Intercomparison of oil spill prediction models for accidental blowout scenarios with and without subsea chemical dispersant injection

Socolofsky, Scott A.; Adams, E. Eric; Boufadel, Michel C.; Aman, Zachary M.; Johansen, Øistein; Konkel, Wolfgang J.; Lindo, David; Madsen, Mads N.; North, Elizabeth W.; Paris, Claire B.; Rasmussen, D.E.; Reed, Mark; Rønningen, Petter; Sim, L.; Uhrenholdt, Thomas; Anderson, Karl; Cooper, Cortis; Nedwed, Tim

doi:10.1016/j.marpolbul.2015.05.039

Cited by 93 publications

(71 citation statements)

References 32 publications

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“…TAMOC predicts mass flow rates for gas of 7.53 kg/s and for liquid of 31.11 kg/s at the release. These values agree well with those reported in Socolofsky et al [31] using the MultiFlash TM software package of 7.4 kg/s of gas and 34.5 kg/s of liquid petroleum fluid at the release. Under these pressure and temperature conditions, 42% of the released mass of methane and light hydrocarbons (C1-C4) is initially present in the petroleum liquid phase; hence, dissolution from both phases should be tracked in the model.…”

Section: Test Casessupporting

confidence: 91%

“…To demonstrate the utility of the TAMOC suite to simulate an accidental oil-well blowout and to compare the SPM and BPM solutions, we apply the models to two test cases defined in Socolofsky et al [31], which reports the results of a model inter-comparison study for oil-well blowout models. The inter-comparison exercise defined several canonical cases of an accidental oil-well blowout in deep water with the purpose to test the performance of predictions for bubble and droplet size distributions at the release and for the fate of oil and gas in the water column over a 14-day simulation.…”

Section: Application: Api Model Inter-comparison Test Casesmentioning

confidence: 99%

“…The release is assumed to be from a broken vertical riser, with an outlet diameter of 0.3 m. In both cases, the ambient velocity profile is assumed uniform over the depth at a velocity of 0.05 m/s. Ambient vertical profiles of salinity, temperature, and oxygen were taken from the worldaverage profile in Sarmiento and Gruber [69] as reported in Socolofsky et al [31]. The complete composition of the oil and gas mixture is given in Socolofsky et al [31] and includes carbon dioxide, nitrogen, methane, ethane, propane, i-butane, n-butane, ipentane, n-pentane, hexane and one pseudo-component labeled as C7?.…”

Section: Test Casesmentioning

confidence: 99%

“…Ambient vertical profiles of salinity, temperature, and oxygen were taken from the worldaverage profile in Sarmiento and Gruber [69] as reported in Socolofsky et al [31]. The complete composition of the oil and gas mixture is given in Socolofsky et al [31] and includes carbon dioxide, nitrogen, methane, ethane, propane, i-butane, n-butane, ipentane, n-pentane, hexane and one pseudo-component labeled as C7?. We apply the DPM in TAMOC to predict the behavior of the gas and liquid phases of this mixture.…”

Section: Test Casesmentioning

confidence: 99%

“…As an application (Sect. 4), we further compare the double-plume and crossflow integral model approaches using simulations of selected test cases defined by Socolofsky et al [31] for hypothetical oil-well blowouts in deep water. These simulations demonstrate the role particle-scale thermodynamics and chemistry processes can have on the multiphase plume physics.…”

Section: Introductionmentioning

confidence: 99%

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Integral models for bubble, droplet, and multiphase plume dynamics in stratification and crossflow

2018

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We present the development and validation of a numerical modeling suite for bubble and droplet dynamics of multiphase plumes in the environment. This modeling suite includes real-fluid equations of state, Lagrangian particle tracking, and two different integral plume models: an Eulerian model for a double-plume integral model in quiescent stratification and a Lagrangian integral model for multiphase plumes in stratified crossflows. Here, we report a particle tracking algorithm for dispersed-phase particles within the Lagrangian integral plume model and a comprehensive validation of the Lagrangian plume model for single-and multiphase buoyant jets. The model utilizes literature values for all entrainment and spreading coefficients and has one remaining calibration parameter j, which reduces the buoyant force of dispersed phase particles as they approach the edge of a Lagrangian plume element, eventually separating from the plume as it bends over in a crossflow. We report the calibrated form j ¼ ½ðb À rÞ=b 4 , where b is the plume halfwidth, and r is the distance of a particle from the plume centerline. We apply the validated modeling suite to simulate two test cases of a subsea oil well blowout in a stratificationdominated crossflow. These tests confirm that errors from overlapping plume elements in the Lagrangian integral model during intrusion formation for a weak crossflow are negligible for predicting intrusion depth and the fate of oil droplets in the plume. The Lagrangian integral model has the added advantages of being able to account for entrainment from an arbitrary crossflow, predict the intrusion of small gas bubbles and oil droplets when appropriate, and track the pathways of individual bubbles and droplets after they separate from the main plume or intrusion layer.

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Section: Test Casessupporting

confidence: 91%

Section: Application: Api Model Inter-comparison Test Casesmentioning

confidence: 99%

Section: Test Casesmentioning

confidence: 99%

Section: Test Casesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integral models for bubble, droplet, and multiphase plume dynamics in stratification and crossflow

2018

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Natural and unnatural oil slicks in the Gulf of Mexico

et al. 2015

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When wind speeds are 2–10 m s −1 , reflective contrasts in the ocean surface make oil slicks visible to synthetic aperture radar (SAR) under all sky conditions. Neural network analysis of satellite SAR images quantified the magnitude and distribution of surface oil in the Gulf of Mexico from persistent, natural seeps and from the Deepwater Horizon (DWH) discharge. This analysis identified 914 natural oil seep zones across the entire Gulf of Mexico in pre‐2010 data. Their ∼0.1 µm slicks covered an aggregated average of 775 km 2 . Assuming an average volume of 77.5 m 3 over an 8–24 h lifespan per oil slick, the floating oil indicates a surface flux of 2.5–9.4 × 10 4 m 3 yr −1 . Oil from natural slicks was regionally concentrated: 68%, 25%, 7%, and <1% of the total was observed in the NW, SW, NE, and SE Gulf, respectively. This reflects differences in basin history and hydrocarbon generation. SAR images from 2010 showed that the 87 day DWH discharge produced a surface‐oil footprint fundamentally different from background seepage, with an average ocean area of 11,200 km 2 (SD 5028) and a volume of 22,600 m 3 (SD 5411). Peak magnitudes of oil were detected during equivalent, ∼14 day intervals around 23 May and 18 June, when wind speeds remained <5 m s −1 . Over this interval, aggregated volume of floating oil decreased by 21%; area covered increased by 49% ( p < 0.1), potentially altering its ecological impact. The most likely causes were increased applications of dispersant and surface burning operations.

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Propagation of uncertainty and sensitivity analysis in an integral oil‐gas plume model

Wang

Iskandarani

Srinivasan³

et al. 2016

JGR Oceans

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Polynomial Chaos expansions are used to analyze uncertainties in an integral oil‐gas plume model simulating the Deepwater Horizon oil spill. The study focuses on six uncertain input parameters—two entrainment parameters, the gas to oil ratio, two parameters associated with the droplet‐size distribution, and the flow rate—that impact the model's estimates of the plume's trap and peel heights, and of its various gas fluxes. The ranges of the uncertain inputs were determined by experimental data. Ensemble calculations were performed to construct polynomial chaos‐based surrogates that describe the variations in the outputs due to variations in the uncertain inputs. The surrogates were then used to estimate reliably the statistics of the model outputs, and to perform an analysis of variance. Two experiments were performed to study the impacts of high and low flow rate uncertainties. The analysis shows that in the former case the flow rate is the largest contributor to output uncertainties, whereas in the latter case, with the uncertainty range constrained by aposteriori analyses, the flow rate's contribution becomes negligible. The trap and peel heights uncertainties are then mainly due to uncertainties in the 95% percentile of the droplet size and in the entrainment parameters.

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Intercomparison of oil spill prediction models for accidental blowout scenarios with and without subsea chemical dispersant injection

Cited by 93 publications

References 32 publications

Integral models for bubble, droplet, and multiphase plume dynamics in stratification and crossflow

Integral models for bubble, droplet, and multiphase plume dynamics in stratification and crossflow

Natural and unnatural oil slicks in the Gulf of Mexico

Propagation of uncertainty and sensitivity analysis in an integral oil‐gas plume model

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