<p><strong>A METHODOLOGY FOR OPTIMIZING MODELING CONFIGURATION IN THE NUMERICAL MODELING OF OIL CONCENTRATIONS IN UNDERWATER BLOWOUTS: A NORTH SEA CASE STUDY</strong></p><p>Andr&#233;s Mart&#237;nez<sup>a,*</sup>, Ana J. Abascal<sup>a</sup>, Andr&#233;s Garc&#237;a<sup>a</sup>, Beatriz P&#233;rez-D&#237;az<sup>a</sup>, Germ&#225;n Arag&#243;n<sup>a</sup>, Ra&#250;l Medina<sup>a</sup></p><p><sup>a</sup>IHCantabria - Instituto de Hidr&#225;ulica Ambiental de la Universidad de Cantabria, Avda. Isabel Torres, 15, 39011 Santander, Spain</p><p><sup>* </sup>Corresponding author: [email protected]</p><p>Underwater oil and gas blowouts are not easy to repair. It may take months before the well is finally capped, releasing large amounts of oil into the marine environment. In addition, persistent oils (crude oil, fuel oil, etc.) break up and dissipate slowly, so they often reach the shore before the cleanup is completed, affecting vasts extension of seas-oceans, just as posing a major threat to marine organisms.</p><p>On account of the above, numerical modeling of underwater blowouts demands great computing power. High-resolution, long-term data bases of wind-ocean currents are needed to be able to properly model the trajectory of the spill at both regional (open sea) and local level (coastline), just as to account for temporal variability. Moreover, a large number of particles, just as a high-resolution grid, are unavoidable in order to ensure accurate modeling of oil concentrations, of utmost importance in risk assessment, so that threshold concentrations can be established (threshold concentrations tell you what level of exposure to a compound could harm marine organisms).</p><p>In this study, an innovative methodology has been accomplished for the purpose of optimizing modeling configuration: number of particles and grid resolution, in the modeling of an underwater blowout, with a view to accurately represent oil concentrations, especially when threshold concentrations are considered. In doing so, statistical analyses (dimensionality reduction and clustering techniques), just as numerical modeling, have been applied.</p><p>It is composed of the following partial steps: (i) classification of i representative clusters of forcing patterns (based on PCA and K-means algorithms) from long-term wind-ocean current hindcast data bases, so that forcing variability in the study area is accounted for; (ii) definition of j modeling scenarios, based on key blowout parameters (oil type, flow rate, etc.) and modeling configuration (number of particles and grid resolution); (iii) Lagrangian trajectory modeling of the combination of the i clusters of forcing patterns and the j modeling scenarios; (iv) sensitivity analysis of the Lagrangian trajectory model output: oil concentrations, &#160;to modeling configuration; (v) finally, as a result, the optimal modeling configuration, given a certain underwater blowout (its key parameters), is provided.</p><p>It has been applied to a hypothetical underwater blowout in the North Sea, one of the world&#8217;s most active seas in terms of offshore oil and gas exploration and production. A 5,000 cubic meter per day-flow rate oil spill, flowing from the well over a 15-day period, has been modeled (assuming a 31-day period of subsequent drift for a 46-day modeling). Moreover, threshold concentrations of 0.1, 0.25, 1 and 10 grams per square meter have been applied in the sensitivity analysis. The findings of this study stress the importance of modeling configuration in accurate modeling of oil concentrations, in particular if lower threshold concentrations are considered.</p>
Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI
