Search Results (101)

Marine oil spills pose a persistent threat to marine ecosystems and coastal economies, and their rapid and unpredictable spread requires timely and reliable monitoring. In X-band marine radar images, oil slicks usually appear as low-contrast dark targets embedded in heterogeneous sea clutter, making accurate segmentation particularly challenging. To address this problem, this study proposes a training-free two-stage oil slick detection framework that combines an improved Slick Boundary Ratio (SBR) feature with an improved Whale Optimization Algorithm (WOA). First, the improved SBR feature is used to extract the oil slick region of interest (ROI). Then, the improved WOA is employed to determine the global threshold for oil slick segmentation. Experimental results show that the proposed method achieves accurate and spatially coherent oil slick segmentation in complex radar backgrounds, with an Accuracy of 99.36%, a Precision of 85.73%, a Recall of 84.42%, an F1-score of 85.07%, and an Intersection over Union (IoU) of 74.01%. These results indicate that the proposed framework can effectively suppress false positives while maintaining strong detection sensitivity, thereby improving segmentation robustness in low-contrast marine radar scenes. Owing to its training-free design, the proposed method shows potential for shipborne and coastal oil spill monitoring applications. Full article

This study aims to improve the process of characterizing oil on the sea surface using synthetic aperture radar (SAR) imagery, seeking to increase the accuracy of oil slick classification as natural or anthropogenic. A set of spatial attributes was obtained using semivariograms and phase-space pictures. This novel approach demonstrated potential to add value for monitoring seepage phenomena, which is of great scientific and environmental importance. The results achieved have potential for operational application as an aid in understanding active petroleum systems, reducing exploration risk in the decision-making process. Different targets display semivariograms with distinct geostatistical parameters, thus expressing contrasting models of spatial data correlation. The research results indicate that trajectories developed by the targets “sea”, “seepage slick”, and “oil spill” showed diagnostic behavior in their respective phase-space pictures. Full article

Oil slicks pose a severe threat to marine ecosystems, making accurate and real-time detection increasingly urgent. Marine X-band radar has become an essential tool for oil slick monitoring due to its high temporal resolution and its ability to sensitively capture the damping of capillary waves on the sea surface caused by oil films. Building upon this, an unsupervised and lightweight SLIC-KMeans-GJO detection framework is proposed. The method first generates superpixels by using Simple Linear Iterative Clustering (SLIC) and then applies K-means clustering to extract region of interest (ROI). An improved Golden Jackal Optimizer (GJO) is adaptively initialized based on the grayscale distribution and information entropy. To enhance optimization performance, Lévy flight and stochastic perturbation mechanisms are incorporated to improve global exploration and local convergence precision. Experimental results demonstrate that the proposed method significantly outperforms conventional thresholding approaches and other intelligent optimization-based segmentation algorithms in terms of noise suppression, target identification accuracy, and discrimination precision for oil slick targets. It effectively mitigates over-segmentation and false detections while preserving fine edge details and the true spatial extent of oil slicks. The proposed framework offers a novel and practical solution for real-time oil slick monitoring, holding strong potential for operational maritime emergency response. Full article

Offshore oil exploration and the volume of imported crude oil shipping have increased steadily, elevating the risk of oil spills. An advanced offshore oil film identification method is proposed to realize the accurate and robust recognition and segmentation of oil films from marine radar images in offshore oil spill detection. This method integrates feature engineering with an improved Beetle Antennae Search (BAS) optimization algorithm, aiming to address the key issues of low discrimination between oil films and complex marine backgrounds and insufficient spill boundary localization accuracy in radar image analysis. First, the raw radar image was transformed into the Cartesian coordinate system, and a filtering procedure was applied to attenuate interference. Subsequently, the gray distribution and local contrast of the denoised image was further improved. Afterwards, the complexity of the grayscale distribution within each feature map was quantified using Shannon entropy. The Top-K feature maps with the highest entropy values were subsequently used to construct an information-rich subset. The subset was then processed through a pixel-wise averaging strategy to generate a coupled feature image. Then, Otsu threshold was used to refine ocean wave regions. Finally, the oil films were segmented with an improved BAS optimization algorithm. The fitness function of the improved BAS algorithm was augmented through the integration of edge fitting accuracy, and a target-proximity penalization scheme. Through an adaptive step-length modulation paradigm and Perceptual Mechanism, it can achieve a marked improvement in search accuracy and achieving precise segmentation of oil slicks. The detection accuracy of the proposed method is significantly enhanced relative to the traditional BAS algorithm and existing marine radar oil spill detection methods. The IOU, Dice, recall and F1-score reached 81.2%, 89.6%, 85.2%, and 90.1% respectively. This method not only advances the methodological rigor of spill detection but also provides critical data support for the development of more effective control and remediation practices. Full article

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