing energy if the view cell 

was placed on a magnetic stir plate. However, because of poor coupling of the stir bar to the magnetic stir 

plate through the thick stainless steel view cell walls, only slow mixing speeds could be maintained: oil 

droplets were still affected by gravity and could stick to the walls of the view cell or to the sapphire glass 

windows, limiting observation times to a few seconds. A fiber optic variable intensity white light source 

provided illumination through the opposing viewing tube from the camera.

One difficulty with the flow cell was the ability to collect high-resolution images through the viewing 

tube. A custom attachment was made with a 3D printer to connect a Canon EOS 70D (20.2 MP) camera 

with an attached 100 mm macro lens (f/16, ISO 6400) to the viewing tube, but the large diameter of the 

lens relative to the diameter of the viewing tube resulted in poor-quality images. A boroscopic camera 

was also tested, but its resolution was too low. A second mount was created to connect a GoPro Hero4 12 

MP camera to the viewing tube. The GoPro has a lens diameter that is similar in size to the viewing tube, 

and images (including video) could be taken; however, the effective FOV at the end of the tube was only 

<1 cm. Increasing the magnification of the image to resolve small droplets (<0.75 mm) was not very 

effective, particularly when the droplets were in motion due to stirring or gravity. The DOF relative to the 
4.10

length of the view cell also diminished the quality of the images. Decreasing the length of the viewing 

tube was not possible because of the minimum focus distance of the camera. Because the challenge of 

collecting images down the view tube of the flow cell is similar to that of collecting images through the 

length of a microscope, an attempt was made to couple a microscope objective and camera mount to the 

viewing tube, but this proved challenging and could not be completed. Of the camera systems and 

methods tested, the GoPro Hero4 camera provided the best quality images and was used to conduct the 

experiments.

4.1.5 Interfacial Tension

When two immiscible liquids are present, the interfacial tension is a measure of force that holds the 

phases separate. Without external mixing forces, the two fluids separate to minimize the surface area 

between the two. The smallest possible surface area exists when the fluids are separated as two phases, 

i.e., all oil in one large drop or slick. As interfacial tension increases, so does the amount of energy 

needed to increase the amount of surface area between the fluids (i.e., creating smaller and smaller 

droplets of oil in the water). In contrast, an interfacial tension value of zero would indicate that the two 

materials may freely mix without an input of energy. Dispersants act as an interface between oil and 

water, reducing surface tension and allowing for greater surface area to be present with lower mixing 

energy.

The interfacial tension of Macondo oil in seawater, with and without different concentrations of 

dispersants, was measured using a ramé-hart 590-U1 Advanced Automated Goniometer/Tensiometer

(Figure 4.3) and the DROPimage Advanced software package. The interfacial tension data may be used to 

help calculate the droplet size (e.g., Young-Laplace equation relating surface tension, droplet radius, and 

pressure differential across the fluid interface) and Weber number (relating fluid density, velocity, surface 

tension, and droplet size) to characterize the mixing media.17 The interfacial tension was measured using 

an inverted pendant drop phase methodology in which a droplet of oil is injected into a cuvette of 

seawater. This approach is used when the density of the drop phase (the oil in this case) is less than that of 

the external phase (the seawater). During the measurements, seawater temperature was held constant at 

17.4°C or 17.8°C, which corresponded to room temperature on different days. The oil was also at room 

temperature or heated to 30°C. All measurements had to be made at 14.7 psi (1 atm). In these 

experiments, the dispersant was thoroughly premixed with the water, providing an idealized situation. 

The interfacial tension of Macondo crude oil at 1 atm, 17.4°C, 0.8561 g/L density, in 17.4°C, 32 ppt 

salinity seawater was determined (N = 3) to be 21.79 mN/m (+/- Std. Dev. 0.68). The Macondo crude oil 

interfacial tension increased upon heating to 30°C and subsequently testing in 17.4°C, 32 ppt salinity 

seawater (N = 3) for a value of 23.39 mN/m (+/- Std. Dev. 0.13). 

With 17.4°C seawater and oil, the addition of Corexit EC9500 at concentrations of 10 ppm or greater 

showed a reduction in the interfacial tension compared to the Macondo crude oil alone. Similarly, with 

30°C oil in 17.4°C seawater, the addition of Corexit at concentrations of 100 ppm or greater reduced the 

interfacial tension compared to the 30°C Macondo alone, but the use of 10 ppm Corexit resulted in a 

slightly higher interfacial tension of 24.15 mN/m. 

When the dispersant Finasol OSR 52 was used with 17.4°C seawater and oil, it reduced the surface 

tension at concentrations of 100 ppm or greater, but concentrations of 10 ppm and 1 ppm yielded surface 

tensions (21.88 mN/m and 21.72 mN/m, respectively) that were comparable to that of the Macondo oil 

alone. The same effect was seen when the oil was heated to 30°C: concentrations of Finasol at 100 ppm 

or greater reduced the surface tension of the oil compared to seawater with no dispersant, but the use of 
4.11

10 ppm and 1 ppm yielded surface tensions (23.72 mN/m and 23.83 mN/m, respectively) that were 

comparable to that of the Macondo oil alone.

In general, interfacial tension was found to increase slightly as the temperature difference between the oil 

and the water increased, and a somewhat greater effect was observed with the Corexit. The temperatureinduced increase was not proportional to changes in DOR and might therefore be attributed to the oil. The 

effect of mismatched and matched dispersant and oil temperatures during mixing in a simulated blowout 

was explored more carefully under Tasks 5 and 6 below (Sections 4.5 and 4.6). Raw data for these 

measurements are provided in Appendix A and summaries for oil with Corexit 9500 and Finasol OSR 52 

are provided below in Figure 4.7 and Figure 4.8, respectively. 
4.12

Dispersant 

Dilution

Concentration 

(ppm)

Interfacial 

Tension 

17.8oC) N

Std 

Dev

Interfacial 

Tension 

(30oC) N

Std 

Dev

1:10 dil 1000 3.67 30 0.45 4.77 3 0.42

1:100 dil 100 13.27 30 2.31 16.86 3 0.74

1:1000 dil 10 20.78 3 0.44 24.15 3 0.19

Figure 4.7. Effect of dispersant (Corexit 9500) concentration and temperature on the interfacial tension 

between oil and water. 

0

5

10

15

20

25

0 100 200 300 400 500 600 700 800 900 1000

Interfaction Tension (mN/m)

Concentration (ppm)

Macondo Interfacial Tension with Corexit EC9500 

in Raw Seawater 17.8°C, 1 atm

0

5

10

15

20

25

0 100 200 300 400 500 600 700 800 900 1000

Interfaction Tension (mN/m)

Concentration (ppm)

Macondo (30°C ) Interfacial Tension with Corexit 

EC9500 in Raw Seawater 17.8°C, 1 atm
4.13

Dispersant Dilution

Concentration 

(ppm)

Interfacial Tension 

17.8oC) N Std Dev

Interfacial Tension 

(30oC) N Std Dev

1:10 dil 1000 3.5 3 * 6.42 3 0.84

1:50 dil 200 13.71 30 0.23 14.2 3 0.75

1:100 dil 100 17.88 30 0.38 18.91 3 0.69

1:1000 dil 10 21.88 30 0.97 23.72 3 0.46

1:10000 dil 1 21.72 30 0.49 23.83 3 0.63

Figure 4.8. Effect of dispersant (Finasol OSR 52) concentration and temperature on the interfacial 

tension between oil and water. *Approximation: the droplet would not remain formed long 

enough to test.

0

5

10

15

20

25

0 200 400 600 800 1000

Interfaction Tension (mN/m)

Concentration (ppm)

Macondo Interfacial Tension with Finasol OSR52 in Raw Seawater 17.8°C, 1 atm

0

5

10

15

20

25

0 200 400 600 800 1000

Interfaction Tension (mN/m)

Concentration (ppm)

Macondo (30°C) Interfacial Tension with Finasol 

OSR-52 in Raw Seawater 17.8°C, 1 atm
4.14

4.1.6 Visualization of the Effect of Pressure on Droplet Size

A series of tests was conducted to examine the effect of static pressure on droplet size once initial mixing 

energy was reduced or eliminated. Dispersants work by reducing the interfacial surface tension between 

oil and water, but mixing energy is also critical to the creation of small droplets. It was suspected that 

pressure might affect droplet size because crude oil can be more compressible than water, and the change 

in density might have a measureable impact on the surface tension of the oil droplet. The objective was to 

compare identical samples of oil, dispersant, and seawater at different pressures and with equivalent or no 

mixing energy to determine whether the observed droplet size distribution varied. To examine the effect 

of pressure on droplet size with different DORs, samples of oil and dispersant in seawater were injected 

into the high-pressure view cells using the Teledyne pumps and subjected to different pressures. The 

samples were premixed, loaded into the view cells, pressurized, and mixed with a stir bar. Stirring was 

stopped and the fluid was immediately imaged using a digital camera to assess the droplet sizes. The 

small view cells were used rather than the large Parr reactors because of the aforementioned difficulties in 

retrieving representative samples from the Parr reactors, particularly with larger drops. In these tests, 

pressure was maintained as a constant and the oil was not observed to degas. Experiments observing the 

effect of depressurization on droplet size and droplet size distributions are described later under Task 6

(Section 4.6). 

For the effect of static pressure tests, Macondo crude was used with Corexit 9500 at DORs of 1:25, 1:50, 

and 1:100. Test solutions used 50μL of oil in 200 mL of seawater; however, some oil was expected to 

have coated the exposed inner surfaces of the flow cell and tubing despite the oleophobic treatment. 

Pressure was held at 14.7 psi (atmospheric), 1000 psi, 2000 psi, and 2900 psi (the equivalent of ~2 km 

water depth). One set of tests was conducted with the temperature held at room temperature (17.8°C) and 

a second set with the temperature held at 6°C to ~8°C. The small view cells have no means for 

temperature regulation, so fluids starting at 4°C warmed by 2°C to 4°C during the cold temperature 

experiments.

After several trials, droplets could not be accurately imaged within the view cell. While mixed with the 

stir bar, droplets may have moved too quickly to be imaged, but without mixing, droplets were still not 

visible; perhaps they rose out of view. A number of trials were conducted in which the concentration of 

oil was increased in the hope of capturing droplets in frame, but an appropriate concentration was not 

found: either no droplets were visible or the window became coated with oil and oil droplets could not be 

clearly resolved through the sheen (Figure 4.9). Neither the automated nor manual processing of the 

images was effective at identifying droplets.

Different coatings and wipes were tested in an attempt to keep the window clean when higher oil 

concentrations were used (Aculon Glass Repellency, Ultraseal ABW, and Tub O’ Towels) without 

success. Interestingly, these treatments worked well on borosilicate glass beakers and flasks used in other 

tests, and it is not clear why the sapphire glass behaved differently. Oil with no dispersant, low DOR, and 

high DOR also did not work. An attempt was also made to mount the view cells onto a rotating axis 

(aligned parallel to the view tubes) in order to keep droplets suspended for a longer period of time, but 

this also failed. It is possible that some large droplets may not have been captured in the images if the 

droplet rapidly rose to the surface. 

Ongoing work at PNNL is attempting to identify better coatings to prevent the buildup of oil on the 

windows. Another possible solution may be to use the Parr reactors with vigorous mixing to create an oil 

in water suspension and to draw pressurized samples through the view cell using the Teledyne pumps. 

This would require further improvements to the camera mount (using custom-designed optics based upon 

those used for microscopy). Another possibility is to use a modified Parr reactor with a large window and 

interior white backdrop positioned within 1–2 cm of the window to enable the use of standard high-speed 
4.15

photography. This latter approach worked well with the blowout tests described in later under Task 6

(Section 4.6).

Figure 4.9. Original (top) and cropped (bottom) images taken of 400 μL oil with 16 μL Corexit in 200 

mL seawater at 17.8°C. In this image, the view cell window has become coated with oil. In 

the original image, the wide outer white area is the plastic coupler used to join the camera to 

the view tube, the silver ring is the top edge of the view tube, the black ring is the inside of

the view tube, and the orange circle is the field of view through the sapphire glass window.
4.16

4.2 Examine the Influence of Droplet Size on Biodegradation

A long-held assumption is that oil droplet size is an important factor controlling the rate of 

biodegradation, and a primary objective of using dispersants is to render oil into small droplet sizes to 

enhance the rate of degradation (National Research Council 2005; Prince et al. 2016). The potential 

benefits of reducing droplet size include, for example: 1) smaller droplets have a larger surface to area 

ratio, allowing microbes to colonize the surface of the oil while still accessing water and oxygen to 

promote the metabolism of hydrocarbons; and 2) dispersion distributes a given quantity of oil through a 

larger volume of water, potentially slowing or reducing the rates at which key nutrients are depleted and 

toxic waste products accumulate. However, an accurate measure of the difference in metabolic rate as a 

function of droplet size does not appear to have been empirically tested. Consequently, it is difficult to 

ascertain the relative improvement in biodegradation rates when trying to establish, for example, 1 mm or 

0.1 mm mean droplet sizes. An understanding of how biodegradation rates scale with droplet size would 

help inform whether a significant benefit can be achieved by using larger amounts of dispersant.

To explore this dynamic, methods were tested to establish different droplet size distributions and to 

maintain a consistent droplet size distribution for many hours or days, or to at least create a stable decay 

in size over time. Ideally, a series of experiments would use cultures in which different oil droplet size 

distributions could be maintained in suspension for a period of days in order to conduct microbial 

biodegradation studies. Alternatively, cultures could be created with different starting size distributions. 

In order to isolate droplet size as the critical variable, tests were conducted to identify means of 

establishing different distributions and to assess how the method of creating the distribution affected 

cellular growth. 

The first section below identifies the different means of creating and maintaining suspensions of droplets 

that were explored. These methods were assessed to examine their ability to stabilize and maintain droplet 

size and for their effect on microbial growth and metabolism. The second section identifies the 

microorganisms used in the assays. Combinations of stabilizers, emulsifiers, and mixing energy could not 

be highly toxic to the cells. However, some promotion or repression of growth and metabolism was 

deemed acceptable, provided the effect could be accounted for and test results could be normalized to 

subtract the effect. The final section outlines the biodegradation study.

4.2.1 Means of Creating and Maintaining Droplet Suspensions

Molten soft agar suspensions using Noble Agar, Bacto Agar, and Gelrite were tested as a means of 

capturing droplets into a semi-solid matrix, but this also failed to create an even droplet distribution: some 

oil separated from the agar before it set and additional oil “bled out” of the agar after it cooled. An 

ultrasonic water bath with variable frequency and intensity control was also tested as a means of 

providing mechanical energy to maintain an emulsion; however, the initial emulsions decayed and formed

surface slicks of oil. Carbon black, which is used to stabilize some emulsions after mechanical mixing, 

was found to be toxic to cell growth and interfered with the ability to take photographs or optical density 

readings. The surfactant sodium dodecyl sulfate, even at low concentrations, resulted in foaming during 

mixing, and also contributed to cell death. Other surfactants, Dawn dish detergent, Corexit, and Finasol,

were tested more extensively as described below. 

4.2.2 Microorganisms Used in the Experiments

A pair of oil metabolizing bacteria, Alcanivorax borkumensis ATCC 700651 and Pseudomonas putida

ATCC 700007, and a single oil metabolizing fungus, Hormoconis resinae ATCC 22711 (Figure 4.10),

were used in the tests as identified below. The bacteria grow as single cells and are believed to only 
4.17

colonize or interact with the surface of oil droplets, and would therefore be most likely to be affected by 

droplet size. H. resinae, on the other hand, is an example of a filamentous fungus whose mycelia can 

penetrate into an oil droplet, and may thus be less limited by droplet size. The use of defined cultures of 

organisms eliminates the potential variability and uncertainty found in natural or complex mixed cultures 

caused by different species growing at different and unpredictable rates. However, for tests requiring a 

greater diversity of metabolism, an undefined mixture of microorganisms was prepared from freshly 

collected seawater. The seawater mix was concentrated 50-fold via filtration to capture the cells followed 

by resuspension in seawater supplemented with glycerol. Individual aliquots were frozen at -80°C until 

needed.

 

Figure 4.10. Left to right: Hormoconis resinae, Alkanivorax borkumensis, and Pseudomonas putida

grown on agar media. Petri dishes are 100 mm across.

4.2.2.1 Characterization of Dispersant Impact on Substrate Biodegradation by 

Microorganisms

For tests using dispersants to control droplet size, it was important to understand the impact the 

dispersants might have on microbial growth (cell division) and metabolism in order to normalize or 

subtract these impacts from the results to assess the impact of other factors such as temperature, pH, or 

droplet size. A number of studies have examined the impact of dispersants, including Corexit 9500, on 

microbial metabolism and the results have been highly variable. Some have shown increased metabolism, 

others no effect, and still other studies have shown inhibition (Prince et al. 2016; Kleindienst et al. 2015;

Macias-Zamora et al. 2014; Prince and Butler 2014). A series of control tests were therefore performed to 

examine the potential effects of the dispersant on cell growth. Assays included direct analysis of toxicity 

using Microtox assays, assessing the impact on growth curves, and assessing any impact on the 

metabolism of various substrates.

To assess the impact on metabolism by the selected organisms and with the same samples of dispersant to 

be used in the droplet size study, an analysis was performed to look for any potential impacts on the 

metabolism of either carbohydrate (sugar) or protein. Glucose was used as a model sugar and bovine 

serum albumin (BSA) as the model protein. Preliminary tests with two different commercial assay kits, 

D-glucose assay kit (Megazyme Inc., IL, USA) and Quick Start Bradford Protein Assay (Bio-Rad, CA, 

USA), were performed to confirm that the kits could detect different concentrations of substrates (Dglucose and BSA). Each substrate was prepared in sterile ultrapure (18 MΩ resistance) water at the 

following concentrations: 0.125, 0.2, 0.5, 1, 2, 5, 10, and 20 mg/mL. The assays were done using 96-well 

plates and absorbance was measured using a Synergy HT microplate reader (Biotek, VT, USA). Figure 

4.11 (glucose assay) and Figure 4.12 (protein assay) show the results from preliminary tests and both 

worked well. All assays were done in triplicate. Average values are shown and error bars represent 

standard deviation values.
4.18

Figure 4.11. Preliminary glucose assays. (A) After 20-minute incubation at 45°C, the D-glucose 

standard and Glucose Determination Reagent (GOPOD reagent) mixture changed color and 

the higher the glucose concentration, the darker the color was. All samples were tested in 

triplicate. (B) Glucose assay result with the manufacturer's glucose standard: absorbance of 

light at wavelength 492 (A492) values were proportional to glucose concentrations. (B) 

Glucose assay result with “homemade” D-glucose standard: A492 values were proportional 

to glucose concentrations. 
4.19

Figure 4.12. Preliminary protein assays. (A) After 5-minute incubation at room temperature, the Bovine 

Albumin Serum (BSA) standard and Dye Reagent mixture changed color and the higher the 

glucose concentration, the darker the color was. All samples were tested in triplicate. (B) 

Protein assay result with the manufacturer's BSA standard: absorbance of light at 

wavelength 590 (A590) values were proportional to protein concentrations. (B) Protein 

assay result with “homemade” BSA standard: A590 values were proportional to protein 

concentrations.

Following the successful test with substrates prepared in ultrapure water, the assays were 

repeated using sterile filtered seawater as a solvent because it would be used later to cultivate the 

microorganisms during the biodegradation tests. Samples prepared for glucose quantification 

included D-glucose in sterile18 MΩ water as a solvent, D-glucose in sterile seawater, D-glucose 

+ BSA in sterile 18 MΩ water, and D-glucose + BSA in sterile seawater. Samples prepared for 

protein quantification included BSA in sterile18 MΩ water as a solvent, BSA in sterile seawater, 

BSA + D-glucose in sterile 18 MΩ water, and BSA + D-glucose in sterile seawater. The two 

standards were combined in some samples to verify that each substrate could be detected in the 

presence of the other, which would allow for a reduction in the number of samples to be prepared 

for the biodegradation assays. Figure 4.13 and Figure 4.14 show new control assay results. The 

conclusion was that neither the use of seawater nor mixing the two substrates had any effect on 

D-glucose and BSA detection. 

After validating the test methods in seawater, samples were created to examine the impact of Corexit, 

Finasol, and Dawn dish detergent on metabolism by H. resinae, A. borkumensis, and an undefined, mixed 

inoculum of seawater microorganisms prepared using filtration to concentrate the microbial content of 
4.20

raw seawater 50-fold. A. borkumensis has not been shown to metabolize glucose or protein in laboratory 

culture and served as a negative control. H. resinae (syn. Cladosporium resinae and Amorphotheca 

resinae) can metabolize glucose, but is unlikely to metabolize protein. The mixed microbial community 

was expected to harbor organisms that metabolize both substrates.

Figure 4.13. Glucose assays. Two different solvents, water and seawater, were used to make various 

concentrations of D-glucose. (A) D-glucose standards dissolved in sterile water and (B) Dglucose standards dissolved in sterile seawater. (C&D) D-glucose and BSA (dissolved in 

either water or seawater) were mixed at a 1:1 ratio and then used to measure D-glucose 

concentrations. All assays were done in triplicate and error bars represent standard 

deviation values.

Corexit, Finasol, and Dawn dish detergent were tested at final concentrations of 0.625%, 1.25%, 2.5% 

and 5%. BSA and D-Glucose were combined at concentrations of 1 mg/mL each, and 0.1 mg/mL each. 

Control sets that included either no inoculum, no dispersant, or no BSA or D-Glucose were also created. 

All samples were prepared using filter-sterilized seawater and a barcode system was used to label and 

track the samples. Tube caps were left loose to prevent the generation of anaerobic conditions. 0.1 mL 

samples were collected at Time 0, 1 day, 5 days, 6 days, and 7 days. Collected samples were stored at -

20 °C until analysis. 

No substrate degradation was observed, even in the samples to which no dispersant or emulsifier was 

added. Samples were also collected at Time 0 and after 5 days to inoculate petri dish media (potato 

dextrose agar for samples inoculated with H. resinae, and marine agar for samples inoculated with A. 

borkumensis) to check cell viability. The Time 0 plates showed visible growth (both A. borkumensis and 

H. resinae) on agar plates. After 5 days, based on spread plate results, however, A. borkumensis was no 

longer viable, while H. resinae still had viability. Results for A. borkumensis and H. resinae are shown in 

Figure 4.15 and Figure 4.16, respectively. All experiments were done in triplicate.
4.21

Figure 4.14. Protein assays. Two different solvents, water and seawater, were used to make various 

concentrations of Bovine Serum Albumin (BSA). (A) BSA standards dissolved in sterile 

water and (B) BSA standards dissolved in sterile seawater. (C&D) D-glucose and BSA 

(dissolved in either water or seawater) were mixed at a 1:1 ratio and then used to measure 

BSA concentrations. All assays were done in triplicate and error bars represent standard 

deviation values.
4.22

Figure 4.15. Protein assays. (A) Protein assay results from the BSA & D-glucose mix: A590 values were 

proportional to protein concentration. (B) Protein assay results from samples containing 1

mg/mL of BSA. (C) Protein assay results from samples containing 0.1 mg/mL of BSA. (D) 

Protein assay results from samples containing no substrate. All experiments were done in 

triplicate and average values are shown. Standard deviation values are shown as error bars.
4.23

Figure 4.16.Glucose assays. (A) Glucose assay results from the BSA & D-glucose mix: A492 values 

were proportional to glucose concentration. (B) Glucose assay results from samples 

containing 1 mg/mL of D-glucose. (C) Glucose assay results from samples containing 

0.1 mg/mL of D-glucose. (D) Glucose assay results from samples containing no substrate. 

All experiments were done in triplicate and average values are shown. Standard deviation 

values are shown as error bars.

The results for A. borkumensis were expected: it is not known to metabolize glucose or protein in 

laboratory culture. The results for H. resinae and the seawater inoculum were more puzzling, because

H. resinae was expected to metabolize glucose but not protein and the seawater mix was expected to 

metabolize both substrates.

A second approach to examining the impact of dispersant/emulsifier on substrate biodegradation by 

microorganisms was to test how dispersant/emulsifier affected cell growth. Cultures were prepared in 96-

well microtiter plates using marine broth growth medium supplemented with Corexit, Finasol, or Dawn 

dish detergent at concentrations of 0%, 0.5%, 0.625 %, 1%, 2%, 2.5%, and 5% (v/v). One plate was 

created inoculated with A. borkumensis and a second plate with the concentrated seawater mixture of 

organisms. H. resinae was not used for this experiment because it has a filamentous growth habit that 

results in uneven turbidity in liquid culture. Figure 4.17 shows the experimental setup. Each concentration 

of dispersant or detergent was tested in triplicate (Cultures 1–3); columns 10–12 were sterile controls (no 

inoculum) used as reference samples for determining the change in optical density (O.D.). The O.D.590

was measured every 10 minutes for 3 days using a Synergy HT microplate reader (Biotek, VT, USA). 

Results are shown in Figure 4.18 and Figure 4.19.
4.24

Figure 4.17. Experimental setup for testing the effect of dispersant/emulsifier on cell growth. The 

growth curve study was done with various concentrations of dispersants and emulsifiers. 

96-well plates and a microplate reader were used. Test organisms were A. borkumensis and 

seawater organism mix and experiments were done in triplicate.

Figure 4.18. Impact of dispersant/emulsifier on A. borkumensis. (A) No dispersant or emulsifier, (B) 

Dawn dish soap, (C) Corexit 9500A, and (D) Finasol OSR 52. (B–D): The red arrow 

indicates the control well (red line): growth curve in the absence of dispersant or emulsifier. 

Each graph presents data for a single column of wells on a microtiter plate; each line 

represents one representative well (culture) on the microtiter plate. 

Each line in panels B–D of Figure 4.18 represents the average of three identical wells (i.e., Cultures 1–3) 

for a given treatment and concentration. Similarly in panel A, each line represents an average of three 

identical wells, although all wells and lines come from identical control cultures. In summary, A. 

borkumensis growth was not inhibited but instead enhanced by both dispersants and the Dawn dish soap. 

Panel A of Figure 4.18 shows consistent growth rates and peak optical densities in the dispersant- and 

detergent-free control cultures. Panels B and C show similar rates of growth and that higher optical cell 
4.25

densities were achieved using Dawn dish detergent (B) and Corexit (C). This implies that the cells were 

able to use either addition as a carbon or nutrient source. Panel D shows a moderate elevation in the final 

optical density with the addition of Finasol. The “no additive” control culture for each panel is shown in 

red and is indicated by a black arrow (Figure 4.18 B–D).

Figure 4.19. Impact of dispersant/emulsifier on seawater microorganism mix. (A) No dispersant or 

emulsifier, (B) Dawn dish soap, (C) Corexit 9500A, and (D) Finasol OSR 52. (B–D): The 

red arrow indicates growth curve (red line) in the absence of dispersant or emulsifier. Each 

graph presents data for a single column of wells on a microtiter plate; each line represents 

one representative well (culture) on the microtiter plate.

The results from the growth experiments using the seawater organism mix are less definitive. The mixed 

inoculum contains many unknown organisms and the distribution of growth curves resulting from the 

control (no additive) cultures in panel A perhaps indicate that there is no dominant, fast-growing species 

in the mixture. In contrast to the results obtained using A. borkumensis, Dawn dish soap had a clearly 

negative impact on the growth of the mixed seawater organisms (Figure 4.19 B). It is possible that with 

additional time, one or more resistant species might have begun to grow, but the effect ruled out the use 

of Dawn dishsoap as a means of manipulating oil droplet size during biodegradation tests. For some 

cultures, Corexit and Finasol appeared to support the rapid onset of growth, but not of higher density 

growth (Figure 4.19 A, C, and D), and some growth curves for each dispersant also indicated inhibition of 

growth by the dispersant. 

As a further test of the toxicity of the additives, Microtox® assays were performed to see whether 

dispersant or emulsifier would have toxic effects. The Microtox assay measures the light output of a 

bioluminescent bacterium, Alivibrio fischeri following a 30-minute exposure to a test sample or solution. 

When a test material is toxic, it diminishes metabolism by A. fischeri, resulting in reduced 

bioluminescence (Girotti et al.2002; Park et al.2016). Toxic levels of a substance are identified by the 

EC50 value, the effective concentration that reduces bioluminescence to ≤50% of the control (no additive) 

value. A culture of A. fischeri was aliquoted into a microtiter plate and individual wells were then spiked 
4.26

with a dispersant or Dawn detergent to create concentrations of 0.5 μL/mL, 0.625 μL/mL, 1 μL/mL, 

1.25μL/mL, 2 μL/mL, 2.5 μL/mL, and 5 μL/mL. Zinc sulfate was used as a positive (lethal) control for 

comparison and validation. Triplicate samples were created and readings were taken using a Synergy HT 

microplate reader (Biotek, VT, USA). 

The Microtox results are shown in Figure 4.20. Both dispersants, Corexit 9500A and Finasol OSR 52 did 

not show toxicity (Figure 4.20 C & D), while Dawn resulted in EC50 at lower concentrations, 0.5μL/mL 

through 1 μL/mL. Surprisingly, at concentrations of 1.25 to 5 μL/mL, Dawn was less toxic to A. fischeri; 

it is unclear why this occurred. The results also demonstrated increased light output with increasing 

concentration for Corexit and an uneven increase in luminescence with increasing concentration for 

Finasol. The rapid (≤30 min) stimulation of luminescence indicates that the response is due to an increase 

in metabolism rather than cell growth. All of the results are pretty consistent with the growth curves, 

indicating that Corexit and Finasol are not toxic.

Figure 4.20. Microtox® assay results.

4.2.3 Experimental Setup to Examine the Effect of Droplet Size on 

Biodegradation

Ultimately, Corexit 9500 was used with constant stirring (by magnetic stir bars) to set the initial droplet 

size distribution. All samples were subjected to the same vigorous mixing speed (identical magnetic stir 

bars at 400 rpm) to create the droplet size distribution and to sustain the suspension of oil in seawater,

while also providing consistent aeration for the cells. The temperature for all samples was kept at 18°C 

and the incubation lasted for 7 days. The flasks and stir bars were all treated with Aculon Glass 

Repellency to minimize oil adhesion to the borosilicate flask wall or Teflon coated stir bar. The Aculon 

coating was previously shown to have no effect on cell growth (Figure 4.4). Samples were kept in a 
4.27

darkened room to prevent photo-degradation of hydrocarbons, except during periods when the samples 

were examined, photographed, or ultimately processed to quantify hydrocarbons.

A preliminary test found seawater to be an inappropriate medium for the test because cell growth was 

stunted within 24–48 hr, presumably due to the limited availability of key nutrients. A modified seawater 

medium enriched with nitrogen and phosphate (0.45 μm filtered seawater supplemented with 5 mM

NH4Cl and 2.2 mM K2HPO4) was thus employed to sustain growth over several days. Tests with this 

medium and oil resulted in dense cell cultures after ~72 hr. 

In one set of tests, each sample was inoculated with the oil metabolizing bacteria, A. borkumensis (ATCC 

700651) and P. putida (ATCC 700007). In a second set of tests, the samples were inoculated with the 

fungus, H. resinae (ATCC 22711). The bacteria and fungi were used separately to test for evidence that 

growth habit (with or without penetrating mycelia) might influence the dependence on droplet size. A set 

of preliminary tests was used to determine growth rates, inoculum volume, and oil in water 

concentrations. Table 4.3 shows the composition of the individual samples that were ultimately created 

and analyzed in the study. Fourteen samples were created on Day 1 and used for the biodegradation study. 

Included were control samples without oil to monitor the growth of the organisms. On the final day of the 

incubations and prior to sample processing to quantify hydrocarbons, a fifteenth sample, Sample O, was 

created with the bacterial inoculum and oil as a “no biodegradation control.” A DOR of 1:20 was 

arbitrarily selected for that sample. The inoculum and Corexit were included in case the presence of cells 

or dispersant might bias the polycyclic aromatic hydrocarbon (PAH) analysis.

Each day after inoculation, the stir plate was stopped briefly to allow for photography that was used to 

calculate droplet size. The volume of fluids was also noted and any missing volume (presumably due to 

evaporation), which was minimal on all days, was replaced with diH2O to maintain salinity.

Quantification of hydrocarbons was performed according to a modified EPA method for dispersed oil in 

water (40 CFR Ch. I, Pt 300, App. C) (EPA 2003) and a previously developed gas chromatography-mass 

spectrometry (GC-MS) method developed at PNNL-MSL (Huesemann et al. 2003, 2004). The collected 

water samples (50–100 mL) were spiked with surrogate recovery standards (5α-androstane, naphthalened8, acenaphthene-d10, phenanthrene-d10, chrysene-d12, and perylene-d12) followed by liquid-liquid 

extraction with dichloromethane three times. The extracts were then dewatered by sodium sulfate and 

carefully concentrated under N2 to ~5 mL. Silica column chromatography was used for the further 

cleanup and fractionation into alkanes and PAHs. The extracts were then concentrated and solventexchanged into hexane. 

GC-internal standards (d10-fluorene and d12-benzo[a]pyrene) were added to the final concentrated 

extracts prior to GC-MS analysis. The GC-MS system was an Agilent 6890N GC interfaced to an Agilent 

5973 inert MS using electron impact in selective ion monitoring mode. A DB5-MS capillary column 

(J&W Inc., 30 m length, 0.25 mm diameter, 0.25 μm thickness) was used to achieve chromatographic 

separation of the alkanes and PAHs. Identification of target analytes was based on their retention times 

and confirmed by the abundance of a secondary ion relative to the molecular ion. Alkanes included 

normal and branched aliphatics ranging in carbon number from 10 to 35 as well as pristane and phytane.

PAHs include 16 parent PAHs (naphthalene, phenanthrene, anthracene, fluorene, dibenzothiophene, 

fluoranthene, pyrene, chrysene, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[e]pyrene, 

benzo[a]pyrene, perylene, indeno[1,2,3-cd]pyrene, dibenzo[a,h]anthracene, benzo[g,h,i]perylene), as well 

as 22 alkylated homologues of naphthalene, fluorene, dibenzothiophene, phenanthrene, anthracene, 

fluoranthene, pyrene, and chrysene. The measured hydrocarbon concentrations were then normalized to 

the concentration of a conserved biomarker within the oil 17α(H),21β(H)-hopane for the quantification of 

biodegradation (Prince et al. 1994).
4.28

Table 4.3. Composition of samples used to study the effect of droplet size on biodegradation.

(a)

Addition Sample A Sample B Sample C Sample D Sample E

mL Seawater + NP medium 200 200 200 200 200

mL Alcanivorax borkumensis 3 3 3 3 3

mL Pseudomonas putida 3 3 3 3 3

mL Hormoconis resinae 0 0 0 0 0

mL Corexit 0 0 1 2 5

mL oil 0 200 200 200 200

DOR NA 0 1:200 1:100 1:40

DWR NA 0 1:200000 1:100000 1:40000

Addition Sample F Sample G Sample H Sample I Sample J

mL Seawater + NP medium 200 200 200 200 200

mL Alcanivorax borkumensis 3 3 0 0 0

mL Pseudomonas putida 3 3 0 0 0

mL Hormoconis resinae 0 0 6 6 6

mL Corexit 10 20 0 0 1

mL oil 200 200 0 200 200

DOR 1:20 1:10 NA 0 1:200

DWR 1:20000 1:10000 NA 0 1:200000

Addition Sample K Sample L Sample M Sample N Sample O

mL Seawater + NP medium 200 200 200 200 200

mL Alcanivorax borkumensis 0 0 0 0 3

mL Pseudomonas putida 0 0 0 0 3

mL Hormoconis resinae 6 6 6 6 0

mL Corexit 2 5 10 20 10

mL oil 200 200 200 200 200

DOR 1:100 1:40 1:20 1:10 1:20

DWR 1:100000 1:40000 1:20000 1:10000 1:20000

(a) Samples with bacteria are highlighted in green and samples with fungi in orange. 

NA = not applicable.

4.2.4 Results of the Droplet Study

The samples prepared as described in the section above and in Table 4.3 were analyzed daily to calculate 

droplet size and at the conclusion of the experiment to measure changes in PAH concentration. The 

complete results of the hydrocarbon analysis are provided in a series of tables in Appendix B and the 

droplet size data are provided in Appendix C. In summary, droplet sizes changed throughout the course of 

the experiment. This may have been caused by a combination of factors: evaporation of aromatic 

hydrocarbons, biodegradation of hydrocarbons, biodegradation of the Corexit, and flocculation of cells or 

cell-oil aggregates. Although changes in droplet size were expected because of these factors, the observed 

changes were inconsistent and do not appear to scale with the DOR and initial sizes. Nonetheless, the data 

do provide a perspective on the influence of different initial DORs and size ranges on biodegradation, and 

a detailed analysis of the biodegradation of specific hydrocarbons as a function of initial DOR. The 
4.29

results are specific to the organisms used in the tests and the test conditions; discrepancies in the findings 

reported by different researchers have been attributed to the experimental setup, organisms, and methods 

of analysis. The control tests described at the beginning of this chapter also demonstrate that the 

organisms tested did not appear to be negatively affected by the presence of Corexit.

The histograms in Appendix C show the distribution of droplet sizes found in the flasks throughout the 

course of the experiment. The graphs follow the method of droplet size distribution representation 

presented by Brandvik et al. 2013 and Johansen et al. 2013, in which the theoretical droplet size range 

was represented as a series of bins of equal logarithmic intervals (Johansen et al. 2013; Brandvik et al. 

2013). After sizing, each particle was placed into its corresponding bin, which was labeled on the x-axis 

by its median diameter. The y-axis (volume fraction) is the percent of the total volume of oil represented 

by all of the droplets in each bin. Table 4.4 and Table 4.5 summarize the droplet size data, showing 

minimum and maximum droplet sizes, mean droplet diameter, and the mode median bin diameter (bin 

representing the greatest fraction of the total volume of oil) from the distributions shown in Appendix C. 

Droplet size measurements were also taken for the two samples with no oil or dispersant (Samples A and 

H). Small aggregates of cells were captured in those photographs and were likely also captured in the 

photos of samples with oil and dispersants. The larger H. resinae cells and mycelia likely account for the 

bimodal distributions seen in Samples I through N. The coloration of the microorganisms was too similar 

to that of similarly sized oil droplets to filter them out. Thus, the cells likely influenced the recorded 

distributions toward smaller size ranges.

The data provided in Table 4.6 and Table 4.7 and in Appendix B indicate that the inoculated samples have 

lower concentrations of several PAHs than the values from the Time 0 sample (Sample O). Some of the 

observed loss could be explained by effects other than biodegradation. For example, 2-ring PAHs 

(especially the naphthalene) can be lost by evaporation more easily than other PAHs. Therefore, the 

significant concentration decrease of those PAHs could be a combined effect of evaporation and 

biodegradation. The concentration of other PAHs, such as phenanthrene, did decrease compared to 

Sample O. The results of the hydrocarbon analysis were noisy and showed no significant trends relative to 

DOR or droplet size, or even to the presence or absence of dispersant. 

If there were any inhibitory effects of the dispersant, they appeared to have been fairly small and perhaps 

constrained to specific hydrocarbons. A more suitable comparison might have been to compare these tests 

with a sample in which the oil was maintained as a surface slick; the actual droplet size distribution may 

not be as important as simply creating a suspension of the oil droplets in the water column. If a future 

study validates that hypothesis, then only the minimum amount of dispersant necessary to prevent slick 

formation (in a deep ocean release) would be needed. Studies described in Section 4.6 of this report 

examine droplet size distributions generated from simulated blowouts with and without added dispersant. 

They note that the turbulent nature of a discharge and subsequent degassing results in some atomization 

and emulsification of oil without added dispersants.
4.30

Table 4.4. Summary of the droplet size data for the Alcanivorax borkumensis and Pseudomonas putida

samples.

Sample A Sample B Sample C Sample D Sample E Sample F Sample G

oil 0 200 uL 200 uL 200 uL 200 uL 200 uL 200 uL

DOR NA 0 1:200 1:100 1:40 1:20 1:10

DWR NA 0 1:200000 1:100000 1:40000 1:20000 1:10000

Time (hrs) Minimum Droplet Diameter (mm)

24 0.008806 0.008892 0.008283 0.007737 0.006605 0.009496 0.009495

48 0.011683 0.011260 0.011161 0.010655 0.010625 0.010220 0.010079

72 0.010477 0.010622 0.010685 0.010565 0.010655 0.010625 0.010625

96 0.011063 0.011312 0.010869 0.011079 0.010966 0.010931 0.010802

120 0.010390 0.010595 0.010536 0.010477 0.010593 0.010419 0.010332

148 0.010595 0.010474 0.010477 0.010535 0.010777 0.010896 0.010777

Maximum Droplet Diameter (mm)

24 0.032948 0.050463 0.035682 0.181946 0.159577 0.192156 0.155536

48 0.050463 0.327035 0.334731 0.425206 0.050463 0.151388 0.231249

72 0.050463 0.276395 0.247215 0.222837 0.107047 0.087404 0.188814

96 0.035682 0.188814 0.259772 0.249777 0.142730 0.142730 0.437019

120 0.035682 0.185412 0.192156 0.228479 0.050463 0.087404 0.178412

148 0.035682 0.198672 0.242010 0.271750 0.100925 0.118345 0.147123

Volume Fraction, Mode Median Bin Diameter (mm)

24 0.008685 0.008685 0.008685 0.00736 0.074735 0.171025 0.14493

48 0.01209 0.331655 0.20182 0.391375 0.010245 0.010245 0.171025

72 0.010245 0.122815 0.14493 0.122815 0.010245 0.010245 0.010245

96 0.010245 0.01209 0.122815 0.122815 0.010245 0.010245 0.46185

120 0.010245 0.171025 0.122815 0.122815 0.010245 0.010245 0.171025

148 0.010245 0.074735 0.074735 0.14493 0.010245 0.122815 0.074735

Mean Droplet Diameter (mm)

24 0.010059 0.010312 0.009319 0.009367 0.009606 0.011245 0.012511

48 0.013742 0.013546 0.016052 0.013968 0.012598 0.012081 0.012213

72 0.012002 0.016551 0.015111 0.016497 0.012900 0.012684 0.013262

96 0.012977 0.014421 0.016773 0.015021 0.012837 0.012692 0.014553

120 0.011680 0.013614 0.014177 0.015690 0.012751 0.012276 0.012863

148 0.011790 0.022674 0.022807 0.020043 0.012087 0.012523 0.018737
4.31

Table 4.5. Summary of the droplet size data for the Hormiconis resinae samples.

Sample H Sample I Sample J Sample K Sample L Sample M Sample N

oil 0 200 uL 200 uL 200 uL 200 uL 200 uL 200 uL

DOR NA 0 1:200 1:100 1:40 1:20 1:10

DWR NA 0 1:200000 1:100000 1:40000 1:20000 1:10000

Time 

(hr) Minimum Droplet Diameter (mm)

24 0.010080 0.010221 0.010386 0.010418 0.011634 0.011861 0.010074

48 0.011227 0.010165 0.010715 0.010418 0.010068 0.010194 0.010476

72 0.010505 0.010505 0.010604 0.010565 0.010445 0.010522 0.010742

96 0.010979 0.010803 0.010924 0.010715 0.010837 0.010652 0.010361

120 0.010655 0.010472 0.010638 0.010443 0.010586 0.010554 0.010432

148 0.010902 0.010770 0.010837 0.010832 0.010559 0.010633 0.011888

Maximum Droplet Diameter (mm)

24 0.112838 0.222837 0.050463 0.123608 0.050463 0.118345 0.174808

48 0.050463 0.262212 0.079788 0.247215 0.147123 0.147123 0.155536

72 0.050463 0.133512 0.071365 0.257310 0.163518 0.133512 0.138198

96 0.050463 0.298541 0.050463 0.208063 0.100925 0.151388 0.112838

120 0.061804 0.321142 0.071365 0.178412 0.151388 0.112838 0.228479

148 0.050463 0.405275 0.050463 0.112838 0.112838 0.123608 0.071365

Volume Fraction, Mode Median Diameter (mm)

24 0.010245 0.14493 0.010245 0.010245 0.012049 0.012049 0.012049

48 0.01209 0.23816 0.010245 0.23816 0.010245 0.010245 0.14493

72 0.010245 0.010245 0.010245 0.23816 0.010245 0.010245 0.14493

96 0.010245 0.281045 0.010245 0.010245 0.010245 0.010245 0.010245

120 0.010245 0.281045 0.010245 0.010245 0.010245 0.010245 0.010245

148 0.010245 0.281045 0.010245 0.010245 0.010245 0.010245 0.010245

Mean Droplet Diameter (mm)

24 0.011598 0.013157 0.011844 0.012391 0.013269 0.013948 0.013411

48 0.012972 0.012420 0.012720 0.012120 0.012316 0.011873 0.012765

72 0.012367 0.012038 0.012130 0.012872 0.012876 0.012260 0.013380

96 0.012680 0.013146 0.013119 0.013002 0.012665 0.012434 0.012251

120 0.012412 0.012993 0.012486 0.012718 0.012667 0.012628 0.012279

148 0.011919 0.020472 0.012001 0.013023 0.011992 0.011812 0.013586
4.32

Table 4.6. Percent loss of hydrocarbon after 148 hrs compared to Sample O; bacterial exposures.

(a)

Compound MDL 

(ug/L)

Sample 

B

Sample 

C

Sample 

D

Sample 

E

Sample

F

Sample 

G

DOR 0 1:200 1:100 1:40 1:20 1:10

% loss % loss % loss % loss % loss % loss

Naphthalene 0.0026 100.00% 100.00% 100.00% 100.00% 100.00% 99.97%

2,6-Dimethylnaphthalene 0.0011 99.59% 99.69% 99.56% 99.46% 99.90% 99.52%

C1-Naphthalenes 0.0023 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

C2-Naphthalenes 0.0096 97.85% 98.19% 97.47% 97.18% 99.04% 97.53%

C3-Naphthalenes 0.0112 78.07% 79.57% 76.44% 82.23% 87.26% 77.47%

C4-Naphthalenes 0.0113 52.57% 53.68% 49.47% 57.07% 64.92% 51.23%

Biphenyl 0.0005 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Fluorene 0.0011 83.60% 84.73% 82.30% 85.67% 88.58% 81.10%

1-Methyl fluorene 0.0010 53.20% 54.79% 50.11% 58.16% 65.72% 53.16%

C1-Fluorenes 0.0035 56.57% 54.88% 53.58% 63.32% 66.14% 52.94%

C2-Fluorenes 0.0026 26.87% 25.57% 19.77% 30.68% 39.25% 24.35%

C3-Fluorenes 0.0131 12.27% 9.22% 6.70% 12.00% 24.42% 13.50%

Dibenzothiophene 0.0012 52.58% 53.67% 51.75% 58.01% 65.50% 49.90%

C1-Dibenzothiophenes 0.0030 31.60% 29.26% 30.34% 35.35% 46.74% 27.58%

C2-Dibenzothiophenes 0.0023 19.66% 15.17% 12.90% 18.93% 33.96% 15.23%

C3-Dibenzothiophenes 0.0031 16.48% 11.68% 12.01% 12.66% 28.77% 13.37%

C4-Dibenzothiophenes 0.0022 19.25% 18.45% 12.37% 17.62% 31.44% 18.30%

Phenanthrene 0.0025 43.82% 45.13% 43.74% 49.58% 59.35% 39.68%

Anthracene 0.0002 26.42% 18.11% 24.67% 36.51% 39.64% 27.24%

1-methyl Phenanthrene 0.0008 20.39% 17.51% 17.99% 21.95% 37.99% 17.08%

3,6-DMP 0.0009 12.53% 12.76% 7.45% 15.35% 31.26% 12.85%

2,6-DMP 0.0003 11.55% 6.84% 3.21% 13.29% 30.90% 5.90%

1,7-DMP 0.0006 11.85% 10.39% -1.04% 13.67% 27.02% 2.60%

C1-Phen/An 0.0034 22.22% 19.80% 18.89% 25.53% 38.75% 21.13%

C2-Phen/An 0.0038 14.49% 11.38% 11.08% 16.66% 31.12% 11.44%

C3-Phen/An 0.0028 11.03% 6.21% 4.88% 11.34% 28.85% 10.05%

C4-Phen/An 0.0041 8.54% 5.86% 4.28% 10.90% 27.41% 7.49%

Fluoranthene 0.0004 20.13% 20.09% 28.50% 25.52% 48.21% 23.67%

Pyrene 0.0015 7.68% 3.71% 3.55% 5.63% 24.73% 7.60%

C1-Fluor/Py 0.0008 9.46% 5.36% 1.81% 5.44% 27.64% 8.98%

C2-Fluor/Py 0.0013 10.64% 4.09% 4.40% 9.28% 28.84% 8.75%

C3-Fluor/Py 0.0022 9.27% 5.73% 5.44% 12.35% 31.66% 10.33%

Benzo [a] anthracene 0.0003 6.70% 17.57% 14.76% 14.49% 52.66% 14.81%

Chrysene 0.0006 10.44% 3.99% 2.51% 8.60% 42.33% 16.45%

C1-Chrysenes 0.0008 13.93% 7.84% 9.69% 10.90% 49.83% 19.18%

C2-Chrysenes 0.0030 13.96% 9.29% 11.13% 7.13% 46.99% 18.24%

C3-Chrysenes 0.0066 10.97% 10.74% 5.50% 7.14% 45.40% 12.14%

C4-Chrysenes 0.0141 17.97% 12.96% 12.87% 10.02% 47.07% 18.74%
4.33

Compound MDL 

(ug/L)

Sample 

B

Sample 

C

Sample 

D

Sample 

E

Sample

F

Sample 

G

DOR 0 1:200 1:100 1:40 1:20 1:10

% loss % loss % loss % loss % loss % loss

Benzo [b] fluoranthene 0.0008 -4.78% -0.70% -1.49% 9.93% 56.32% 1.67%

Benzo [k] fluoranthene 0.0010 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Benzo [e] pyrene 0.0011 18.58% 10.29% 21.50% 14.70% 58.71% 25.60%

Benzo [a] pyrene 0.0010 41.48% 39.52% 47.21% 64.77% 81.12% 76.79%

Perylene 0.0010 8.25% -0.99% 1.69% 7.38% 51.48% 8.05%

Indeno [1,2,3-c,d] pyrene 0.0002 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Dibenzo [a,h] anthracene 0.0003 79.74% 87.29% 92.29% 90.67% 100.00% 90.31%

Benzo [g,h,i] perylene 0.0002 73.93% 73.97% 84.06% 81.18% 92.50% 81.88%

Recovery

d8-Naphthalene 77% 68% 63% 70% 45% 76%

d10-Acenaphthene 91% 84% 80% 90% 51% 84%

d10-Phenanthrene 105% 98% 95% 105% 64% 96%

d12-Chrysene 78% 67% 74% 69% 70% 79%

d12-Perylene 85% 67% 77% 80% 77% 84%

(a) Green, orange, and blue highlighting provided solely to color-code samples

Table 4.7. Percent loss of hydrocarbon after 148 hrs compared to Sample O; fungal exposures.

(a)

Compound MDL 

(ug/L)

Sample 

I

Sample 

J

Sample 

K

Sample 

L

Sample 

M

Sample 

N

DOR 0 1:200 1:100 1:40 1:20 1:10

% loss % loss % loss % loss % loss % loss

Naphthalene 0.0026 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

2,6-Dimethylnaphthalene 0.0011 99.43% 97.57% 95.37% 93.11% 92.00% 96.74%

C1-Naphthalenes 0.0023 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

C2-Naphthalenes 0.0096 98.75% 96.09% 94.05% 91.12% 89.96% 95.11%

C3-Naphthalenes 0.0112 83.85% 75.87% 68.55% 64.38% 64.74% 72.58%

C4-Naphthalenes 0.0113 49.83% 46.05% 38.54% 33.93% 38.59% 42.79%

Biphenyl 0.0005 100.00% 100.00% 100.00% 99.28% 100.00% 100.00%

Fluorene 0.0011 85.55% 79.87% 78.30% 71.02% 72.25% 77.06%

1-Methyl fluorene 0.0010 54.97% 52.33% 46.18% 39.03% 44.17% 47.63%

C1-Fluorenes 0.0035 52.47% 48.89% 42.39% 36.46% 40.03% 45.93%

C2-Fluorenes 0.0026 21.82% 23.99% 18.74% 13.91% 20.10% 21.69%

C3-Fluorenes 0.0131 -1.05% 13.66% 11.55% 8.27% 8.28% 5.71%

Dibenzothiophene 0.0012 56.89% 49.45% 46.23% 38.96% 42.98% 45.67%

C1-Dibenzothiophenes 0.0030 27.94% 24.07% 22.24% 14.46% 18.82% 19.51%

C2-Dibenzothiophenes 0.0023 9.82% 14.78% 10.80% 7.22% 11.90% 10.27%

C3-Dibenzothiophenes 0.0031 4.56% 13.06% 7.77% 2.86% 5.12% 7.46%

C4-Dibenzothiophenes 0.0022 -0.44% 7.88% 4.63% 0.44% 3.77% 2.70%

Phenanthrene 0.0025 48.28% 41.91% 39.09% 30.54% 38.10% 39.84%
4.34

Compound MDL 

(ug/L)

Sample 

I

Sample 

J

Sample 

K

Sample 

L

Sample 

M

Sample 

N

DOR 0 1:200 1:100 1:40 1:20 1:10

% loss % loss % loss % loss % loss % loss

Anthracene 0.0002 44.79% 21.18% 24.50% 5.20% 25.57% 23.41%

1-methyl Phenanthrene 0.0008 17.26% 20.70% 17.44% 11.77% 17.37% 18.12%

3,6-DMP 0.0009 8.45% 10.77% 8.59% 7.16% 11.62% 10.44%

2,6-DMP 0.0003 7.01% 8.52% 11.82% 0.24% 10.71% 2.20%

1,7-DMP 0.0006 8.35% 7.68% 5.81% 5.70% 10.29% 2.78%

C1-Phen/An 0.0034 19.70% 20.46% 17.09% 13.02% 17.62% 19.20%

C2-Phen/An 0.0038 7.91% 12.18% 10.82% 7.52% 9.74% 12.28%

C3-Phen/An 0.0028 3.73% 9.41% 6.87% 3.48% 4.88% 6.50%

C4-Phen/An 0.0041 2.94% 8.11% 3.95% 0.31% 2.38% 1.89%

Fluoranthene 0.0004 17.58% 31.74% 23.34% 27.24% 33.67% 21.64%

Pyrene 0.0015 7.30% 14.81% 12.53% 10.96% 14.23% 14.06%

C1-Fluor/Py 0.0008 -0.04% 3.93% 3.10% -2.70% 3.69% -0.08%

C2-Fluor/Py 0.0013 0.81% 9.39% 5.86% 4.64% 3.89% 6.07%

C3-Fluor/Py 0.0022 1.01% 5.13% 6.56% 4.66% 6.69% 3.16%

Benzo [a] anthracene 0.0003 3.12% 18.92% 2.01% 4.48% 20.99% 14.95%

Chrysene 0.0006 6.73% 12.32% 12.43% 6.85% 10.99% 11.10%

C1-Chrysenes 0.0008 6.96% 12.84% 12.38% 10.79% 11.17% 11.17%

C2-Chrysenes 0.0030 6.64% 12.84% 13.67% 9.10% 14.43% 12.23%

C3-Chrysenes 0.0066 5.66% 13.92% 16.30% 10.45% 14.24% 12.37%

C4-Chrysenes 0.0141 9.74% 14.45% 20.43% 18.10% 19.27% 13.37%

Benzo [b] fluoranthene 0.0008 12.96% 18.12% 14.25% 25.78% 21.98% 27.78%

Benzo [k] fluoranthene 0.0010 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Benzo [e] pyrene 0.0011 11.60% 24.20% 11.30% 17.94% 11.64% 19.32%

Benzo [a] pyrene 0.0010 43.75% 64.91% 33.72% 56.22% 52.66% 66.55%

Perylene 0.0010 11.82% 16.90% 7.79% 3.15% 12.59% 15.39%

Indeno [1,2,3-c,d] pyrene 0.0002 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Dibenzo [a,h] anthracene 0.0003 88.95% 90.22% 5.78% 100.00% 100.00% 89.74%

Benzo [g,h,i] perylene 0.0002 78.02% 77.72% 5.63% 78.49% 85.76% 85.42%

Recovery

d8-Naphthalene 68% 64% 71% 67% 72% 80%

d10-Acenaphthene 89% 88% 94% 88% 95% 99%

d10-Phenanthrene 110% 101% 112% 102% 109% 114%

d12-Chrysene 74% 76% 86% 80% 81% 81%

d12-Perylene 86% 85% 88% 81% 86% 93%

(a) Green, orange, and blue highlighting provided solely to color-code samples
4.35

4.3 Explore Novel, Noninvasive Approaches to Characterizing

Emulsions and Oil Degradation in Pressure Chambers

One challenging aspect of hyperbaric research is that withdrawing samples from a pressure system may 

result in alteration of the sample. This held particularly true for the high-pressure studies using oil 

because of the degassing of the oil following a drop in pressure and changes in oil droplet size due to the 

change in pressure and mixing energies introduced by sample extraction and pressure release. Although 

changes in pressure during sample extraction were not expected to significantly affect the chemical state 

of the oil (other than the rapid loss of dissolved and free gas), the aforementioned physical changes would 

have interfered with the analysis of dispersant effectiveness (oil droplet size), and the process of sample 

extraction and fluid exchange is particularly slow.

A few methods used for other high-pressure studies were explored to determine whether they could be 

used to characterize the physical and chemical properties of the pressurized fluids and gases. The methods 

and tests undertaken are described in the sections that follow.

4.3.1 Nuclear Magnetic Resonance Spectroscopy

NMR spectroscopy is a powerful tool that allows the determination of average molecular and chemical 

contributions of complex mixtures. Recently, 1

H and 13C{1H} NMR spectroscopy were used to 

thoroughly characterize the molecular sub-structures contained within a number of diesel fuels and coal 

liquids (Canella et al. 2013a, b; Bays 2014). Fuels and coal liquids are complex mixtures in which

contributions from individual species cannot readily be resolved because the mixture comprises thousands 

of unique molecular species. Similarly for an oil spill, an understanding of the interactions of dispersants, 

such as Corexit and Finasol, with the crude or processed oil will mean observing the influence of the oil 

on the dispersant. These interactions will have a time and chemical condition (temperature, pressure, 

dispersant concentration, etc.) dependency that should be measured while the process is occurring, 

operando, and at the conditions most representative of those being studied, in situ, as opposed to ex situ

measurements where the results must be extended to conditions observed in the environment.

Proton nuclear magnetic resonance (1

H-NMR) techniques were applied as a means of measuring

droplet/micelle size and formation of oil/dispersant in water at high pressures. High-pressure reaction 

vessels are fully enclosed so there are not many means for performing in situ analyses. Samples were 

tested using both 1

H-NMR and 1H-DOSY (diffusion ordered spectroscopy). Before the tests were 

performed, several aspects were investigated, including 1) the feasibility of using NMR with the high salt 

concentration found in seawater of the Gulf of Mexico, which could have caused electrical grounding of 

the NMR cell, resulting in arcing and precluding its use; and 2) whether salt concentrations were within 

range for testing and whether accurate particle sizes could be measured. A 300 MHz 1

H frequency 

Agilent Varian NMR System (VNMRS) equipped with a direct detect dual band probe was used for 

diffusion experiments. Diffusion coefficients were measured using the 1

HDOSY (Wu et al. 1995) in the 

VnmrJ 4.2 software suite. Experimental parameters included 128 scans, 2–3 ms gradient pulse lengths,

and diffusion delays of 0.05–0.2 s. The gradient strengths were varied from 0–20 G cm−1.

Initial 1

H-DOSY testing of Corexit EC9500A and Finasol OSR 52 was conducted in pure deuterated 

water (D2O) and the results for Finasol OSR 52 are shown in Figure 4.21. 
4.36

Figure 4.21. Diffusion ordered spectrum of Finasol OSR 52 in deuterium oxide (D2O). The x-axis 

represents 1

H-NMR resonances indicative of the structural characteristics of each Finasol 

OSR 52 component. The y-axis represents the rate at which each of the Finasol OSR 52 

components diffuses through the deuterated water. The same rate of diffusion for different 

resonances suggests that they are part of the same molecule or same cluster of molecules, 

as would be the case for a micelle. Clearly, two major chemical components make up 

Finasol OSR 52: one with a diffusion constant (D) slightly less than 1 × 10-10 m2

/s and one 

with a diffusion constant of 4.4 × 10-10 m2

/s. HDO is D2O that has exchanged a proton for a 

deuteron with residual water. The diffusion rate of HDO in D2O serves as an internal 

standard in all samples. 

Sampling in the pure D2O indicated that the compositional components of the Finasol OSR 52 could be 

detected. Using the Stokes-Einstein equation, which assumes that each of the particles behaves as a 

spherical agglomeration or micelle, the radius of the agglomerations or micelles was calculated to be 3–5 

nm for the Finasol OSR 52 in D2O, which is reasonable for a cluster of molecules below the critical 

micelle concentration. The dispersant samples were subsequently tested in salt-modified D2O. Two salt 

concentrations made by adding Instant Ocean® sea salt to D2O were tested for proof of concept: 36 ppt 

(mimicking that of Gulf of Mexico seawater) and 360 ppt (an extreme brine environment; ocean brine 

pools are around 150 ppt). The high salt concentrations did not cause grounding or arcing in the NMR cell 

and a good signal was obtained. After mixing Instant Ocean® sea salt into the Finasol-D2O mixture to 

create a solution with a salinity of 36 ppt, the spectral components became broader, the diffusion 

coefficient became smaller (corresponding to a higher diffusion rate), and the radius of the micelles or 

agglomerations decreased from 0.3–5.5 nm to 0.61 nm. The radius is much smaller with the salt than 

without it. These results indicate that the two dispersants tested may behave in a different manner when 

applied in variable salt conditions (e.g., in a high salt environment like a brine pool).

To confirm that signal information could be obtained in the increased salt solution, 1 μm diameter 

polystyrene beads from Magsphere (Pasadena, CA) were tested (Figure 4.22). While good peak signals 

could be obtained for the polystyrene beads in 1

H-NMR testing, the beads were not amenable to particle 

sizing in 1

H-DOSY, because the material was (and behaved like) a solid particle and 1H-DOSY testing 

requires diffusion of materials to be able to measure particles in the test chamber.
4.37

Figure 4.22. 1H-NMR spectrum of 1 micron diameter polystyrene beads.

1

H-NMR spectroscopy was used to measure stock samples of dispersant to determine whether

they comprised any materials that would show up as distinct signals that could be tracked in a 

mixed component sample. Dioctyl sodium sulfosuccinate (DOSS), a primary component of 

many dispersants that is often used to track dispersant in the environment, was also tested. 1

HNMR comparison results for DOSS and the dispersants are shown in Figure 4.23. The results 

show that the DOSS spectra are visible in the spectra obtained for Finasol and Corexit. Particle 

sizes of the dispersants in pure D2O and in salt-modified D2O could be measured, so varying 

concentrations of dispersant in D2O were tested as well as the addition of oil (WTI crude) to the 

samples. Table 4.8 provides a list of samples and results. 

Figure 4.23. Three spectra aligned vertically obtained by 1

H-NMR spectroscopy for Finasol OSR 52, 

DOSS, and Corexit EC9500A (from top to bottom, respectively). Each DOSS 1

H-NMR 

component showed overlapping signal with other dispersant components.

At high dispersant concentrations (≥1:25 dispersant to D2O) the phases separated in the NMR 

cell. The addition of crude oil resulted in an oil layer on top of the D2O with a rapid phase 

separation. This separation and top layer formation prevented particle size determination using 

the 1H-DOSY method. In an attempt to maintain dispersion and micelle formation after mixing, 

Finasol OSR-52

DOSS

Corexit EC9500A
4.38

higher DOR ratios were used. Diffusion behaviors of species observable by 1H-NMR were not 

sensitive to the DOR ratios up to 3:1 

Table 4.8. Tests performed on the 1H-NMR spectrometer for in situ particle size determination and 

micelle diffusion analysis. The testing time given is the time for a single test. Each experiment 

required 4–5 tests for proper analysis.

Sample

Dispersant: 

D2O ratio 

(v/v)

Oil:D2O 

ratio 

(v/v)

Salinity 

(ppt)

Relaxation 

Time (s)

Diffusion 

Coefficient 

x10^-10 

(m^2/s)

Particle 

Size 

(nm)

Testing 

Time 

(hr)

Finasol 

OSR 52

1:50 Not 

Tested

0 Not 

Measured

0.3-5.5 0.3-5.5 2

Finasol 

OSR 52

1:25 Not 

Tested

36 0.6-8 2.5-5.6 0.3-0.7 1-2

Corexit 

EC9500A

1:25 Not 

Tested

36 0.6-2 2-5.5 0.3-0.8 1-2

Finasol 

OSR 52

1:5000 Not 

Tested

0 Not 

Measured

0.2-5.5 0.3-8 >12

Corexit 

EC9500A

1:5000 Not 

Tested

0 Not 

Measured

0.8-3 0.2-3 >12

Finasol 

OSR 52

1:1000 Not 

Tested

0 Not 

Measured

0.5-6.0 0.3-3.5 8

Corexit 

EC9500A

1:1000 Not 

Tested

0 Not 

Measured

0.3-5.2 0.3-5.8 8

Finasol 

OSR 52

1:1000 1:100 36 Not 

Measured

0.5-5.1 0.3-3.5 8

Dioctyl 

Sodium 

Sulfosuccinate

1:25 Not 

Tested

0 Not 

Measured

2 0.9 1-2

(Table 4.9). It is possible that some micelle formation did occur, but the majority of the species 

in solution seemed to be those formed with Finasol OSR 52 alone and due to resonance overlap 

in the 1H-NMR spectrum, any difference in speciation was not observable by this technique. 

Figure 4.24 shows the 1H-DOSY spectra of A) 1:50 Finasol OSR 52: D2O ratio and B) 1:1 ODR 

in a 1:50 analyte mixture:D2O (analyte mixture = 1:1 oil-dispersant mixture), which are 

essentially identical. The addition of oil in a 3:1 DOR and 1:50 oil:D2O ratios resulted in rapid 

phase separation and the particle size could not be determined by DOSY methods.

Ultimately, the use of NMR was abandoned because of the number of difficulties and the conclusion that 

oil droplets and other fluids would need to be maintained in a motionless state for several minutes or 

longer to achieve the resolution needed to make accurate characterization of either physical or chemical 

properties.
4.39

Table 4.9. Samples and ratios used to try and force micelle formation for particle size determination and 

diffusion analysis.

Sample

Dispersant: 

D2O ratio 

(v/v)

DOR 

(v/v)

Analyte 

Mixture: 

D2O 

ratio 

(v/v)

Diffusion 

Coefficient 

x10^-10 

(m^2/s)

Particle 

Size 

(nm)

Finasol 

OSR 52 1:50 1:50 1:50 0.3-5.5 0.3-5.5

Finasol 

OSR 52 1:50 1:50 1:50 0.4-5 0.3-4.5

Finasol 

OSR 52 1:100 1:1 1:50 0.4-5 0.3-4.5

Finasol 

OSR 52 1:100 3:1 1:50 0.4-5 0.3-4.5

Figure 4.24. 1

H-DOSY spectra of A) 1:50 Finasol OSR 52: D2O ratio and B) 1:1 DOR in a 1:50 analyte 

mixture:D2O (analyte mixture = 1:1 oil-dispersant mixture). No significant changes were 

observed upon addition of oil. The expansion of dot size and the few additional dots that 

arise were data noise peaks and not significant.
4.40

4.3.2 Investigation of Other Spectroscopic Approaches

A rapid and low-cost method of quickly determining oil:dispersant:seawater ratios was desired because 

typical methods (e.g., liquid chromatography [LC]-MS, GC-MS) are time-/labor-intensive and produce 

significant chemical waste in the form of harsh solvents. Spectroscopic approaches (i.e., light-based) were 

investigated as a solution for on-the-fly quantitation of oil:dispersant:seawater and to detect changes in 

the chemical composition of samples subject to degradation. Briefly, spectroscopy approaches exploit the 

optical characteristics that are unique to each compound (e.g., absorption spectra of water, Figure 4.25). 

With the introduction of the sapphire glass view cell into the pressure system (Figure 4.6), light 

spectroscopic methods were also explored in an attempt to identify a means of characterizing pressurized 

samples of seawater, oil, and dispersant without having to withdraw or perform solvent extractions on the 

samples. 

Fluorescence spectroscopy, for example, provides a very high level of sensitivity capable of parts per 

billion and lower limits of detection. Fluorescence spectroscopy has been used for the high-pressure study 

of the solubility of organometallic compounds, chemical extraction processes, catalytic reactions, new 

materials synthesis and purification, and the characterization of fundamental chemical processes. All 

conjugated organic compounds fluoresce to some degree and the emission spectrum, emission lifetime,

and emission efficiency are largely a function of the molecular structure. Consequently the fluorescence 

spectrum and lifetime collected from the high-pressure system could provide a means of determining the 

gross molecular composition of the seawater-oil-dispersant emulsion. Shorter wavelength fluorescence 

spectra with shorter lifetimes would indicate smaller organic compounds with simple conjugation. Longer 

wavelength spectra (in the red for very large polyaromatic hydrocarbons) with longer lifetimes would 

indicate a higher degree of conjugation, a higher content of polyaromatic hydrocarbons, and in general 

higher molecular weight (lower volatility) components of oil.

Figure 4.25. Absorption spectra of water.

For each of the methods described below, the first round of evaluation was carried out in unpressurized 

systems to facilitate movement and rapid exchange of samples between systems. Studies using 

pressurized systems were then performed with any methods that were successful. 
4.41

When a compound absorbs light of a particular wavelength, it may emit light at a different wavelength—a 

phenomenon known as fluorescence. Oil, dispersant, and seawater all fluoresce; seawater, for example, 

fluoresces because of the presence of organic contaminants (e.g., proteins and DNA). The particular 

excitation and emission spectra, as well as the timing of the fluorescence, all have the potential to help 

identify compounds, and the intensity of the fluorescence may also be used to quantify the compounds. 

To determine the fluorescent spectra of a compound, two components are needed: a monochromatic 

illumination source to produce light of a single wavelength and a spectrophotometer to detect light of a 

particular wavelength.

A BioTek Synergy4 multi-detection microplate reader was explored as a high-throughput tool for 

measuring fluorescence. It provided both the illumination source and the spectrophotometer with a plate 

reader and attached automated microplate stacker for rapid, automated analysis of hundreds of samples. It 

provided three measurement modes: absorption, transmission, and fluorescence illumination from 250 nm 

to 650 nm; detection from 300 nm to 700 nm, and the ability to perform automated excitation/ emission 

scans of compounds.

An experiment was carried out to determine whether the instrument could detect unique fluorescent 

signatures from oil or dispersant in seawater. The rationale was that if each compound has a unique 

fluorescent signature, then ratios of fluorescence measurements should be directly related to ratios of the 

compounds. Test materials included dispersants (Corexit and Finasol), crude oil (ANS and WTI), 

unfiltered and 0.22 μm filtered seawater, and specific dispersant components (1,2-propanediol, kerosene, 

dipropylene glycol methyl ether, 2-butoxyethanol, and dioctyl sulfosuccinate). 

The spectra are provided in Appendix D and results are summarized here. Seawater, Corexit, Finasol, 

ANS, and WTI all fluoresced in ultraviolet light. Peak excitation differed between compounds, but there 

was some overlap and the peaks were not particularly sharp. This might be resolved by using a different 

instrument. The dispersant components did not display any significant fluorescence. The conclusion was 

that fluorescence spectroscopy might be a viable approach if background fluorescence does not wash out 

unique signatures.

A second experiment was aimed at determining whether pre-determined ratios of oil, dispersant, and 

seawater mixtures could be related to fluorescence ratios. The rationale was based on each compound 

fluorescing at unique wavelengths. For this set of experiments, test materials included dispersants

(Corexit and Finaso), crude oil (ANS and WTI), and unfiltered seawater. Samples consisted of 180 uL 

raw seawater + 10 uL oil + 10 uL dispersant. Spectra are provided in Appendix E and results are 

summarized here. Mixtures of oil, dispersant, and seawater fluoresced across a range of wavelengths. 

Inconsistent results were acquired across dilutions of dispersant, most likely due to an inability to 

stabilize emulsions of oil/dispersant/seawater because the oil tended to accumulate on edges of the wells

and/or stick to bottom of the plates. The conclusion was that at dilutions at which dispersant would be 

used in the ocean it would be unlikely to be able to distinguish the dispersant’s fluorescence from 

background seawater/oil fluorescence.

Hyperspectral analysis was also performed to determine whether more complex spectral profiles might be 

useful. Using a contrast dye in the water allowed visualization of the dye in a more discriminatory manner 

than the oil. However, dispersant and oil emulsions quickly blocked the imaging system. The available 

system only went to ~1100 nm and most infrared testing of oil occurs at ~3300 nm. With an extended 

spectrum system, it may be possible to measure oil directly. It may also be possible to use a “void in 

water” signal to measure the size of oil droplets at low concentrations, or even to measure droplets in 

higher concentration emulsions using a very short path length.
4.42

4.4 Examine the Effect of Deep Water Conditions on Cell Growth and 

the Biodegradation of High Concentrations of Oil

Compared with studies at atmospheric pressure, only a very small number of studies have examined the 

effect of high pressure on cellular metabolism (Kumar and Libchaber 2013; Marietou and Bartlett 2014; 

Follonier et al. 2012). Fewer still have examined the biodegradation of oil under high pressure (Schedler 

et al. 2014; Juarez et al.2015; Scoma et al. 2016). In the deep ocean5

, high pressure is accompanied by 

cold temperatures (3–4°C), a lack of sunlight, and other confounding factors that may include elevated 

salinity, reduced oxygen, and less mixing energy than in wave- and wind-driven surface waters. Each of 

these factors has the potential to slow down microbial growth and metabolism, particularly on complex 

carbon sources. 

Microorganisms in the deep pelagic ocean are typically slow-growing heterotrophic and 

chemoautotrophic organisms that live off of detritus and other nutrients sourced from the upper water 

column or gradients created by thermal or chemical seeps on the ocean floor. Many of the organisms 

found in the deep ocean are also believed to be transient; i.e., transported to the deep ocean from the 

upper water column attached to sinking particulates. Studies have also shown that many organisms from 

surface environments are capable of growth at elevated pressures (Kumar and Libchaber 2013; Marietou 

and Bartlett 2014; Scoma et al 2016) and upwelling events bring microbial communities from the deep 

ocean up to the surface (Kerkhof et al. 1999), thereby adding to the diversity of these environments.

A set of experiments was designed to explore the relative importance of temperature, pressure, dispersant 

concentration, and droplet size as independent variables on microbial growth and metabolism. The effects 

of dispersants and droplet size were described previously in Chapter 2.0. Studies concerning the 

metabolism of hydrocarbons have typically used very low concentrations of oil (e.g., Schedler et al. 

[2014]used <2 mM; McFarlin et al. [2014] used 2.5 and 15 ppm) to facilitate the monitoring of the 

hydrocarbons and to control the suspension of hydrocarbons in the test chamber. A key objective of this 

study that was established by the sponsor was to examine oil degradation with >1% (v:v) crude oil in 

seawater with little mixing energy. Such conditions would likely occur in close proximity to a natural 

seep or leak plume.

4.4.1 Microbiology: Strain Selection

The objective was to use a natural consortium of microorganisms from seawater rather than to rely on a 

single organism or specified mix that would necessarily fail to represent the genetic and metabolic 

diversity of the natural environment. However, natural microbial communities collected at a single 

location constantly vary in composition due to many factors, including seasonal, tidal, and diurnal cycles. 

This variation could introduce inconsistencies between experiments started on different dates using 

inocula freshly collected from the field each time. Therefore, to maintain consistency across all 

experiments, microbes were collected from 252 L of seawater at PNNL-MSL through serial filtration onto 

0.45 μm and 0.22 μm filters. The microorganisms were then resuspended from the filters into 2520 mL of 

seawater + 2520 mL 50% glycerol. The suspension was then divided into both 0.8 mL and 40 mL sample 

tubes that were frozen at -80°C to create a supply of virtually identical inocula. The preparation also 

provided a 50x concentration factor to help when inoculating a large volume of sterile medium. A frozen 

sample was used to inoculate sterile growth media to demonstrate the viability of the frozen organisms.

Another was used to inoculate an oil-containing sample of seawater to show that at least some of the 

microorganisms were in fact capable of consuming oil.

 5 Microbiologists generally define the deep ocean as greater than 1000 m (Jannasch and Taylor 1984; Prieur 2011)
4.43

A DNA fingerprint of the frozen stock was also generated to create a profile of the prokaryote and 

eukaryote content. The fingerprint method used polymerase chain reaction (PCR) amplification of a 

hyper-variable region of the small subunit rRNA encoding domain to generate DNA fragments of 

different lengths for the different organisms in the sample. The presence of a fragment identifies the 

presence of a corresponding organism and thus the collection of fragments represents the community. 

This allowed for tracking and comparison of changes in microbial content during the biodegradation tests.

In some instances where tighter control of variables was desired, cultures of single, pairs, or triplets of 

isolated species were used. Two bacteria, A. borkumensis (ATCC 700651) and P. putida (ATCC 700007),

and one fungus, H. resinae (ATCC 22711), were used. All are known to metabolize petroleum products. 

A. borkumensis is globally distributed in the ocean and has been isolated in the Gulf of Mexico (Kosita et 

al. 2011). Strains of P. putida and H. resinae have also been isolated from marine environments, but may 

not have been specifically identified in Gulf of Mexico isolations to date. Both a bacterium and a fungus 

were used to consider the effects on prokaryotes and eukaryotes and differences between unicellular 

species (A. borkumensis) that might be expected to only colonize the surface of an oil droplet and a 

filamentous fungus potentially capable of extending mycelia to penetrate into droplets of oil or tar.

The initial plan was to conduct a series of studies of emulsified oil in seawater using different 

concentrations of dispersant and under different pressures. Numerous studies have examined the effect of 

these and other factors on very low concentrations of oil in seawater, with typical values of parts per 

million and below (Prince et al. 2013; McFarlin et . 2014; Kleindienst et al. 2015; Schedler et al. 2014).

In this study, the objective was to examine degradation using much higher concentrations of oil—parts 

per thousand or parts per hundred—and to use low-mixing conditions. The study described in Chapter

2.0, and the work of others have generated inconsistent results concerning the effect of dispersants on 

cellular growth and metabolism, particularly on oil (Kleindienst et al. 2015; Prince et al. 2016; 

Kleindienst et al. 2015; Macias-Zamora et al. 2014; Prince and Butler 2014). Consequently, a decision 

was made to not proceed with further studies concerning dispersant effects. 

Oil sticking to the inner walls of the Parr reactors and forming a surface slick remained problematic. A 

similar effect was observed during another study for BSEE that examined the use of an absorbant-like 

particulate material as a means to deliver microorganisms and promote the biodegradation of oil. One 

observation from that study was that because microorganisms did not degrade the small quantities of oil 

that formed a slick on the sides of glassware and on the water surface slick as quickly as oil droplets in 

suspension, the non-degraded slick oil confounded the ability to conduct high sensitivity measurements of 

hydrocarbon degradation in the water column (Bonheyo et al. 2017). Due to time and funding constraints 

and in consultation with BSEE, a decision was made to perform a literature review regarding hyperbaric 

microbiology and the impact of pressure on metabolism and to conduct an experiment comparing the 

relative impact of pressure and temperature on the growth of microorganisms from surface environments 

(i.e., that may be introduced into a deep water setting). 

Each liter of growth medium consisted of 1 L of sterile filtered seawater supplemented with 1 g pyruvate, 

50 mg yeast extract, 5 mg tryptone, and 50 mg NH4Cl. The 2 liter Parr reactors were filled with 1.6 L of 

growth medium and inoculated with 40 mL of the frozen seawater microbial community and 10 mL of an 

overnight A. borkumensis culture. The reactors were then sealed and incubated at either room temperature 

(~17.8°C) or 4°C. For each pair of temperatures, one reactor was kept at atmospheric pressure and 

another at 2500 psi (equivalent to ~1700m depth). The mixing impellers were used to provide gentle 

mixing. Samples were collected every 24 hr to measure turbidity (O.D.600), at which time pressure was 

first slowly released from the pressurized reactors, a sample was withdrawn, and then the system was 

repressurized. This allowed for fresh gas exchange on a daily basis.
4.44

With the exception of the atmospheric pressure culture at 4°C, all other cultures showed signs of growth 

within 24 hr. Not surprisingly, the culture maintained at atmospheric pressure and room temperature 

exhibited the fastest growth and achieved the highest optical density reading, which peaked at 48 hr at 

0.179. Surprisingly, the unpressurized sample at 4°C showed no increase in O.D. throughout the test. The 

growth profile of the samples pressurized at 2500 psi were all similar, regardless of temperature: the 4°C 

culture reached an O.D. of 0.020 after 24 hr then fell slightly to 0.019, whereas the room temperature 

sample reached an O.D. of 0.018 at 24 hr and 0.019 after 48 hr. Samples spread onto agar plates and 

incubated at 18°C showed unidentifiable, but mixed colony morphotypes.

4.5 Characterize the Effects of Pressure, Time, DOR, and Oil:Water 

Ratio on Droplet Size

Dispersants reduce the interfacial tension between the oil and water, permitting small droplets of oil to 

exist without coalescence. A number of factors influence droplet size and the potential for coalescence;

among these are the viscosity and density of the fluids, surfactant concentration, dilution of the oil in 

water, and interfacial shear (Auflem et al. 2001). 

As described in Section 4.1, an investigation was conducted to determine whether pressure affected oil 

droplet size over a range of DORs. A range of oil in water concentrations was tested using the small view 

cells for imaging, but the oil was either not visible, or quickly obscured the windows. Greater success was 

found using a series of simulated blowouts to characterize droplet sizes (described in Section 4.6) and we 

were instead tasked with characterizing the viscosity and interfacial tension of the oil and dispersants.

4.5.1 Oil and Dispersant Physical Characteristics: Rheometrics

To understand how the dispersants (Finasol OSR 52 and Corexit EC9500) and crude oils (ANS and 

Macondo) may behave in a blowout scenario we obtained rheology data on each material. One factor that 

was examined was whether matching the temperature and density of the dispersant with the ejected oil 

had any effect on the extent of oil and dispersant mixing and thus the emulsification of the oil. Although 

data for each of the materials could be found online, experiments were conducted to obtain high-quality 

density and viscosity data as a function of temperature, recognizing that age (weathering, degassing), and 

batch (dispersant) or date of extraction (oil) can influence the properties of the materials The initial 

rheological data were obtained through automated sample analysis at the Southwest Research Institute

(SwRI, San Antonio, TX). SwRI was able to deliver density values for each of the four materials at 20°C 

and at 70°C (except for Macondo, which had too much off-gassing to obtain an accurate density 

measurement) as well as dynamic (absolute) viscosity from 4°C to 100°C at a shear rate of 100/s, 

consistent with previous literature. It is necessary to maintain consistency between tests because these 

materials are non-Newtonian and thus instead of exhibiting a consistent dynamic viscosity no matter what 

the applied shear force, these materials exhibited shear-thinning behavior (Figure 4.26).
4.45

Figure 4.26. Non-Newtonian shear-thinning behavior exhibited by Corexit EC9500 during a viscosity 

vs. shear rate sweep. Viscosity is shown in mPascal-seconds; 1 mPa-s equals 1 cP.

Both stock samples as well as weathered samples were evaluated using a single shear rate for all 

samples as opposed to different rates for fresh vs. weathered samples. As can be seen in Figure 

4.26 however, this choice will not necessarily result in a large change in the measured viscosity,

because at low shear rates the samples are close to Newtonian. During a blowout scenario, shearthinning behavior may drastically change the oil-dispersant-water interactions as the viscosity of 

each sample reaches that of water (1.61 cP), a Newtonian fluid, during high stress. This behavior 

may increase phase separation, thereby decreasing dispersant effectiveness, or it could minimize 

interfacial tension between the oil and dispersant to ultimately increase dispersant effectiveness.

Of the four materials tested, the viscosity of Corexit EC9500 was approximately 10–20 times higher than 

the other materials (Figures 4.27, 4.28, 4.29, and 4.30). Finasol OSR 52 had an average dynamic viscosity 

of 223.67 cP at 4°C. Corexit EC9500 had an average dynamic viscosity of 2057 cP at 4°C. ANS crude oil 

had an average dynamic viscosity of 110.33 cP at 4°C. Macondo crude oil had an average dynamic 

viscosity of 70.1 cP at 4°C. While this is the lowest viscosity of the samples tested, it is still ~35x that of 

raw seawater, which is 1.61 cP at 5°C and 33 ppt.
4.46

Figure 4.27. Finasol OSR 52 viscosity vs. temperature curve. Testing performed by SwRI.

Figure 4.28. Corexit EC9500 viscosity vs. temperature curve. Testing performed by SwRI.

0

50

100

150

200

250

300

cP



0 10 20 30 40 50 60 70 80 90 100 °C 110

Temperature T

Finasol OSR52

Viscosity Vs. Temperature Curve

Anton Paar GmbH

Finasol OSR52 1

 Viscosity

Finasol OSR52 2

 Viscosity

Finasol OSR52 3

 Viscosity

0

200

400

600

800

1,000

1,200

1,400

1,600

2,000

cP



0 10 20 30 40 50 60 70 80 90 100 °C 110

Temperature T

Corexit 9500

Viscosity Vs. Temperature Curve

Anton Paar GmbH

Corexit 9500 1

 Viscosity

Corexit 9500 2

 Viscosity

Corexit 9500 3

 Viscosity
4.47

Figure 4.29. Alaska North Slope crude oil viscosity vs. temperature curve. Testing performed by SwRI.

Figure 4.30. Macondo crude oil viscosity vs temperature curve. The Macondo sample was tested four

times because the first test yielded an anomalous result (blue line). Testing performed by 

SwRI.

The measured density of each material is given in Table 4.10. Raw seawater at 20°C and 33 ppt 

salinity has a density of 1.023 g/cm3 at the surface and a density of 1.036 g/cm3 at 4°C and 35 ppt 

salinity at a depth of 2000 m. Of the four materials tested, only Corexit EC9500 had a density 

greater than that of seawater. Density of a material is proportional to the temperature of the 

material and, while density measurements were not made at 70°C by SwRI, the Radwag USA 

(Miami, FL) density determination kit was used to track the Macondo oil density as it cooled 

from 52.5°C to 19.5°C (Figure 4.31). The value obtained at 20°C (0.8575) has a percent 

difference equal to 0.17% derived from the SwRI data confirming the accuracy of the results.

0

10

20

30

40

50

60

70

80

90

100

120

cP



0 10 20 30 40 50 60 70 80 90 100 °C 110

Temperature T

Alaska North Slope Crude

Viscosity Vs. Temperature Curve

Anton Paar GmbH

Alaska North Slope Crude 1

 Viscosity

Alaska North Slope Crude 2

 Viscosity

Alaska North Slope Crude 3

 Viscosity

0

10

20

30

40

50

60

70

80

cP



0 10 20 30 40 50 60 70 80 90 100 °C 110

Temperature T

Macondo

Viscosity Vs. Temperature Curve

Anton Paar GmbH

Macondo Crude 1

 Viscosity

Macondo Crude 2

 Viscosity

Macondo Crude 3

 Viscosity

Macondo Crude 4

 Viscosity
4.48

Table 4.10. Density measurements of Finasol OSR 52, Corexit EC9500, Alaska North Slope crude oil,

and Macondo crude oil obtained at 20°C and at 70°C.

Density (g/cm3) Measurements

Sample 20°C Std. Dev. 70°C Std. Dev.

Finasol OSR 52 1.0045 0.0001 0.9650 0.0003

Corexit EC9500 1.0635 0.0000 1.0249 0.0000

Alaska North 

Slope Crude Oil

0.8909 0.0002 0.8562 0.0002

Macondo Crude 

Oil

0.8561 0.0001 Not Measured Not Measured

Figure 4.31. Graph illustrating the increase in density of Macondo crude oil while cooling from 52.5°C 

to 19.5°C.

4.6 Characterize the Effect of Pressure Drop and Temperature on Oil 

Droplet Size

To better understand various characteristics of oil blowouts at depth, a small-scale system was developed

to simulate the ejection of oil at a higher initial pressure than the hydrostatic pressure of the marine 

environment. Specifically, tests were designed to examine droplet size distributions immediately 

following ejection and to explore whether different methods of dispersant injection would affect the size 

distribution. The experimental system developed used a pressurized 2 L Parr reactor to eject oil (starting 

pressure of 2900 psi) into an aquarium filled with unfiltered seawater. Initial tests attempted to use the 

Teledyne syringe pumps, which are rated to 7500 psi and can be used to generate accurate pressures in 

closed systems, but pressure could not be sustained during the injection.

In several of the planned tests, heated oil and or dispersant was to be ejected into cold seawater. One 

possible method for tracking the movement of the fluids would be to monitor temperature throughout the 

system. Thermal imaging cameras were unsuitable for the task due to the absorption and poor 

transmission of infrared radiation in water and shielding by the aquarium glass. Instead, a thermal probe 

sensor array was developed to track the temperature of fluids in the aquarium (Figure 4.32), which is 

described in more detail below. Visible spectrum videography was performed using the GoPro cameras, 

0.8250

0.8300

0.8350

0.8400

0.8450

0.8500

0.8550

0.8600

0 10 20 30 40 50 60

Density (g/cm^3) 

Temperature (°C) 

Macondo Crude Oil Density vs Temperature
4.49

still images for assessing droplet sizes were taken using the Canon EOS camera, and lighting was 

provided by two LimoStudio 500 watt equivalent, 5500 K temperature light-emitting diode (LED) light 

banks (Figure 4.33). The aquarium setup was placed inside a chemical safety cabinet for ventilation and 

containment.

Figure 4.32. Test tank showing a 4×3 thermal probe array. Each wire had three thermistors along its 

length to interrogate the top, middle, and bottom depths of the tank.

Natural, unfiltered seawater (33 ppt) was stored in 5 gallon pails in a walk-in refrigerator until needed to 

provide chilled seawater of 7.8°C to 8.2°C during the experiments. When heating was needed for the oil, 

a Parr reactor containing the oil or oil and dispersant premixture was stored overnight in a 65°C 

incubator. The measured temperature of the oil or oil:dispersant mix during the injection was typically 

~55.4°C because of rapid cooling during setup or ineffective heating in the incubator. This still provided a 

significant temperature differential with the cold water.

Oil was ejected through 1⁄4 in. OD (6.35 mm) 0.172 in. ID (4.37 mm) high-pressure stainless steel tubing. 

Dispersant was ejected through 1

/8 in. OD (3.18 mm) 0.069 in. ID (1.27 mm) stainless steel tubing. The 

duration of the blowout was 2–3 seconds during which 121 g to 165 g of oil and gas were released. Oil in 

the Parr reactor was pressurized either by injecting the gas through the oil, which resulted in considerable 

solvation of the gas, or by injecting the gas into the head space above the oil, which resulted in less gas 

dissolving into the oil (measured by weight gain during pressurization).
4.50

Figure 4.33. Laboratory setup for the blowout tank.

4.6.1 Sensor Array Development

An inexpensive sensor array system for tracking temperature was developed using thermistors (MF58H 

10K) for the sensing element. Thermistors were chosen over thermocouples because they are easily 

multiplexed, allowing a single analog-to-digital converter (ADC) to read as many thermistors as there are 

digital output pins. An ATmega2560 microcontroller was chosen to drive the system because it has a 10-

bit ADC and up to 56 digital output pins. The system was developed to have 36 thermistors, thereby 

allowing for future expandability (up to 56 thermistors can be driven). The thermistors were arrayed in a 

4×3×3 (depth, width, height) arrangement. Clamps to attach lead weights to the data cables were designed 

and 3D printed. Software was developed in C to run on the microcontroller, which returned temperature 

data a universal serial bus. A host script was written in Python to run on a PC, which collected

temperature data from the microcontroller. The host script also operated a camera (see Section 4.6.3 on 

particle size analysis), which took photos at regular intervals. Multiple photos were taken in quick 

succession during the initial 10 s of the blowout, and then the rate of photography decreased over the 

remaining 10 min of the experiment. Photos and temperature data were synchronized to the PC’s clock.

Lighting was provided by a pair of LimoStudio 500-watt equivalent, 5500 K temperature LED lights.

4.6.2 Results from the Thermal Array

Results indicated a change in temperature in the water after the blowout, but the spatial resolution was not 

high enough to provide a good picture of the mixing. Both heating and cooling were observed in the array 

from the injection of warm or hot oil and the release of rapidly expanding gas (Figure 4.34).
4.51

Figure 4.34. Sample data from the thermal array indicating the temperature of the surrounding water. 

Sensors closest to the point of oil injection recorded a rise in temperature, but a few sensors 

farther from the point of injection recorded a drop in temperature.

4.6.3 Blowout Testing: Particle Size Analysis

Blowout particle size analysis was performed using images taken with a Canon EOS 70D digital camera 

with the attached 100 mm macro lens (f/16, ISO 6400). The camera was located 38 cm from the tank. 

Images were obtained as a time series that started at a time coinciding with the initiation of a simulated oil 

blowout and ended 10 minutes after initiation. Particle size analysis was performed using images 

collected at 5 s after blowout to assess blowout dynamics and again at 10 min after blowout (well after the 

initial turbulent release) to assess the evolution of the size distribution and to maintain consistency and 

correlation of data with research using swirling and baffled flask tests, in which data are collected after 10 

minutes of mixing. 

Images were scaled to a submerged ruler affixed to a polytetrafluoroethylene (PTFE) board. The PTFE 

board was placed 7 cm from the tank wall to reduce the overall path length through which a portion of the 

oil plume was imaged. Even with this reduced path length, blowouts with increased concentrations of oil 

or highly dispersed (small particle cloud) could easily obscure the FOV such that direct imaging was 

ineffective (Figure 4.35).
4.52

Figure 4.35. Image acquired at 10 min after blowout (136 g of 60°C Macondo oil at 2400 psi [390 psi 

drop from blowout] into 63226.5 g of raw seawater [33 ppt; 11.8°C]). The particles are too 

fine (sub 0.01 μm) and too dense to effectively image and quantitate oil distribution in the 

water column. One of the temperature sensors emplaced in the water tank is barely visible 

in the center-right of the image.

As described in detail in Section 3.0, images with sizable particles were converted to 8-bit grayscale in 

Adobe Photoshop and then the particle size distribution was calculated using Fiji (Image J with particle 

size analysis plugin). The submerged ruler was used to scale the resultant images. Identified particles 

were required to have a circularity of at least 0.30 to prevent overlapping particles from being measured 

as a single large particle.

Figure 4.36 shows sample images collected from a test blowout analysis of approximately 50 g of 60°C 

Macodo crude oil released at 2200 psi into 60,000 g of raw seawater at 12°C.

Qualitatively tracking the blowout plume in the tank, large particles of oil can be seen within a cloud of 

smaller particles. When the large particles reach the surface of the water, some spread out along the 

surface forming a thick layer, but the blowout turbulence induces some mixing in the tank as well. This 

mixing of the large and small particles resulted in a distribution of fine oil particles in the water column 

10 min after blowout (see Table 4.11 and data described later in Figure 4.7 through Figure 4.15 in Section 

4.6.6). At both 5 s and 10 min after blowout droplets were measured at the smallest size discernable with 

this imaging system; however, the maximum particle size observed at 5-seconds had a cross-sectional 

area almost 2 orders of magnitude higher than the largest particle observed at 10-minutes.
4.53

Figure 4.36. A) A subsection of the camera field of view imaged at 5 s post-blowout and B) a subsection 

of the camera field of view imaged at 10 min post-blowout.

Table 4.11. Cross-sectional area of oil particles at 5 s and 10 min after a test blowout with no dispersant.

Values in mm2 5-Second Image 10-Minute Image

Mean Area 0.003 0.001

SD 0.021 0.001

Min 4.53E-04 4.53E-04

Max 1.113 0.033

4.6.4 Dispersant Injection

The timing, and placement of dispersant injection was critical to facilitate mixing with the oil and 

seawater and ultimately dispersion of the oil throughout the water column. Figure 4.37 shows a scenario 

whereby little to no mixing may occur even though the dispersant and oil are co-injected at approximately 

the same time and the same XYZ coordinates in a water column. In this instance, the rapid degassing of 

the oil prevented the dispersant from mixing with the oil.
4.54

Figure 4.37. Time series of images during a Macondo crude oil blowout (43 grams released at 60°C and 

2400 psi) with coinjection of 1.0 g of Corexit EC9500 (17.4°C and 1 atm) into raw seawater 

(11.8°C and 1 atm). The still image A) represents Time 0 at which point the blowout had

initiated and the Corexit was injected; B) is at a time approximately 0.5 s after blowout 

initiation; and C) is an image captured approximately 1 s after blowout initiation. The 

Corexit dispersant is encircled in red and tracked through the three images.

As can be seen in this small-scale blowout, injecting dispersant at the same XYZ coordinate as the 

blowout was initiated did not result in efficient mixing. The dispersant did not mix with the oil or disperse 

into the water column. It was instead carried up through the water column as a single large droplet with 

the gas. Upon reaching the surface of the water the droplet of dispersant was noted to break up into 

smaller droplets that later dispersed surface oil. If the oil and dispersant were evenly mixed throughout 

the water column the concentration of oil would have been 43 g in 51,419 total grams of solution or 

836.27 ppm and the DOR would have been 1:43.

4.6.5 The Tests

A number of preliminary tests were conducted to optimize the photography, process, location, and 

orientation for releasing the oil and injecting dispersant. The simulated blowouts described in section 

4.6.6 were performed to assess the impact of temperature, DOR, and method of introducing dispersant 

into the blowout. In instances where the oil and dispersant were “premixed,” this was done by adding 

dispersant to a 40 mL subvolume of the oil in a 50 mL centrifuge tube, mixing with a vortex, then adding 

that mix to the remaining oil and stirring to mix in the dispersant. This created an idealized mixing 

situation.

4.6.6 Test Results

Table 4.12 shows the test parameters that were used for the simulated blowouts and Figure 4.38 – Figure 

4.46 provide the droplet size distributions (following the methods used by Brandvik et al. 2013 and 

Johansen et al. 2013) at approximately 5 s after the start of the blowout and again after 10 min after 

blowout start. In each case, the seawater was chilled to an initial temperature of 4°C. Dispersant was 

either premixed with the oil or injected into the oil plume to allow mixing. Tests analyzed whether it was 
4.55

important to match the temperature of the dispersant to the oil to enable better mixing. The Parr reactor 

with the oil and gas was weighed before and after the injection to calculate the amount of material 

injected into the tank.

In all blowout tests, a cloud of atomized oil droplets <10 μm (with many droplets <1 μm) was created and 

lingered for >10 minutes in the tank. In the absence of dispersant, the cloud eventually formed a gravitybased gradient with some clearing at the bottom of the tank and increased density of particles (opaque 

appearance) at the top of the tank. Although the cloud was visually dense, the oil entrained in the cloud 

amounted to a very small volume, typically less than 0.1 percent of the volume of water. Also during the 

tests, rising oil droplets created a slick on the water surface. In the presence of dispersant (co-injected or 

premixed), this slick was discontinuous. With the introduction of dispersant, particularly with premixed 

dispersant, a chemocline envelope formed around the plume that kept the oil:dispersant:entrained 

seawater separate from the surrounding seawater. Despite the initial highly energetic turbulence and 

continued degassing and rising of buoyant droplets, this chemocline remained stable for 30 to 90 minutes 

in the tank, but eventually “collapsed” and oil freely dispersed throughout the tank before density 

differences eventually caused droplets to rise. The initial sinking and spread of the oil droplets was 

presumed to be driven by interfacial tension. However, there was no significant difference in droplet size 

distributions when the dispersant was premixed or co-injected with the oil.

A bimodal distribution frequently resulted when no dispersant was added. This was observed as the cloud 

of small, <800 μm droplets that were very numerous, but represented only a small fraction of the total 

volume of oil. Only a small number of large (>2 mm) droplets are needed to represent a significant 

fraction of the oil released. With or without dispersant, the force of the blowout generated small-sized 

droplets. Additionally, the mode of introducing the dispersant, the ultimate DOR, and any temperature 

differential between the dispersant and the oil did not appear to have any effect. This latter finding is 

encouraging for modelers in that it simplifies the number of parameters that need to be considered when 

building a particle size distribution and spill migration model. 

It is important to note that some or all of the larger droplets released in the absence of dispersant might 

break up into smaller droplets while rising through the water column because of the turbulence from 

continued degassing and the continued upwelling of gas and oil from below. This study did not examine 

the fate of the smaller droplets over an extended period of time. The dispersants may act to prevent 

coalescence of the droplets during the first minutes to hours following oil release and before physical 

separation alone might prevent droplets from contacting each other. 
4.56

Table 4.12. Test parameters used for the simulated blowouts.

(a)

Test # 2 2.2 3 3.2 3.3

Seawater Temp 4oC 4oC 4oC 4oC 4oC

Oil Temp 60oC 16.8oC 16.8oC 16.8oC 16.8oC

Dispersant Temp 60oC 16.8oC none none none

Injection Style premixed premixed NA NA NA

Pressurization Headspace Headspace in oil Headspace Headspace

Starting Pressure 2370 2300 2390 2290 2330

Ending Pressure 1705 1950 1920 1890 1940

Drop 665 450 470 400 390

Dispersant 1.575 1.32 0 0 0

Oil + Gas released 165 132 161 149.5 139 g

Oil Released 157.5 132 161 149.5 139 g

DOR 1:100 1:100 NA NA NA

Seawater 62013.5 63215.5 64519 64948.5 64433.5

Test # 4 4.2 5 6

Seawater Temp 4oC 4oC 4oC 4oC

Oil Temp 60oC 60oC 60oC 60oC

Dispersant Temp none none 4oC 60oC

Injection Style NA NA Coinjection Coinjection

Pressurization Headspace Headspace Headspace Headspace

Starting Pressure 2345 2410 2300 2370 psi

Ending Pressure 1775 1930 1850 1705 psi

Drop 570 480 410 665 psi

Dispersant 0 0 2.13 g 1.43 g

Oil + Gas released 151 g 121 g 131.5 g 157.5 g

Oil Released 151 g 118.5 g 128 g 165 g*

DOR NA NA 1:60 1:110

Seawater 64020.5 63994 63106 62014.5

(a) Blue (4oC) and red (60oC) font is used for ease of identifying temperature combinations

*Likely due to continued off-gassing from reactor.

NA = not applicable.
4.57

Figure 4.38. Droplet size distributions for blowout #2 at approximately 5 s after the start of the blowout and again after 10 min after blowout start.
4.58

Figure 4.39. Droplet size distributions for blowout #2-2 at approximately 5 s after the start of the blowout and again after 10 min after blowout 

start.
4.59

Figure 4.40. Droplet size distributions for blowout #3 at approximately 5 s after the start of the blowout and again after 10 min after blowout 

start.
4.60

Figure 4.41. Droplet size distributions for blowout #3-2 at approximately 5 s after the start of the blowout and again after 10 min after blowout 

start.
4.61

Figure 4.42. Droplet size distributions for blowout #3-3 at approximately 5 s after the start of the blowout and again after 10 min after blowout 

start.
4.62

Figure 4.43. Droplet size distributions for blowout #4 at approximately 5 s after the start of the blowout and again after 10 min after blowout 

start.
4.63

Figure 4.44. Droplet size distributions for blowout #4-2 at approximately 5 s after the start of the blowout and again after 10 min after blowout 

start.
4.64

Figure 4.45. Droplet size distributions for blowout #5 at approximately 5 s after the start of the blowout and again after 10 min after blowout 

start.
4.65

Figure 4.46. Droplet size distributions for blowout #6 at approximately 5 s after the start of the blowout and again after 10 min after blowout 

start.
4.66

4.7 Explore the Effect of Sediments on Droplet Size

Organic and inorganic particles found in crude oil and the water column may have an impact on droplet 

size during a blowout. The formation of stable oil-particle-aggregates (OPAs) in coastal environments 

following a spill is well-documented (Fitzpatrick et al. 2015; Lee et al. 2003; Khelifa et al. 2002; StoffynEgli 2002; Le Floch et al. 2002). This phenomenon is, however, typically noted with weathered oil, and in 

particular with oil that has sunk to the benthos. One important factor in the formation of these aggregates 

along the shoreline is turbulence, and the turbulent discharge of oil from a leaking well might promote the 

formation of aggregates. 

The potential formation of OPAs during a deep ocean release would have important ramifications for the 

fate and transport of the oil. OPAs have a lower interfacial tension with water than does oil alone, and are 

thus less likely to coalesce (Lee 2002). OPAs also tend to have a higher specific gravity and to sink in 

water (Fitzpatrick et al. 2015). OPAs may form with a wide range of sediment and mineral types, or with 

phytoplankton, and will form when the concentration of sediment is as low as 100 mg sediment per liter 

of seawater (Fitzpatrick et al. 2015).

To examine the impact that particulates might have on freshly ejected (unweathered) oil, the initial plan 

was to mix sediments with crude oil samples to be ejected at high pressure into the seawater test tank. 

However, safety concerns arose about the potential for creating a blockage in the pressure lines. Instead, 

200 μL of oil with and without 50 mg of fine sediment (diatomaceous earth) was added to 200 mL of 

seawater (1:1000 oil to water ratio) in 500 mL glass beakers, and mixing energy was provided by a 

magnetic stir bar at 240 rpm. The oil-sediment-seawater mix was agitated for 5 min, and then the mixing 

energy was stopped to allow for photography (within a few seconds of stopping) and particle size 

determination. Photos of the water surface and potential oil slick formation were taken after 2–3 min 

without mixing. Mixing was restored and then 4 μL of Corexit 9500 was added to each beaker to achieve 

a DOR of 1:50. Mixing continued for 5 min at which point a second set of photographs were taken. 

Mixing was again restored and an additional 4 μL of Corexit was added to achieve a DOR of 1:25. A 

control flask with the sediment remained turbid when mixing ceased; a large portion of the diatom 

frustules are of low enough density to remain suspended for at least a few minutes without active mixing 

and would have a rapid impact on causing an adhered portion of the oil to rise or sink in the water 

column.

The results (Table 4.13 and Figures 4.47 through 4.49) show that prior to the addition of the dispersant, 

samples with and without sediment had a fairly similar droplet size distribution in the smaller bin sizes, 

but the sample with no sediment had larger droplets representing a significant fraction of the total volume. 

With the addition of Corexit at a DOR of 1:50, however, the samples with sediment had a distinctly lower 

size distribution with a maximum diameter of 62 μm, while the sample without sediment had droplets 

with a maximum diameter of 359 μm. Similarly, the mode median diameter (representing a large fraction 

of the total volume of oil) of the sediment containing sample was 12 μm, while that of the sample without 

sediment was 202 μm. With a DOR of 1:25, the sediment sample had a bimodal distribution with larger 

droplets, up to 160 μm, while the sediment-free sample still had larger droplets of up to 257 μm and a 

more even distribution (not including the largest bin size). Droplet sizes in the 10 to 20 μm range may be 

associated with the diatomaceous earth, whose particles fell in that size range. The smaller sizes may be 

due to oil associating with the diatom frustules to form OPAs, or perhaps due to some physical action in 

the mixing in which the diatom frustules “bombard” and break apart the droplets. Photographs of the 

beakers (Figure 4.50 and Figure 4.51) show that the stir bar in the beaker with the sediment was much 

cleaner than the stir bar in the beaker without sediment, which had a very apparent coating of oil. 

However, images of the water surface taken a few minutes after the mixing was stopped showed that in 
4.67

the absence of sediment, much of the oil with dispersant remained in suspension and did not readily form 

a slick. In the presence of sediment, the water column remained an orange-tinted turbid color, but oil 

droplets began to aggregate at the surface to form a slick. This would appear to fit the bimodal 

distribution with the larger droplets rising to the surface, but it was surprising that the largest droplets in 

the sediment mix were ~40% smaller than the largest droplets in the flask without sediment.

Table 4.13. Summary of droplet size data (mm).

Minimum Diameter Maximum Diameter

Treatment

Without 

Sediment

With 

Sediment Treatment

Without 

Sediment

With 

Sediment

No Corexit 0.01045 0.01022 No Corexit 0.25231 0.12866

1:50 DOR 0.01045 0.01022 1:50 DOR 0.35860 0.06180

1:25 DOR 0.01057 0.01033 1:25 DOR 0.25731 0.15958

Mean Diameter Mode Median Bin Diameter

Treatment

Without 

Sediment

With 

Sediment Treatment

Without 

Sediment

With 

Sediment

No Corexit 0.01264 0.01285 No Corexit 0.23816 0.01245

1:50 DOR 0.01869 0.01233 1:50 DOR 0.20182 0.01245

1:25 DOR 0.01426 0.01193 1:25 DOR 0.23816 0.14493

Geometric Mean

Treatment

Without 

Sediment

With 

Sediment

No Corexit 0.01210 0.01222

1:50 DOR 0.01438 0.01186

1:25 DOR 0.01313 0.01156

While this study demonstrated that sediments entrained in oil during a blowout can alter droplet size 

distributions when compared to oil without sediment, the results are based upon a single sediment type. 

The experiments used diatomaceous earth, a silicate, with particles ranging from10 to 20 μm in diameter. 

Further studies might examine particles of varying sizes and compositions, including carbonates, other 

silicates, organic particles, and mixtures. Crude oil adheres to virtually any surface, but may preferentially 

adhere to other sediment types. Intact diatom frustules are hollow and have a low density compared with 

solid silicates or other mineral types. The frustules also have very intricate nano-scale surface features 

that could potentially increase or decrease oil adsorption and retention. Both the chemistry and surface 

structure of sediment particles would likely affect the formation of OPAs during blowouts. The density of 

the sediment would also likely affect the migration of the oil.
4.68

Figure 4.47. Size distributions for samples with and without sediment and no dispersant.

0

5

10

15

20

25

30

35

"< 2.5

3.215

4.48

6.2358.68512.0916.84

23.455

32.66

45.475

63.33

88.195

122.815171.025

238.16

331.655

461.85

643.155895.635

1247.2

1736.852418.65

3368.14690.36531.69095.6

Volume fraction (%)

Median Bin Size (um)

Droplets without sediment, no Corexit

0

5

10

15

20

25

"< 2.5

3.215

4.48

6.2358.68512.0916.84

23.455

32.66

45.475

63.33

88.195

122.815171.025

238.16

331.655

461.85

643.155895.635

1247.2

1736.852418.65

3368.14690.36531.69095.6

Volume fraction (%)

Median Bin Size (um)

Droplets with sediment, no Corexit
4.69

Figure 4.48. Size distributions for samples with and without sediment and a DOR of 1:50.

0

5

10

15

20

25

30

"< 2.5

3.215

4.48

6.2358.68512.0916.84

23.455

32.66

45.475

63.33

88.195

122.815171.025

238.16

331.655

461.85

643.155895.635

1247.2

1736.852418.65

3368.14690.36531.69095.6

Volume fraction (%)

Median Bin Size (um)

Droplets without sediment, DOR=1:50

0

5

10

15

20

25

30

"< 2.5

3.215

4.48

6.2358.68512.0916.84

23.455

32.66

45.475

63.33

88.195

122.815171.025

238.16

331.655

461.85

643.155895.635

1247.2

1736.852418.65

3368.14690.36531.69095.6

Volume fraction (%)

Median Bin Size (um)

Droplets with sediment, DOR=1:50
4.70

Figure 4.49. Size distributions for samples with and without sediment and a DOR of 1:25.

0

5

10

15

20

25

"< 2.5

3.215

4.48

6.2358.68512.0916.84

23.455

32.66

45.475

63.33

88.195

122.815171.025

238.16

331.655

461.85

643.155895.635

1247.2

1736.852418.65

3368.14690.36531.69095.6

Volume fraction (%)

Median Bin Size (um)

Droplets without sediment, DOR=1:25

0

5

10

15

20

25

"< 2.5

3.215

4.48

6.2358.68512.0916.84

23.455

32.66

45.475

63.33

88.195

122.815171.025

238.16

331.655

461.85

643.155895.635

1247.2

1736.852418.65

3368.14690.36531.69095.6

Volume fraction (%)

Median Bin Size (um)

Droplets with sediment, DOR=1:25
4.71

Figure 4.50. Oil suspensions without sediment (A) and with sediment (B) after mixing but prior to the 

addition of Corexit. The stir bar in the flask without sediment became coated with oil and 

remained coated even after the addition of Corexit.
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Figure 4.51. Surface expression of oil without sediment (top) and with sediment (bottom) after the 

addition of dispersant.

4.8 Update BLOSOM (Blowout and Spill Occurrence Model) to 

Incorporate Dispersant Effects on Droplet Size Distributions

Numerical simulations are a critical part of scientific progress when complicated natural systems are 

studied, along with observations at field and measurements in controlled laboratory conditions. 

Simulations allow for the testing of hypotheses that cannot be tested in laboratories. Likewise, the 

comprehensive information in space and time obtained as a numerical solution is simply not possible in 

real-life conditions. These realities have long motivated the development and testing of numerical models 

and will continue to do so into the foreseeable future.

Numerical models are complicated enough that they present several challenges of their own. While 

verification, validation, and confirmation of numerical models of natural systems is impossible (Oreskes 

et al. 1994), their evaluation is fundamentally important (Oreskes et al. 1998). Testing a model is critical 

to making sure that it model is behaving as expected. It also is important to test a model’s validity, in 

particular to avoid a misleading sense of truth that may arise from not paying due attention to the 

limitations that are inherent to all models (Sterman 2994). 

As part of this study, the numerical BLOSOM was upgraded to simulate the addition of dispersants at 

depth during a blowout—a scenario that was first experienced during the Deepwater Horizon accident. 

During the time of the accident, different models were used to track oil at the sea surface to support policy 

decisions and emergency response planning and implementation. However, no model was readily 
4.73

accessible at that time to help evaluate the consequences a priori, or to help guide the process during the 

administration of dispersants. Experiments, observational campaigns, and the development of models 

have resulted in enough progress in our understanding of complicated deep sea blowouts that we can now 

begin to simulate the application of dispersants during a blowout event. This effort—one of the first of its 

kind—implements a dispersant module in BLOSOM and presents some simulations highlighting its 

capabilities and limitations. As mentioned above, understanding the capabilities and limitations of any 

model is an important part of the progress made toward using them efficiently in policy and scientific 

applications.

At the beginning of this effort, BLOSOM already computed a droplet size distribution (DSD) as 

described by Johansen (2003). The construction of BLOSOM’s dispersant module began with the 

addition of the DSD proposed by Johansen et al. (2013; Figure 4.52). Two versions of the Johansen et al. 

(2013) distribution were added to BLOSOM: the original formulation as proposed in the 2013 paper and a 

variant of the droplet distribution with corrected coefficient values as outlined by Brandvik et al. (2014). 

These updated coefficients were described as preliminary in the Scope of Work. With the addition of the 

updated coefficients, BLOSOM provides the capability for comparing the original and updated versions, 

and other DSDs.

Some proprietary oil-spill models are able to simulate the addition of dispersants at depth (e.g., 

OSCAR, MIKE and OILMAP), while freely distributed models are only able to include the use 

of dispersants at the sea surface. Thus, to the best of our knowledge, BLOSOM is the only opensource blowout and oil-spill model capable of simulating the addition of dispersants at depth. The depth 

should only be limited by the bathymetry and ocean model data that is provided.

Figure 4.52. Johansen et al (2013) distributions for oil droplet size (ordinate, fraction mass) as a function 

of droplet size (abcissa, microns) as computed by BLOSOM internally. 
4.74

BLOSOM requires a minimum droplet size value to be supplied and this functionality was already present

in the original model; however, an optional upper boundary on droplet size was also added as a user 

argument. Two preset droplet size ranges, one preset representing droplet sizes in the presence of 

dispersants and another representing sizes in the absence of dispersants, were also added. The values used 

for the presets were taken from Johansen et al. (2013).17 A separate option was added to BLOSOM’s 

settings to allow users to select which droplet model to use to initiate the simulation. 

In addition to adding the option to select the Johansen 2013 DSD, steps were taken to further enhance 

BLOSOM’s dispersant module. To simulate the effectiveness of dispersant application, an efficiency 

coefficient needs to be present, as well as two DSDs: one to apply to untreated oil and one to apply to 

treated oil. For the efficiency coefficient, users can supply their own value, or it can be derived from 

relationships extracted from the literature (see findings below in Section 4.8.1.4). The capability to 

specify a second DSD for treated oil and an efficiency value were both added to BLOSOM. Explicit 

spatiality was introduced by allowing the user to specify regions of dispersant influence—as a circle on 

the surface (simulating surface application) or as a sphere below the surface (simulating subsurface 

application). Any spill parcel or jet/plume control volume that intersects a dispersant application region 

may be marked as having been treated, modifying the DSD according to the selected treated DSD and 

efficiency value.

Several simulations were carried out to compare the PNNL experimental DSDs to those derived from 

literature that have been implemented as part of BLOSOM. A series of BLOSOM simulations were 

carried out: two for each DSD built into BLOSOM and each experimental DSD provided by the PNNL 

experiments. Each DSD was tested under static ambient conditions (i.e., with no ocean currents or wind), 

as well as with hydrodynamic ocean model data derived from the NCOM-AmSeas model for the 

simulated period from May 20, 2010 through July 19, 2010 on 6 hr intervals. For each simulation in the 

constant and hydrodynamic categories, only the selected DSD and droplet size boundaries varied 

according to the input information.

The PNNL experiments fall into two main categories: oil at 16.8°C and 60°C. Within each category, DSD 

from blowouts with dispersant and DSD from blowouts without dispersant were calculated (Figure 4.53

and Figure 4.54). Table 4.14 includes the main experimental characteristics of each blowout. 
4.75

Figure 4.53. Droplet size distributions (fraction mas, ordinate) as a function of droplet size (abcissa, 

microns) for experiments using oil at 16.8°C with application of dispersant (D, left panel) 

and without dispersant (ND, right panel). The details for each experiment can be seen in 

Table 4.14.

Figure 4.54. Droplet size distributions (fraction mas, ordinate) for blowouts with oil at 60°C as a 

function of droplet size (abcissa, microns). The three panels on the left are for distributions 

using the application of dispersant, and the distribution on the right is under comparable 

conditions but with no application of dispersants. The details for each experiment can be 

seen in Table 4.14.
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Table 4.14. Experimental setup for each blowout.(a)

Blowout number 2 2.2 3 4 4.2 5 6

Seawater Temp 4oC 4oC 4oC 4oC 4oC 4oC 4oC

Oil Temp 60oC 16.8oC 16.8oC 60oC 60oC 60oC 60oC

Dispersant Temp 60oC 16.8oC none none none 4oC 60oC

Injection Style premixed premixed NA NA NA Coinjection Coinjection

Pressurization Headspace Headspace in oil Headspace Headspace Headspace Headspace

Starting Pressure 2370 2300 2390 2345 2410 2300 2370 psi

Ending Pressure 1705 1950 1920 1775 1930 1850 1705 psi

Drop 665 450 470 570 480 410 665 psi

Dispersant 1.575 1.32 0 0 0 2.13 g 1.43 g

Oil + Gas 

released 165 132 161 151 g 121 g 131.5 g 157.5 g

Oil Released 157.5 132 161 151 g 118.5 g 128 g 165 g

DOR 1:100 1:100 NA NA NA 1:60 1:110

Seawater 62013.5 63215.5 64519 64020.5 63994 63106 62014.5

(a) Blue (4oC) and red (60oC) font is used for ease of identifying temperature combinations

NA = not applicable.

The oil temperature did not seem to have a strong effect on the droplet distribution; the DSDs for 

blowouts without dispersant (Figure 4.55) and with dispersant (Figure 4.56) were similar. Therefore, two 

distributions were enough to represent the DSDs for when no dispersant is applied (blowout 3 was used 

for this case) and for when dispersant is applied (blowout 2.2 was used for this case). By plotting these 

two representative distributions together, the effect of the dispersant can be evaluated (Figure 4.57).

Figure 4.55. Normalized droplet size distribution (fraction mas, ordinate) as a function of droplet size 

(abcissa, microns) for two experiments (blowouts 3 and 4) without any dispersant applied. 

Gray indicates the intersection of the two distributions.
4.77

Figure 4.56. Normalized droplet size distribution (fraction mas, ordinate) as a function of droplet size 

(abcissa, microns) for two experiments (blowouts 2 and 5) with dispersant applied. Gray 

indicates the intersection of the two distributions.

Figure 4.57. Droplet size distribution (fraction mas, ordinate) as a function of droplet size (abcissa, 

microns) for the blowout without dispersant (cyan) and the blowout with dispersant (red) 

used for BLOSOM simulations. Gray indicates the intersection of the two distributions.

The viscosity of oil has a strong effect on the effectiveness of applied dispersant; specifically, the more 

viscous the oil, the less effective dispersant application will be (ITOPF 2011). Viscosity can be seen as a 

measure of mechanical resistance to mechanical breakup, and how readily a body of oil breaks into 
4.78

smaller droplets under mechanical stress (ITOPF 2011). Because BLOSOM tracks the viscosity of 

simulated crude oil, it can be used as a proxy of the efficiency of applied dispersant. This relationship is 

explored through literature research.

4.8.1 Results: Numerical Simulations

Numerical simulations were conducted for static and hydrodynamic conditions.

4.8.1.1 Static Conditions

The output from BLOSOM simulations using constant ambient conditions was used to confirm that the 

distributions of droplet sizes when using the J2013 (Figure 4.58) and PNNL2017 (Figure 4.59) 

distributions agreed with the distributions obtained in the corresponding studies (Figure 4.52 and Figure 

4.57, respectively). When oil is treated with dispersant, the PNNL2017 droplet radii range between about 

20 and 250 μm, while the treated J2013 distribution ranges between 50 and 1800 μm (red bars in Figures

4.58 and Figure 4.59). When oil was not treated with dispersant, PNNL2017 ranged between 13 and 1700 

μm, while J2013 ranged between 200 and 72,000 μm (cyan bars in Figure 4.58 and Figure 4.59). 

Figure 4.58. Droplet size distributions from Johansen et al. (2013) as implemented in BLOSOM for both 

dispersant-treated and untreated oil.
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Figure 4.59. The droplet size distributions from the experimentally derived observations, as 

implemented in BLOSOM. Both dispersant-treated and untreated oil droplets are 

represented.

4.8.1.2 Hydrodynamic Conditions

When the output from a numerical ocean model is included, a realistic simulation of a deep blowout 

becomes possible. For this purpose, output from the Navy Coastal Ocean Model (NCOM) simulating 

ocean currents during the Deepwater Horizon event (May 20–July 18, 2010) was used. 

Figure 4.60, Figure 4.61, Figure 4.62, and Figure 4.63 show the final outcomes for simulations using the 

J2013 distribution with untreated droplet sizes, the J2013 distribution using treated droplet sizes, the 

PNNL2017 untreated distribution and droplet sizes, and the PNNL2017 treated distribution and droplet 

sizes, respectively. As can be seen in the figures, a much greater amount of oil reached the surface in the 

J2013 simulations (Figure 4.60 and Figure 4.61), while the PNNL2017 simulations (Figure 4.62 and 

Figure 4.63) produced a much sparser surface slick. This discrepancy can be attributed to the fact that the 

PNNL2017 distributions produce droplet sizes that are roughly an order of magnitude smaller than those 

from the J2013 simulations. Both the dispersant-treated simulations (Figure 4.61 and Figure 4.63) show a 

larger concentration of oil below the water surface than their untreated counterparts (Figure 4.60 and 

Figure 4.62). This is expected, because dispersants reduce the size of droplets in an oil plume to a point at 

which they will stay suspended in the water column, thereby increasing the likelihood of colliding with 

the ocean floor (Johansen et al. 2013; McNutt et al. 2012).
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Figure 4.60. Oil blowout as simulated with BLOSOM after a 30-day simulation using the distribution 

from Johansen et al. (2013) for untreated droplet sizes.17 a) Plan view of the blowout at the sea-surface 

after a 30-day simulation originating at the Macondo Well initialized on May 2010 using realistic 

hydrodynamic conditions as simulated by NCOM, and using the J2013 distribution for oil not treated with 

dispersant. The red line indicates the location of a vertical cross-section. b) Vertical cross-section along 

the red line in panel a) showing the distribution as a function of depth (ordinate). The color scale indicates 

the depth of each droplet. c) Three-dimensional view of the blowout shown in panels a) and b), the color 

again indicates the depth of the droplet.
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Figure 4.61. Oil blowout as a function of longitude, latitude, and depth, after a 30-day simulation 

with BLOSOM, using the distribution from Johansen et al. (2013) for treated droplet 

sizes.17 a) Plan view of the blowout at the sea-surface after a 30-day simulation originating 

at the Macondo Well initialized on May 2010 using realistic hydrodynamic conditions as 

simulated by NCOM, and using the J2013 distribution for oil treated with dispersant. The 

red line indicates the location of a vertical cross-section. b) Vertical cross-section along the 

red line in panel a) showing the distribution as a function of depth (ordinate). The color

scale indicates the depth of each droplet. c) Three-dimensional view of the blowout shown 

in panels a) and b), the color again indicates the depth of the droplet.
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Figure 4.62. Oil blowout as simulated with BLOSOM after a 30-day simulation using the distribution 

from PNNL for untreated droplet sizes. a) Plan view of the blowout at the sea-surface after 

a 30-day simulation originating at the Macondo Well initialized on May 2010 using 

realistic hydrodynamic conditions as simulated by NCOM, and using the PNNL

distribution for oil not treated with dispersant. The red line indicates the location of a 

vertical cross-section. b) Vertical cross-section along the red line in panel a) showing the 

distribution as a function of depth (ordinate). The color scale indicates the depth of each 

droplet. c) Three-dimensional view of the blowout shown in panels a) and b), the color 

again indicates the depth of the droplet.
4.83

Figure 4.63. Oil blowout as simulated with BLOSOM after a 30-day simulation using the distribution 

from PNNL for treated droplet sizes. a) Plan view of the blowout at the sea-surface after a 

30-day simulation originating at the Macondo Well initialized on May 2010 using realistic 

hydrodynamic conditions as simulated by NCOM, and using the PNNL distribution for oil 

treated with dispersant. The red line indicates the location of a vertical cross-section. b) 

Vertical cross-section along the red line in panel a) showing the distribution as a function of 

depth (ordinate). The color scale indicates the depth of each droplet. c) Three-dimensional 

view of the blowout shown in panels a) and b), the color again indicates the depth of the 

droplet.
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Table 4.15. Percent of oil mass by depth for each of the four BLOSOM simulations

0–250 m 250–500 m 500–750 m

750–1000

m

1000–1250

m <-1250 m

J2013 T 42.1 0.6 0.9 1.3 2.2 52.9

J2013 NT 87.1 0.6 0.7 0.8 1 9.9

PNNL T 2.7 0 0 0 0 97.3

PNNL NT 3.8 0.3 0.3 0.6 1.8 93

Johansen et al. 2013 distribution treated (J2013 T) and untreated (J2013 NT), and PNNL experimental 

distributions treated (PNNL T) and untreated (PNNL NT).

4.8.1.3 Discussion

As noted, the PNNL2017 distributions produced droplet sizes that were roughly an order of magnitude 

smaller than those from the J2013 simulations. This was likely due to the experimental conditions under 

which the DSDs were derived. In this study we compared them “as derived.”

Because the J2013 DSDs were comparable to Deepwater Horizon conditions, BLOSOM successfully 

simulated oil remaining at depth when using the J2013 DSD for treated oil. The amount of oil remaining 

at depth is about 53%, which is roughly consistent with the amount of oil believed to never have surfaced

(McNutt et al. 2012). It should be noted that most of the oil in this simulation becomes sunk as the ocean 

currents and turbulent diffusion moved oil onto the bottom, but only when oil droplets remained at depth 

long enough. Oil remaining at depth was more limited (roughly 10% of oil) when the untreated J2013 

DSD was used. Because both PNNL distributions are small compared to the J2013 distribution for treated 

oil, the PNNL2017 simulations resulted in a large amount of oil remaining at depth.

4.8.1.4 Literature Findings

There has recently been a large body of literature published discussing the behavior of dispersants as they 

interact with plumes of crude oil. A literature review was undertaken to tease out any common variables 

that appear to have an influence on dispersant-crude oil interactions.

6 Several articles describing physical 

experimentation with dispersants were evaluated, with a number of experimental variables recorded 

which could potentially effect dispersant effectiveness (DE). DE is measured by the reduction in droplet 

sizes (Mukherjee 2011) and therefore, the inverse relationship between DE and droplet size occurs by 

definition. This relationship is useful if you know the reduction in DSDs (before vs after dispersant 

application) and you want to calculate DE. Relevant variables captured during the literature review 

include dispersant brand, dispersant concentration, crude oil type, pressure, temperature, mixing method 

and rate, mixing time, salinity, SARA concentrations, and degree of crude weathering. While there was 

some signal associated with temperature and salinity, the strongest signals came from mixing efficiency 

and oil viscosity. While mixing efficiency had the strongest signal, the viscosity correlation was chosen to 

include in BLOSOM since viscosity is already explicitly included as part of the BLOSOM simulation.

A three-parameter model incorporating the hyperbolic tangent was found to give a good fit to the 

experimental viscosity data in Figure 4.64. The generalized relationship is given in terms of the 

hyperbolic tangent function as well as the adjustable parameters A, B, and C.

 6 Abdelrahim et al. 2014; Belore et al. 2009; Brandvik et al. 2013; Brandvik et al. 2016; Chandraskar et al. 2003; Fu 

et al. 2014; Moles et al. 2002; Mukherjee et al. 2011; Nagamine 2014; Nyankson et al 2015a; Nyankson et al 2015b; 

Pan et al. 2016; Wang et al. 2013. 
4.85







 





 



 



 − = +

C

B Effectiveness A μ % 1 tanh

(4.1)

Here, μ is the oil viscosity in units of centipoise (cP).

Figure 4.64. Curve fit for dispersant effectiveness (percentage, ordinate) as a function of oil viscosity 

(cP, abcissa).

The parameters A, B, and C in Equation (4.1) are dependent on the type of test protocol used to determine 

the viscosity. Both the Institut Français du Pétrole and Mackay, Nadeau, and Steelman test protocols are 

commonly used. Values for A, B, and C for use in the prediction of dispersant percent effectiveness 

associated with viscosity values obtained using either of these protocols are given in Table 4.16.

Table 4.16. Values for parameters A, B, and C used to determine dispersant effectiveness as a function 

of viscosity values obtained using either the IFP or MNS test protocol.

Test Protocol

Parameter IFP MNS

A 49.3 50

B 1890 6000

C 3330 4000

4.8.2 Future Enhancements: BLOSOM

Many enhancements can be made to increase BLOSOM’s utility when simulating the application of 

dispersants on oil spills. One such enhancement would be to treat the dispersant application sites as actual 

clouds, rather than just spheres of influence. This would mean that they could be affected by ambient 

0

10

20

30

40

50

60

70

80

90

100

10 100 1000 10000 100000

Percent Effectiveness

Viscosity cP

Dispersant Effectiveness vs Viscosity (MNS)
5.86

factors, and perhaps dissipate over time. Such a modification would greatly increase the complexity of the 

present implementation, but would yield more physically defensible results.

Another enhancement would be to add a means to heuristically calculate the minimum droplet size used 

to anchor the active DSD. Currently, this is a user-supplied value, or a specific, fixed value taken from 

literature. Ideally, this value could be derived from relationships as they exist within a simulation. Such 

an automated setup would enable less-knowledgeable users to simulate the application of dispersants that 

to achieve desirable results. To achieve this degree of emergent behavior, additional correlations would 

need to be derived from the present (or future) literature.

Still another enhancement would be to further refine the heuristics used to derive dispersant effectiveness 

to include the input from a wider range of environmental variables. During the literature review, several 

promising leads were noted for additional correlative relationships that could be boiled down into 

equations, but there was insufficient evidence to sufficiently justify their use. An expanded literature 

review could further elucidate such relationships to the point where they are defensible, and they could be 

incorporated into BLOSOM’s dispersant module.

Finally, the PNNL2017 DSDs could be further integrated into BLOSOM’s framework to better take 

advantage of user adjustments. For the purposes of this project the droplet sizes were fixed to a specific 

range, because these values were derived from direct observational results. Modifying the droplet size 

boundaries for the PNNL2017 DSDs to be adjustable by the modeler (as they are for the current J2013 

DSD) opens up the possibility of fitting the derived distributions to match observations from any other 

experimental setup.

5.0 Conclusions and Next Steps

Ongoing exploration in the deep ocean environment of the U.S. Outer Continental Shelf requires 

continued high pressure research to characterize the phenomena affecting blowouts and response 

strategies. New and improved methods and capabilities will be needed to accurately simulate 

conditions and to collect meaningful data that may be used to inform oil spill models and 

response. Particular needs include equipment for simulating blowouts and oil suspensions in 

closed cells and methods for measuring and monitoring the evolution of DSDs within pressure 

cells. Non-invasive methods for monitoring oil and dispersant chemistry would also be 

beneficial. Field tests in the open ocean would also help reduce some uncertainties regarding the 

correlation and scaling of laboratory tests with real world scenarios, as noted below.

The DSDs derived from the PNNL experimental results produced droplets that were an order of 

magnitude smaller than the droplets sizes observed during Deepwater Horizon (Johansen et al. 

2013; compare Figs. 4 8.7 and 4.8.8). This is due to the experimental design, including the 

methods and equipment that were used (e.g., nozzle size, GOR, discharge rate). The physical 

constraints and limitations of tank experiments will invariably produce DSDs of a different scale 

than are produced by a full-scale blowout in the open ocean. Challenges therefore exist in 

understanding how to properly establish equivalencies of scale between the experimental 

parameters and full scale ‘real world’ parameters, and for how to transform data and 
5.87

observations from an experiment to the equivalent real-world scale. Finding the proper 

extrapolation method for the lab results from PNNL is therefore encouraged as a direction for 

future research. Another important direction for future research is simulating the evolution of the 

initial DSD (known as a dynamic DSD) to capture variations in size as the blowout evolves 

within the ocean, and at its surface. 

The results of the experiment examining the effect of droplet size on the rate of biodegradation 

indicate that the scale of the droplets may not have a significant effect on the rate of 

biodegradation, but this may only be true within the scale examined (<10 μm through ~500 μm). 

There may be a critical threshold of larger droplet size that may significantly impact the rate of 

biodegradation. Additional work is necessary, however, to identify an approach for generating 

and maintaining larger droplet size distributions in laboratory scale environments. The formation 

of OPAs when mixing diatomaceous earth with oil or oil and dispersant may have identified an 

alternative approach and it may be possible to use the size of the particulate to control scale. 

Hollow (as with the diatom frustules) or porous particulate matter would be preferable and it 

would be necessary to quantify what fraction of the volume of the OPAs consists of oil.

The blowout experiments demonstrated that the vigorous and turbulent action of the blowout 

results in the atomization of a fraction of the oil. Differences in DSD between the PNNL and 

Johansen et al. 2013 study also demonstrate that the specific physical aspects of the blowout 

(e.g., orifice diameter, discharge rate, GOR) will impact the resulting initial droplet size 

distribution. It would likely be very important to monitor the DSD some distance from the 

blowout with and without the introduction of dispersant. Results from the BLOSOM model 

indicate that generating very small DSDs would result in driving a greater portion of the oil to 

the seafloor. Deep ocean seafloor sequestration of a significant portion of the oil may be 

beneficial, but this would need to be evaluated for the specific location. Biodegradation of the oil 

at the seafloor (due to cold temperatures and low oxygen) would be considerably slower than 

biodegradation of oil in the water column. Results of the BLOSOM model and the droplet size 

biodegradation study indicate that the motivation for controlling droplet size with DOR should 

be to direct what fraction of the oil is in the water column, water surface, or benthos: reducing 

droplet size below 500 μm is unlikely to increase the rate of biodegradation.

The blowout experiments also demonstrated that the extent of mixing of dispersant into the oil 

plume at or near the location of the blowout orifice may have little consequence. Premixing the 

dispersant with the oil (as was also done by Johansen et al. 2013), perhaps simulating an 

injection of dispersant into the well or riser pipe below a blowout orifice did not have a 

significant effect on the DSD when compared with injecting the dispersant into the ejection 

plume. An important caveat is to monitor the dispersant injection to assure that degassing of the 

oil does not separate the dispersant from the plume. 

Another possible phenomenon that might impact oil-dispersant-seawater mixing and 

emulsification is the formation of methane hydrates during a deep ocean blowout. Methane 

hydrates can form when liquid or gaseous methane comes in contact with cold water and 

elevated pressures. The hydrates will form on the surface of gas bubbles, but can also nucleate on 

other surfaces, including sediments carried with the oil. Hydrate formation during a deep ocean 

oil release would reduce the amount of gas ordinarily contributing to the upward driving force on 
5.88

the oil; if a significant quantity of gas was affected, the upward mobility of the plume would 

become increasingly dependent upon droplet size and buoyancy. The effect of dispersant 

concentration on hydrate formation is also poorly characterized. Further, the formation of 

hydrates within the ejection plume may pull water out of the mixture and thus impede the 

emulsification of the oil by the dispersant at the very location where turbulent mixing energy is 

available. 
6.1

6.0 References

Abdelrahim, M. A., and Rao, D. N. Measurement of Interfacial Tension in 

Hydrocarbon/Water/Dispersant Systems at Deepwater Conditions. From Oil Spill Remediation: 

Colloid Chemistry-based Principles and Solutions. 2014. 1st Ed. John Wiley & Sons, Inc. pp 295-

315.

Auflem, I. H.; Kallevik, H.; Westvik, A.; Sjöblom, J., Influence of pressure and solvency on the 

separation of water-in-crude-oil emulsions from the North Sea. Journal of Petroleum Science 

and Engineering 2001, 31 (1), 1-12.

Backhaus, T.; Scholze, M.; Grimme, L. H., The single substance and mixture toxicity of 

quinolones to the bioluminescent bacterium Vibrio fischeri. Aquatic Toxicology 2000, 49 (1–2), 

49-61.

Bays, J. 13C NMR Analysis of Blind Canyon and Illinois #6 Coal Liquids; Pacific Northwest 

National Laboratory: Richland, WA, 2014.

Becker, K. W.; Lindblom, G. P., PERFORMANCE EVALUATION OF A NEW VERSATILE 

OIL SPILL DISPERSANT. International Oil Spill Conference Proceedings 1983, 1983 (1), 61-

64.

Belore, R. C.; Trudel, K., Guarino, A., Large-scale cold water dispersant effectiveness 

experiments with Alaskan crude oils and Corexit 9500 and 9527 dispersants. Marine Pollution 

Bulletin: 2009, 58 (1), 118-128.

Bianchi, T. S.; Osburn, C.; Shields, M. R.; Yvon-Lewis, S.; Young, J.; Guo, L.; Zhou, Z., 

Deepwater Horizon Oil in Gulf of Mexico Waters after 2 Years: Transformation into the 

Dissolved Organic Matter Pool. Environmental Science & Technology 2014, 48 (16), 9288-9297.

Bonheyo, G.; Winder, E.; Park, J.; Avila, A.; Jeters, R.; Larimer, C. Advanced Materials and 

Manufacturing Reliability: Performance Analysis of Antifouling Coatings; Pacific Northwest 

National Laboratory: Richland, WA, 2015; p 30.

Bonheyo, G.; Jeters, R.; Shin, Y.; Park, J.; Kuo, L.; Avila, A.; Symes, M. E15PG00037: 

Multifunctional Herding-Sorbent Agents for Use in Icy Water; PNNL-26504; Pacific Northwest 

National Laboratory: Richland, WA, May, 2017, 2017; pp 1-84.

Brandvik, P. J.; Johansen, Ø.; Leirvik, F.; Farooq, U.; Daling, P. S., Droplet breakup in 

subsurface oil releases – Part 1: Experimental study of droplet breakup and effectiveness of 

dispersant injection. Marine Pollution Bulletin 2013, 73 (1), 319-326.

Brandvik, P. J.; Johansen, Ø.; Farooq, U.; Angell, G.; Leirvik, F. Sub-surface oil releases –

Experimental study of droplet distributions and different dispersant injection techniques- version 

2. A scaled experimental approach using the SINTEF Tower basin; SINTEF report no: A26122; 

SINTEF Trondheim, Norway 2014; pp 1-134.
6.2

Brandvik, P. J., Davies, E., Bradly, C., Storey, C. Leirvik, F., Subsurface oil releases –

Verification of dispersant effectiveness under high pressure. 2016. Final Report. SINTEF 

Materials and Chemistry, pp 1-62.

Broje, V.; Gala, W.; Nedwed, T.; Twomey, J., A Consensus on the State of the Knowledge and 

Research Recommendations on the Fate and Effects of Deep Water Releases of Oil, Dispersants 

and Dispersed Oil. International Oil Spill Conference Proceedings 2014, 2014 (1), 225-237.

Campo, P.; Venosa, A. D.; Suidan, M. T., Biodegradability of Corexit 9500 and Dispersed South 

Louisiana Crude Oil at 5 and 25 °C. Environmental Science & Technology 2013, 47 (4), 1960-

1967.

Cannella, W. F., C.; Arboleda, P.; Bays, T.; Dettman, H.; Foster, M.; Gieleciak, R.; Gunter, G.; 

Hager, D.; Lay, C.; Lewis, S.; Luecke, J.; Sluder, S.; Zigler, B. Detailed Characterization of the 

Physical and Chemical Properties of the Reformulated FACE Diesel Fuels: FD2B, FD4B, and 

FD7B; AVFL-19-1 Coordinating Research Council, Inc: Alpharetta, GA, Jan 2013, 2013; p 150.

Cannella, W. F., C.; Gieleciak, R.; Arboleda, P.; Bays, T.; Dettman, H.; Foster, M.; Gunter, G.; 

Hager, D.; King, D.; Lay, C.; Lewis, S.; Luecke, J.; Sluder, S.; Zigler, B.; Natarajan, M. 

Advanced Alternative and Renewable Diesel Fuels: Detailed Characterization of Physical and 

Chemical Properties; CRC Report No. AVFL-19-2; Coordinating Research Council: Alpharetta, 

GA, 2013.

Chandraskar, S., Sorial, G., and Weaver, J. W. 2003. Determining Dispersant Effectiveness Data 

for a Suite of Environmental Conditions. Presented at International Oil Spill Conference, 

Vancouver, Canada, April 7-10, 2003.

EPA method 40 CFR Ch. 1, part 300, Appendix C. Swirling flask dispersant effectiveness test, 

revised standard dispersant toxicity test, and bioremediation agent effectiveness test. . 7-1-03 ed.; 

US-EPA, Ed.

Fingas, M. A Review of Literature Related to Oil Spill Dispersants 2011-2014; Anchorage, 

Alaska, 2014; p 52.

Fitzpatrick, F. A.; Boufadel, M. C.; Johnson, R.; Lee, K. W.; Graan, T. P.; Bejarano, A. C.; Zhu, 

Z.; Waterman, D.; Capone, D. M.; Hayter, E.; Hamilton, S. K.; Dekker, T.; Garcia, M. H.; 

Hassan, J. S. Oil-particle interactions and submergence from crude oil spills in marine and 

freshwater environments: review of the science and future research needs; 2015-1076; Reston, 

VA, 2015; p 44.

Follonier, S.; Panke, S.; Zinn, M., Pressure to kill or pressure to boost: a review on the various 

effects and applications of hydrostatic pressure in bacterial biotechnology. Appl Microbiol 

Biotechnol. 2012, 93 (5), 1805-1815.
6.3

Fu, J., Gong, Y., Zhao, X., O’Reilly, S.E., Zhao, D., Effects of Oil and Dispersant on Formation 

of Marine Oil Snow and Transport of Oil Hydrocarbons. Environmental Science and Technology

2014, 48 (24), 14392-14399.

Fulladosa, E.; Murat, J.-C.; Villaescusa, I., Study on the toxicity of binary equitoxic mixtures of 

metals using the luminescent bacteria Vibrio fischeri as a biological target. Chemosphere 2005,

58 (5), 551-557.

Gill, G. A.; Kuo, L.-J.; Janke, C. J.; Park, J.; Jeters, R. T.; Bonheyo, G. T.; Pan, H.-B.; Wai, C.; 

Khangaonkar, T.; Bianucci, L.; Wood, J. R.; Warner, M. G.; Peterson, S.; Abrecht, D. G.; 

Mayes, R. T.; Tsouris, C.; Oyola, Y.; Strivens, J. E.; Schlafer, N. J.; Addleman, R. S.; Chouyyok, 

W.; Das, S.; Kim, J.; Buesseler, K.; Breier, C.; D’Alessandro, E., The Uranium from Seawater 

Program at the Pacific Northwest National Laboratory: Overview of Marine Testing, Adsorbent 

Characterization, Adsorbent Durability, Adsorbent Toxicity, and Deployment Studies. Industrial 

& Engineering Chemistry Research 2016, 55 (15), 4264-4277.

Girotti, S.; Bolelli, L.; Roda, A.; Gentilomi, G.; Musiani, M., Improved detection of toxic 

chemicals using bioluminescent bacteria. Analytica Chimica Acta 2002, 471 (1), 113-120.

Government_Accountability_Office. Oil Dispersants: Additional Research Needed, Particularly 

on Subsurface and Arctic Applications; Government Accountability Office: May 30, 2012, 2012; 

p 73.

Huesemann, M. H.; Hausmann, T. S.; Fortman, T. J., Assessment of bioavailability limitations 

during slurry biodegradation of petroleum hydrocarbons in aged soils. Environmental Toxicology 

and Chemistry 2003, 22 (12), 2853-2860.

Huesemann, M. H.; Hausmann, T. S.; Fortman, T. J., Does Bioavailability Limit 

Biodegradation? A Comparison of Hydrocarbon Biodegradation and Desorption Rates in Aged 

Soils. Biodegradation 2004, 15 (4), 261-274.

Use of Dispersants to Treat Oil Spills. Technical Information paper ed.; International Tanker 

Owners Pollution Federation Limited (ITOPF) 2011; pp 4-5.

Jannasch, H.W. and Taylor, C.D. Deep-Sea Microbiology. Ann. Rev. Microbiol. 1984, 38, 487-

514.

Johansen, Ø., Development and verification of deep-water blowout models. Marine Pollution 

Bulletin 2003, 47 (9), 360-368.

Johansen, Ø.; Brandvik, P. J.; Farooq, U., Droplet breakup in subsea oil releases – Part 2: 

Predictions of droplet size distributions with and without injection of chemical dispersants. 

Marine Pollution Bulletin 2013, 73 (1), 327-335.
6.4

Juarez, A. G. V.; Kadimesetty, H. S.; Achatz, D. E.; Schedler, M.; Muller, R., Online Monitoring 

of Crude Oil Biodegradation at Elevated Pressures. IEEE Journal Of Selected Topics In Applied 

Earth Observations And Remote Sensing 2015, 8 (2), 872-878.

Kerkhof, L. J.; Voytek, M. A.; Sherrell, R. M.; Millie, D.; Schofield, O., Variability in bacterial 

community structure during upwelling in the coastal ocean. In Molecular Ecology of Aquatic 

Communities, Zehr, J. P.; Voytek, M. A., Eds. Springer Netherlands: Dordrecht, 1999; pp 139-

148.

Khelifa, A.; Stoffyn-Egli, P.; Hill, P. S.; Lee, K., Characteristics of Oil Droplets Stabilized by 

Mineral Particles: Effects of Oil Type and Temperature. Spill Science & Technology Bulletin 

2002, 8 (1), 19-30.

Kleindienst, S.; Seidel, M.; Ziervogel, K.; Grim, S.; Loftis, K.; Harrison, S.; Malkin, S. Y.; 

Perkins, M. J.; Field, J.; Sogin, M. L.; Dittmar, T.; Passow, U.; Medeiros, P. M.; Joye, S. B., 

Chemical dispersants can suppress the activity of natural oil-degrading microorganisms. 

Proceedings of the National Academy of Sciences of the United States of America 2015, 112

(48), 14900-14905.

Kleindienst, S.; Paul, J. H.; Joye, S. B., Using dispersants after oil spills: impacts on the 

composition and activity of microbial communities. Nat Rev Micro 2015, 13 (6), 388-396.

Klump, E., Oil Drillers Rush Back to the Gulf of Mexico: BP’s oil spill is a distant memory as 

companies invest billions. Businessweek November 14, 2013, 2013.

Kostka, J.E., Prakash, O.,

 Overholt, W.A., Green, S.J., Gina Freyer, G., Canion, A., Delgardio, 

J., Norton, N., Hazen, T.C., Huettel, M. Hydrocarbon-Degrading Bacteria and the Bacterial 

Community Response in Gulf of Mexico Beach Sands Impacted by the Deepwater Horizon Oil 

Spill. Appl Environ Microbiol. 2011 77(22), 7962–7974.

Kumar, P.; Libchaber, A., Pressure and Temperature Dependence of Growth and Morphology 

of Escherichia coli: Experiments and Stochastic Model. Biophysical Journal 2013, 105 (3), 783-

793.

Le Floch, S.; Guyomarch, J.; Merlin, F.-X.; Stoffyn-Egli, P.; Dixon, J.; Lee, K., The Influence of 

Salinity on Oil–Mineral Aggregate Formation. Spill Science & Technology Bulletin 2002, 8 (1), 

65-71.

Lee, K., Oil–Particle Interactions in Aquatic Environments: Influence on the Transport, Fate, 

Effect and Remediation of Oil Spills. Spill Science & Technology Bulletin 2002, 8 (1), 3-8.

Lee, K.; Stoffyn-Egli, P.; Tremblay, G. H.; Owens, E. H.; Sergy, G. A.; Guénette, C. C.; Prince, 

R. C., Oil–Mineral Aggregate Formation on Oiled Beaches: Natural Attenuation and Sediment 

Relocation. Spill Science & Technology Bulletin 2003, 8 (3), 285-296.
6.5

Lei, L.; Aoyama, I., Effect-directed investigation and interactive effect of organic toxicants in 

landfill leachates combining Microtox test with RP-HPLC fractionation and GC/MS analysis. 

Ecotoxicology 2010, 19 (7), 1268-1276.

Lessard, R. R.; DeMarco, G., The Significance of Oil Spill Dispersants. Spill Science & 

Technology Bulletin 2000, 6 (1), 59-68.

Jon E. Lindstrom, D. M. W., Joan F. Braddock Biodegradation of dispersed Oil Using COREXIT 

9500; The Alaska Department of Environmental Conservation Division of Spill Prevention and 

Response: 1999.

Lindstrom, J. E.; Braddock, J. F., Biodegradation of petroleum hydrocarbons at low temperature 

in the presence of the dispersant Corexit 9500. Marine Pollution Bulletin 2002, 44 (8), 739-747.

Macías-Zamora, J. V.; Meléndez-Sánchez, A. L.; Ramírez-Álvarez, N.; Gutiérrez-Galindo, E. 

A.; Orozco-Borbón, M. V., On the effects of the dispersant Corexit 9500© during the 

degradation process of n-alkanes and PAHs in marine sediments. Environmental Monitoring and 

Assessment 2014, 186 (2), 1051-1061.

Marietou, A.; Bartlett, D. H., Effects of High Hydrostatic Pressure on Coastal Bacterial 

Community Abundance and Diversity. Applied and Environmental Microbiology 2014, 80 (19), 

5992-6003.

McFarlin, K. M.; Prince, R. C.; Perkins, R.; Leigh, M. B., Biodegradation of Dispersed Oil in 

Arctic Seawater at -1°C. PLoS ONE 2014, 9 (1), e84297.

McNutt, M. K.; Camilli, R.; Crone, T. J.; Guthrie, G. D.; Hsieh, P. A.; Ryerson, T. B.; Savas, O.; 

Shaffer, F., Review of flow rate estimates of the Deepwater Horizon oil spill. Proceedings of the 

National Academy of Sciences 2012, 109 (50), 20260-20267.

Mikkelsen, O. A.; Hill, P. S.; Milligan, T. G.; Chant, R. J., In situ particle size distributions and 

volume concentrations from a LISST-100 laser particle sizer and a digital floc camera. 

Continental Shelf Research 2005, 25 (16), 1959-1978.

Moles, A., Holland, L., and Short, J., Effectiveness in the Laboratory of Corexit 9527 and 9500 

in Dispersing Fresh, Weathered, and Emulsion of Alaska North Slope Crude Oil under Subarctic 

Conditions. Spill Science & Technology Bulletin 2002, 7(5–6), 241–247.

Mukherjee, B., Turner, J., Wrenn, B. A., Effect of Oil Composition on Chemical Dispersion of 

Crude Oil. Environmental Engineering Science 2011, 28(7), 496-507.

Mulkins-Phillips, G. J.; Stewart, J. E., Effect of Four Dispersants on Biodegradation and Growth 

of Bacteria on Crude Oil. Applied Microbiology 1974, 28 (4), 547-552.

Nagamine, S. I. 2014. ‘The Effects of Chemical Dispersants on Buoyant Oil Droplets’. MS 

thesis. University of Hawai’i, Mānoa.
6.6

Using Oil Dispersants on the Sea; National Research Council; National Academy Press: 

Washington, DC, 1989, 1989; pp 24-69.

Oil Spill Dispersants: Efficacy and Effects; National Research Council; National Academy Press: 

Washington, D.C., 2005; p 400.

Nyankson, E., DeCuir, M. J., and Gupta, R.B., Soybean Lecithin as a Dispersant for Crude Oil 

Spills. ACS Sustainable Chemical Engineering 2015, 3(5), 920-931.

Nyankson, E., Olasehinde, O., John, T. V., and Gupta, R. B., Surfactant-Loaded Halloysite Clay 

Nanotube Dispersants for Crude Oil Spill Remediation. Industrial and Engineering Chemistry 

Research 2015, 54(38), 9328-9341

Oreskes, N.; Shrader-Frechette, K.; Belitz, K., Verification, Validation, and Confirmation of 

Numerical Models in the Earth Sciences. Science 1994, 263 (5147), 641.

Oreskes, N., Evaluation (not validation) of quantitative models. Environmental Health 

Perspectives 1998, 106 (Suppl 6), 1453-1460.

Pan, Z., Zhao, L., Boufadel, M.C., King, T., Conmy, R., Lee, K., Impact of mixing time and 

energy on the dispersion effectiveness and droplets size of oil. Chemosphere 2017, 166, 246-254.

Park, J.; Jeters, R. T.; Kuo, L.-J.; Strivens, J. E.; Gill, G. A.; Schlafer, N. J.; Bonheyo, G. T., 

Potential Impact of Seawater Uranium Extraction on Marine Life. Industrial & Engineering 

Chemistry Research 2016, 55 (15), 4278-4284.

Prieur, D., 2011, Deep-Sea Microbiology. Encyclopedia of Astrobiology. M. Gargaud, R. Amils, 

J. C. Quintanilla et al. Berlin, Heidelberg, Springer Berlin Heidelberg: 414-415.

Prince, R. C.; Elmendorf, D. L.; Lute, J. R.; Hsu, C. S.; Haith, C. E.; Senius, J. D.; Dechert, G. J.; 

Douglas, G. S.; Butler, E. L., 17.alpha.(H)-21.beta.(H)-hopane as a conserved internal marker for 

estimating the biodegradation of crude oil. Environmental Science & Technology 1994, 28 (1), 

142-145.

Prince, R. C.; McFarlin, K. M.; Butler, J. D.; Febbo, E. J.; Wang, F. C. Y.; Nedwed, T. J., The 

primary biodegradation of dispersed crude oil in the sea. Chemosphere 2013, 90 (2), 521-526.

Prince, R. C.; Butler, J. D., A protocol for assessing the effectiveness of oil spill dispersants in 

stimulating the biodegradation of oil. Environmental Science and Pollution Research 2014, 21

(16), 9506-9510.

Prince RC, K. B. a. B. J., Three Widely-Available Dispersants Substantially Increase the

Biodegradation of otherwise Undispersed Oil. Journal of Marine Science: Research & 

Development 2016, 6 (1), 4.
6.7

Schedler, M.; Hiessl, R.; Valladares Juárez, A. G.; Gust, G.; Müller, R., Effect of high pressure 

on hydrocarbon-degrading bacteria. AMB Express 2014, 4, 77-77.

Schindelin, J.; Arganda-Carreras, I.; Frise, E.; Kaynig, V.; Longair, M.; Pietzsch, T.; Preibisch, 

S.; Rueden, C.; Saalfeld, S.; Schmid, B.; Tinevez, J.-Y.; White, D. J.; Hartenstein, V.; Eliceiri, 

K.; Tomancak, P.; Cardona, A., Fiji: an open-source platform for biological-image analysis. Nat 

Meth 2012, 9 (7), 676-682.

Scoma, A.; Barbato, M.; Hernandez-Sanabria, E.; Mapelli, F.; Daffonchio, D.; Borin, S.; Boon, 

N., Microbial oil-degradation under mild hydrostatic pressure (10 MPa): which pathways are 

impacted in piezosensitive hydrocarbonoclastic bacteria? Scientific Reports 2016, 6, 23526.

Sterman, J. D., The Meaning of Models. Science 1994, 264 (5157), 329.

Stoffyn-Egli, P.; Lee, K., Formation and Characterization of Oil–Mineral Aggregates. Spill 

Science & Technology Bulletin 2002, 8 (1), 31-44.

Tamburini, C.; Goutx, M.; Guigue, C.; Garel, M.; Lefevre, D.; Charriere, B.; Sempere, R.; Pepa, 

S.; L.Peterson, M.; Wakeham, S.; Lee, C., Effects 

ofhydrostaticpressureonmicrobialalterationofsinkingfecalpellets. Deep-SeaResearchII 2009, 56, 

1533–1546.

Tsiridis, V.; Petala, M.; Samaras, P.; Hadjispyrou, S.; Sakellaropoulos, G.; Kungolos, A., 

Interactive toxic effects of heavy metals and humic acids on Vibrio fischeri. Ecotoxicology and 

Environmental Safety 2006, 63 (1), 158-167.

U.S._Coast_Guard. On scene coordinator report: Deepwater Horizon oil spill; U.S. Department 

of Homeland Security, U.S. Coast Guard: Washington, DC, 2011.

U.S. Crude Oil and Natural Gas Proved Reserves; US Energy Information Administration (USEIA) 2011; p 10.

Gulf of Mexico Fact Sheet; US Energy Information Administration (US-EIA) 2013 Accessed 

March 31, 2014 at http://www.eia.gov/special/gulf_of_mexico/data.cfm (updated July 1, 2013).

US Energy Information Administration (US-EIA). 2017. ‘Gulf of Mexico crude oil production, 

already at annual high, expected to keep increasing.’ Today in Energy. Accessed 9/24/2017 at 

https://www.eia.gov/todayinenergy/detail.php?id=30752 (last updated April 12, 2017)

USNC. Macondo: the Gulf oil disaster: Chief Counsel's report; United States National 

Commission On The BP Deepwater Horizon Oil Spill And Offshore Drilling: Washington, D.C., 

2011; p 371.

Venosa, A. D.; Holder, E. L., Biodegradability of dispersed crude oil at two different 

temperatures. Marine Pollution Bulletin 2007, 54 (5), 545-553.
6.8

Wang, W., Zheng, Y., and Lee, K. 2013. Chemical dispersion of oil with mineral fines in a low 

temperature environment. Marine Pollution Bulletin 2013, 72(1), 205-212.

Wu, D. H.; Chen, A. D.; Johnson, C. S., An Improved Diffusion-Ordered Spectroscopy 

Experiment Incorporating Bipolar-Gradient Pulses. Journal of Magnetic Resonance, Series A 

1995, 115 (2), 260-264.

Zeinstra-Helfrich, M.; Koops, W.; Murk, A. J., The NET effect of dispersants — a critical 

review of testing and modelling of surface oil dispersion. Marine Pollution Bulletin 2015, 100

(1), 102-111.
Appendix A

–

Interfacial Tension Data
A.1

Appendix A

Interfacial Tension Data

Sample: Macondo Crude in unfiltered seawater at 17.8°C with Corexit 9500 1:10 dilution at 1 atm 

pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 4.44 0.252 0.826 9.89 3.14 127.14 1.925 1.732 2

2 4.36 0.256 0.825 9.93 3.15 126.56 1.935 1.732 2

3 4.29 0.259 0.824 9.96 3.16 126.00 1.943 1.732 2

4 4.28 0.260 0.824 9.99 3.17 125.79 1.952 1.732 2

5 4.21 0.264 0.823 10.02 3.18 125.19 1.959 1.731 2

6 4.19 0.266 0.823 10.04 3.19 124.92 1.966 1.732 2

7 4.13 0.268 0.822 10.06 3.20 124.43 1.972 1.731 2

8 4.11 0.270 0.822 10.08 3.20 124.12 1.979 1.731 2

9 4.11 0.270 0.822 10.11 3.21 124.00 1.985 1.732 2

10 4.05 0.273 0.821 10.12 3.22 123.47 1.991 1.731 2

11 3.18 0.305 0.770 9.31 2.81 117.36 1.958 1.633 2

12 3.19 0.305 0.770 9.30 2.80 117.36 1.960 1.633 2

13 3.16 0.306 0.769 9.30 2.80 117.03 1.962 1.632 2

14 3.18 0.306 0.769 9.31 2.81 117.12 1.963 1.636 2

15 3.17 0.306 0.769 9.30 2.80 116.96 1.965 1.636 2

16 3.16 0.307 0.769 9.32 2.81 116.71 1.968 1.637 2

17 3.13 0.309 0.768 9.31 2.81 116.36 1.971 1.638 2

18 3.12 0.310 0.768 9.35 2.81 116.12 1.974 1.639 2

19 3.09 0.313 0.767 9.33 2.81 115.54 1.980 1.640 2

20 3.15 0.308 0.768 9.34 2.81 116.32 1.975 1.641 2

21 3.76 0.291 0.816 10.31 3.27 119.36 2.057 1.728 2

22 3.72 0.293 0.815 10.32 3.27 118.94 2.061 1.727 2

23 3.72 0.293 0.815 10.33 3.28 118.74 2.064 1.727 2

24 3.69 0.295 0.814 10.34 3.28 118.32 2.069 1.724 2

25 3.71 0.294 0.815 10.36 3.28 118.4 2.072 1.728 2

26 3.67 0.296 0.814 10.36 3.28 118.01 2.075 1.725 2

27 3.63 0.298 0.813 10.35 3.28 117.42 2.080 1.723 2

28 3.61 0.300 0.812 10.37 3.28 117.01 2.086 1.722 2

29 3.67 0.296 0.814 10.39 3.29 117.41 2.088 1.726 2

30 3.61 0.300 0.812 10.39 3.29 116.79 2.092 1.724 2

Mean: 3.67 0.290 0.800 9.89 3.09 119.84 2.000 1.700 2

Std. Dev: 0.45 0.02 0.02 0.45 0.21 3.89 0.06 0.04 0
A.2

Sample: Macondo Crude in unfiltered seawater at 17.8°C with Corexit 9500 1:100 dilution at 1 atm 

pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 14.26 0.212 1.357 27.09 13.48 126.44 3.333 2.823 2

2 14.03 0.215 1.356 27.11 13.48 125.65 3.343 2.822 2

3 14.06 0.215 1.356 27.14 13.50 125.29 3.350 2.823 2

4 14.05 0.215 1.356 27.17 13.51 124.96 3.357 2.823 2

5 13.83 0.217 1.354 27.18 13.51 124.19 3.367 2.821 2

6 13.66 0.220 1.353 27.19 13.50 123.50 3.375 2.818 2

7 1.620 0.220 1.352 27.21 13.51 123.07 3.382 2.818 2

8 13.59 0.221 1.352 27.23 13.51 122.57 3.389 2.818 2

9 13.43 0.223 1.350 27.23 13.51 121.88 3.398 2.815 2

10 13.32 0.224 1.349 27.25 13.51 121.27 3.406 2.814 2

11 13.17 0.218 1.323 25.88 12.58 125.11 3.272 2.754 2

12 13.12 0.219 1.322 25.89 12.58 124.68 3.278 2.754 2

13 13.00 0.220 1.321 25.90 12.58 124.19 3.285 2.753 2

14 12.92 0.221 1.320 25.91 12.58 123.81 3.290 2.752 2

15 12.86 0.222 1.320 25.92 12.58 123.39 3.296 2.751 2

16 12.78 0.223 1.319 25.95 12.59 122.95 3.303 2.750 2

17 12.75 0.224 1.319 25.96 12.59 122.58 3.308 2.750 2

18 12.63 0.225 1.318 25.96 12.59 122.05 3.315 2.748 2

19 12.70 0.224 1.318 25.98 12.60 121.87 3.319 2.750 2

20 12.56 0.227 1.317 26.00 12.60 121.28 3.327 2.748 2

21 14.95 0.212 1.389 28.44 14.45 125.21 3.433 2.889 2

22 14.75 0.214 1.387 28.46 14.45 124.33 3.444 2.887 2

23 14.57 0.216 1.386 28.47 14.45 123.65 3.453 2.885 2

24 14.56 0.216 1.386 28.49 14.46 123.13 3.461 2.886 2

25 14.47 0.217 1.385 28.52 14.47 122.62 3.470 2.885 2

26 14.29 0.220 1.384 28.53 14.47 121.78 3.480 2.882 2

27 14.17 0.221 1.382 28.55 14.47 121.06 3.489 2.881 2

28 14.15 0.221 1.382 28.58 14.48 120.52 3.498 2.882 2

29 14.08 0.222 1.382 28.6 14.48 119.83 3.508 2.881 2

30 13.87 0.225 1.380 28.62 14.49 118.89 3.519 2.878 2

Mean: 13.27 0.220 1.350 27.21 13.52 123.06 3.380 2.820 2

Std. 

Dev: 2.31 0.00 0.03 1.08 0.78 1.80 0.08 0.06 0.00
A.3

Sample: Macondo Crude in unfiltered seawater at 17.8°C with Corexit 9500 1:1000 dilution at 1 atm 

pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 20.48 0.211 1.622 39.37 23.09 110.99 4.203 3.373 2

2 20.58 0.212 1.631 40.03 23.57 104.81 4.296 3.394 2

3 21.28 0.209 1.647 40.58 24.15 109.79 4.276 3.425 2

Mean: 20.78 0.210 1.630 39.99 23.60 108.53 4.260 3.400 2

Std. Dev: 0.44 0.00 0.01 0.61 0.53 3.28 0.05 0.03 0.00

Sample: Macondo Crude in unfiltered seawater at 17.8o°C with Corexit 9500 1:10,000 dilution at 1 

atm pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 21.49 0.202 1.628 39.21 23.06 116.92 4.128 3.381 2

2 22.65 0.204 1.680 41.99 25.43 110.84 4.332 3.490 2

3 21.77 0.206 1.655 40.79 24.35 110.87 4.273 3.438 2

Mean: 21.97 0.200 1.650 40.66 24.28 112.88 4.240 3.440 2

Std. Dev: 0.61 0.00 0.03 1.39 1.19 3.50 0.10 0.05 0.00

Sample: Macondo Crude in unfiltered seawater at 17.8°C with Corexit 9500 1:100,000 dilution at 1 

atm pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 20.83 0.207 1.622 39.05 22.94 117.12 4.122 3.370 2

2 21.15 0.204 1.621 38.75 22.75 120.37 4.069 3.365 2

3 20.29 0.203 1.586 36.99 21.29 122.76 3.948 3.293 2

Mean: 20.76 0.200 1.610 38.26 22.33 120.08 4.050 3.340 2

Std. Dev: 0.43 0.00 0.02 1.11 0.90 2.83 0.09 0.04 0.00
A.4

Sample: Macondo Crude in unfiltered seawater at 17.8°C with Finasol OSR 52 1:50 dilution at 1 atm 

pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 14.82 0.221 1.412 29.92 15.43 117.48 3.612 2.942 2

2 14.63 0.223 1.410 29.95 15.43 115.98 3.631 2.941 2

3 14.54 0.224 1.410 30.03 15.47 114.64 3.650 2.942 2

4 14.36 0.227 1.409 30.09 15.49 112.86 3.673 2.94 2

5 14.20 0.229 1.407 30.14 15.50 110.85 3.697 2.938 2

6 14.09 0.230 1.406 30.21 15.52 108.69 3.723 2.937 2

7 13.98 0.232 1.405 30.29 15.54 106.05 3.754 2.936 2

8 13.87 0.233 1.404 30.38 15.55 102.17 3.795 2.935 2

9 13.93 0.232 1.405 30.50 15.55 95.31 3.859 2.934 2

10 19.78 0.175 1.452 30.94 15.62 48.07 4.077 2.976 2

11 13.93 0.222 1.372 28.17 14.15 119.78 3.481 2.858 2

12 13.86 0.222 1.371 28.20 14.16 118.96 3.492 2.859 2

13 13.67 0.225 1.369 28.25 14.17 117.71 3.509 2.857 2

14 13.60 0.226 1.369 28.28 14.18 116.67 3.522 2.856 2

15 13.40 0.229 1.367 28.32 14.19 115.28 3.541 2.853 2

16 13.34 0.23 1.366 28.36 14.20 114.11 3.555 2.852 2

17 13.19 0.232 1.365 28.39 14.20 112.73 3.573 2.851 2

18 13.11 0.233 1.364 28.44 14.21 111.17 3.591 2.85 2

19 12.99 0.235 1.363 28.49 14.23 109.43 3.613 2.848 2

20 12.92 0.236 1.362 28.53 14.23 107.66 3.632 2.848 2

21 14.01 0.220 1.372 28.13 14.14 120.70 3.468 2.859 2

22 14.01 0.220 1.372 28.17 14.15 120.03 3.478 2.860 2

23 13.79 0.223 1.371 28.22 14.17 118.71 3.496 2.859 2

24 13.68 0.225 1.37 28.25 14.18 117.6 3.511 2.859 2

25 13.56 0.227 1.369 28.31 14.20 116.6 3.526 2.858 2

26 13.48 0.228 1.368 28.34 14.21 115.52 3.539 2.856 2

27 13.31 0.230 1.367 28.38 14.22 114.09 3.557 2.853 2

28 13.22 0.231 1.366 28.42 14.23 112.66 3.575 2.853 2

29 13.13 0.233 1.366 28.47 14.24 111.29 3.593 2.852 2

30 13.01 0.234 1.364 28.51 14.20 109.51 3.613 2.850 2

Mean: 13.71 0.230 1.380 28.90 14.60 113.25 3.600 2.880 2

Std. 

Dev: 0.51 0.00 0.02 0.88 0.62 5.66 0.10 0.04 0
A.5

Sample: Macondo Crude in unfiltered seawater at 17.8o

°C with Finasol OSR 52 1:100 dilution at 1 atm 

pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 18.39 0.203 1.509 33.53 18.35 122.07 3.766 3.113 2

2 18.32 0.204 1.509 33.56 18.36 121.39 3.776 3.135 2

3 18.09 0.206 1.508 33.62 18.40 120.50 3.790 3.133 2

4 18 0.207 1.508 33.66 18.42 119.98 3.798 3.133 2

5 17.84 0.209 1.507 33.71 18.45 118.95 3.813 3.133 2

6 17.8 0.209 1.507 33.73 18.45 118.52 3.818 3.134 2

7 17.64 0.211 1.506 33.78 18.48 117.41 3.834 3.133 2

8 17.58 0.212 1.506 33.81 18.49 116.88 3.841 3.133 2

9 17.46 0.213 1.505 33.85 18.51 115.83 3.855 3.132 2

10 17.3 0.215 1.504 33.87 18.51 115.10 3.865 3.131 2

11 18.27 0.204 1.507 33.45 18.28 121.79 3.765 3.128 2

12 18.2 0.204 1.506 33.47 18.29 121.19 3.773 3.129 2

13 18.08 0.206 1.506 33.50 18.30 120.53 3.786 3.128 2

14 17.98 0.207 1.505 33.52 18.31 119.86 3.792 3.127 2

15 17.7 0.210 1.504 33.54 18.31 119.04 3.804 3.124 2

16 17.58 0.211 1.503 33.56 18.32 118.32 3.811 3.124 2

17 17.54 0.211 1.502 33.59 18.33 117.67 3.820 3.124 2

18 17.47 0.212 1.502 33.62 18.34 116.95 3.830 3.124 2

19 17.31 0.213 1.501 33.65 18.35 116.12 3.841 3.122 2

20 17.19 0.215 1.500 33.66 18.35 115.40 3.850 3.120 2

21 18.6 0.210 1.540 35.39 19.79 116.26 3.935 3.206 2

22 18.47 0.211 1.541 35.42 19.80 115.52 3.945 3.206 2

23 18.34 0.212 1.541 35.47 19.82 114.32 3.96 3.206 2

24 18.24 0.213 1.540 35.51 19.84 113.33 3.973 3.205 2

25 18.09 0.215 1.539 35.55 19.85 111.98 3.991 3.204 2

26 17.96 0.216 1.538 35.59 19.86 110.65 4.006 3.203 2

27 17.87 0.217 1.538 35.64 19.88 109.19 4.023 3.202 2

28 17.78 0.218 1.537 35.68 19.88 107.64 4.041 3.202 2

29 17.66 0.219 1.536 35.73 19.89 105.68 4.063 3.200 2

30 17.58 0.220 1.535 35.78 19.90 103.22 4.089 3.199 2

Mean: 17.88 0.210 1.520 34.28 18.87 116.04 3.880 3.150 2

Std. 

Dev: 0.38 0.00 0.02 0.94 0.71 4.83 0.10 0.04 0.00
A.6

Sample: Macondo Crude in unfiltered seawater at 17.8°C with Finasol OSR 52 1:1000 dilution at 1 

atm pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 22.97 0.199 1.670 41.00 24.72 119.84 4.118 3.464 2

2 22.86 0.200 1.670 41.06 24.76 118.74 4.203 3.465 2

3 22.92 0.200 1.671 41.10 24.79 118.75 4.205 3.467 2

4 22.76 0.201 1.670 41.16 24.83 118.02 4.217 3.466 2

5 22.80 0.201 1.671 41.20 24.86 117.80 4.221 3.468 2

6 22.80 0.201 1.672 41.24 24.89 117.43 4.227 3.470 2

7 22.64 0.202 1.671 41.25 24.89 116.85 4.235 3.469 2

8 22.63 0.202 1.671 41.30 24.93 116.45 4.241 3.470 2

9 22.64 0.202 1.672 41.33 24.95 116.19 4.245 3.470 2

10 22.61 0.203 1.671 41.35 24.95 115.85 4.250 3.471 2

11 22.51 0.199 1.653 40.13 23.96 120.6 4.135 3.429 2

12 22.38 0.200 1.652 40.16 23.98 120.21 4.142 3.429 2

13 22.52 0.199 1.653 40.20 24.01 120.01 4.145 3.431 2

14 22.46 0.200 1.654 40.24 24.04 119.79 4.150 3.431 2

15 22.38 0.200 1.653 40.26 24.05 119.46 4.155 3.431 2

16 22.24 0.202 1.653 40.27 24.06 119.1 4.161 3.430 2

17 22.30 0.201 1.653 40.30 24.08 118.9 4.163 3.432 2

18 22.26 0.201 1.654 40.32 24.09 118.67 4.168 3.432 2

19 22.10 0.203 1.653 40.33 24.10 118.29 4.174 3.431 2

20 22.12 0.203 1.653 40.34 24.10 118.06 4.176 3.432 2

21 20.62 0.206 1.609 38.36 22.35 117.69 4.078 3.343 2

22 20.56 0.206 1.609 38.36 22.35 117.57 4.080 3.342 2

23 20.63 0.206 1.609 38.37 22.35 117.62 4.080 3.343 2

24 20.57 0.206 1.609 38.37 22.35 117.48 4.082 3.343 2

25 20.58 0.206 1.609 38.37 22.35 117.32 4.083 3.343 2

26 20.57 0.206 1.609 38.38 22.36 117.27 4.084 3.343 2

27 20.56 0.206 1.609 38.38 22.36 117.23 4.085 3.343 2

28 20.48 0.207 1.608 38.38 22.35 117.05 4.087 3.342 2

29 20.47 0.207 1.608 38.37 22.35 116.91 4.088 3.341 2

30 20.58 0.206 1.609 38.39 22.36 116.97 4.088 3.343 2

Mean: 21.88 0.200 1.640 39.94 23.75 118.07 4.150 3.410 2

Std. 

Dev: 0.97 0.00 0.03 1.20 1.06 1.25 0.06 0.05 0.00
A.7

Sample: Macondo Crude in unfiltered seawater at 17.8°C with Finasol OSR 52 1:10,000 dilution at 1 

atm pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 22.49 0.199 1.654 40.31 24.04 117.16 4.180 3.431 2

2 22.51 0.199 1.654 40.32 24.04 117.06 4.181 3.432 2

3 22.43 0.200 1.653 40.32 24.05 116.88 4.184 3.431 2

4 22.52 0.199 1.654 40.35 24.07 116.74 4.186 3.433 2

5 22.39 0.200 1.654 40.36 24.07 116.56 4.189 3.431 2

6 22.40 0.200 1.653 40.36 24.07 116.42 4.190 3.432 2

7 22.33 0.201 1.653 40.37 24.08 116.27 4.193 3.431 2

8 22.40 0.200 1.654 40.38 24.08 116.18 4.194 3.432 2

9 22.19 0.202 1.653 40.38 24.08 115.93 4.199 3.430 2

10 22.25 0.201 1.653 40.38 24.08 115.91 4.198 3.431 2

11 21.42 0.208 1.649 40.69 24.20 107.42 4.301 3.428 2

12 21.45 0.208 1.649 40.70 24.21 107.30 4.302 3.429 2

13 21.43 0.208 1.649 40.71 24.21 107.17 4.304 3.429 2

14 21.38 0.209 1.649 40.68 24.19 107.10 4.305 3.427 2

15 21.47 0.208 1.649 40.71 24.21 106.85 4.306 3.429 2

16 21.45 0.208 1.649 40.73 24.22 106.66 4.309 3.429 2

17 21.44 0.208 1.649 40.72 24.21 106.53 4.310 3.429 2

18 21.37 0.209 1.649 40.72 24.21 106.36 4.313 3.428 2

19 21.37 0.209 1.649 40.72 24.21 106.15 4.315 3.428 2

20 21.34 0.209 1.649 40.73 24.21 106.06 4.317 3.427 2

21 21.32 0.209 1.647 40.61 24.13 107.53 4.297 3.423 2

22 21.36 0.208 1.647 40.62 24.13 107.36 4.298 3.425 2

23 21.39 0.208 1.648 40.63 24.14 107.17 4.300 3.425 2

24 21.37 0.208 1.647 40.62 24.14 107.02 4.301 3.425 2

25 21.36 0.208 1.648 40.63 24.14 106.90 4.303 3.424 2

26 21.36 0.208 1.648 40.64 24.15 106.68 4.305 3.425 2

27 21.32 0.209 1.647 40.64 24.14 106.48 4.307 3.424 2

28 21.28 0.209 1.647 40.63 24.13 106.39 4.308 3.424 2

29 21.32 0.209 1.647 40.64 24.14 106.23 4.310 3.424 2

30 21.34 0.209 1.647 40.63 24.14 105.99 4.313 3.425 2

Mean: 21.72 0.210 1.650 40.56 24.14 110.02 4.270 3.430 2

Std. 

Dev: 0.49 0.00 0.00 0.16 0.06 4.69 0.06 0.00 0.00
A.8

Sample: 30°C Macondo Crude in unfiltered seawater at 17.8°C with Finasol OSR 52 1:10 dilution at 1 

atm pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 5.53 0.266 0.927 13.24 4.66 120.02 2.361 1.949 2

2 6.51 0.274 1.021 16.59 6.36 109.32 2.768 2.154 2

3 7.21 0.267 1.060 17.77 7.05 109.49 2.864 2.232 2

Mean: 6.42 0.270 1.000 15.87 6.02 112.94 2.660 2.110 2

Std. Dev: 0.84 0.00 0.07 2.35 1.23 6.13 0.27 0.15 0.00

Sample: 30°C Macondo Crude in unfiltered seawater at 17.8°C with Finasol OSR 52 1:50 dilution at 1 

atm pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 15.06 0.225 1.408 30.00 15.39 111.31 3.681 2.934 2

2 13.72 0.227 1.347 27.47 13.54 114.66 3.493 2.811 2

3 13.81 0.227 1.352 27.68 13.68 114.21 3.510 2.821 2

Mean: 14.20 0.230 1.370 28.38 14.20 113.39 3.560 2.860 2

Std. Dev: 0.75 0.00 0.03 1.40 1.03 1.82 0.10 0.07 0.00

Sample: 30°C Macondo Crude in unfiltered seawater at 17.8°C with Finasol OSR 52 1:100 dilution at 

1 atm pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 18.76 0.211 1.52 34.55 19.01 112.07 3.928 3.161 2

2 19.66 0.207 1.54 35.30 19.66 114.16 3.947 3.201 2

3 18.31 0.216 1.52 34.87 19.19 106.28 4.004 3.164 2

Mean: 18.91 0.210 1.53 34.91 19.29 110.84 3.960 3.180 2

Std. Dev: 0.69 0.00 0.01 0.38 0.34 4.08 0.04 0.02 0.00
A.9

Sample: 30°C Macondo Crude in unfiltered seawater at 17.8°C with Finasol OSR 52 1:1000 dilution at 

1 atm pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 23.58 0.206 1.683 41.97 25.57 117.09 4.273 3.496 2

2 23.34 0.205 1.672 41.45 25.08 116.95 4.247 3.474 2

3 24.23 0.205 1.700 42.88 26.34 115.31 4.335 3.532 2

Mean: 23.72 0.210 1.690 42.10 25.66 116.45 4.290 3.500 2

Std. Dev: 0.46 0.00 0.01 0.72 0.64 0.99 0.05 0.03 0.00

Sample: 30°C Macondo Crude in unfiltered seawater at 17.8°C with Finasol OSR 52 1:10,000 dilution 

at 1 atm pressure.

.Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 23.32 0.206 1.675 41.75 25.26 113.01 4.303 3.480 2

2 24.54 0.205 1.712 43.66 26.95 110.20 4.424 3.556 2

3 23.63 0.204 1.677 41.65 25.22 115.88 4.270 3.482 2

Mean: 23.83 0.210 1.690 42.35 25.81 113.03 4.330 3.510 2

Std. Dev: 0.63 0.00 0.02 1.13 0.99 2.84 0.08 0.04 0.00
A.10

Sample: 30°C Macondo Crude in unfiltered seawater at 17.8°C with Corexit 9500 1:10 dilution at 1 

atm pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 4.29 0.297 0.863 11.86 3.95 114.32 2.275 1.829 2

2 4.93 0.249 0.846 10.40 3.37 127.47 1.978 1.774 2

3 5.08 0.279 0.909 12.83 4.47 118.87 2.326 1.920 2

Mean: 4.77 0.280 0.870 11.70 3.93 120.22 2.190 1.840 2

Std. 

Dev: 0.42 0.02 0.03 1.22 0.55 6.68 0.19 0.07 0.00

Sample: 30°C Macondo Crude in unfiltered seawater at 17.8°C with Corexit 9500 1:100 dilution at 1 

atm pressure

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 17.10 0.205 1.429 30.00 15.60 124.44 3.537 2.969 2

2 17.45 0.213 1.472 32.27 17.27 118.01 3.742 3.061 2

3 16.03 0.213 1.413 29.67 15.28 120.46 3.563 2.940 2

Mean: 16.86 0.210 1.440 30.65 16.05 120.97 3.610 2.990 2

Std. 

Dev: 0.74 0.00 0.03 1.42 1.07 3.25 0.11 0.06 0.00

Sample: 30°C Macondo Crude in unfiltered seawater at 17.8°C with Corexit 9500 1:1000 dilution at 1 

atm pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 24.37 0.205 1.707 43.54 26.72 103.83 4.474 3.546 2

2 24.09 0.204 1.695 42.8 26.13 108.50 4.395 3.520 2

3 24.00 0.206 1.697 43.19 26.32 98.91 4.498 3.526 2

Mean: 24.15 0.210 1.700 43.18 26.39 103.75 4.460 3.530 2

Std. 

Dev: 0.19 0.00 0.01 0.37 0.30 4.80 0.05 0.01 0.00
A.11

Sample: 30°C Macondo Crude in unfiltered seawater at 17.4°C at 1 atm pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 23.38 0.204 1.67 41.49 24.98 110.83 4.307 3.468 2

2 23.52 0.207 1.684 42.38 25.72 106.89 4.392 3.499 2

3 23.26 0.208 1.682 42.57 25.73 97.03 4.488 3.496 2

Mean: 23.39 0.210 1.68 42.15 25.48 104.92 4.400 3.490 2

Std. 

Dev: 0.13 0.00 0.01 0.58 0.43 7.11 0.09 0.02 0.00

Sample: 17.4°C Macondo Crude in unfiltered seawater at 17.4°C at 1 atm pressure.

Number Gamma Beta R0 Area Volume Theta Height Width Opt

1 22.54 0.202 1.666 41.12 24.69 112.89 4.265 3.457 2

2 21.22 0.195 1.547 38.22 23.17 90.11 4.031 3.199 2

3 21.61 0.203 1.634 39.54 23.30 114.05 4.173 3.391 2

Mean: 21.79 0.200 1.620 39.63 23.72 105.68 4.160 3.350 2

Std. 

Dev: 0.68 0.00 0.06 1.45 0.84 13.50 0.12 0.13 0.00
Appendix B

–

PAH Analysis Following Biodegradation

The data provided in Table 4.6 and Table 4.7 and here in Appendix B indicate that the inoculated samples 

have lower concentrations of several PAHs than the values from the Time 0 sample (Sample O). Some of 

the observed loss could be explained by effects other than biodegradation. For example, 2-ring PAHs 

(especially the naphthalene) can be lost by evaporation more easily than other PAHs. Therefore, the 

significant concentration decrease of those PAHs could be a combined effect of evaporation and 

biodegradation. The concentration of other PAHs, such as phenanthrene, did decrease compared to 

Sample O. The results of the hydrocarbon analysis were noisy and showed no significant trends relative to 

DOR or droplet size, or even to the presence or absence of dispersant.
Appendix B

PAH Analysis Following Biodegradation

# Compound

MDL 

(ug/L)

PAHs (ug/L)

Sample 

A

Sample 

B

Sample 

C

Sample 

D

Sample 

E

1 Naphthalene 0.0026 U U U U U

2 2,6-

Dimethylnaphthalene 0.0011 U 2.95 2.27 3.17 3.96

3 C1-Naphthalenes 0.0023 U U U U U

4 C2-Naphthalenes 0.0096 U 57.98 48.90 68.15 76.10

5 C3-Naphthalenes 0.0112 U 444.57 414.24 477.71 360.26

6 C4-Naphthalenes 0.0113 U 376.09 367.33 400.64 340.37

7 Biphenyl 0.0005 U U U U U

8 Acenaphthylene 0.0004 U U U U U

9 Acenaphthene 0.0004 U U U U U

10 Fluorene 0.0011 U 16.09 14.97 17.36 14.05

11 1-Methyl fluorene 0.0010 U 109.58 105.85 116.82 97.96

12 C1-Fluorenes 0.0035 U 149.49 155.31 159.78 126.25

13 C2-Fluorenes 0.0026 U 344.82 350.95 378.28 326.82

14 C3-Fluorenes 0.0131 U 332.46 344.02 353.57 333.46

15 Dibenzothiophene 0.0012 U 10.78 10.53 10.97 9.54

16 C1-Dibenzothiophenes 0.0030 U 58.69 60.70 59.77 55.47

17 C2-Dibenzothiophenes 0.0023 U 106.04 111.98 114.98 107.01

18 C3-Dibenzothiophenes 0.0031 U 90.04 95.21 94.87 94.16

19 C4-Dibenzothiophenes 0.0022 U 37.75 38.13 40.97 38.51

20 Phenanthrene 0.0025 U 92.04 89.90 92.18 82.61

21 Anthracene 0.0002 U 4.01 4.46 4.10 3.46

22 1-methyl Phenanthrene 0.0008 U 117.38 121.62 120.91 115.07

23 3,6-DMP 0.0009 U 57.27 57.11 60.59 55.42

24 2,6-DMP 0.0003 U 59.94 63.13 65.59 58.76

25 1,7-DMP 0.0006 U 68.76 69.90 78.82 67.34

26 C1-Phen/An 0.0034 U 557.88 575.23 581.75 534.15

27 C2-Phen/An 0.0038 U 798.99 828.03 830.88 778.72

28 C3-Phen/An 0.0028 U 508.88 536.43 544.05 507.08

29 C4-Phen/An 0.0041 U 141.18 145.32 147.75 137.53

30 Fluoranthene 0.0004 U 3.70 3.70 3.31 3.45
# Compound

PAHs (ug/L)

Sample 

F

Sample 

G

Sample 

H

Sample 

I

Sample 

J

1 Naphthalene U 0.07 U 0.00 U

2 2,6-

Dimethylnaphthalene 0.71 3.52 U 4.11 17.63

3 C1-Naphthalenes U U U U U

4 C2-Naphthalenes 25.91 66.73 U 33.62 105.55

5 C3-Naphthalenes 258.30 456.86 U 327.39 489.13

6 C4-Naphthalenes 278.18 386.71 U 397.84 427.82

7 Biphenyl U U U U U

8 Acenaphthylene U U U U U

9 Acenaphthene U U U U U

10 Fluorene 11.20 18.54 U 14.17 19.74

11 1-Methyl fluorene 80.27 109.66 U 105.43 111.62

12 C1-Fluorenes 116.55 161.95 U 163.57 175.92

13 C2-Fluorenes 286.42 356.68 U 368.60 358.38

14 C3-Fluorenes 286.40 327.78 U 382.92 327.18

15 Dibenzothiophene 7.84 11.39 U 9.80 11.49

16 C1-Dibenzothiophenes 45.70 62.14 U 61.83 65.16

17 C2-Dibenzothiophenes 87.17 111.89 U 119.04 112.49

18 C3-Dibenzothiophenes 76.79 93.40 U 102.90 93.73

19 C4-Dibenzothiophenes 32.05 38.20 U 46.96 43.07

20 Phenanthrene 66.60 98.83 U 84.74 95.17

21 Anthracene 3.29 3.96 U 3.01 4.29

22 1-methyl Phenanthrene 91.43 122.25 U 121.99 116.91

23 3,6-DMP 45.00 57.06 U 59.94 58.42

24 2,6-DMP 46.82 63.76 U 63.01 61.98

25 1,7-DMP 56.93 75.98 U 71.49 72.02

26 C1-Phen/An 439.28 565.65 U 575.94 570.47

27 C2-Phen/An 643.63 827.50 U 860.50 820.57

28 C3-Phen/An 406.97 514.46 U 550.62 518.12

29 C4-Phen/An 112.05 142.80 U 149.83 141.83

30 Fluoranthene 2.40 3.54 U 3.82 3.16
# Compound

PAHs (ug/L)

Sample 

K

Sample 

L

Sample 

M

Sample 

N

Sample 

O

1 Naphthalene U U U U 227.85

2 2,6-

Dimethylnaphthalene 33.60 50.05 58.10 23.67 726.26

3 C1-Naphthalenes U U U U 1011.24

4 C2-Naphthalenes 160.50 239.48 270.90 131.75 2696.94

5 C3-Naphthalenes 637.65 722.20 714.97 555.89 2027.47

6 C4-Naphthalenes 487.33 523.92 486.94 453.68 792.95

7 Biphenyl U 1.03 U U 142.76

8 Acenaphthylene U U U U U

9 Acenaphthene U U U U U

10 Fluorene 21.29 28.42 27.22 22.50 98.07

11 1-Methyl fluorene 126.01 142.76 130.71 122.61 234.14

12 C1-Fluorenes 198.28 218.68 206.40 186.10 344.17

13 C2-Fluorenes 383.14 405.91 376.73 369.21 471.48

14 C3-Fluorenes 335.19 347.61 347.57 357.29 378.95

15 Dibenzothiophene 12.22 13.87 12.96 12.35 22.73

16 C1-Dibenzothiophenes 66.72 73.40 69.65 69.07 85.81

17 C2-Dibenzothiophenes 117.74 122.47 116.28 118.45 132.00

18 C3-Dibenzothiophenes 99.44 104.73 102.29 99.77 107.81

19 C4-Dibenzothiophenes 44.59 46.54 44.99 45.49 46.75

20 Phenanthrene 99.81 113.80 101.43 98.57 163.85

21 Anthracene 4.11 5.16 4.05 4.17 5.45

22 1-methyl Phenanthrene 121.72 130.08 121.82 120.72 147.43

23 3,6-DMP 59.84 60.78 57.87 58.64 65.47

24 2,6-DMP 59.75 67.60 60.50 66.27 67.76

25 1,7-DMP 73.47 73.56 69.98 75.84 78.01

26 C1-Phen/An 594.62 623.83 590.85 579.48 717.22

27 C2-Phen/An 833.24 864.14 843.40 819.60 934.37

28 C3-Phen/An 532.64 552.04 544.04 534.77 571.95

29 C4-Phen/An 148.27 153.87 150.68 151.44 154.36

30 Fluoranthene 3.55 3.37 3.07 3.63 4.63
# Compound

MDL 

(ug/L)

PAHs (ug/L)

Sample 

A

Sample 

B

Sample 

C

Sample 

D

Sample 

E

31 Pyrene 0.0015 U 9.48 9.89 9.91 9.69

32 C1-Fluor/Py 0.0008 U 60.89 63.65 66.04 63.60

33 C2-Fluor/Py 0.0013 U 93.10 99.93 99.60 94.52

34 C3-Fluor/Py 0.0022 U 102.95 106.96 107.29 99.46

35 Benzo [a] anthracene 0.0003 U 7.96 7.03 7.27 7.29

36 Chrysene 0.0006 U 33.13 35.52 36.07 33.82

37 C1-Chrysenes 0.0008 U 74.40 79.67 78.07 77.03

38 C2-Chrysenes 0.0030 U 94.94 100.10 98.07 102.48

39 C3-Chrysenes 0.0066 U 64.72 64.89 68.70 67.50

40 C4-Chrysenes 0.0141 U 38.67 41.03 41.07 42.41

41 Benzo [b] fluoranthene 0.0008 U 5.95 5.72 5.77 5.12

42 Benzo [k] fluoranthene 0.0010 U U U U U

43 Benzo [e] pyrene 0.0011 U 6.69 7.37 6.45 7.00

44 Benzo [a] pyrene 0.0010 U 1.29 1.33 1.16 0.78

45 Perylene 0.0010 U 9.27 10.21 9.94 9.36

46 Indeno [1,2,3-c,d] 

pyrene

0.0002 U U U U U

47 Dibenzo [a,h] 

anthracene 0.0003 U 2.93 1.84 1.12 1.35

48 Benzo [g,h,i] perylene 0.0002 U 1.87 1.87 1.15 1.35

U: below method detection limit

Recovery MDL 

(ug/L)

Sample 

A

Sample 

B

Sample 

C

Sample 

D

Sample 

E

d8-Naphthalene 84% 77% 68% 63% 70%

d10-Acenaphthene 111% 91% 84% 80% 90%

d10-Phenanthrene 51% 105% 98% 95% 105%

d12-Chrysene 84% 78% 67% 74% 69%

d12-Perylene 219% 85% 67% 77% 80%
# Compound

PAHs (ug/L)

Sample 

F

Sample 

G

Sample 

H

Sample 

I

Sample 

J

31 Pyrene 7.73 9.49 U 9.52 8.75

32 C1-Fluor/Py 48.67 61.22 U 67.28 64.61

33 C2-Fluor/Py 74.14 95.07 U 103.34 94.41

34 C3-Fluor/Py 77.54 101.74 U 112.32 107.64

35 Benzo [a] anthracene 4.04 7.26 U 8.26 6.91

36 Chrysene 21.34 30.91 U 34.50 32.44

37 C1-Chrysenes 43.37 69.87 U 80.43 75.35

38 C2-Chrysenes 58.50 90.22 U 103.01 96.18

39 C3-Chrysenes 39.69 63.87 U 68.58 62.58

40 C4-Chrysenes 24.95 38.31 U 42.55 40.33

41 Benzo [b] fluoranthene 2.48 5.59 U 4.94 4.65

42 Benzo [k] fluoranthene U U U 3.43 2.06

43 Benzo [e] pyrene 3.39 6.11 U 7.26 6.22

44 Benzo [a] pyrene 0.42 0.51 U 1.24 0.77

45 Perylene 4.91 9.30 U 8.91 8.40

46 Indeno [1,2,3-c,d] 

pyrene

U U U U U

47 Dibenzo [a,h] 

anthracene U 1.40 U 1.60 1.42

48 Benzo [g,h,i] perylene 0.54 1.30 U 1.58 1.60

U: below method detection limit

Recovery Sample 

F

Sample 

G

Sample 

H

Sample 

I

Sample 

J

d8-Naphthalene 45% 76% 94% 68% 64%

d10-Acenaphthene 51% 84% 120% 89% 88%

d10-Phenanthrene 64% 96% 73% 110% 101%

d12-Chrysene 70% 79% 131% 74% 76%

d12-Perylene 77% 84% 240% 86% 85%
# Compound

PAHs (ug/L)

Sample 

K

Sample 

L

Sample 

M

Sample 

N

Sample 

O

31 Pyrene 8.98 9.14 8.81 8.83 10.27

32 C1-Fluor/Py 65.17 69.07 64.77 67.31 67.25

33 C2-Fluor/Py 98.08 99.35 100.13 97.87 104.19

34 C3-Fluor/Py 106.02 108.18 105.87 109.88 113.46

35 Benzo [a] anthracene 8.36 8.14 6.74 7.25 8.53

36 Chrysene 32.40 34.46 32.93 32.89 37.00

37 C1-Chrysenes 75.75 77.12 76.79 76.79 86.45

38 C2-Chrysenes 95.26 100.30 94.42 96.85 110.35

39 C3-Chrysenes 60.85 65.10 62.35 63.70 72.69

40 C4-Chrysenes 37.51 38.61 38.06 40.83 47.14

41 Benzo [b] fluoranthene 4.87 4.22 4.43 4.10 5.68

42 Benzo [k] fluoranthene 3.68 3.52 3.27 3.54 4.49

43 Benzo [e] pyrene 7.28 6.74 7.26 6.62 8.21

44 Benzo [a] pyrene 1.46 0.96 1.04 0.74 2.20

45 Perylene 9.32 9.79 8.84 8.55 10.11

46 Indeno [1,2,3-c,d] 

pyrene

5.89 U U U 6.13

47 Dibenzo [a,h] 

anthracene 13.65 U U 1.49 14.48

48 Benzo [g,h,i] perylene 6.78 1.55 1.02 1.05 7.19

U: below method detection limit

Recovery Sample 

K

Sample 

L

Sample 

M

Sample 

N

Sample 

O

d8-Naphthalene 71% 67% 72% 80% 91%

d10-Acenaphthene 94% 88% 95% 99% 95%

d10-Phenanthrene 112% 102% 109% 114% 107%

d12-Chrysene 86% 80% 81% 81% 77%

d12-Perylene 88% 81% 86% 93% 91%
Appendix C

–

Droplet Size Distribution
C.1

Bacteria; no dispersant
C.2

Bacteria; no dispersant
C.3

Bacteria; no dispersant
C.4

Bacteria; DOR = 1:100
C.5

Bacteria; DOR = 1:40
C.6

Bacteria; DOR = 1:20
C.7

Bacteria; DOR = 1:10
C.8

Fungi; No oil, no dispersant
C.9

Fungi; No dispersant
C.10

Fungi; DOR= 1:200
C.11

Fungi; DOR= 1:100
C.12

Fungi; DOR= 1:40
C.13

Fungi; DOR= 1:20
C.14

Fungi; DOR= 1:10
Appendix D

–

Determination of Oil, Dispersant, and Seawater Fluorescent 

Spectra
Appendix D

Determination of Oil, Dispersant, and Seawater Fluorescent 

Spectra


Appendix E

–

Determination of Oil, Dispersant, and Seawater Ratios Using 

Fluorescence
E.1

Appendix E

Determination of Oil, Dispersant, and Seawater Ratios Using 

Fluorescence
E.2
E.3
E.4
E.5
E.6
E.7
E.8
PNNL-XXXXX

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