Development and application of an oil spill model with wave-current interactions in coastal areas

An innovative coupling technique for integrating oil spill prediction model with finite volume method-based ocean model

Marine oil spill pollution have increased recent years, threatening the safety of the marine environment. This paper proposes a coupling technique: Fast Mapping & Addressing(FMA) that integrates the oil spill model with oceanographic model. The FMA technique is based on hash function and spatial quadtree algorithm to achieve efficient addressing from an unstructured to a structured grid. The oil spill model simulates the oil spill process, while the ocean model simulates the ocean currents. The efficiency is improved about ten times compared to the interpolation algorithm. Results reveal the difference between the simulation results of the ocean model and the measured data is minimal, with an MAE(Mean Absolute Error) and RMSE(Root Mean Square Error) of about 0.06 m. Moreover, two oil spill events in China's nearshore were selected to simulate and verify the results. Indeed, our model's results agree with the observed data, demonstrating that our model can achieve a satisfied simulation of oil spill.

Impact of ocean waves on transport of underwater spilled oil in the Bohai Sea

To investigate the impact of ocean waves on transport of underwater spilled oil, a wave action module is developed and embedded into an underwater oil spill model. Combined with a validated hydrodynamic background data, the model is applied to a short-term numerical simulation of the 2011 Penglai 19-3 oil spill event in the Bohai Sea. Numerical result shows that the model result is rationally consistent with the field observation. In addition, waves can influence surface oil slick in both horizontal and vertical directions: in horizontal direction, Stokes drift tends to make oil slick spread more widely under the action of wind-waves; in vertical direction, breaking waves tend to entrain the surface oil into water column, and then affects the vertical distribution of spilled oil. Furthermore, sensitivity study shows that using different parameterizations of wave entrainment can make some difference to the model output, but the effect is limited. Besides, the impact of input wave errors varies on different model outputs. This study is expected to provide offshore oil spill emergency responders and researchers with useful information on oil spill monitoring, emergency plans and environmental damage assessment.

Oil spill trajectory modelling and environmental vulnerability mapping using GNOME model and GIS

This study develops an oil spill environmental vulnerability model for predicting and mapping the oil slick trajectory pattern in Kota Tinggi, Malaysia. The impact of seasonal variations on the vulnerability of the coastal resources to oil spill was modelled by estimating the quantity of coastal resources affected across three climatic seasons (northeast monsoon, southwest monsoon and pre-monsoon). Twelve 100 m³ (10,000 splots) medium oil spill scenarios were simulated using General National Oceanic and Atmospheric Administration Operational Oil Modeling Environment (GNOME) model. The output was integrated with coastal resources, comprising biological, socio-economic and physical shoreline features. Results revealed that the speed of an oil slick (40.8 m per minute) is higher during the pre-monsoon period in a southwestern direction and lower during the northeast monsoon (36.9 m per minute). Evaporation, floating and spreading are the major weathering processes identified in this study, with approximately 70% of the slick reaching the shoreline or remaining in the water column during the first 24 h (h) of the spill. Oil spill impacts were most severe during the southwest monsoon, and physical shoreline resources are the most vulnerable to oil spill in the study area. The study concluded that variation in climatic seasons significantly influence the vulnerability of coastal resources to marine oil spill.

Extending Voronoi-diagram based modeling of oil slick spreading to surface tension-viscous spreading regime

An earlier paper demonstrated a methodology for modeling the spreading process with a Gaussian random walk procedure, but was limited to the gravity-viscous spreading regime. Here we extend the methodology of representing spread and transport of oil slicks on calm sea surface by updated Voronoi diagrams to account for the surface tension-viscous spreading regime as well. We have utilized the analogy between diffusion and spreading processes by defining a step length for the particle-based random walk scheme. In this study, calculation of the diffusive length is improved by including the surface tension term in the numerical solution method. The results from the numerical simulation of the spreading oil slick agree very well with the analytical solutions. The solution is robust in that good agreement is achieved for a large range of model and numerical solution parameters. This modeling procedure remains valid only for passive, quiescent spreading. The inclusion of spreading due to important horizontal and vertical turbulent shear processes in the Voronoi diagram paradigm remains a challenge for future work.

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

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.

Estimating offshore exposure to oil spill impacts based on a statistical forecast model

A statistical oil spill risk forecast model in support of emergency response and environmental risk assessment is presented by combing the deterministic model, probabilistic strategy and frequency estimation. When applied to evaluate various potential spill sources (oil port, fairway, anchorage and pipeline) in the Zhoushan offshore area, the model provides the probability of slick spatial position, oil slick thickness, and exposure duration of floating slick. An oil spill risk map is generated after integrating multiple spill sources, which is a powerful tool for identifying high-risk areas and developing contingency plan. Impact scope and damage degree vary among different sources because of special local topographical, hydrological, and meteorological conditions, where generally exists high pollution intensity of point-source and wide range of line-source. Huge Changjiang River runoff prevents coastal sea in the north from being contaminated by spilled oil from the southern Zhoushan offshore area.

Deep oil spill hazard assessment based on spatio-temporal met-ocean patterns

Oil spill risk assessments are important tools for the offshore oil and gas industries to minimize the consequences of deep spills. The stochastic modeling required in this kind of studies, is generally centered on surface transport and based on a Monte Carlo selection of hundreds or thousands of met-ocean scenarios from reanalysis databases, to create an ensemble of spill simulations. We propose a new integrated stochastic modeling methodology including both surface and subsurface transport, based on the specific selection of the most relevant environmental conditions through data-mining techniques. The methodology was applied to evaluate oil contamination probability as a consequence of a simulated deep release in the North Sea. Our results show the effectiveness of the proposed methodology to select representative evolutions of met-ocean conditions and to obtain pollution probabilities from an integrated subsurface and surface oil spill stochastic modeling, while assuring a manageable computational effort.

Quantitative oil spill risk from offshore fields in the Bohai Sea, China

The oil spill risk in the environments surrounding the Bohai Sea is aggravated by increasing marine petroleum exploitation. There is a growing need to assess the risk of oil pollution in sensitive marine areas and coastlines. Both the recurrence frequency of spill accidents and the duration of exposure in marine areas and coastal shorelines are considered in our statistical model, which consists of a probabilistic oil spill model used to simulate various oil spill scenarios at twenty oil fields in the Bohai Sea based on high-resolution oceanographic, meteorological, and geomorphological data. The statistical results of the risk to marine areas integrate the probability of pollution in marine areas, oil slick thickness, and duration of water surface covered by a floating slick. The coastal risk index includes the probability of a shoreline hit, average mass of stranding oil, and time of oil beaching. The spatial distributions of environmental triggering factors and coastal/marine vulnerability are merged, and a map of the oil spill risk in the Bohai Sea is presented, which is useful for contingency planning and the assessment of environmental risk of marine petroleum exploitation.

Mid-long term oil spill forecast based on logistic regression modelling of met-ocean forcings

Past major oil spill disasters, such as the Prestige or the Deepwater Horizon accidents, have shown that spilled oil may drift across the ocean for months before being controlled or reaching the coast. However, existing oil spill modelling systems can only provide short-term trajectory simulations, being limited by the typical met-ocean forecast time coverage. In this paper, we propose a methodology for mid-long term (1-6 months) probabilistic predictions of oil spill trajectories, based on a combination of data mining techniques, statistical pattern modelling and probabilistic Lagrangian simulations. Its main features are logistic regression modelling of wind and current patterns and a probabilistic trajectory map simulation. The proposed technique is applied to simulate the trajectory of drifting buoys deployed during the Prestige accident in the Bay of Biscay. The benefits of the proposed methodology with respect to existing oil spill statistical simulation techniques are analysed.

Using a genetic algorithm to improve oil spill prediction

The performance of oil spill models is strongly influenced by multiple parameters. In this study, we explored the ability of a genetic algorithm (GA) to determine optimal parameters without the need for time-consuming manual attempts. An evaluation function integrating the percentage of coincidence between the predicted polluted area and the observed spill area was proposed for measuring the performance of a Lagrangian oil particle model. To maximise the objective function, the oil spill was run numerous times with continuously optimised parameters. After many generations, the GA effectively reduced discrepancies between model results and observations of a real oil spill. Subsequent validation indicated that the oil spill model predicted oil slick patterns with reasonable accuracy when equipped with optimal parameters. Furthermore, multiple objective optimisation for observations at different times contributed to better model performance.

Numerical modelling of temporal and spatial patterns of petroleum hydrocarbons concentration in the Bohai Sea

The discharge of petroleum hydrocarbons (PHs; ~10,000tons annually) into the Bohai Sea, a shallow inland sea in China, presents a serious threat to the marine environment. To evaluate the effects of PHs pollution and estimate the corresponding environmental capacity, we have developed a genetic algorithm-based coupled hydrodynamic/transport for simulating PHs concentration evolution and distribution from July 2006 to October 2007, with the predicted values being in good agreement with monitoring results. Importantly, the mean PHs concentrations and seasonal concentration variations were primarily determined by external loading, i.e., currents were shown to drive PHs transport, reconfigure local PHs patterns, and increase PHs concentration in water masses, even at large distances from discharge sources. The developed model could realistically simulate PHs distribution and evolution, thus being a useful tool for estimating the seasonal environmental capacity of PHs.

Development of a statistical oil spill model for risk assessment

To gain a better understanding of the impacts from potential risk sources, we developed an oil spill model using probabilistic method, which simulates numerous oil spill trajectories under varying environmental conditions. The statistical results were quantified from hypothetical oil spills under multiple scenarios, including area affected probability, mean oil slick thickness, and duration of water surface exposed to floating oil. The three sub-indices together with marine area vulnerability are merged to compute the composite index, characterizing the spatial distribution of risk degree. Integral of the index can be used to identify the overall risk from an emission source. The developed model has been successfully applied in comparison to and selection of an appropriate oil port construction location adjacent to a marine protected area for Phoca largha in China. The results highlight the importance of selection of candidates before project construction, since that risk estimation from two adjacent potential sources may turn out to be significantly different regarding hydrodynamic conditions and eco-environmental sensitivity.

Modeling spreading of oil slicks based on random walk methods and Voronoi diagrams

We introduce a methodology for representation of a surface oil slick using a Voronoi diagram updated at each time step. The Voronoi cells scale the Gaussian random walk procedure representing the spreading process by individual particle stepping. The step length of stochastically moving particles is based on a theoretical model of the spreading process, establishing a relationship between the step length of diffusive spreading and the thickness of the slick at the particle locations. The Voronoi tessellation provides the areal extent of the slick particles and in turn the thicknesses of the slick and the diffusive-type spreading length for all particles. The algorithm successfully simulates the spreading process and results show very good agreement with the analytical solution. Moreover, the results are robust for a wide range of values for computational time step and total number of particles.