GRIIDC | R6.x812.000:0005

Dataset Available

Subsurface Oil Simulator (SOSim) source code, documentation, and input examples

Authors: Ji, Chao and Mary Jacketti

Published On: Feb 03 2022 19:57 UTC

DOI: https://doi.org/10.7266/n7-dgsh-kh04

UDI: R6.x812.000:0005

Files

Individual Files

No. of Downloads: 16

No. of Files: 76

File Size: 4.53 GB

File Format(s):
py, nc, txt, pdf, docx, mp4, csv, png, ico, cpg, jpg, sbx, xbn, prj, shp, shx, xml, ps1

Project Information

Funded By:
Gulf of Mexico Research Initiative

Funding Cycle:
RFP-VI

Research Group:
Inferential/Parametric Forecasting of Subsurface Oil Trajectory Integrating Limited Reconnaissance Data with Flow Field Information for Emergency Response

Point of Contact

James Englehardt
University of Miami / Department of Civil, Architectural and Environmental Engineering
jenglehardt@miami.edu

Theme keywords

Subsurface oil model, Bayesian model, Sunken oil model, Submerged oil model, oil spreading prediction, Subsurface Oil Simulator (SOSim)

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Abstract:

This dataset includes source code, documentation, and input examples for Subsurface Oil Simulator (SOSim), a Bayesian probabilistic model which predicts the spatial probability of the location of spreading oil, in both the water column and sediments, after a spill. The model code is written in Python 2.7 and requires QGIS 2.14. Installation procedures are described in a ReadMe File and a user manual, which also includes information to aid users in running SOSim. Two video clips detail the installation procedure for Windows and Mac operating systems. A number of example input files are included. Case studies using SOSim may be downloaded from associated datasets R6.x812.000:0002 (Deepwater Horizon oil spill, doi:10.7266/n7-qsr6-gr54), R6.x812.000:0008 (T/V Athos I spill, doi: 10.7266/n7-8rhz-c860), and R6.x812.000:0001 (Tank Barge DBL 152 oil spill, doi: 10.7266/n7-d7cg-md27). The SOSim source code is also archived on Github. This dataset supports the publications: Ji, C.; Englehardt, J.D.; Beegle-Krause, C.J. (2020). Design of Real-Time Sampling Strategies for Submerged Oil Based on Probabilistic Model Predictions. Journal of Marine Science and Engineering, 8, 12, 984. doi:10.3390/jmse8120984; and Jacketti, M., Englehardt, J. D., & Beegle-Krause, C. J. (2020). Application of the SOSim v2 Model to Spills of Sunken Oil in Rivers. Journal of Marine Science and Engineering, 8(9), 729. doi:10.3390/jmse8090729.

Suggested Citation:

Ji, Chao and Mary Jacketti. 2022. Subsurface Oil Simulator (SOSim) source code, documentation, and input examples. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/n7-dgsh-kh04

Publications:

Jacketti, M., Englehardt, J. D., & Beegle-Krause, C. J. (2020). Application of the SOSim v2 Model to Spills of Sunken Oil in Rivers. Journal of Marine Science and Engineering, 8(9), 729. doi:10.3390/jmse8090729

Ji, C., Englehardt, J. D., & Beegle-Krause, C. J. (2020). Design of Real—Time Sampling Strategies for Submerged Oil Based on Probabilistic Model Predictions. Journal of Marine Science and Engineering, 8(12), 984. doi:10.3390/jmse8120984

Purpose:

The purpose of this dataset is to provide a new subsurface oil model (SOSim) to modelers to be used as an emergency response tool after an oil spill.

Data Parameters and Units:

N/A

Methods:

Bayesian inference is used with field and prior information to predict the location and movement of subsurface oil. The model is written in Python 2.7 and requires QGIS 2.14. SOSim runs under Windows and Mac operating systems only.