TRAJECTORY PREDICTION FOR BARGE BUFFALO 292 SPILL

ABSTRACT The oil spill trajectory prediction for the barge Buffalo 292 spill was provided by NOAA and TGLO. The bulk of the 5000 barrels of IFO 380 that was leaked moved rapidly through the Galveston Channel entrance and into the Gulf of Mexico as a result of a strong meteorological event. Because of the nature of the product, it was possible to track the resulting slicks for more than 3 weeks. Initially, the oil trailed east away from shore. Changing winds and currents moved the oil south and west, leading to sporadic impacts along the shore from east of Galveston to south of Corpus Christi. Trajectory forecasts were used to alert response personnel of impending beach impacts and to direct offshore skimming operations. Real-time current and wind meters, oil-tracking drifters, visual overflights, and remote-sensing observations provided an unusual amount of calibrating data for trajectory forecasting. This fact, along with detailed analysis assisted by computer models, allowed for a surprisingly high degree of accuracy in trajectory prediction in spite of complex current and wind patterns and changing wind drift factors for the product as it weathered. In this paper, these favorable results are compared with results of an earlier spill in the region where fewer resources were available for trajectory analysis.

Download Full-text

BARGE BUFFALO 292: OBSERVATIONS OF SATELLITE-TRACKED SURFACE DRIFTERS

International Oil Spill Conference Proceedings ◽

10.7901/2169-3358-1997-1-916 ◽

1997 ◽

Vol 1997 (1) ◽

pp. 916-919

Author(s):

Debra A. Simecek-Beatty ◽

William J. Lehr ◽

Walter R. Johnson ◽

James M. Price

Keyword(s):

Gulf Of Mexico ◽

Oil Spill ◽

Oil Spills ◽

Management Service ◽

Wind Drift ◽

Vector Correlation ◽

Joint Program ◽

Surface Drifters ◽

Visual Observations

ABSTRACT As part of a joint program to use satellite-tracked drifters at accidental oil spills, the National Oceanic and Atmospheric Administration deployed three drifters supplied by the Minerals Management Service during the barge Buffalo 292 spill in the Gulf of Mexico. The deployments complemented visual observations of the oil spill and provided data for calibrating the on-scene spill model. The data-rich environment of this particular spill response made it possible to calculate the vector correlation between the drifters and a hindcast of the oil movement and to estimate the wind-drift factors for the oil-tracking drifters.

Download Full-text

Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

Remote Sensing ◽

10.3390/rs12203416 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3416

Author(s):

Shamsudeen Temitope Yekeen ◽

Abdul-Lateef Balogun

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Oil Spill ◽

Oil Spills ◽

Biogenic Elements ◽

Trajectory Prediction ◽

Oil Spill Detection ◽

Remote Sensing Technology ◽

Sensing Technology

Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.

Download Full-text

Remote sensing monitoring of Gulf of Mexico oil spill using ENVISAT ASAR images

2013 21st International Conference on Geoinformatics ◽

10.1109/geoinformatics.2013.6626165 ◽

2013 ◽

Cited By ~ 4

Author(s):

Jianhua Wan ◽

Yang Cheng

Keyword(s):

Remote Sensing ◽

Gulf Of Mexico ◽

Oil Spill ◽

Remote Sensing Monitoring ◽

Envisat Asar

Download Full-text

Numerical Simulation of Oil Spill in the Gulf of Mexico Based on the GNOME and ADIOS

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.295-298.1535 ◽

2013 ◽

Vol 295-298 ◽

pp. 1535-1542 ◽

Cited By ~ 4

Author(s):

Yi Yang ◽

Zhi Li Chen ◽

Ying Li ◽

Xiao Xiao ◽

Qi Dan ◽

...

Keyword(s):

Remote Sensing ◽

Numerical Simulation ◽

Gulf Of Mexico ◽

Oil Spill ◽

Future Study ◽

Environmental Information ◽

Geographical Information ◽

Remote Sensing Monitoring ◽

Simulation Results ◽

Weathering Process

This paper analyzes the mechanism of the GNOME and ADIOS models. On this basis, GNOME and ADIOS model are applied to the simulation of the oil drift and the weathering process of the early oil spill in the Gulf of Mexico respectively by correctly adding the Gulf of geographical information and environmental information. The simulated oil spill trajectories agree well with remote sensing monitoring results and the simulation results of ADIOS are in line with the oil spill weathering study conclusions. This paper also analyzes the reasons of simulation mistakes and the shortcomings of the model itself so as to figure out the direction for the future study.

Download Full-text

Mapping spatial and temporal variation of seafloor organic matter Δ14C and δ13C in the Northern Gulf of Mexico following the Deepwater Horizon Oil Spill

Marine Pollution Bulletin ◽

10.1016/j.marpolbul.2021.112076 ◽

2021 ◽

Vol 164 ◽

pp. 112076

Author(s):

Kelsey L. Rogers ◽

Samantha H. Bosman ◽

Natalie Wildermann ◽

Brad E. Rosenheim ◽

Joseph P. Montoya ◽

...

Keyword(s):

Organic Matter ◽

Gulf Of Mexico ◽

Temporal Variation ◽

Oil Spill ◽

Deepwater Horizon ◽

Spatial And Temporal Variation ◽

Deepwater Horizon Oil Spill ◽

Northern Gulf Of Mexico

Download Full-text

Impact of the Deepwater Horizon oil spill on a deep-water coral community in the Gulf of Mexico

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1118029109 ◽

2012 ◽

Vol 109 (50) ◽

pp. 20303-20308 ◽

Cited By ~ 217

Author(s):

H. K. White ◽

P.-Y. Hsing ◽

W. Cho ◽

T. M. Shank ◽

E. E. Cordes ◽

...

Keyword(s):

Gulf Of Mexico ◽

Oil Spill ◽

Deep Water ◽

Deepwater Horizon ◽

Coral Community ◽

Deepwater Horizon Oil Spill ◽

Water Coral

Download Full-text

Persistent organic pollutants (POPs) in blood and blubber of common bottlenose dolphins (Tursiops truncatus) at three northern Gulf of Mexico sites following the Deepwater Horizon oil spill

The Science of The Total Environment ◽

10.1016/j.scitotenv.2017.11.209 ◽

2018 ◽

Vol 621 ◽

pp. 130-137 ◽

Cited By ~ 6

Author(s):

Jennifer E. Balmer ◽

Gina M. Ylitalo ◽

Teresa K. Rowles ◽

Keith D. Mullin ◽

Randall S. Wells ◽

...

Keyword(s):

Gulf Of Mexico ◽

Organic Pollutants ◽

Oil Spill ◽

Persistent Organic Pollutants ◽

Tursiops Truncatus ◽

Bottlenose Dolphins ◽

Deepwater Horizon ◽

Deepwater Horizon Oil Spill ◽

Northern Gulf Of Mexico

Download Full-text

Remote Sensing in Oil Spill Handling in Offshore North West Java

10.2118/205607-ms ◽

2021 ◽

Author(s):

Audra Ligafinza ◽

Farasdaq Muchibbus Sajjad ◽

Mohammad Abdul Jabbar ◽

Anggia Fatmawati ◽

Alvin Derry Wirawan ◽

...

Keyword(s):

Remote Sensing ◽

Real Time ◽

Oil Spill ◽

Environmental Damage ◽

Satellite Imaging ◽

North West ◽

Novel Technologies ◽

Oil Boom ◽

Remediation Strategies ◽

Restoration Strategies

Abstract During the blowout event, it is critical to track the oil spill to minimize environmental damage and optimize restoration cost. In this paper, we deliver our success story in handling oil spill from recent experiences. We utilize remote sensing technologies to establish our analysis and plan the remediation strategies. We also comprehensively discuss the techniques to analyze big data from the satellites, to utilize the downloaded data for forecasting, and to align the satellite information with restoration strategies. PHE relies on its principle to maintain minimum damage and ensures safety by dividing the steps into several aspects of monitoring, response (offshore and onshore), shoreline management and waste management. PHE utilizes latest development in survey by using satellite imaging, survey boat, chopper and UAV drone. Spill containment is done using several layers of oil boom to recover oil spill, complemented with skimmers and storage tanks. PHE encourages shoreline remediation using nets and manual recovery for capturing oil sludge. Using this combination of technologies, PHE is able to model and anticipate oil spill movement from the source up until the farthest shoreline. This enables real time monitoring and handling, therefore minimum environmental damage is ensured. PHE also employs prudent engineering design based on real time field condition in order to ensure the equipment are highly suited for the condition, as well as ensuring good supply chain of the material availability. This publication addresses the first offshore blowout mitigation and handling in Indonesia that uses novel technologies such as static oil boom, satellite imaging and integrated effort in handling shoreline damage. It is hoped that the experience can be replicated for other offshore operating contractors in Indonesia in designing blowout remediation.

Download Full-text