Search Results (19)

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

The development and application of predictive models to distinguish seepage slicks from oil spills are challenging, since Synthetic Aperture Radars (SAR) detect these events as dark spots on the sea surface. Traditional Machine Learning (ML) has been used to discriminate the Oil Slick Source (OSS) as natural or anthropic assuming that the samples employed to train and test the models in the source domain (D_S) follow the same statistical distribution of unknown samples to be predicted in the target domain (D_T). When such assumptions are not held, Transfer Learning (TL) allows the extraction of knowledge from validated models and the prediction of new samples, thus improving performances even in scenarios never seen before. A database with 26 geometric features extracted from 6279 validated oil slicks was used to develop predictive models in the Gulf of Mexico (GoM) and its Mexican portion (GMex). Innovatively, these well-trained models were applied to predict the OSS of unknown events in the GoM, the American (GAm) portion of the GoM, and in the Brazilian continental margin (BR). When the D_S and D_T domains are similar, the TL and generalization are null, being equivalent to the usual ML. However, when domains are different but statically related, TL outdoes ML (58.91%), attaining 87% of global accuracy when using compatible SAR sensors in the D_S and D_T domains. Conversely, incompatible SAR sensors produce domains statistically divergent, causing negative transfers and generalizations. From an operational standpoint, the evidenced generalization capacity of these models to recognize geometric patterns across different geographic regions using TL may allow saving time and budget, avoiding the collection of validated and annotated new training samples, as well as the models re-training from scratch. When looking for new exploratory frontiers, automatic prediction is a value-added product that strengthens the knowledge-driven classifications and the decision-making processes. Moreover, the prompt identification of an oil spill can speed up the response actions to clean up and protect sensitive areas against oil pollution. Full article

Ocean surface monitoring, emphasizing oil slick detection, has become essential due to its importance for oil exploration and ecosystem risk prevention. Automation is now mandatory since the manual annotation process of oil by photo-interpreters is time-consuming and cannot process the data collected continuously by the available spaceborne sensors. Studies on automatic detection methods mainly focus on Synthetic Aperture Radar (SAR) data exclusively to detect anthropogenic (spills) or natural (seeps) oil slicks, all using limited datasets. The main goal is to maximize the detection of oil slicks of both natures while being robust to other phenomena that generate false alarms, called “lookalikes”. To this end, this paper presents the automation of offshore oil slick detection on an extensive database of real and recent oil slick monitoring scenarios, including both types of slicks. It relies on slick annotations performed by expert photo-interpreters on Sentinel-1 SAR data over four years and three areas worldwide. In addition, contextual data such as wind estimates and infrastructure positions are included in the database as they are relevant data for oil detection. The contributions of this paper are: (i) A comparative study of deep learning approaches using SAR data. A semantic and instance segmentation analysis via FC-DenseNet and Mask R-CNN, respectively. (ii) A proposal for Fuse-FC-DenseNet, an extension of FC-DenseNet that fuses heterogeneous SAR and wind speed data for enhanced oil slick segmentation. (iii) An improved set of evaluation metrics dedicated to the task that considers contextual information. (iv) A visual explanation of deep learning predictions based on the SHapley Additive exPlanation (SHAP) method adapted to semantic segmentation. The proposed approach yields a detection performance of up to $94\%$ of good detection with a false alarm reduction ranging from $14\%$ to $34\%$ compared to mono-modal models. These results provide new solutions to improve the detection of natural and anthropogenic oil slicks by providing tools that allow photo-interpreters to work more efficiently on a wide range of marine surfaces to be monitored worldwide. Such a tool will accelerate the oil slick detection task to keep up with the continuous sensor acquisition. This upstream work will allow us to study its possible integration into an industrial production pipeline. In addition, a prediction explanation is proposed, which can be integrated as a step to identify the appropriate methodology for presenting the predictions to the experts and understanding the obtained predictions and their sensitivity to contextual information. Thus it helps them to optimize their way of working. Full article

The Gulf of Mexico is a hydrocarbon-rich region characterized by the presence of floating oil slicks from persistent natural hydrocarbon seeps, which are reliably captured by synthetic aperture radar (SAR) satellite imaging. Improving the state of knowledge of hydrocarbon seepage in the Gulf of Mexico improves the understanding and quantification of natural seepage rates in North America. We used data derived from SAR scenes collected over the Gulf of Mexico from 1978 to 2018 to locate oil slick origins (OSOs), cluster the OSOs into discrete seep zones, estimate the flux of individual seepage events, and calculate seep recurrence rates. In total, 1618 discrete seep zones were identified, primarily concentrated in the northern Gulf of Mexico within the Louann salt formation, with a secondary concentration in the Campeche region. The centerline method was used to estimate flux based on the drift length of the slick (centerline), the slick area, and average current and wind speeds. Flux estimates from the surface area of oil slicks varied geographically and temporally; on average, seep zones exhibited an 11% recurrence rate, suggesting possible intermittent discharge from natural seeps. The estimated average instantaneous flux for natural seeps is 9.8 mL s⁻¹ (1.9 × 10³ bbl yr⁻¹), with an annual discharge of 1.73–6.69 × 10⁵ bbl yr⁻¹ (2.75–10.63 × 10⁴ m³ yr⁻¹) for the entire Gulf of Mexico. The temporal variability of average flux suggests a potential decrease following 1995; however, analysis of flux in four lease blocks indicates that flux has not changed substantially over time. It is unlikely that production activities in the Gulf of Mexico impact natural seepage on a human timescale. Of the 1618 identified seep zones, 1401 are located within U.S. waters, with 70 identified as having flux and recurrence rates significantly higher than the average. Seep zones exhibiting high recurrence rates are more likely to be associated with positive seismic anomalies. Many of the methods developed for this study can be applied to SAR-detected oil slicks in other marine settings to better assess the magnitude of global hydrocarbon seepage. Full article

Remote sensing techniques offer significant potential for generating accurate thick oil slick maps critical for marine oil spill response. However, field validation and methodology assessment challenges remain. Here, we report on an approach to leveraging oil emissions from the Coal Oil Point (COP) natural marine hydrocarbon seepage offshore of southern California, where prolific oil seepage produces thick oil slicks stretching many kilometers. Specifically, we demonstrate and validate a remote sensing approach as part of the Seep Assessment Study (SAS). Thick oil is sufficient for effective mitigation strategies and is set at 0.15 mm. The brightness temperature of thick oil, T_BO, is warmer than oil-free seawater, T_BW, allowing segregation of oil from seawater. High spatial-resolution airborne thermal and visible slick imagery were acquired as part of the SAS; including along-slick “streamer” surveys and cross-slick calibration surveys. Several cross-slick survey-imaged short oil slick segments that were collected by a customized harbor oil skimmer; termed “collects”. The brightness temperature contrast, ΔT_B (T_BO − T_BW), for oil pixels (based on a semi-supervised classification of oil pixels) and oil thickness, h, from collected oil for each collect provided the empirical calibration of ΔT_B(h). The T_B probability distributions provided T_BO and T_BW, whereas a spatial model of T_BW provided ΔT_B for the streamer analysis. Complicating T_BW was the fact that streamers were located at current shears where two water masses intersect, leading to a T_B discontinuity at the slick. This current shear arose from a persistent eddy down current of the COP that provides critical steering of oil slicks from the Coal Oil Point. The total floating thick oil in a streamer observed on 23 May and a streamer observed on 25 May 2016 was estimated at 311 (2.3 bbl) and 2671 kg (20 bbl) with mean linear floating oil 0.14 and 2.4 kg m⁻¹ with uncertainties by Monte Carlo simulations of 25% and 7%, respectively. Based on typical currents, the average of these two streamers corresponds to 265 g s⁻¹ (~200 bbl day⁻¹) in a range of 60–340 bbl day⁻¹, with significant short-term temporal variability that suggests slug flow for the seep oil emissions. Given that there are typically four or five streamers, these data are consistent with field emissions that are higher than the literature estimates. Full article

This paper presents the results of a long-term survey of the Caspian Sea using satellite SAR and multispectral sensors. The primary environmental problem of the Caspian Sea is oil pollution which is determined by its natural properties, mainly by the presence of big oil and gas deposits beneath the seabed. Our research focuses on natural oil slicks (NOS), i.e., oil showings on the sea surface due to natural hydrocarbon emission from seabed seeps. The spatial and temporal variability of NOS in the Caspian Sea and the possibilities of their reliable detection using satellite data are examined. NOS frequency and detectability in satellite images depending on sensor type, season and geographical region are assessed. It is shown that both parameters vary significantly, and largely depend on sensor type and season, with season being most pronounced in visible (VIS) data. The locations of two offshore seep sites at the Iranian and Turkmenian shelves are accurately estimated. Statistics on individual sizes of NOS are drawn. The release rates of crude oil from the seabed to the sea surface are compared. Detailed maps of NOS are put together, and areas exposed to high risk of sea surface oil pollution are determined. Full article

Distinguishing between natural and anthropic oil slicks is a challenging task, especially in the Gulf of Mexico, where these events can be simultaneously observed and recognized as seeps or spills. In this study, a powerful data analysis provided by machine learning (ML) methods was employed to develop, test, and implement a classification model (CM) to distinguish an oil slick source (OSS) as natural or anthropic. A robust database containing 4916 validated oil samples, detected using synthetic aperture radar (SAR), was employed for this task. Six ML algorithms were evaluated, including artificial neural networks (ANN), random forest (RF), decision trees (DT), naive Bayes (NB), linear discriminant analysis (LDA), and logistic regression (LR). Using RF, the global CM achieved a maximum accuracy value of 73.15. An innovative approach evaluated how external factors, such as seasonality, satellite configurations, and the synergy between them, limit or improve OSS predictions. To accomplish this, specific classification models (SCMs) were derived from the global ones (CMs), tuning the best algorithms and parameters according to different scenarios. Median accuracies revealed winter and spring to be the best seasons and ScanSAR Narrow B (SCNB) as the best beam mode. The maximum median accuracy to distinguish seeps from spills was achieved in winter using SCNB (83.05). Among the tested algorithms, RF was the most robust, with a better performance in 81% of the investigated scenarios. The accuracy increment provided by the well-fitted models may minimize the confusion between seeps and spills. This represents a concrete contribution to reducing economic and geologic risks derived from exploration activities in offshore areas. Additionally, from an operational standpoint, specific models support specialists to select the best SAR products and seasons for new acquisitions, as well as to optimize performances according to the available data. Full article

In the contrary to surface oil slicks, dispersed oil pollution is not yet detected or monitored on regular basis. The possible range of changes of the local optical properties of seawater caused by the occurrence of dispersed oil, as well as the dependencies of changes on various physical and environmental factors, can be estimated using simulation techniques. Two models were combined to examine the influence of oceanic water type on the visibility of dispersed oil: the Monte Carlo radiative transfer model and the Lorenz–Mie model for spherical oil droplets suspended in seawater. Remote sensing reflectance, R_rs, was compared for natural ocean water models representing oligotrophic, mesotrophic and eutrophic environments (characterized by chlorophyll-a concentrations of 0.1, 1 and 10 mg/m³, respectively) and polluted by three different kinds of oils: biodiesel, lubricant oil and crude oil. We found out that dispersed oil usually increases R_rs values for all types of seawater, with the highest effect for the oligotrophic ocean. In the clearest studied waters, the absolute values of R_rs increased 2–6 times after simulated dispersed oil pollution, while R_rs band ratios routinely applied in bio-optical models decreased up to 80%. The color index, CI, was nearly double reduced by dispersed biodiesel BD and lubricant oil CL, but more than doubled by crude oil FL. Full article

Satellite synthetic aperture radar (SAR) is a unique tool to collect measurements over sea surface but the physical interpretation of such data is not always straightforward. Among the different sea targets of interest, low-backscattering areas are often associated to marine oil pollution even if several physical phenomena may also result in low-backscattering patches at sea. In this study, the effects of low-backscattering areas of anthropogenic and natural origin on the azimuth autocorrelation function (AACF) are analyzed using VV-polarized SAR measurements. Two objective metrics are introduced to quantify the deviation of the AACF evaluated over low-backscattering areas with reference to slick-free sea surface. Experiments, undertaken on six Sentinel-1 SAR scenes, collected in Interferometric Wide Swath VV+VH imaging mode over large low-backscattering areas of different origin under low-to-moderate wind conditions (speed ≤ 7 m/s), spanning a wide range of incidence angles (from about 30° up to 46°), demonstrated that the AACF evaluated within low-backscattering sea areas remarkably deviates from the slick-free sea surface one and the largest deviation is observed over oil slicks. Full article

During marine oil spills, chemical dispersants are used routinely to disperse surface slicks, transferring the hydrocarbon constituents of oil into the aqueous phase. Nonetheless, a comprehensive understanding of how dispersants affect natural populations of hydrocarbon-degrading bacteria, particularly under environmentally relevant conditions, is lacking. We investigated the impacts of the dispersant Corexit EC9500A on the marine hydrocarbon degrader Marinobacter sp. TT1 when pre-adapted to either low n-hexadecane concentrations (starved culture) or high n-hexadecane concentrations (well-fed culture). The growth of previously starved cells was inhibited when exposed to the dispersant, as evidenced by 55% lower cell numbers and 30% lower n-hexadecane biodegradation efficiency compared to cells grown on n-hexadecane alone. Cultures that were well-fed did not exhibit dispersant-induced inhibition of growth or n-hexadecane degradation. In addition, fluorescence microscopy revealed amorphous cell aggregate structures when the starved culture was exposed to dispersants, suggesting that Corexit affected the biofilm formation behavior of starved cells. Our findings indicate that (previous) substrate limitation, resembling oligotrophic open ocean conditions, can impact the response and hydrocarbon-degrading activities of oil-degrading organisms when exposed to Corexit, and highlight the need for further work to better understand the implications of environmental stressors on oil biodegradation and microbial community dynamics. Full article

Recovery modeling and countermeasures for oil spilled at sea have been extensively researched, but research remains insufficient on recovery potential estimation methods. It is required to access the mechanical recovery potential by considering the relationship between oil behavior, environmental conditions, and the performance of clean-up activities. Two response-planning models were developed in this study. One is a spatially uniform recovery model for estimating recovery potential that reflects weathering, oil properties, and equipment efficiency. The other is a spatially nonuniform recovery model that considers not only the above characteristics but also local thickness reduction by skimming. A comparison between the two models and an analysis of their effects on response was carried out through the calculation using an accident scenario. It is possible to analyze the effect of the thin slicks, natural dissipation, and the quantification of deployable skimming systems with the spatially nonuniform recovery model. Finally, we analyzed interrelationships among residual oil volume on the sea, response time, and the number of skimming systems. Full article

Ocean surveillance is one of the important applications of synthetic aperture radar (SAR). Polarimetric SAR provides multi-channel information and shows great potential for monitoring ocean dynamic environments. Oil spills are a form of pollution that can seriously affect the marine ecosystem. Dual-polarimetric SAR systems are usually used for routine ocean surface monitoring. The hybrid dual-pol SAR imaging mode, known as compact polarimetry, can provide more information than the conventional dual-pol imaging modes. However, backscatter measurements of the hybrid dual-pol mode depend on the transmit wave polarization, which results in lacking consistent interpretation for various compact polarimetric (CP) images. In this study, we will explore the capability of different CP modes for oil spill detection and discrimination. Firstly, we introduce the general CP formalism method to formulate an arbitrary CP backscattered wave, such that the target scattering vector is characterized in the same framework for all CP modes. Then, a recently proposed CP decomposition method is investigated to reveal the backscattering properties of oil spills and their look-alikes. Both intensity and polarimetric features are studied to analyze the optimal CP mode for oil spill observation. Spaceborne polarimetric SAR data sets collected over natural oil slicks and experimental biogenic slicks are used to demonstrate the capability of the general CP mode for ocean surface surveillance. Full article

Oil spill is considered one of the main threats to marine and coastal environments. Efficient monitoring and early identification of oil slicks are vital for the corresponding authorities to react expediently, confine the environmental pollution and avoid further damage. Synthetic aperture radar (SAR) sensors are commonly used for this objective due to their capability for operating efficiently regardless of the weather and illumination conditions. Black spots probably related to oil spills can be clearly captured by SAR sensors, yet their discrimination from look-alikes poses a challenging objective. A variety of different methods have been proposed to automatically detect and classify these dark spots. Most of them employ custom-made datasets posing results as non-comparable. Moreover, in most cases, a single label is assigned to the entire SAR image resulting in a difficulties when manipulating complex scenarios or extracting further information from the depicted content. To overcome these limitations, semantic segmentation with deep convolutional neural networks (DCNNs) is proposed as an efficient approach. Moreover, a publicly available SAR image dataset is introduced, aiming to consist a benchmark for future oil spill detection methods. The presented dataset is employed to review the performance of well-known DCNN segmentation models in the specific task. DeepLabv3+ presented the best performance, in terms of test set accuracy and related inference time. Furthermore, the complex nature of the specific problem, especially due to the challenging task of discriminating oil spills and look-alikes is discussed and illustrated, utilizing the introduced dataset. Results imply that DCNN segmentation models, trained and evaluated on the provided dataset, can be utilized to implement efficient oil spill detectors. Current work is expected to contribute significantly to the future research activity regarding oil spill identification and SAR image processing. Full article

Damping Bragg scattering from the ocean surface is the basic underlying principle of synthetic aperture radar (SAR) oil slick detection, and they produce dark spots on SAR images. Dark spot detection is the first step in oil spill detection, which affects the accuracy of oil spill detection. However, some natural phenomena (such as waves, ocean currents, and low wind belts, as well as human factors) may change the backscatter intensity on the surface of the sea, resulting in uneven intensity, high noise, and blurred boundaries of oil slicks or lookalikes. In this paper, Segnet is used as a semantic segmentation model to detect dark spots in oil spill areas. The proposed method is applied to a data set of 4200 from five original SAR images of an oil spill. The effectiveness of the method is demonstrated through the comparison with fully convolutional networks (FCN), an initiator of semantic segmentation models, and some other segmentation methods. It is here observed that the proposed method can not only accurately identify the dark spots in SAR images, but also show a higher robustness under high noise and fuzzy boundary conditions. Full article

Deep carbonate reservoirs are rich in oil and gas resources. However, due to poor pore connectivity and low permeability, it is necessary to adopt hydraulic fracturing technology for their development. The mechanism of hydraulic fracturing for fracture initiation and propagation in carbonate rocks remains unclear, especially with regard to selection of the type of fracturing fluid and the fracturing parameters. In this article, an experimental study focusing on the mechanisms of hydraulic fracturing fracture initiation and propagation is discussed. Several factors were studied, including the type of injecting fracturing fluids, pump flow rate, fracturing pressure curve characteristics, and fracture morphology. The results showed the following: (1) The viscosity of fracturing fluid had a significant effect on fracturing breakdown pressure. Under the same pump flow rate, the fracturing breakdown pressure of slick water was the lowest. Fracturing fluids with low viscosity could easily activate weakly natural fractures or filled fractures, leading to open microcracks, and could effectively reduce the fracturing breakdown pressure. (2) The fluctuations in fracturing pump pressure corresponded with the acoustic emission hits and changes in radial strain; for every drop of fracturing pressure, acoustic emission hits and changes in radial strain were mutated. (3) Under the same fracturing fluid, the pump flow rate mainly affected fracturing breakdown pressure and had little effect on fracture morphology. (4) The width of the main fracture was affected by the viscosity and pump flow rate. Maximum changes in radial strain at the fracturing breakdown pressure point occurred when the fracturing fluid was guar gum. (5) With gelled acid and cross-linked acid fracturing, the main fractures were observed on the surface. The extension of the fracturing crack was mainly focused near the crack initiation parts. The crack expanded asymmetrically; the wormhole was dissolved to break through to the surface of the specimen. (6) The dissolution of gelled acid solution could increase the width of the fracturing crack and improve the conductivity of carbonate reservoirs. Full article