Deep oil spill hazard assessment based on spatio-temporal met-ocean patterns

Abstract: Oil spill risk assessments are important tools for the offshore oil and gas industries to minimize the consequences of deep spills. The stochastic modeling required in this kind of studies, is generally centered on surface transport and based on a Monte Carlo selection of hundreds or thousands of met-ocean scenarios from reanalysis databases, to create an ensemble of spill simulations. We propose a new integrated stochastic modeling methodology including both surface and subsurface transport, based on the specific selection of the most relevant environmental conditions through data-mining techniques. The methodology was applied to evaluate oil contamination probability as a consequence of a simulated deep release in the North Sea. Our results show the effectiveness of the proposed methodology to select representative evolutions of met-ocean conditions and to obtain pollution probabilities from an integrated subsurface and surface oil spill stochastic modeling, while assuring a manageable computational effort.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Fuente: Marine Pollution Bulletin Volume 154, May 2020, 111123

Editorial: Elsevier Ltd

 Fecha de publicación: 01/05/2020

Nº de páginas: 13

Tipo de publicación: Artículo de Revista

DOI: 10.1016/j.marpolbul.2020.111123

ISSN: 0025-326X,1879-3363

Proyecto español: TRA2017-89164-R

Url de la publicación: https://doi.org/10.1016/j.marpolbul.2020.111123