Three-dimensional modeling of the Macondo well blowout

Claire B. Paris1,2 and Matthieu Le Hénaff1,3

1Rosenstiel School of Marine and Atmospheric Science2Applied Marine Physics, 3CIMAS

NSF RAPID 104869

Zachary Aman4 , Ajit Subramaniam5, Dong-Ping Wang6, Judith Helgers1,7, Ashwanth Srinivasan1,8 , Villy H.

Kourafalou1,8, P. Hogan9, Joost de Gouw10,Jerome Brioude10

4Colorado School of Mines, 5Columbia University, 6State university of New York, 7Center for Computational Science, 8MPO, 9NRL, 10Boulder Colorado Univ.

Outline

• Far-field oil fate and transport model

• Evaluation of surface dispersion

• Formation and evaluation of deep intrusions

• Predicted effect of synthetic dispersants on the oil partition in the water column

•April 20 - explosion of the DWH rig causing rupture of the riser pipe

•April 20- July 15 - approximately 2 M gallons oil /day oil gushing into the Gulf of Mexico, a total of 175-222 M gallons (~5M barrels) of sweet Louisiana crude oil (Crone and Tolstoy 2010)

•Sept 18, - permanent closure of the Macondo well, nearly 5 mo. later

•July 15 - capping of the well after 86 days (relief well still leaking?)

•May - July 15 - injection of about 2 M gallons dispersant at the wellhead

✓ The Macondo well, in the Mississippi Canyon block 252, is located at 1522 m

Facts timeline

Far-Field Model: Connectivity Modeling System (CMS)

Far-Field TransportFate

TidesWaves

Near-Field BC

Linking near- & far-field models: Boundary Conditions

Multi-phase plumeinput variables

hs = separation height ht = trap heighthp = peel heightQi = flow rate to 1st intrusionhi = thickness of 1st intrusionbi = width of 1st intrusionxi = upstream penetrationf = peeling fractionus = slip velocity, f(droplet size)ua = flow velocity

(Akar & Jirka 1995; Socolofski & Adams 2003, 2005Socolofski & Bhaumik 2008, Socolofski et al. 2011)

Figure 4. Time!series comparison of fluorescence measurements (colored dots) with predicted trap height hT (black dots;upper and lower estimates shown with gray crosses). Size of the colored dots represents the amount of “excess” fluorescence;color of the dots indicates potential density as st (r ! 1000 kg/m3).

Figure 3. Profiles of fluorescence for (a) the field measurement of the R/V Brooks McCall at Station BM54 on May 30,2010 and (b) laboratory experiment T04 reported by Socolofsky and Adams [2003, 2005]. Fluorescence in Figure 3a isrelated to the concentration of dissolved and dispersed liquid hydrocarbons; fluorescence in Figure 3b is the concentration ofRhodamine 6G dye tracer injected at the laboratory diffuser as a tracer for entrained ambient water. The lower and uppergray bars in Figure 3a are the predicted trap height hT and peel height hP, respectively.

SOCOLOFSKY ET AL.: SUBSURFACE INTRUSIONS FOR THE DH BLOWOUT L09602L09602

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chemical composition of petroleum: multi-fractions model

(DeGouw et al. 2011, Science)

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Chemical composition of petroleum: multi-fractions model

(DeGouw et al. 2011, Science)

Oil Fate Model: Multi-fractions

• Oil fraction, density, evaporation rate: The oil is composed of 3 fractions,

light (35% C1), medium (25% C2) and heavy (40% C3) with different properties of

both density and evaporation

• Oil/gas droplet/bubble size: Each particle size is assigned randomly between a

minimum and maximum diameter, dmin = 1 µm and dmax = 300 µm (from laboratory

experiments for high shear light crude oil-in-water emulsions)

• Integrated approach of field-calibrated blowout model to compute the terminal

velocity of spherical shape (size range < 1.2mm ):

UT = Re µ/gd

where Re is the Reynolds number, µ is the dynamic viscosity of the ambient fluid, d is the spherical

particle diameter, and g the acceleration due to gravity.

• Flow rate (Qi): high release frequency of 1000 particles every 2 hours

(Greaves et al. 2008, Zheng et al. 2002, Paris et al. 2011, ES&T in review)

• Hydrodynamic sub-model GoM-HYCOM,1/25 deg. horizontal res., 20 vertical layers- forced by the 0.5 deg. NOGAPS winds and surface fluxes and by large

scale HYCOM- Navy Coupled Ocean Data Assimilation (NCODA) system (SSH,

SST, in-situ profiles)- current velocity, temperature, and salinity (critical to oil

displacement and degradation)- COAMPS winds (27 km res) to simulate surface wind and wind-

induced wave drift (Le Hénaff et al. 2012)

• Stochastic Particle Tracking sub-model: IBM with subgrid mixing (Khor = 1 m2.s-1 Kvert = 10-5 m2.s-1) and oil behavior (Paris et al. 2012)

• Uncertainties analyses: error estimation based on droplet size and hydrocarbon fractions using polynomial method. 1000 CPUs used to simulate the fate of ~10M oil particles released from April 20 to July 15 in a 36-simulation ensemble (Srinivasan et al. 2011)

Oil Transport Model

10

SURFACE: Model Evaluation

(Le Hénaff et al. 2011, ES&T in review)

Observed Simulated Observed Simulated

ROFFSROFFS

SURFACE: Effect of Wind Drift

• with wind-induced drift: spread toward the NE • without wind-induced drift: advected around the frontal cyclone & reach the edge of the LC

(Le Henaff et al., ES&T in review)

WD Apr 30 ND Apr 30

ND May 3WD May 3

• GOM-HYCOM SSH on April 20, 2010•LC well extended•frontal eddies

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Effect of wind drift: Animation of surface slicks - No Wind (Apr.20- Sep.16)

(Le Hénaff et al. 2011, EST in review )

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Effect of Wind Drift: Animation of surface slicks - Wind Drift (Apr.20- Sep.16)

(Le Hénaff et al. 2011, submit. EST)

SURFACE: Model Evaluation with observed and simulated oil landfall

(Le Henaff et al. 2011, submit. EST)

Model

Observations

CSTARS

15

0 0.5 1 1.5 2 2.5x 104

0

500

1000

1500

Number of particles

De

pth

(m

)

June 20, 2010

0 0.5 1 1.5 2 2.5x 104

0

500

1000

1500

Number of particles

De

pth

(m

)

June 28, 2010

Observation-Model Comparison for the first intrusion (deep plume)SUBSEA: Model Evaluation for vertical profile

Depth profile of total oil particles (1-500 μm) for oil density =0.82-0.96 kg.m-3)

Composite depth profile of surfactant (DOSS) concentrations observed in samples (May/June, green circles; June, blue diamonds; red square June) Kujawsnski et al. (2011 ES&T)

SUBSEA: Formation of the submerged oil plumes (Apr.20-Sep.26, 2010)

(Paris et al. 2011, EST in review)

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Depth (m)

(Paris et al. 2011,Submitted EST)

SUBSEA: Model Evaluation of the “deep plume” (Aug-Sep. 2010, 1000-1100m)

No. Particles

O2 Anom.

(Kessler et al., 2011)

Oxygen anomalyfrom Pogo float (A. Subramaniam, NSF-RAPID)

Sampling domain for observations in B)

A) modelled intrusion and float measurements of O2

B) Intrusion observations

A(Paris et al. 2011, EST)

SUBSEA: 3D animation of the Macondo well blowout (Apr.20-Sep.6, 2010)

(Paris et al. 2011, EST in review)

Oil droplet diameter (µm)

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(Subramanian et al. In prep.)

Dep

th (

m)

Evaluation of Deep Plumes: Observations of O2 anomalies (Aug - Sept 2010)

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D. ∅ < 50µm

C. ∅ = 50-60µm

B. ∅ = 60-70 µm

A. ∅ = 1-300µm

Dep

th (m

)

D. ∅ < 50µm

C.. ∅ = 50-60µm

B. ∅ = 60-70 µm

A ∅ = 1-300µm

Dep

th (m

)

Time (month/day)

Effect of droplet size & vertical velocity of OGCMs on oil vertical distribution

Ut = buoyancyUt = buoyancy + w

GOM-HYCOM (1/25o)(u,v,w) velocity field components

GOM-HYCOM (1/25o)(u,v) velocity field components

∅ 1-300 µm ∅ 1-300 µm∅ 50-60 µm ∅ 50-60 µm

∅ <50 µm∅ 60-70 µm ∅ <50 µm ∅ 60-70 µm

Time (month/day) Time (month/day)

➡ Most surfaced droplets/bubbles must have been > 100 µm ➡Droplets < 50 µm are nearly neutrally buoyant

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D. ∅ < 50µm

C.. ∅ = 50-60µm

B. ∅ = 60-70 µm

A ∅ = 1-300µm

Dep

th (m

)

Time (month/day)

GOM-HYCOM (1/25o) GOM-HYCOM (1/50o) nested to NGOM-HYCOM

Effect of the Horizontal Resolution of OGCMs

Time (month/day) Time (month/day)

∅ 1-300 µm ∅ 1-300 µm∅ 50-60 µm∅ 50-60 µm

∅ <50 µm∅ 60-70 µm ∅ <50 µm∅ 60-70 µm

Predicted Effect of Synthetic Dispersants

Oil Droplet Size (µm)

(Greaves et al. 2008)

Time (month/day)

Droplet distributionUj jet velocityDo initial droplet/bubble sizeσ the surface tension between oil and water (decreases with temperature & dispersants)

10-20 µm mode

50-70 µm mode

Prop

ortio

n of

tota

l oil

spille

d

The predicted oil partition through time with(solid lines) and without (dotted lines) synthetic dispersants

Oil in suspension below 1000 m

Total oil in suspension in the water column

Oil Partition Through Time

Prop

ortio

n of

tota

l oil

spille

dAtmosphereSurfaceAshoreWater columnDecay and Settled

The predicted oil partition through time with (solid lines) and without (dotted lines) synthetic dispersants

(Paris et al. 2011, ES&T under review)

Dai

ly p

ropo

rtio

n of

tota

l oil

(%)

Submerged oil

Surfaced oil

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SUMMARY of FINDINGS

• the prevailing winds, through their effect to induce direct and wave drift, played a major role in pushing the rising oil toward the coasts along the northern Gulf, and, in synergy with the surface currents, prevented the oil from reaching the Florida Straits.

• The dynamic nature of the system showing revealed 1) how the layering and patchiness happened, predicting the formation of multiple stratified plumes and 2) hitherto unexplored mechanisms of local transport, bursting dense packets of oil westward towards the interior of the GOM. Oceanic events may further cause upwelling or subduction of the neutrally buoyant submerged oil

• while the entire water column was contaminated to some degree by the oil mixture, through time, a large fraction of the oil could have stayed submerged

• numerical experiments on the size distribution of the oil droplets suggested that applying Corexit at the wellhead may not have significantly changed the amount of crude oil rising to the surface. The synthetic dispersant contributed significantly to trap the oil below 1000m

• Regional models are efficient in representing general patterns of oil transport from deep blowouts

• Results are sensible to model vertical velocity and resolution

Future Improvements

Oil Transport Model(1) Better estimate of surface and coastal circulation: verify model ocean currents with existing drifters (Global Drifter Program) and drifter experiments (i.e., CARTHE), XCP, shipboard ADCP

(2) Better estimate of deep ocean: apply spatial filtering (OA) to remove spurious vertical velocity, implement subsurface 4D DA for both hydrographic and velocity observations

(3) implement a wave model run to the surface stochastic particle tracking

(4) verify oil droplet size with available field and laboratory data

(5) sensitivity analyses on the number of chemical fractions

(6) Oil weathering: implement time varying fraction-dependent dissolution rate of oil droplets, implement settling rate based on DOR

(7) improve modeling of the effect of synthetic dispersantʼs injection on oil droplet size distribution, biodegradation, and settlement

Oil Fate Model

Acknowledgements

This study was funded through the NSF-RAPID OCE-10-48697 to C.B. Paris, V. Kourafalou, and A. Srinivasan

Observations were supported by the NSF-RAPID OCE-10-48482 and OCE-10-58233 to A. Subramaniam

We thank the University of Miami Center for Computational Science (CCS) for operational support with multi-processors

We thank P. Hogan and S. Secada for the hydrodynamic fields provided by the Ocean Prediction Branch of NRL-SSC.