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

Shitao Wang, Mohamed Iskandarani, Ashwanth Srinivasan, W. Carlisle Thacker, Justin Winokur, Omar Knio

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.
Original languageEnglish (US)
Pages (from-to)3488-3501
Number of pages14
JournalJournal of Geophysical Research: Oceans
Volume121
Issue number5
DOIs
StatePublished - May 27 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We 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/).

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