Propagation of uncertainty and sensitivity analysis in an integral oil-gas plume model
Thacker, W. Carlisle
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
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AbstractPolynomial 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.
CitationPropagation of uncertainty and sensitivity analysis in an integral oil-gas plume model 2016 Journal of Geophysical Research: Oceans
SponsorsWe thank the two anonymous reviewers for their constructive suggestions which improve this manuscript. This work was made possible in part by a grant from BP/ The Gulf of Mexico Research Initiative, and by the Office of Naval Research, Award N00014-101-0498. J. Winokur and O. M. Knio were also supported in part by the U.S. Department of Energy (DOE), Office of Science, Office of Advanced Scientific Computing Research, under Award DE-SC0008789. This research was conducted in collaboration with and using the resources of the University of Miami Center for Computational Science. The model data are publicly available in the Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC) repository (https://data. gulfresearchinitiative.org/data/R4.x265. 252:0002/).