Search Results (97)

Oil spills represent a significant environmental hazard, particularly in marine ecosystems, where their impacts extend to coastal infrastructure, biodiversity, and economic activities. This study utilizes GNOME v.47.2 (General NOAA Operational Modeling Environment) and ADIOS2 v.2.10.2 (Automated Data Inquiry for Oil Spills) to simulate and analyze oil spill dynamics in the Romanian sector of the Black Sea, focusing on trajectory prediction, hydrocarbon weathering, and shoreline contamination risk assessment. The research explores multiple spill scenarios involving different hydrocarbon types (light vs. heavy oils), vessel dynamics, and real-time environmental variables (wind, currents, temperature). The findings reveal that lighter hydrocarbons (e.g., gasoline, aviation fuel) tend to evaporate quickly, while heavier fractions (e.g., crude oil, fuel oil #6) persist in the marine environment and pose a higher risk of coastal pollution. In the first case study, a spill of 10,000 metric tons of medium oil (Arabian Medium EXXON) was simulated using GNOME v.47.2, showing that after 22 h, the slick reached the shoreline. Under forecasted hydro-meteorological conditions, 27% evaporated, 1% dispersed, and 72% remained for mechanical or chemical intervention. In the second simulation, 10,000 metric tons of gasoline were released, and within 6 h, 98% evaporated, with only minor residues reaching the shore. A real-world validation case was also conducted using the December 2024 Kerch Strait oil spill incident, where the model accurately predicted the early arrival of light fractions and delayed coastal contamination by fuel oil carried by subsurface currents. These results emphasize the need for future research focused on the vertical dispersion dynamics of heavier hydrocarbon fractions. Full article

The presence of oil slicks in the ocean presents significant environmental and regulatory challenges for offshore oil processing operations. During primary oil–water separation, produced water is discharged into the ocean, carrying residual oil, which is measured using the total oil and grease (TOG) method. The formation and spread of oil slicks are influenced by metoceanographic variables, including wind direction (WD), wind speed (WS), current direction (CD), current speed (CS), wind wave direction (WWD), and peak period (PP). In Brazil, regulatory limits impose sanctions on companies when oil slicks exceed 500 m in length, making accurate prediction of their occurrence and extent crucial for offshore operators. This study follows three main stages. First, the performance of five machine learning classification algorithms is evaluated, selecting the most efficient method based on performance metrics from a Brazilian company’s oil slick database. Second, the best-performing model is used to analyze the influence of metoceanographic variables and TOG levels on oil slick occurrence and detection probability. Finally, the third stage examines the extent of detected oil slicks to identify key contributing factors. The prediction results enhance decision-support frameworks, improving monitoring and mitigation strategies for offshore oil discharges. Full article

Monitoring oil drift by integrating multi-source satellite imagery has been a relatively underexplored practice due to the limited time-sampling of datasets. However, this limitation has been mitigated by the emergence of new satellite constellations equipped with both Synthetic Aperture Radar (SAR) and optical sensors. In this manuscript, we take advantage of multi-temporal and multi-source satellite imagery, incorporating SAR (Sentinel-1 and ICEYE-X) and optical data (Sentinel-2/3 and Landsat-8/9), to provide insights into the spatio-temporal variations of oil spills. We also analyze the impact of met–ocean conditions on oil drift, focusing on two specific scenarios: marine floating oil slicks off the coast of Qatar and oil spills resulting from a shipwreck off the coast of Mauritius. By overlaying oils detected from various sources, we observe their short-term and long-term evolution. Our analysis highlights the finding that changes in oil structure and size are influenced by strong surface winds, while surface currents predominantly affect the spread of oil spills. Moreover, to detect oil slicks across different datasets, we propose an innovative unsupervised algorithm that combines a Bayesian approach used to detect oil and look-alike objects with an oil contours approach distinguishing oil from look-alikes. This algorithm can be applied to both SAR and optical data, and the results demonstrate its ability to accurately identify oil slicks, even in the presence of oil look-alikes and under varying met–ocean conditions. Full article

This paper presents an original approach to predicting oil slick movement and dispersion at the water surface. Special emphasis is placed on the impact of evolving hydro-meteorological conditions and the thickness of the oil spill layer. The main gap addressed by this study lies in the need for a comprehensive understanding of how changing environmental conditions and oil thickness interact to influence the movement and dispersion of oil slicks. By focusing on this aspect, this study aims to provide valuable insights into the complex dynamics of oil spill behaviour, enhancing the ability to predict and mitigate the environmental impacts of such incidents. Self-designed software was applied to develop and modify previously established mathematical probabilistic models for predicting changes in the shape of the oil trajectory. First, a semi-Markov model of the process is constructed, and the oil thickness is analysed at the sea surface over time. Next, a stochastic-based procedure to forecast the horizontal movement and dispersion of an oil slick in diverse hydro-meteorological conditions considering a varying oil layer thickness is presented. This involves determining the trajectory and movement of a slick domain, which consists of an elliptical combination of domains undergoing temporal changes. By applying the procedure and program, a short-term forecast of the horizontal movement and dispersion of an oil slick provided its trajectory at the Bornholm Basin of the Baltic Sea within two days. The research results obtained are preliminary prediction results, although the approach considered in this paper can help responders understand the scope of the problem and mitigate the effects of environmental damage if the oil discharge reaches sensitive ecosystems. Finally, further perspectives of this research are given. Full article

Pollution caused by marine oil spills can lead to persistent ecological disasters and severe social and economic damages. Numerical simulations are useful and essential tools for accurate decision making during emergencies and planning response actions. In this study, we applied the Princeton Ocean Model (POM) to determine current data, including seawater velocity, salinity, and temperature, and we obtained the fate and trajectory of spilled oil using OpenOil. Several probable oil slicks around Taiwan were simulated over time (12 months) and space (four spill locations in the marine area of each coastal city or county) using the model. The percentage risk under the effect of an oil spill is estimated. The risk zone of the coastal waters of Taiwan was identified based on the frequency of simulated oil slicks hitting the coast and sensitive resources. This information not only helps authorities guide the preparation of effective plans to minimise the impacts of oil spill incidents but could also be used to improve regulations related to shipping and vessel navigation in regional seas. Full article

The oil and gas resources in the deep Paleogene system of the South China Sea are abundant. However, due to its poor reservoir physical properties and strong heterogeneity, the deep Paleogene system needs to be commercially exploited by hydraulic fracturing technology. In view of the challenges of offshore low-permeability reservoirs, large-scale fracturing is not allowed because of the limited operation sites and complex string structure. Taking the H oilfield in the South China Sea as the target, based on the concept of the integration of geologic and engineering techniques, parameters such as the number of fracturing stages and the fracture length were optimized by a numerical simulation, and a study on the slurry rate and fracturing scale was carried out based on the type of fracturing and the pipe string structure. The results show that multistage fracturing technology is available in low-permeability offshore oil fields. It is suggested to adopt networking fracturing technology with a “slick water + high slurry rate” framework. A higher rate is recommended, and the fracturing scale of each stage should be 50 m³ of the sands and 700 m³ of the fluids. This research provides a new model for offshore low-permeability oilfield development. Full article

Air–sea interactions within the ocean’s near-surface layer play a pivotal role in climate regulation and are essential for understanding the dispersion of marine pollutants such as microplastics and oil slicks. Despite its significance, high-resolution data exploring the physical dynamics near the air–sea interface are noticeably sparse. To address this, we introduced a novel Lagrangian observational platform, outfitted with an upward-facing high-resolution ADCP, designed to measure current shear within the top 2 m of the surface water. Through two short field experiments, we identified enhanced currents and shear in the near-surface layer, and observed a negative vertical momentum flux aligned with the wind direction and a positive one orthogonal to it. The measurement suggest that Stokes drift contributes to 10% of horizontal mass transport and 20% of shear in the top surface layer, with the direct and local wind-driven current being the predominant influence. To accurately model the physical behavior of buoyant microplastics, this observation underscores the necessity of parameterizations that account for both the Stokes drift and the direct, local wind-driven current, a factor that is often overlooked in many models. Full article

The paper introduces the Oil-Slick Hub (OSH), a computational platform to facilitate the data visualization of a large database of petroleum signatures observed on the surface of the ocean with synthetic aperture radar (SAR) measurements. This Internet platform offers an information search and retrieval system of a database resulting from >20 years of scientific projects that interpreted ~15 thousand offshore mineral oil “slicks”: natural oil “seeps” versus operational oil “spills”. Such a Digital Mega-Collection Database consists of satellite images and oil-slick polygons identified in the Gulf of Mexico (GMex) and the Brazilian Continental Margin (BCM). A series of attributes describing the interpreted slicks are also included, along with technical reports and scientific papers. Two experiments illustrate the use of the OSH to facilitate the selection of data subsets from the mega collection (GMex variables and BCM samples), in which artificial intelligence techniques—machine learning (ML)—classify slicks into seeps or spills. The GMex variable dataset was analyzed with simple linear discriminant analyses (LDAs), and a three-fold accuracy performance pattern was observed: (i) the least accurate subset (~65%) solely used acquisition aspects (e.g., acquisition beam mode, date, and time, satellite name, etc.); (ii) the best results (>90%) were achieved with the inclusion of location attributes (i.e., latitude, longitude, and bathymetry); and (iii) moderate performances (~70%) were reached using only morphological information (e.g., area, perimeter, perimeter to area ratio, etc.). The BCM sample dataset was analyzed with six traditional ML methods, namely naive Bayes (NB), K-nearest neighbors (KNN), decision trees (DT), random forests (RF), support vector machines (SVM), and artificial neural networks (ANN), and the most effective algorithms per sample subsets were: (i) RF (86.8%) for Campos, Santos, and Ceará Basins; (ii) NB (87.2%) for Campos with Santos Basins; (iii) SVM (86.9%) for Campos with Ceará Basins; and (iv) SVM (87.8%) for only Campos Basin. The OSH can assist in different concerns (general public, social, economic, political, ecological, and scientific) related to petroleum exploration and production activities, serving as an important aid in discovering new offshore exploratory frontiers, avoiding legal penalties on oil-seep events, supporting oceanic monitoring systems, and providing valuable information to environmental studies. Full article

An environmentally friendly pore size-controlled, superhydrophobic polylactic acid (PLA) membrane was successfully prepared by a simpler freeze solidification phase separation method (FSPS) and solution impregnation, which has application prospects in the field of oil–water separation. The pore size and structure of the membrane were adjusted by different solvent ratios and solution impregnation ratios. The PLA-FSPS membrane after solution impregnation (S-PLA-FSPS) had the characteristics of uniform pore size, superhydrophobicity and super lipophilicity, its surface roughness Ra was 338 nm, and the contact angle to water was 151°. The S-PLA-FSPS membrane was used for the oil–water separation. The membrane oil flux reached 16,084 L·m⁻²·h⁻¹, and the water separation efficiency was 99.7%, which was much higher than that of other oil–water separation materials. In addition, the S-PLA-FSPS membrane could also be applied for the adsorption and removal of oil slicks and underwater heavy oil. The S-PLA-FSPS membrane has great application potential in the field of oil–water separation. Full article

The in situ measurement of thick oil slick thickness (>0.5 mm) and area in real time in order to estimate the volume of an oil spill is very important for determining the oil spill response strategy and evaluating the oil spill disposal efficiency. In this article, a method is proposed to assess the volume of oil slicks by simultaneously measuring the thick oil slick thickness and area using ultrasonic inspection and image processing methods, respectively. A remotely operated vehicle (ROV), integrating two ultrasonic immersion transducers, was implemented as a platform to receive ultrasonic reflections from an oil slick. The oil slick thickness was determined by multiplying the speed of sound by the ultrasonic traveling time within the oil slick, which was calculated using the cross-correlation method. Images of the oil slick were captured by an optical camera using an airborne drone. The oil slick area was calculated by conducting image processing on images of the oil slick using the proposed image processing algorithms. Multiple measurements were performed to verify the proposed method in the laboratory experiments. The results show that the thickness, area and volume of a thick oil slick can be accurately measured with the proposed method. The method could potentially be used as an applicable tool for measuring the volume of an oil slick during an oil spill response. Full article

Oil spills may have devastating effects on marine ecosystems, public health, the economy, and coastal communities. As a consequence, scientific literature contains various up-to-date, advanced oil spill predictive models, capable of simulating the trajectory and evolution of an oil slick generated by the accidental release from ships, hydrocarbon production, or other activities. To predict in near real time oil spill transport and fate with increased reliability, these models are usually coupled operationally to synoptic meteorological, hydrodynamic, and wave models. The present study reviews the available different met-ocean forcings that have been used in oil-spill modeling, simulating hypothetical or real oil spill scenarios, worldwide. Seven state-of-the-art oil-spill models are critically examined in terms of the met-ocean data used as forcing inputs in the simulation of twenty-three case studies. The results illustrate that most oil spill models are coupled to different resolution, forecasting meteorological and hydrodynamic models, posing, however, limited consideration in the forecasted wave field (expressed as the significant wave height, the wave period, and the Stokes drift) that may affect oil transport, especially at the coastal areas. Moreover, the majority of oil spill models lack any linkage to the background biogeochemical conditions; hence, limited consideration is given to processes such as oil biodegradation, photo-oxidation, and sedimentation. Future advancements in oil-spill modeling should be directed towards the full operational coupling with high-resolution atmospheric, hydrodynamic, wave, and biogeochemical models, improving our understanding of the relative impact of each physical and oil weathering process. Full article

The paper deals with an important issue related to the identification, modelling, and prediction of environmental pollution in aquatic ecosystems of the Baltic Sea caused by anthropopressure. Water ecosystems are in danger nowadays because of the negative influence of chemical releases in seas, oceans, or inland waters. The crucial issue is to prevent the oil spills and mitigate their consequences. Thus, there is a need for methods capable of reducing the water pollution and enhancing the effectiveness of port and marine environment preservation. The challenge in implementing actions to remove and prevent horizontal oil discharge lies in accurately determining its shape and direction of oil spreading. The author employed a self-designed software utilizing modified and developed mathematical probabilistic models to forecast the movement and dispersion of an oil spill in diverse hydrological and meteorological conditions. This involved determining the trajectory and movement of a spill domain, which consists of elliptical sub-domains undergoing temporal changes. The research results obtained are the initial results in the oil spill simulation problem. This approach represents an expanded and innovative method for determining the spill domain and tracking its movement, applicable to oceans and seas worldwide. It expands upon the methodologies firstly discussed, thereby broadening the range of available techniques in this field. A simple model of an oil spill trajectory simulation and a surface oil slick as an ellipse is illustrated using a time-series of selected hydro-meteorological factors that change at random times. The author proposes a Monte Carlo simulation method to determine the extent of an oil spill in an aquatic ecosystem, taking into account the influence of varying hydro-meteorological conditions. A semi-Markov model is defined to capture the dynamics of these conditions within the spill area and develop an enhanced algorithm for predicting changes in the shape and movement of the spill domain under changing these conditions. By applying the algorithm, a simulation is conducted to provide short-term prediction of the oil discharge trajectory in a selected Baltic Sea waterway. To enhance the accuracy of predicting the process of changing conditions, uniformly tested joint datasets from the open sea water area were incorporated. Finally, the potential future prospects and directions for further research in this field are discussed. Full article

Oil spills are one of the most hazardous pollutants in marine environments with potentially devastating impacts on ecosystems, human health, and socio-economic sectors. Therefore, it is of the utmost importance to establish a prompt and efficient system for forecasting and monitoring such spills, in order to minimize their impacts. The present work focuses on the numerical simulation of the drift and spread of oil slicks in marine environments. The specific area of interest is the Azemmour estuary, located on Morocco’s Atlantic Coast. According to the environmental sensitivity index (ESI), given its geographical location at the intersection of the World’s Shipping Lines of oil transport, this area, as with many other sites in Morocco, has been classified as a high-risk area for oil spill accidents. By taking into account a range of factors, including the ocean currents, the weather conditions, and the oil properties, detailed numerical simulations were conducted, using the hydrodynamic TELEMAC-2D model, to predict the behavior and spread of an oil spill event in the aforementioned coastal region. The simulation results help to understand the spatial–temporal evolution of the spilled oil, the effect of wind on the spreading process, as well as the coastal areas that are most likely to be affected in the event of an oil spill accident. The simulations were performed with and without wind effects. The results showed that three days after the oil spill only 31% of the spilled oil remained on the sea surface. The wind was found to be the main factor responsible for oil drifting offshore. The results indicated that rapid action is needed to address the oil spill before it causes significant environmental damage and makes the oil cleanup process more challenging and expensive. The results of the present study are highly valuable for the management and prevention of environmental disasters in the Azemmour estuary area. The findings can be used to assess the efficacy of various response strategies, such as containment and cleanup measures, and to develop more effective emergency response plans. Full article

This paper focuses on the proof and application of discriminating between oil spills and seawater (including the “look-alikes”, named low wind areas) based on the polarization ratio. A new relative polarization ratio (${PR}_r$) method is proposed, which is based on the difference between the scattering mechanism and the dielectric constant for oil spills compared to that of seawater. The case study found that (1) ${PR}_r$ numerically amplifies the contrast between oil spills and seawater, reduces the difference between low wind areas and ordinary seawater, and exhibits better details of the image; (2) the threshold method based on Euclidean distance can obtain the highest classification overall accuracy within the allowable error range, and can be widely used in the study of different incidence angles and environmental conditions; and (3) the identification of oil spills and seawater by the proposed methods can largely avoid the misjudgment of low wind areas as oil spills. Considering visual interpretation as the reference ‘ground truth’, the overall classification accuracy of all cases is more than 95%; only the edge of the diffuse thin oil slick and oil–water mixture is difficult to identify. This method can serve as an effective supplement to existing oil spill detection methods. Full article

Due to the recent rapid growth of ocean oil development and transportation, the offshore oil spill risk accident probability has increased unevenly. The marine oil spill poses a great threat to the development of coastal cities. Therefore, effective and reliable technologies must be used to monitor oil spills to minimize disaster losses. Based on YOLO deep learning network, an automatic oil spill detection method was proposed. The experimental data preprocessing operations include noise reduction, gray adjustment, and local contrast enhancement. Then, real and synthetically generated marine radar oil spill images were used to make slice samples for training the model in the YOLOv5 network. The detection model can identify the effective oil spill monitoring region. Finally, an adaptive threshold was applied to extract the oil slicks in the effective oil spill monitoring regions. The YOLOv5 detection model generated had the advantage of high efficiency compared with existing methods. The offshore oil spill detection method proposed can support real-time and effective data for routine patrol inspection and accident emergency response. Full article