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The goal of the present work is to improve shale reservoir stimulation treatment by using ultra light weight proppants in fracturing fluids. Slickwater has become the most popular fracturing fluid for fracturing shales in recent times because it creates long and skinny fractures and it is relatively cheap. The problem with slickwater is the high rate of settling of common proppants, e.g. sand, which results in propped fractures which are much smaller than the original fractures. Use of gels can help in proppant transport but introduce large formation damage by blocking pores in nano-darcy shales. Gel trapping in the proppant pack causes reduction in permeability of the proppant pack. The light weight proppants which can easily be transported by slickwater and at the same time be able to provide enough fracture conductivity may solve this problem. Three ultra light weight proppants (ULW1, ULW2, and ULW3) have been studied. The mechanical properties of the proppant packs as well as single proppants have been measured. Conductivity of proppant packs has been determined as a function of proppant concentration and confining stress at an average Barnett shale temperature of 95oC. The crush strengths of all the three proppant packs are higher than typical stresses encountered (e.g., Barnett). ULW1 and ULW2 are highly deformable and do not produce many fines. ULW3 has a higher Young’s modulus and produces fines. Conventionally, the proppant conductivity decreases with decreasing proppant concentration and increasing confining stress. But in cases of ULWs, for a partial monolayer, conductivity can be as large as that of a thick proppant pack. The settling velocity is the lowest for ULW1, intermediate for ULW2 and the highest for ULW3. This work contributes new mechanical, conductivity, and settling data on three ultra light weight proppants. Application of light weight proppants in stimulation treatments in shale reservoirs can lead to large propped fractures, which can improve the productivity of fractured shale reservoirs. / text

The aim of this thesis is to develop a methodology for detection of naturally occurring offshore oil slicks originating from hydrocarbon seeps using satellite remote sensing techniques. In this scope, Synthetic Aperture Radar (SAR) imagery has been utilized. Case study area was Andrusov High in the Central Black Sea. Hydrocarbon seepage from tectonic or stratigraphic origin at the sea floor causes oily gas plumes to rise up to the sea surface. They form thin oil films on the sea surface called oil slicks. Presence of seeps and surface oil slicks for the offshore basins is a trace of depleted oil traps. Spatial distribution of oil slicks is closely related to sea waves, dominant wind patterns and weathering factors. Even though, there are oil slick detection techniques available with optical remote sensing, laser fluorosensors, and hyperspectral remote sensing, the most efficient results can be obtained from active microwave sensors like synthetic aperture radar (SAR). SAR sensors simply measure the backscattered radiation from the surface and show the roughness of the terrain. Oil slicks dampen the sea waves creating dark patches in the SAR image. In this context an adapted methodology has been proposed, including three levels namely / visual inspection, image filtering and object based fuzzy classification. With visual inspection, targets have been identified and subset scenes have been created. Subset scenes have been categorized into 3 cases based on contrast difference of dark spots to the surroundings. Then object based classification has been utilized with the fuzzy membership functions defined by extracted features of layer values, shape and texture from segmented and filtered SAR subsets. As a result, oil slicks have been discriminated from look-alikes which are the phenomena resembling oil slicks. The overall classification accuracy obtained by averaging three different cases is 83 % for oil slicks and 77 % for look-alikes. The results of this study can considered to be a preliminary work and supplementary information for determining the best operational procedure of offshore hydrocarbon exploration.

Ce travail de thèse traite de l’optimisation des analyses et des prétraitements des images radar pour la détection des nappes d'huile en domaine océanique (communément appelés "oil slicks" en anglais) ainsi que la localisation des sources de suintements d’huiles d'origine naturelle ("oil seeps") sur le plancher océanique. Moyens, méthodes et difficultés des divers traitements y sont exposés. Il se compose des trois axes de recherche distincts expliqués et détaillés ci-dessous :1- Une approche stochastique pour le prétraitement et l'amélioration des images radar en bande C afin de détecter automatiquement les nappes d'huile.2- Une approche stochastique utilisant une grande quantité d'images radar pour évaluer l'influence de la vitesse du vent et les différents modes de l'instrument (SAR) pour l'optimisation de la détection des nappes d'hydrocarbures.3- La localisation précise de la source des émissions d'hydrocarbures marins à l'aide d'un nouveau modèle de dérive verticale, appliqué au Golfe du Mexique (USA).En premier, nous nous sommes intéressés à l'optimisation des prétraitements et l'amélioration des images radar en bande C par des méthodes stochastiques. La méthodologie proposée comprend trois niveaux de traitement: prétraitement, seuillage et nettoyage binaire. Le premier niveau s’attèle à corriger l'hétérogénéité de la luminosité dans les images radar due à la réflexion non lambertienne du signal radar sur la surface de la mer. Le deuxième niveau consiste en une étape de seuillage qui vise à produire des objets noirs aussi proches que possible de l'ensemble de données d'apprentissage manuellement élaborées. Le troisième niveau, quant à lui, vise à nettoyer les images binaires de sortie des résidus de bruit. Plusieurs méthodes de prétraitement et de nettoyage ont été testées et évaluées par un moteur de qualification qui compare les objets détectés automatiquement avec les zones des objets noirs détectées manuellement. Par la suite, nous nous sommes penchés sur l'évaluation de l'influence de la vitesse du vent et des modes de l'instrument sur la détection des nappes d'hydrocarbures sur les images radar en utilisant une approche stochastique. Cette étude a été dictée par le besoin de définir les conditions météorologiques à même de permettre une détection optimale des nappes d’huiles, à partir des images radar. L’objectif a été de déterminer l’intervalle de vitesse du vent qui optimise la détection des nappes d'huiles dans toutes les images radar utilisant du BigData et une approche stochastique. Ce travail a également été une occasion de nous intéresser aux propriétés des modes d'acquisition radar employés dans la détection des nappes d'huile. Ainsi, un ordre de performance de 5 modes est établi (IW, APP, PRI, IMP et WSM) et montre que le mode IW (Sentinel-1), avec la meilleure résolution spatiale (supérieure à 5x20m), est la plus approprié pour détecter une nappe d'huile à forte vitesse du vent. Enfin, nous nous sommes focalisés sur l'estimation de la localisation des sources de pétrole naturel marin à l'aide d'un nouveau modèle de dérive verticale. Les manifestations de suintements d'hydrocarbures sur la surface de la mer sont généralement décalées de leur source sur les fonds marins de plusieurs centaines de mètres ou même de kilomètres. Ce décalage est fonction de la vitesse ascensionnelle et des courants marins le long de la colonne d'eau. Dans cette étude, le diamètre des gouttelettes ne nous est pas connu à priori. Pour combler ce manque d’information, on a appliqué une nouvelle méthode appelée «le chemin des sources». Si ces trois études peuvent être prises chacune indépendamment des autres, elles sont solidement interconnectées et complémentaires. Elles forment une sorte de processus allant de l'optimisation de la détection d’une nappe (les moyens et les outils les plus adéquats pour une meilleure détection) jusqu’à la localisation de sa source sur le plancher océanique / This thesis deals with the preprocessing of radar images and their optimization for the analyzes in order to detect natural marine oil slicks (Sea surface Outbreak/SSO) as well as better determine their source location at the Sea Floor Source (SFS). We explained herein means, methods and difficulties encountered. This thesis consists of the following three distinct research axes represented by three submitted papers :1- A stochastic approach for pre-processing and improvement of C-band radar images to automatically detect oil slicks;2- A stochastic approach using a large quantity of radar images to evaluate the influence of wind speed and the different modes of the instrument (SAR) on the delectability of marine oil slicks ;3- Accurate location of the Sea Floor Source of marine hydrocarbon emissions using a new vertical drift model within the water column, applied to the northern Gulf of Mexico (southern USA).So first, we focused on the optimization of pre-processing and the improvement of C-band radar images by stochastic methods to automatically detect oil slicks. The proposed methodology includes three processing levels : preprocessing, thresholding, and binary cleaning. The first level consists of correcting the heterogeneity of the luminosity in the radar images resulting from the non-Lambertian reflection of the radar signal on the sea surface. The second level consists of a thresholding step which aims to produce dark objects as close as possible to the manually developed training data set. The third level consists of cleaning the output binary images from the noise residuals. Several preprocessing and cleaning methods have been tested and evaluated by a qualification engine that compares the objects automatically detected with the manual detection. Then, we focus in a second chapter in the evaluation of the influence of wind speed and instrument modes on the detection of oil slicks from radar images by using a stochastic approach. This study was dictated by the need to define the meteorological conditions capable for an optimal detection of oil slicks, from the radar images. The objective was to determine the wind speed range which optimizes the detection of oil slicks in all radar images using BigData and a stochastic approach. This work was also an opportunity to investigate the properties of the radar acquisition modes used in the detection of oil slicks. Thus, a 5-mode performance order is established (IW, APP, PRI, IMP and WSM) and shows that the IW (Sentinel-1) mode, with the best spatial resolution (greater than 5x20m) detects oil slicks at high wind speed. Finally, we focused on estimating the location of marine natural oil seeps sources using a new vertical drift model, applied in the Gulf of Mexico. Thus, we have developed a new method for detecting the source of oil seeps from natural sources on the seafloor according to the vertical drift model. Occurrences of oil seeps on the sea surface are generally offset from their sources on the seabed by several hundred meters or even kilometers. This deflection is dependent on the upward velocity of the oil and marine currents along the water column. In this study, the diameter of the droplets is not known to us a priori. To fill this gap, a new method called "the sources path" was applied herein that propose the Sea Floor Source taking into account the droplet size and the vertical drift within the water column before their Sea surface Outbreak (SSO).If these three studies can be taken independently of each other, they are firmly interconnected and complementary. They form a sort of process ranging from the optimization of the detection of an oil slick (the most appropriate means and tools for better detection) to the location of its source on the seafloor

Made available in DSpace on 2014-12-17T14:56:21Z (GMT). No. of bitstreams: 1 DaniloLS.pdf: 2499617 bytes, checksum: 328b5ce6d56f5a92a61ad220565411c7 (MD5) Previous issue date: 2006-07-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Oil spill on the sea, accidental or not, generates enormous negative consequences for the affected area. The damages are ambient and economic, mainly with the proximity of these spots of preservation areas and/or coastal zones. The development of automatic techniques for identification of oil spots on the sea surface, captured through Radar images, assist in a complete monitoring of the oceans and seas. However spots of different origins can be visualized in this type of imaging, which is a very difficult task. The system proposed in this work, based on techniques of digital image processing and artificial neural network, has the objective to identify the analyzed spot and to discern between oil and other generating phenomena of spot. Tests in functional blocks that compose the proposed system allow the implementation of different algorithms, as well as its detailed and prompt analysis. The algorithms of digital image processing (speckle filtering and gradient), as well as classifier algorithms (Multilayer Perceptron, Radial Basis Function, Support Vector Machine and Committe Machine) are presented and commented.The final performance of the system, with different kind of classifiers, is presented by ROC curve. The true positive rates are considered agreed with the literature about oil slick detection through SAR images presents / Derramamentos de ?leo sobre o mar, mesmo que acidentais, geram enormes conseq??ncias negativas para a ?rea afetada. Os preju?zos s?o ambientais e econ?micos, principalmente com a proximidade dessas manchas de ?reas de preserva??o e/ou zonas costeiras. O desenvolvimento de t?cnicas autom?ticas para a identifica??o de manchas de ?leo sobre a superf?cie marinha, capturadas atrav?s de imagens de Radar, auxiliam num completo monitoramento dos oceanos e mares. Contudo, manchas de diferentes origens podem ser visualizadas nesse tipo de produ??o de imagem, tornando o monitoramento dif?cil. O sistema proposto neste trabalho, baseado em t?cnicas de processamento digital de imagens e redes neurais artificiais, tem o objetivo de identificar a mancha analisada e discernir entre ?leo e os demais fen?menos geradores de mancha. Testes nos blocos funcionais que comp?em o sistema proposto permitem a implementa??o de diferentes algoritmos, assim como sua an?lise detalhada e pontual. Os algoritmos que tratam do processamento digital de imagem (filtragem do ru?do speckle e gradiente), assim como o de classifica??o (Perceptron de M?ltiplas Camadas, rede de fun??o de Base Radial, M?quina de Vetor de Suporte e M?quina de comit?) s?o apresentados e comentados.O desempenho final do sistema, com diferentes tipos de classificadores, ? apresentado atrav?s da curva ROC. As taxas de acertos s?o consideradas condizentes com o que a literatura de detec??o de manchas de ?leo na superf?cie oce?nica atrav?s de imagens de SAR apresenta

Les instruments, satellites ou systèmes aéroportés, actuellement utilisés pour la détection et la caractérisation d'hydrocarbure sur la mer sont basés sur des moyens optiques ou radars. Ces moyens présentent une performance dégradée due à une fréquence encore trop importante de fausses alarmes ou à un temps de traitement des données trop conséquent. Les méthodes de détection, d'identification et de quantification des fuites d'hydrocarbures offshores peuvent donc être améliorées en associant robustesse et réactivité. Cette amélioration suppose une compréhension approfondie des phénomènes océanographiques et électromagnétiques à l'œuvre dans cette scène particulière. La thèse s'appuie sur des données regroupant des images optiques et SAR aéroportées ou satellites ainsi que des mesures réalisées en laboratoire. Ce jeu de données permet de vérifier la cohérence des résultats obtenus par modélisation. L'objectif de la thèse est de distinguer une surface de mer polluée d'une surface de mer propre à l'aide de la signature électromagnétique de la surface totale puis de détailler le type et la quantité d'hydrocarbure présent. La thèse se divise en deux domaines, à savoir modélisation océanographique et modélisation électromagnétique. La modélisation océanographique intègre la simulation de la surface rugueuse imitant une surface de mer propre, et polluée. Cette surface de mer doit être générée sur une superficie importante et doit conserver une résolution restituant les petites vagues avec un temps de génération minimal. La partie électromagnétique est centrée sur les modèles asymptotiques de diffusion des ondes électromagnétiques par une interface rugueuse. Ces modèles sont adaptés au contexte de la thèse, complexité de la scène et rapidité du traitement, mais nécessitent plusieurs hypothèses pour être appliqués. / Satellites or airborne systems currently used for the detection and characterization of oil slicks on sea surface are based on optical or radar means. These means have a lack of performance due to a too high frequency of false alarms or to an excessively long data processing time. The methods for detecting, identifying and quantifying offshore pollutant can therefore be improved by combining robustness and reactivity. This improvement implies an in-depth understanding of the oceanographic and electromagnetic phenomena at work in this particular scene. The thesis is based on data gathering aerial and satellite images and SAR as well as measurements carried out in laboratory. This dataset makes it possible to check the consistency of the results obtained by modeling. The objective of the thesis is to distinguish a polluted sea surface from a clean sea surface using the electromagnetic signature of the total surface and then to detail the type and quantity of pollutant. The thesis is divided into two domains, namely oceanographic modeling and electromagnetic modeling. Oceanographic modeling integrates the simulation of the rough surface imitating a clean or polluted sea surface. This sea surface must be generated over a large area with a thin resolution. The electromagnetic part is centered on the asymptotic models for the electromagnetic waves diffraction by a rough interface. These models are adapted to the context of the thesis, the complexity of the scene and the speed of processing, but require several hypotheses to be applied.