Decision support tools for oil spill response (OSR-DSTs): Approaches, challenges, and future research perspectives

Environmental hazard and preliminary risk assessment of herding agents used in next generation oil spill response

Herding agents offer a significant advance in oil spill response by overcoming past barriers limiting effectiveness of in-situ burning. This paper reviews the use, environmental fate and effects of two commercial herders, Siltech OP-40 and ThickSlick 6535. A conceptual model is proposed to describe herder fate followed by a screening exposure analysis. Hazard concentrations intended to protect aquatic life are derived using empirical toxicity data, interspecies correlation estimation and group target site models. Using exposure and hazard evaluations, a preliminary risk assessment is performed demonstrating acceptable risk to aquatic life. Hazards posed to wildlife are also reviewed. Potential harm to wildlife can be avoided or minimized by adopting best management application practices. This synthesis is intended to provide a valuable resource describing the rationale for herder use, evaluating environmental risk trade-offs and informing future oil spill response planning and decision-making. Priorities for further research are identified.

Strategic location model for oil spill response vessels (OSRVs) considering oil transportation and weather uncertainties

In the event of oil spills in offshore oil and gas projects, containment and dispersion equipment must be sent to the affected areas within a critical time by vessels known as oil spill response vessels (OSRVs). Here, we developed an optimization tool, integrated with an oil spill trajectory simulation model, both in deterministic and stochastic alternatives, to support decision-making during the strategic planning of OSRV operations. The tool was constructed in Python using GNOME for oil spill simulations and the GUROBI to solve the optimization model. The tool was applied to a case study in Brazil and afforded relevant recommendations. In terms of research contributions, we proved the viability of the integration between oil spill simulation and mathematical modeling for OSRV strategic operation planning, we explored the stochasticity of the problem with an innovative strategy and we demonstrated flexibility and easy applicability of the framework on real operations.

Dispersion analysis of the 2017 Persian Gulf oil spill based on remote sensing data and numerical modelling

Oil spills, detected by SAR sensors as dark areas, are highly effective marine pollutants that affect the ocean surface. These spills change the water surface tension, attenuating capillary gravitational waves and causing specular reflections. We conducted a case study in the Persian Gulf (Arabian Sea to the Strait of Hormuz), where approximately 163,900 gal of crude oil spilled in March 2017. Our study examined the relationship between oil weathering processes and extracted backscatter values using zonal slices projected over SAR-detected oil spills. Internal backscatter values ranged from -22.5 to -23.5, indicating an oil chemical binding and minimal interaction with seawater. MEDSLIK-II simulations indicated increased oil solubilization and radar attenuation rates with wind, facilitating coastal dispersion. Higher backscatter at the spill edges compared to the core reflected different stages of oil weathering. These results highlight the complex dynamics of oil spills and their environmental impact on marine ecosystems.

A multi-criteria simulation-optimization coupling approach for effective emergency response in marine oil spill accidents

Effective marine oil spill responses are vital to reduce environmental, societal, and economic damage. This study developed a Multi-Criteria Emergency Response System (MC-ERS) to comprehensively evaluate response efficiency, operational costs, and environmental losses. The proposed system integrates dynamic multiphase simulation of oil weathering and oil cleanup processes and further provides effective planning for multi-stage resource allocation through system optimization. The developed weight-sum model improved the performance of response operations by reducing the complexity of multi-criteria decision-making. Particle Swarm Optimization (PSO) was chosen as the foundational optimization algorithm due to its efficiency in rapid convergence and suitability for complex problems. From extensive comparisons of PSO variants across benchmark functions and inertia strategies, the C-PSO algorithm was developed, demonstrating enhanced optimization performance for MC-ERS. The developed modelling system performance was demonstrated and evaluated through a representative case study. The optimization plan coordinated resource allocation from onshore warehouses to harbors and spill sites, balancing oil recovery efficiency, costs, and ecological losses. Optimized results indicate an oil recovery of up to 76.50% in five days. Additionally, the system cuts costs by 3.45% and environmental losses by 15.75%. The findings enhance the efficiency of marine oil spill emergency response and provide support for such incidents.

Sustainability and capability-based assessment of marine oil spill response technologies using a decision support system under decomposed fuzzy set

Marine oil spills are a practically challenging environmental occurrence to remediate. The critical stage of managing marine oil spills is choosing an appropriate response technology. This research implements a novel sustainability and capability policy-oriented decision support system (DSS) and the decomposed fuzzy set-based ORESTE model. First, an integrated criteria system on the basis of the sustainability and capability aspects is employed by considering four social, environmental, economic, and capability dimensions. Second, the ORESTE model under the decomposed fuzzy environment is extended to deal with the uncertainty data, evaluate the conflicts between the candidate alternatives, and choose the optimal scenario. Seven technologies of bioremediation, solidifiers, dispersants, adsorbents, in situ burning, booms, and skimmers for remediating oil spills in the Persian Gulf, Iran, have been assessed. The assessment results indicate that the bioremediation technology is the optimal scenario for the oil spill-contaminated sites management and the suggested DSS is feasible and applicable.

Magnetic, self-heating and superhydrophobic sponge for solar-driven high-viscosity oil-water separation

In this work, a novel oil-adsorption sponge with superhydrophobicity was fabricated using polymer-assisted electroless deposition and dip-coating techniques for depositing a rough polydopamine layer, magnetic particles, and low surface energy polydimethylsiloxane onto the surface of a sponge skeleton. The as-prepared superhydrophobic sponge (WCA > 150° and SA < 5°) exhibited rapid adsorption behavior, large adsorption capacity (up to 50.6 times its own dry weight or above 90% of its own volume), excellent durability (above 80% of the adsorption capacity after 80 recycles), and a self-cleaning property owing to sufficient open-cell pores and superelasticity provided by the melamine-formaldehyde host as well as the hierarchical roughness and convenient magnetic recovery enabled by the polymer-assisted electroless deposition approach. The pump-, gravity-, and solar-driven oil-water separation devices based on the fabricated cubic composites were also demonstrated, particularly the separation of high-viscosity oil-water mixtures via the solar-driven mode, demonstrating the broad prospects of such modified sponges in actual applications. This study provides a new avenue for rationally designing novel oil adsorption and separation materials.

Oil spills: Detection and concentration estimation in satellite imagery, a machine learning approach

The method's development to detect oil-spills, and concentration monitoring of marine environments, are essential in emergency response. To develop a classification model, this work was based on the spectral response of surfaces using reflectance data, and machine learning (ML) techniques, with the objective of detecting oil in Landsat imagery. Additionally, different concentration oil data were used to obtain a concentration-estimation model. In the classification, K-Nearest Neighbor (KNN) obtained the best approximations in oil detection using Blue (0.453-0.520 μm), NIR (0.790-0.891 μm), SWIR1 (1.557-1.717 μm), and SWIR2 (1.960-2.162 μm) bands for 2010 spill images. In the concentration model, the mean absolute error (MAE) was 1.41 and 3.34, for training and validation data. When testing the concentration-estimation model in images where oil was detected, the concentration-estimation obtained was between 40 and 60 %. This demonstrates the potential use of ML techniques and spectral response data to detect and estimate the concentration of oil-spills.

Development and testing of a SOSM approach to examine the natural attenuation of oil spillage on shorelines

Understanding the fate and behavior of spilled oil on the shoreline is crucial for helping decision-makers make trade-offs between natural attenuation and shoreline treatments. In this study, primary processes of oil attenuation and translocation on the shoreline, including penetration, washout, and biodegradation, have been considered to develop a numerical model, Shoreline Oil Spill Model (SOSM), for predicting the fate of spilled oil in various shoreline geological compartments. A holistic system design is employed to develop a user-friendly graphical interface system for SOSM. Global and local sensitivity analyses are used to examine the importance of the input factors on different outputs in SOSM. The results of the sensitivity analysis indicate that the input of oil spill mass correlates positively with the oil retention and surface and subsurface oil mass, while the oil concentration and penetration depth are sensitive to the grain size of the beach sediment. Subsequently, the SOSM is tested to hindcast the fate of stranded oil from the Exxon Valdez oil spill. Reasonable validation results have been obtained for the study site. It confirms the utility of the SOSM to examine and predict the level of shoreline oil contamination in the field following accidental spill events.

Exploring the effects of substrate mineral fines on oil translocation in the shoreline environment: Experimental analysis, numerical simulation, and implications for spill response

Mineral fines act a pivotal part in determining the fate and behavior of oil. In this study, the infiltrations of oil emulsion in simulated sediments and natural shoreline sediments were investigated using a fixed bed experiment. Oil infiltration process was simulated based on fixed-bed dispersion model. The role of mineral fines in oil release was explored using simulated and natural sediments. Although mineral fines exhibited a higher affinity for oil, it was found that increasing fines fractions decreased the flow rate of oil emulsion, thereby decreasing the oil retention in the sediment column. In terms of oil release from the sediment, the highest level of oil mass was observed in the oil-mineral flocculation phase compared to the water column and the water surface compartments. Compared to light crude oil, the release of engine oil from sediment was less. The effects of mineral fines on oil infiltration and release were also confirmed by using natural shoreline sediments. Results of our detailed field studies also showed that current shoreline classification datasets do not characterize the presence and fraction of mineral fines at a level of detail required to accurately predict the significance of oil translocation following spill incidents.

Numerical Study on the Influence of Model Uncertainties on the Transport of Underwater Spilled Oil

Oil pollution influences marine biology, ecology, and regional sustainable development capacity, but model uncertainties limit the ability of the numerical model to accurately predict the transport and fate of the underwater oil spill. Based on a three-dimensional underwater oil spill model validated by satellite images of the oil slick at the sea surface, the Penglai 19-3 oil spill accident in the Bohai Sea was simulated; in addition, several sensitivity experiments were set up to investigate the influence of model uncertainties in the background wind, current, start time of the oil spill, and spill site on the transport of underwater spilled oil in the Penglai 19-3 oil spill accident. The experimental results indicate that the uncertainty in the background wind has a certain impact on the simulated centroid position at the sea surface, and little effect on the simulated underwater results, while the uncertainty in the background current has a significant influence on the transport of the underwater spilled oil both at the sea surface and underwater. An uncertainty of 24 h in the start time of the oil spill can cause more than 1 time larger than the benchmark case displacement of the oil spill centroid point and sweeping area at the sea surface, as the periodic tidal current is the main constituent of the ocean current in the Bohai Sea. The uncertainty in the spill site has a large influence on the final position of the oil spill centroid point, but the oil spill trajectories do not intersect with each other within 48 h, which makes it possible to identify the oil spill platform from the actual observations. The influence of uncertainties in the important model inputs and key model parameters on the transport of underwater spilled oil in the Penglai 19-3 oil spill accident is evaluated for the first time, which is of substantial significance for improving the prediction accuracy of the transport and fate of underwater oil spills.

Climate Change Impacts on Coastal and Offshore Petroleum Infrastructure and the Associated Oil Spill Risk: A Review

Climate change has been observed worldwide in recent decades, posing challenges to the coastal and offshore oil and gas infrastructure. It is crucial to identify how climate change affects these infrastructures and the associated oil spill risk. This paper provides an analysis of the vulnerability of coastal and offshore oil and gas infrastructure in response to climate change. The paper examines oil spill incidents worldwide and addresses climate change’s possible influences on oil spill risk. Moreover, available oil spill modeling and decision support tools for oil spill response are reviewed considering climate change. The paper signals the need for emerging decision and modeling tools considering climate change effects, which can help decision-makers to evaluate the risk on time and provide early warnings to adapt or prevent the unforeseen impacts on the oil industry partially resulting from global warming, including oil spill accidents.

Environmental Impacts and Challenges Associated with Oil Spills on Shorelines

Oil spills are of great concern because they impose a threat to the marine ecosystem, including shorelines. As oil spilled at sea is transported to the shoreline, and after its arrival, its behavior and physicochemical characteristics change because of natural weathering phenomena. Additionally, the fate of the oil depends on shoreline type, tidal energy, and environmental conditions. This paper critically overviews the vulnerability of shorelines to oil spill impact and the implication of seasonal variations with the natural attenuation of oil. A comprehensive review of various monitoring techniques, including GIS tools and remote sensing, is discussed for tracking, and mapping oil spills. A comparison of various remote sensors shows that laser fluorosensors can detect oil on various types of substrates, including snow and ice. Moreover, current methods to prevent oil from reaching the shoreline, including physical booms, sorbents, and dispersants, are examined. The advantages and limitations of various physical, chemical, and biological treatment methods and their application suitability for different shore types are discussed. The paper highlights some of the challenges faced while managing oil spills, including viewpoints on the lack of monitoring data, the need for integrated decision-making systems, and the development of rapid response strategies to optimize the protection of shorelines from oil spills.

DSS-OSM: An Integrated Decision Support System for Offshore Oil Spill Management

The marine ecosystem, human health and social economy are always severely impacted once an offshore oil spill event has occurred. Thus, the management of oil spills is of importance but is difficult due to constraints from a number of dynamic and interactive processes under uncertain conditions. An integrated decision support system is significantly helpful for offshore oil spill management, but it is yet to be developed. Therefore, this study aims at developing an integrated decision support system for supporting offshore oil spill management (DSS-OSM). The DSS-OSM was developed with the integration of a Monte Carlo simulation, artificial neural network and simulation-optimization coupling approach to provide timely and effective decision support to offshore oil spill vulnerability analysis, response technology screening and response devices/equipment allocation. In addition, the uncertainties and their interactions were also analyzed throughout the modeling of the DSS-OSM. Finally, an offshore oil spill management case study was conducted on the south coast of Newfoundland, Canada, demonstrating the feasibility of the developed DSS-OSM.

Efficient and Fast Removal of Oils from Water Surfaces via Highly Oleophilic Polyurethane Composites

In this study we evaluated the oil adsorption capacity of an aliphatic polyurethane foam (PU 1) and two of its composites, produced through surface coating using microparticles of silica (PU-Si 2) and activated carbon (PU-ac 3). The oil adsorption capacity in diesel was improved up to 36% using the composite with silica and up to 50% using the composite with activated carbon with respect to the initial PU 1. Excellent performances were retained in gasoline and motor oil. The adsorption was complete after a few seconds. The process follows a monolayer adsorption fitted by the Langmuir isotherm, with a maximum adsorption capacity of 29.50 g/g of diesel for the composite with activated carbon (PU-ac 3). These materials were proved to be highly oleophilic for oil removal from fresh water and sea water samples. Regeneration and reuse can be repeated up to 50 times by centrifugation, without a significant loss in adsorption capacity.