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Journal articles on the topic 'Oil-spill modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Sohpal, Vipan Kumar, and Amarpal Singh. "Biodegradation: Study of Modelling and Simulative of Oil Spill in Marine." International Journal of Scientific Research 1, no. 5 (June 1, 2012): 55–57. http://dx.doi.org/10.15373/22778179/oct2012/18.

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2

Quon, Tony K. S., and George E. Bushell. "Modelling Navigational Risk and Oil Spill Probabilities." Journal of Navigation 47, no. 3 (September 1994): 390–402. http://dx.doi.org/10.1017/s0373463300012339.

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This paper examines first a statistical modelling of the number of navigational accidents (collisons, groundings and strikings) involving tankers and freighters in Canadian waters; it then looks at a direct method of modelling oil spill frequency and magnitude, given a navigational accident. The former is used to examine various hypotheses in the literature regarding the impact of various factors on navigational risk. The latter is based on a direct probability tree analysis which allows the estimation of detailed oil spill impacts by facilitating the examination of representative spill sizes.

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3

Aznar, F., M. Sempere, M. Pujol, R. Rizo, and M. J. Pujol. "Modelling Oil-Spill Detection with Swarm Drones." Abstract and Applied Analysis 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/949407.

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Nowadays, swarm robotics research is having a great increase due to the benefits derived from its use, such as robustness, parallelism, and flexibility. Unlike distributed robotic systems, swarm robotics emphasizes a large number of robots, and promotes scalability. Among the multiple applications of such systems we could find are exploring unstructured environments, resource monitoring, or distributed sensing. Two of these applications, monitoring, and perimeter/area detection of a given resource, have several ecological uses. One of them is the detection and monitoring of pollutants to delimit their perimeter and area accurately. Maritime activity has been increasing gradually in recent years. Many ships carry products such as oil that can adversely affect the environment. Such products can produce high levels of pollution in case of being spilled into sea. In this paper we will present a distributed system which monitors, covers, and surrounds a resource by using a swarm of homogeneous low cost drones. These drones only use their local sensory information and do not require any direct communication between them. Taking into account the properties of this kind of oil spills we will present a microscopic model for a swarm of drones, capable of monitoring these spills properly. Furthermore, we will analyse the proper macroscopic operation of the swarm. The analytical and experimental results presented here show the proper evolution of our system.

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4

Hartmann, T. "Oil spill modelling—Lessons to be learned?" Marine Pollution Bulletin 18, no. 2 (February 1987): 58–59. http://dx.doi.org/10.1016/0025-326x(87)90550-9.

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5

Masciangioli, Panfilo, German Febres, and María Elena Viale-Rigo. "Nissos Amorgos Oil Spill Experience." International Oil Spill Conference Proceedings 1999, no. 1 (March 1, 1999): 1127–31. http://dx.doi.org/10.7901/2169-3358-1999-1-1127.

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ABSTRACT The behavior of the Bachaquero crude oil spilled by tanker Nissos Amorgos, when it grounded in the Gulf of Venezuela, was studied through modelling its fate using the SIMAP model (Applied Science Associates, Inc.). The purpose of the simulations was to improve the cleanup activities, and to help in evaluating the environmental impact.

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6

Griffiths, Jonathan, Liam Harrington-Missin, and Sarah Hall. "What makes a great oil spill forecast?" International Oil Spill Conference Proceedings 2017, no. 1 (May 1, 2017): 2017–190. http://dx.doi.org/10.7901/2169-3358-2017.1.2017-190.

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Abstract (ID2017-190)This paper identifies the key information that should be included in an operational oil spill forecast. It shows how modellers convert huge quantities of data into a readily accessible modelling forecast report that can be rapidly interpreted and incorporated into the incident action plan.Effective response strategies can minimise the potentially devastating consequences of an oil spill. To protect nearby socio-economic and ecological sensitivities a response strategy needs to be implemented quickly.Oil spill forecasts help predict the behaviour of oil that has been spilled into the marine environment. These predictions are highly useful when planning response strategies for the coming days. Key operational information derived from oil spill forecasts can be broken down into two main areas:1. Where is oil expected to travel?By knowing where oil is expected to travel, response organisations can decide which sensitivities need protection. Forecasts will show if oil is predicted to reach fisheries, shorelines and other important resources. This information can be used within a Net Environmental Benefit Analysis (NEBA) to decide which sensitivities to prioritise for protection.2. What is expected to happen to the physical properties of the spilled oil?Once oil enters the marine environment it is subjected to weathering processes such as spreading, evaporation and biodegradation. These processes change the chemical makeup of the oil which usually becomes more viscous and can form emulsion. Information obtained from modelling can help decision makers choose the correct response options and equipment to use during a spill. For example, if a modelling forecast shows oil is likely to become highly viscous responders will know heavy oil skimmers will be needed if offshore recovery is to take place.The information referred to above can be extracted from the vast amount of data that is created when an oil spill model is run. As Incident Managers need to make informed decisions quickly, it is essential that oil spill forecasts are presented in a clear and concise fashion. From experience, it is often this extra step of making the data easily accessible to the decision maker that is overlooked and is of most value in a response.This means that response modellers need to be trained, not only in the science of oil spill modelling, but also in the art of conveying complex information to a range of end users from oil spill experts to interested members of the public.

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7

Nordam, Tor, CJ Beegle-Krause, Jørgen Skancke, Raymond Nepstad, and Mark Reed. "Improving oil spill trajectory modelling in the Arctic." Marine Pollution Bulletin 140 (March 2019): 65–74. http://dx.doi.org/10.1016/j.marpolbul.2019.01.019.

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8

Turrell, W. R. "Modelling the Braer oil spill—A retrospective view." Marine Pollution Bulletin 28, no. 4 (April 1994): 211–18. http://dx.doi.org/10.1016/0025-326x(94)90096-5.

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9

Toz, Ali Cemal, Burak Koseoglu, and Cenk Sakar. "Numerical modelling of oil spill in New York Bay." Archives of Environmental Protection 42, no. 4 (December 1, 2016): 22–31. http://dx.doi.org/10.1515/aep-2016-0037.

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Abstract New York Bay is one of the most important transition regions of ships trading to east America. The region plays an important role in the commerce of the New York metropolitan area. The area is surrounded with the coasts that have various levels of environmental sensitivity. The area accommodates high diversity of native ecosystems and species that are rather vulnerable in case of oil spill. Thus getting well informed about the likelihood, or fate, of oil spills around this region is of great importance so that proactive measures can be taken. The purpose of this study is to investigate the oil spill and predict the future accidents likely to be encountered around the Bay of New York. Two trajectory models have been conducted for the study. ADIOS (Automated Data Inquiry for Oil Spills), has been conducted for natural degradation calculations, and, GNOME (General NOAA Operational Modeling Environment), has been conducted for surface spread simulation. The results gained through these efforts are hoped to be useful for many organizations dealing with oil spill response operations and contribute to an effective and efficient coordination among the relevant institutions.

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10

Kileso, Alexander, Boris Chubarenko, Petras Zemlys, and Igor Kuzmenko. "Oil spill modelling methods: application to the south–eastern part of the Baltic Sea." Baltica 27, special (February 20, 2014): 15–22. http://dx.doi.org/10.5200/baltica.2014.27.11.

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The state-of-art in oil spill modelling methods is summarized, focusing on development since 2000. Some recommendations for possible application of these methods to the south–eastern part of the Baltic Sea are prepared. Particular attention is paid on the methods of parameterization of volume of oil spill and calculation of advection of the oil spills. Consideration is also given to methods used in oil weathering models.

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11

Audu, Henry A. P., and J. O. Ehiorobo. "Geoinformation for Oil Spill Disaster Management in the Niger Delta Region of Nigeria." Advanced Materials Research 62-64 (February 2009): 432–38. http://dx.doi.org/10.4028/www.scientific.net/amr.62-64.432.

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One of the major ecological and environmental problems confronting the Niger Delta region of Nigeria today is degradation and hazard arising from oil spill. Spillage occurs in this region either from vandalisation or sabotage by ethnic militants, youths who are jobless and therefore deliberately break open crude oil transport pipelines to scoop fuel for sale in the black market to earn a living or rupture of pipes due to ageing and mechanical malfunction. In most spillages, farmlands are lost, aquatic and wildlife is affected, and people are in many cases displaced from their homes. In extreme cases as occurred in Jesse, lives were lost. This paper examines the use of Geoinformation technology in oil spill response modelling and management. The use Global Positioning System (GPS) derived data for the creation of a management database is discussed. Data generated from the Jesse spill and fire site covering these areas were used to generate case study scenario for oil spill response modelling and clean up management operation.

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12

Price, James M., Mark Reed, Matthew K. Howard, Walter R. Johnson, Zhen-Gang Ji, Charles F. Marshall, Norman L. Guinasso, and Gail B. Rainey. "Preliminary assessment of an oil-spill trajectory model using satellite-tracked, oil-spill-simulating drifters." Environmental Modelling & Software 21, no. 2 (February 2006): 258–70. http://dx.doi.org/10.1016/j.envsoft.2004.04.025.

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13

Snow, B. J., I. Moulitsas, A. J. Kolios, and M. De Dominicis. "CranSLIK v1.0: stochastic prediction of oil spill transport and fate using approximation methods." Geoscientific Model Development 7, no. 4 (July 22, 2014): 1507–16. http://dx.doi.org/10.5194/gmd-7-1507-2014.

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Abstract. This paper investigates the development of a model, called CranSLIK, to predict the transport and transformations of a point mass oil spill via a stochastic approach. Initially the various effects on destination are considered and key parameters are chosen which are expected to dominate the displacement. The variables considered are: wind velocity, surface water velocity, spill size, and spill age. For a point mass oil spill, it is found that the centre of mass can be determined by the wind and current data only, and the spill size and age can then be used to reconstruct the surface of the spill. These variables are sampled and simulations are performed using an open-source Lagrangian approach-based code, MEDSLIK II. Regression modelling is applied to create two sets of polynomials: one for the centre of mass, and one for the spill size. Simulations performed for a real oil spill case show that a minimum of approximately 80% of the oil is captured by CranSLIK. Finally, Monte Carlo simulation is implemented to allow for consideration of the most likely destination for the oil spill, when the distributions for the oceanographic conditions are known.

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14

Abascal, Ana J., Sonia Castanedo, Raul Medina, Inigo J. Losada, and Enrique Alvarez-Fanjul. "Application of HF radar currents to oil spill modelling." Marine Pollution Bulletin 58, no. 2 (February 2009): 238–48. http://dx.doi.org/10.1016/j.marpolbul.2008.09.020.

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15

Li, Yang. "Control of spatial discretisation in coastal oil spill modelling." International Journal of Applied Earth Observation and Geoinformation 9, no. 4 (December 2007): 392–402. http://dx.doi.org/10.1016/j.jag.2007.02.003.

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16

Violeau, D., C. Buvat, K. Abed-Meraim, and E. de Nanteuil. "Numerical modelling of boom and oil spill with SPH." Coastal Engineering 54, no. 12 (December 2007): 895–913. http://dx.doi.org/10.1016/j.coastaleng.2007.06.001.

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17

Copeland, Graham, and Wee Thiam-Yew. "Current data assimilation modelling for oil spill contingency planning." Environmental Modelling & Software 21, no. 2 (February 2006): 142–55. http://dx.doi.org/10.1016/j.envsoft.2004.04.022.

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18

Marghany, M. "RADARSAT for oil spill trajectory model." Environmental Modelling & Software 19, no. 5 (May 2004): 473–83. http://dx.doi.org/10.1016/s1364-8152(03)00162-2.

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19

Jones, HFE, MTS Poot, JC Mullarney, WP de Lange, and KR Bryan. "Oil dispersal modelling: reanalysis of theRenaoil spill using open-source modelling tools." New Zealand Journal of Marine and Freshwater Research 50, no. 1 (January 2, 2016): 10–27. http://dx.doi.org/10.1080/00288330.2015.1112819.

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20

Qiao, Fangli, Guansuo Wang, Liping Yin, Kan Zeng, Yuanling Zhang, Min Zhang, Bin Xiao, Shumin Jiang, Haibo Chen, and Ge Chen. "Modelling oil trajectories and potentially contaminated areas from the Sanchi oil spill." Science of The Total Environment 685 (October 2019): 856–66. http://dx.doi.org/10.1016/j.scitotenv.2019.06.255.

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21

Fingas, M. "Formation of water-in-oil emulsions and application to oil spill modelling." Journal of Hazardous Materials 107, no. 1-2 (February 27, 2004): 37–50. http://dx.doi.org/10.1016/j.jhazmat.2003.11.008.

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22

Kalibatiene, D., A. Burmakova, and V. Smelov. "On Knowledge-Based Forecasting Approach for Predicting the Effects of Oil Spills on the Ground." Digital Transformation, no. 4 (January 7, 2021): 44–56. http://dx.doi.org/10.38086/2522-9613-2020-4-44-56.

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The oil industry carries enormous environmental risks and can cause consequences at different levels: water, air, soil, and, therefore, all living things on our planet. In this regard, forecasting the environmental consequences of oil spill accidents becomes relevant. Moreover, forecasting of oil spill accidents can be used to quickly assess the consequences of an accident that has already occurred, as well as to develop a plan of operational measures to eliminate possible accidents, facilities under construction, associated with the transportation, storage or processing of petroleum products. Consequently, the aim of this paper is to present a knowledge-based approach and its implementing system for forecasting the consequences of an accidental oil spills on the ground and groundwater. The novelty of the proposed approach is that it allows us to forecast the oil spill in a complex and systematic way. It consists of components for modelling geological environment (i.e., geological layers, oil spill form, the oil migration with groundwater), forecasting component for an oil spill and pollution mitigation component. Moreover, the forecasting component is based on experts’ knowledge on oil spill. In addition, the paper presents a general architecture for the implementation of the proposed knowledge-based approach and its implementation into a prototype named SoS-Ground.

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23

Snow, B. J., I. Moulitsas, A. J. Kolios, and M. De Dominicis. "CranSLIK v1.0: stochastic prediction of oil spill transport and fate using approximation methods." Geoscientific Model Development Discussions 6, no. 4 (December 20, 2013): 7047–76. http://dx.doi.org/10.5194/gmdd-6-7047-2013.

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Abstract. This paper investigates the development of a model, called CranSLIK, to predict the transport and transformations of a point mass oil spill via a stochastic approach. Initially the various effects that affect the destination are considered and key parameters are chosen which are expected to dominate the displacement. The variables considered are: wind velocity, surface water velocity, spill size, and spill age. For a point mass oil spill, it is found that the centre of mass can be determined by the wind and current data only, and the spill size and age can then be used to reconstruct the surface of the spill. These variables are sampled and simulations are performed using an open-source Lagrangian approach-based code, MEDSLIK II. Regression modelling is applied to create two sets of polynomials: one for the centre of mass, and one for the spill size. A minimum of approximately 80% of the oil is captured for the Algeria scenario. Finally, Monte-Carlo simulation is implemented to allow for consideration of most likely destination for the oil spill, when the distributions for the oceanographic conditions are known.

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24

Williams, Jack, Garnet Hooper, and Gregg Hamilton. "Automating the process of net environmental benefit analysis (NEBA) for emergency response and environmental plans." APPEA Journal 54, no. 2 (2014): 497. http://dx.doi.org/10.1071/aj13070.

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Each year in Australia, hundreds of referrals and environmental plans (EPs) are submitted to the regulatory agencies by oil and gas explorers. The plans assess multiple spill response options. One assessment tool is NEBA. This extended abstract presents a new approach for NEBAs and an automated NEBA process. This NEBA method has two scenarios: pre-spill and spill response. The pre-spill NEBA uses EP-defined sensitive receptors as appropriate examples in the region of interest, uses the modelling outputs to identify potential exposure zones, defines critical inputs (season, spill size), defines and ranks priority response according to the modelling output, and assesses spill response strategies. The output of the pre-spill NEBA is a table of response tactics to be assessed with respect to reducing risk to as low as reasonably practicable (ALARP). The spill response NEBA is similar to the pre-spill, but it uses real-time spill modelling data as an additional input to strengthen the data evaluation. Traditionally, both pre- and spill response NEBAs require considerable data input, extensive mathematical modelling, and human interpretation. Automating the NEBA process saves time, reduces errors, and minimises human biases in interpretation. ConocoPhillips has recently developed a tool to quickly conduct NEBAs. The NEBA tool also provides a means to document the progress of operational and scientific monitoring programs to measure the performance of the spill response. The automated process output can be time/date stamped to ensure critical steps are fully documented and auditable.

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25

Grimes, Alexandra, and Nicholas Olden. "CONSIDERATION OF TOTAL VOLATILE HYDROCARBON EXPOSURE DURING OIL SPILL RESPONSE1." International Oil Spill Conference Proceedings 2005, no. 1 (May 1, 2005): 979–82. http://dx.doi.org/10.7901/2169-3358-2005-1-979.

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Public perception and environmental awareness place increasing demands on the petroleum industry to facilitate fast and efficient oil spill containment and recovery to mitigate environmental damage. HSE legislation also places increasing demands on Oil Spill Response Organisations to ensure a safe working environment for responders. This paper looks at the trade-offs facing oil spill response planning from the perspective of occupational exposure to Total Volatile Hydrocarbons (TVH). TVH is a term used to represent a large group consisting of hundreds of chemical compounds that derive from crude oil. Under certain circumstances, in-situ response measures represent a significant risk to local air quality and human health. Mechanical and manual oil spill recovery in close proximity with TVHs place spill responders and potentially the general public at an increased risk from fire/ explosions as well as acute and chronic health implications. Over the course of a spill, physical and chemical processes are continuously changing TVH composition. This requires rapid on-scene monitoring and/ or predictive modelling to optimise spill counter measures and responder safety. The use of personal and area TVH monitoring equipment is discussed in a practical spill recovery context, and an overview is provided of portable gaseous testing equipment with respect to key criteria such as; conformity, configuration, user-friendliness and robustness. Current developments in TVH monitoring models are reviewed and their contribution to future oil spill contingency planning assessed. Consideration is given to hazardous vapour exposure and the resulting health and safety issues that were faced by OSRL during the Tasmin Spirit and an inland well-blow out in Georgia.

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26

Li, Daming, Xingchen Tang, Yanqing Li, Xiao Wang, and Hongqiang Zhang. "Mathematical Modeling of Marine Oil Spills in the Luanjiakou District, near the Port of Yantai." Discrete Dynamics in Nature and Society 2018 (2018): 1–22. http://dx.doi.org/10.1155/2018/2736102.

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This paper presents a simulation method for oil spills in a multi-island area. The simulation considers three parts, which consist of(1)the spreading of an oil slick on its edge as well as the diffusion and drift under dynamic actions,(2)the evaporation and spreading thickness of an oil slick in its interior, and(3)the adsorption and emulsification near shorelines and islands. The Euler-Lagrange method is adopted to track the spill location and particles positions on the edge of oil slicks. A mathematical model of marine oil spills is established for the Luanjiakou District of the Port of Yantai. The flow field verification shows that the BIAS of tidal level, flow velocity, and flow direction is below ±10 cm, 0.11 m/s, and ±2°, respectively, and the oil spill verification captures satisfactory results. Hence, the proposed model could reproduce the oil spill process in this region. Then, we simulate oil spills under various operating conditions. It is concluded that the transport of oil slicks is mainly influenced by flood/ebb currents, whereas the wind plays a major role in the drift and thickness of oil slicks. The study provides an important reference to controlling and handling of accidental oil spills.

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27

Fernandes, Rodrigo, Francisco Campuzano, David Brito, Manuela Juliano, Frank Braunschweig, and Ramiro Neves. "AUTOMATED SYSTEM FOR NEAR-REAL TIME PREDICTION OF OIL SPILLS FROM EU SATELLITE-BASED DETECTION SERVICE." International Oil Spill Conference Proceedings 2017, no. 1 (May 1, 2017): 1574–93. http://dx.doi.org/10.7901/2169-3358-2017.1.1574.

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ABSTRACT 2017-244: The state-of-the-art in both operational oceanography, remote sensing, and computational capacity, enables now the possibility of developing near-real time, holistic automated services capable of dramatically improving maritime situational awareness to responding to oil spill emergencies. Based on the European satellite-based oil spill and vessel detection service – CleanSeaNet (EMSA – European Maritime Safety Agency), which distributes oil pollution detection standardized notification packages in less than 30 minutes, a new automated early warning system (EWS) for near-real time modelling and prediction of the detected oil spills was developed. This EWS provides 48-hour oil spill forecasts + 24-hour backward simulations, delivering results 5–10 minutes after the reception of the oil spill detection notifications. These forecasts are then distributed in multiple formats and platforms (e.g. Google Earth, e-mail). The oil spill fate and behaviour model used in this EWS is part of MOHID modelling system, and is coupled offline with metocean forecast solutions, taking advantage of autonomous models previously run in multiple institutions. The system is currently able to integrate various metocean forecasting systems, being agnostic about the data sources and applied locations, as long as their outputs comply with commonly adopted formats, including CF compliant files or CMEMS (Copernicus Marine Environment Monitoring Service). The EWS is currently operational in western Iberia, supporting Portuguese Maritime Authority, and is being expanded to neighbourhood regions (from Spain and Morocco) with high resolution metocean models (MARPOCS project funded by European Union Humanitarian Aid & Civil Protection). Taking advantage of the coupling of MOHID oil spill model and CleanSeaNet, an oil spill hazard assessment is made in the Portuguese continental coast, based on the cumulative analysis of drift model simulations from previously detected spills using metocean model data, for a period between 2011–2016. Although this EWS doesn’t replace on-demand operational oil spill forecasting systems, it supports maritime authorities with a fast first-guess forecast solution, allowing:Anticipation of tactical response (including visual inspection of the spill) and mitigation of the pollution episode;A more effective identification of the pollution source, and in case of suspected illegal spill, earlier actions towards effective prosecution of the polluter;In the other hand, the hazard assessment generated is a valuable instrument for the development of efficient planning and prevention strategies. The EWS can be connected to any satellite-based detection service (inside or outside Europe) as long as the detected oil slicks are automatically distributed in a structured and standardized data format similar to CleanSeaNet.

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Skåtun, Helge Mohn, Jesse Uzzell, and Gjermund Gravir. "EMDROPS: An Integrated Tool for Risk Analysis and Response Planning." International Oil Spill Conference Proceedings 1999, no. 1 (March 1, 1999): 909–12. http://dx.doi.org/10.7901/2169-3358-1999-1-909.

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ABSTRACT It has been pointed out that one of the pitfalls associated with using a combination of computerised oil spill planning tools is the general lack of integration between the different sub-tasks. Det Norske Veritas (DNV) has been developing and using the EMDROPS tool for Environmental Risk Analysis, Response Analysis, and Contingency Planning. EMDROPS employs the MIRA method for Environmental Risk Assessment (ERA), adopted by OLF, the Norwegian Oil Industry Association, thus the results from the ERA can be put directly into the Response Analysis/Response Planning, using the same oil spill and environmental data as used for the ERA. EMDROPS is integrated into a standard GIS (ArcView) which allows the different modules (Oil Spill Modelling, Vulnerable Resources, Environmental Risk Assessment, and Response Planning) to be used in a seamless manner. Some of the data available to the user is the Norwegian Marine Resources Database (MRDB) and an oil spill equipment database which is used by the Environmental Risk Analysis and response effectiveness analysis.

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29

Lončar, Goran, Gordana Beg Paklar, and Ivica Janeković. "Numerical Modelling of Oil Spills in the Area of Kvarner and Rijeka Bay (The Northern Adriatic Sea)." Journal of Applied Mathematics 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/497936.

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Several hypothetical cases of oil spills from tankers in the Kvarner and Rijeka Bay were analyzed using three-dimensional circulation models coupled with oil spill model. Two circulation models—local one covering the area of Kvarner Bay, Rijeka Bay, and Vinodol channel along with the basin-wide one covering the whole Adriatic Sea—are connected through the one-way nesting procedure by imposing the results from the Adriatic model to the open boundaries of the local one. Oil spill model relays on the current fields obtained by the local circulation model during all our simulations. Spreading of the oil pollution from three hypothetical positions of tanker accidents in the local model domain was simulated for the periods of 10 “winter-season” and “summer-season” days. The oil spill model results show that the hypothetical tanker accidents in the center of the Rijeka Bay are the most dangerous for the studied area in both seasons. Summer-season case shows significantly worse situation from the ecological point of view, oil spills spread on the larger area simply because stratification and mixing present during the winter period reduce oil slick effect.

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30

Toz, Ali Cemal. "Modelling Oil Spill around Bay of Samsun, Turkey, with the Use of Oilmap and Adios Software Systems." Polish Maritime Research 24, no. 3 (September 1, 2017): 115–25. http://dx.doi.org/10.1515/pomr-2017-0096.

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Abstract Bay of Samsun is one of the most important oil transport gateways in Black Sea. The region is surrounded with the coasts which have various levels of environmental sensitivity. The purpose of this study is to investigate the oil spill and predict the future accidents likely to be encountered around the Bay of Samsun. To be well informed about fate, this study makes the best possible use of two trajectory models. One of them, ADIOS (Automated Data Inquiry for Oil Spills), has been applied to natural degradation calculations, and the other one, OILMAP (oil spill model and response system), has been used for surface spread simulation. Hence in order to identify the risky areas three scenarios have been developed. Their results reveal that in case of oil spills, with average environmental conditions, there is a risk of contamination for the city of Samsun. Although the area under the risk is the same, contamination density is totally different depending upon the quantity and the type of spilt oil. The results gained through these efforts are hoped to be useful for many organizations dealing with oil spill response operations and contribute to an effective coordination among the relevant institutions.

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31

Lehr, William, Robert Jones, Mary Evans, Debra Simecek-Beatty, and Roy Overstreet. "Revisions of the ADIOS oil spill model." Environmental Modelling & Software 17, no. 2 (January 2002): 189–97. http://dx.doi.org/10.1016/s1364-8152(01)00064-0.

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32

Tkalich, Pavel. "A CFD solution of oil spill problems." Environmental Modelling & Software 21, no. 2 (February 2006): 271–82. http://dx.doi.org/10.1016/j.envsoft.2004.04.024.

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33

Soussi, Abdellatif, Chiara Bersani, Roberto Sacile, Dounia Bouchta, Ahmed El Amarti, Hamid Seghiouer, Driss Nachite, and Jaouad Al Miys. "Coastal Risk Modelling for Oil Spill in The Mediterranean Sea." Advances in Science, Technology and Engineering Systems Journal 5, no. 4 (2020): 273–86. http://dx.doi.org/10.25046/aj050434.

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34

Lončar, Goran, Nenad Leder, and Marin Paladin. "Numerical modelling of an oil spill in the northern Adriatic." Oceanologia 54, no. 2 (April 2012): 143–73. http://dx.doi.org/10.5697/oc.54-2.143.

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35

Gottinger, Hans W. "Econometric modelling, estimation and policy analysis of oil spill processes." International Journal of Environment and Pollution 15, no. 3 (2001): 333. http://dx.doi.org/10.1504/ijep.2001.005271.

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36

Eke, Chijioke D., Babatunde Anifowose, Marco J. Van De Wiel, Damian Lawler, and Michiel A. F. Knaapen. "Numerical Modelling of Oil Spill Transport in Tide-Dominated Estuaries: A Case Study of Humber Estuary, UK." Journal of Marine Science and Engineering 9, no. 9 (September 19, 2021): 1034. http://dx.doi.org/10.3390/jmse9091034.

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Oil spills in estuaries are less studied and less understood than their oceanic counterparts. To address this gap, we present a detailed analysis of estuarine oil spill transport. We develop and analyse a range of simulations for the Humber Estuary, using a coupled hydrodynamic and oil spill model. The models were driven by river discharge at the river boundaries and tidal height data at the offshore boundary. Satisfactory model performance was obtained for both model calibration and validation. Some novel findings were made: (a) there is a statistically significant (p < 0.05) difference in the influence of hydrodynamic conditions (tidal range, stage and river discharge) on oil slick transport; and (b) because of seasonal variation in river discharge, winter slicks released at high water did not exhibit any upstream displacement over repeated tidal cycles, while summer slicks travelled upstream into the estuary over repeated tidal cycles. The implications of these findings for operational oil spill response are: (i) the need to take cognisance of time of oil release within a tidal cycle; and (ii) the need to understand how the interaction of river discharge and tidal range influences oil slick dynamics, as this will aid responders in assessing the likely oil trajectories.

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Castanedo, Sonia, Beatriz Perez-Diaz, Ana J. Abascal, Mar Cardenas, Maitane Olabarrieta, Raul Medina, Justine Receveur, Esterine Evrard, and Julien Guyomarch. "A HIGH RESOLUTION OPERATIONAL OIL SPILL MODEL AT SANTANDER BAY (SPAIN): IMPLEMENTATION AND VALIDATION." International Oil Spill Conference Proceedings 2014, no. 1 (May 1, 2014): 516–30. http://dx.doi.org/10.7901/2169-3358-2014.1.516.

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ABSTRACT A High Resolution Operational Oceanography System that provides decision makers with short-term (within 48 hours) oil spill trajectory forecasting at local scale, has been developed in the Bay of Santander (Spain). The system is based on process models applied in a set of nested grids. Hydrodynamics in the study area are calculated with the COAWST modelling system which uses daily boundary conditions and meteorological forcing obtained from the European network MYOCEAN (http://www.myocean.eu/) and from the Spanish met office, AEMET, respectively. Daily COAWST's outputs and meteorological forecast are ready to be used by the oil spill transport and fate model, TESEO. A web service that manages the operational system and allows the user to run hypothetical as well as real oil spill trajectories has been implemented. Data from two hydrodynamics field campaigns and from experimental tests carried out with two types of oils have been used to validate the hydrodynamic and the oil spill models.

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Smith, Barry, Julie Martine, and Graeme D. Hubbert. "ENVIRONMENTAL STUDIES FOR A PERMIT-WIDE DRILLING PROGRAM IN A SENSITIVE MARINE ENVIRONMENT." APPEA Journal 34, no. 1 (1994): 750. http://dx.doi.org/10.1071/aj93055.

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An integrated program of environmental studies was undertaken in support of an application to conduct permit-wide exploratory drilling in a shallow, sensitive marine environment. The program comprised permit and well site specific work which was carried out in parallel with a corporate due diligence program established by the permit Operator.Scientific studies undertaken for permit area EP 341 off the north west coast of Western Australia comprised underwater surveys, aerial video reconnaissance, surface current tracking surveys and oil spill trajectory modelling to characterise the local environment and identify sensitive resources at risk of impact from oil spills. Survey data were used to prepare an environmental impact assessment report and an oil spill contingency plan, which provide a set of site-specific environmental management guidelines for use by field and office personnel involved with the drilling program. A 3D oil spill trajectory model successfully predicted current flows in the complex hydrodynamic environment of the permit areas, demonstrating its usefulness as a real time tool for oil spill response planning.

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Luk, G. K., and H. F. Kuan. "Modelling the behaviour of oil spills in natural waters." Canadian Journal of Civil Engineering 20, no. 2 (April 1, 1993): 210–19. http://dx.doi.org/10.1139/l93-026.

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This paper is a state-of-the-art review of the formulations for the different processes responsible for the transport and mixing of petroleum oil spilled in natural waters. Processes accounting for the transfer and loss of the surface oil, such as initial spreading, evaporation, dissolution, emulsification, dispersion, photo-oxidation, and sedimentation, are included. Based on the findings, a dynamic mathematical model describing the fate of spilled oil was developed. To reflect field observations, the surface oil composition in the model is allowed to vary with time as a result of weathering. Initial results for model testing are presented. Key words: oil spill, mathematical model, fate model, weathering processes.

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Skeie, G. M., T. Sørnes, F. Engen, A. Boye, A. L. Heggø, S. Rasmussen, and C. S. Spikkerud. "From Reservoir Characteristics, Through Environmental Risk Assessments to Oil Spill Response – Lessons Learned from a Comprehensive Systematic Development by an Operator." International Oil Spill Conference Proceedings 2014, no. 1 (May 1, 2014): 293386. http://dx.doi.org/10.7901/2169-3358-2014-1-293386.1.

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Statoil is the largest operator on the Norwegian Continental Shelf, is operating 42 assets and has an exploration activity in the order of 20-25 wells per year. In 2011, Statoil decided to undertake a full review of the basis for the oil spill response level for their activities, and establish a fully documented, transparent documentation of their approach. In Norway, operators are required to perform environmental risk and oil spill emergency preparedness assessments for all activities, as a basis for oil spill response plans. These assessments are of a complex and quantitative nature, and it has for a while been realised that there needed to be a better link between well characteristics and the oil spill response level. In the early phase of the development, it was decided to separate issues according to their nature. Issues related to company policy on overall level of spill response were identified and addressed in a separate process, as were issues relating to science and technology. From this, further work proceeded on the three main topics a) Policy decisions on spill response scaling criteria, b) algorithms for calculating effect of various response measures, and c) fact finding on issues of operational windows, capacities and effectiveness. As part of the development, all Company fields in production were reviewed, and oil spill response level adjusted and extended to include near shore and shoreline response plans. Sensitivity studies were undertaken to identify critical elements in the progression from blowout and kill studies to near shore spill response plans. Lessons learned include the overall importance of selecting correct data on reservoir characteristics, sufficient resolution in oil spill modelling, and applying consensus values for spill response capabilities throughout the assessment process. To ensure an adequate basis for near shore spill response planning, GIS technology was applied to produce a set of thematic spill response maps, in A1 size PDF format, for 37 of the most sensitive areas along the Norwegian coast. A strategic plan was also developed for each area. All maps, documentation, GIS data sets and other results of this development work has actively been made available to operators on the NCS, to the Authorities and to spill response organizations.

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Fingas, Merv, Ben Fieldhouse, and Joe Mullin. "Water-in-oil Emulsions Results of Formation Studies and Applicability to Oil Spill Modelling." Spill Science & Technology Bulletin 5, no. 1 (April 1999): 81–91. http://dx.doi.org/10.1016/s1353-2561(98)00016-4.

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42

Eide, Magnus S., Øyvind Endresen, Øyvind Breivik, Odd Willy Brude, Ingrid H. Ellingsen, Kjell Røang, Jarle Hauge, and Per Olaf Brett. "Prevention of oil spill from shipping by modelling of dynamic risk." Marine Pollution Bulletin 54, no. 10 (October 2007): 1619–33. http://dx.doi.org/10.1016/j.marpolbul.2007.06.013.

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Nordam, Tor, Raymond Nepstad, Emma Litzler, and Johannes Röhrs. "On the use of random walk schemes in oil spill modelling." Marine Pollution Bulletin 146 (September 2019): 631–38. http://dx.doi.org/10.1016/j.marpolbul.2019.07.002.

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44

Dinariyana, A. A. B., M. A. Avesina, K. Sambodho, and F. Fahrirozan. "Collision Risk Analysis and Oil Spill Dispersion Modelling in Lombok Strait." IOP Conference Series: Earth and Environmental Science 557 (September 15, 2020): 012002. http://dx.doi.org/10.1088/1755-1315/557/1/012002.

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45

Glen, David. "Modelling the impact of double hull technology on oil spill numbers." Maritime Policy & Management 37, no. 5 (September 2010): 475–87. http://dx.doi.org/10.1080/03088839.2010.509050.

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Cabrera Aguilera, Maria Victoria, Bernardo Bastos da Fonseca, Thomas Keith Ferris, Mario Cesar Rodriguez Vidal, and Paulo Victor Rodrigues de Carvalho. "Modelling performance variabilities in oil spill response to improve system resilience." Journal of Loss Prevention in the Process Industries 41 (May 2016): 18–30. http://dx.doi.org/10.1016/j.jlp.2016.02.018.

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O'Laughlin, Casey M., Brent A. Law, Vanessa S. Zions, Thomas L. King, Brian Robinson, and Yongsheng Wu. "Settling of dilbit-derived oil-mineral aggregates (OMAs) & transport parameters for oil spill modelling." Marine Pollution Bulletin 124, no. 1 (November 2017): 292–302. http://dx.doi.org/10.1016/j.marpolbul.2017.07.042.

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LeProvost, Ian. "MARINE ENVIRONMENTAL MANAGEMENT OF THREE OFFSHORE OILFIELDS IN TROPICAL WATERS OF NORTH-WEST AUSTRALIA." APPEA Journal 31, no. 1 (1991): 423. http://dx.doi.org/10.1071/aj90036.

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Environmentally safe management of hydrocarbon exploration and production activities is becoming increasingly important, particularly in sensitive marine areas. LeProvost Environmental Consultants have been working closely with Hadson Energy Limited, Western Mining Corporation Ltd and West Australian Petroleum Pty Ltd to produce environmental impact assessments, environmental management plans and monitoring programs for oilfields recently developed on the North West Shelf. Many of the well sites are located in areas with sensitive ecological habitats, including coral reefs, seagrass beds, mangroves and prawn spawning and fishing grounds. Consequently, operators in these areas have been required to produce comprehensive Environmental Management Plans and Oil Spill Contingency Plans, to gain development and operating approvals from the Western Australian Government.Formulation of these plans begins with baseline surveys of the biological, physical and social characteristics of the study area. Hydrodynamic modelling of the metocean conditions produces oil spill prediction envelopes to highlight the areas that may potentially be affected by an oil spill, if one should occur. Site-specific oil spill response procedures are then designed to cater for the sensitive marine habitats of the area, using the resources that are available in the region to deal with an oil spill.Results to date from the Marine Biological Monitoring Programs for three oilfields have supported predictions that no significant adverse impacts on the environment would result from the development and operation of the oilfields.

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Reed, Mark, Morten H. Emilsen, Ben Hetland, Øistein Johansen, Sharon Buffington, and Boye Høverstad. "Numerical model for estimation of pipeline oil spill volumes." Environmental Modelling & Software 21, no. 2 (February 2006): 178–89. http://dx.doi.org/10.1016/j.envsoft.2004.04.019.

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Ainsworth, C. H., E. P. Chassignet, D. French-McCay, C. J. Beegle-Krause, I. Berenshtein, J. Englehardt, T. Fiddaman, et al. "Ten years of modeling the Deepwater Horizon oil spill." Environmental Modelling & Software 142 (August 2021): 105070. http://dx.doi.org/10.1016/j.envsoft.2021.105070.

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