In this dissertation, 3D surface seismic data was integrated with the well logs to be able to define the properties in every location for the reservoir under investigation. To accomplish this task, geostatistical and artificial neural networks (ANN) techniques were employed. First, missing log sets in the study area were estimated using common empirical relationships and ANN. Empirical estimations showed linear dependent results that cannot be generalized. On the other hand, ANNs predicted missing logs with an very high accuracy. Sonic logs were predicted using resistivity logs with 90% correlation coefficient. Second, acoustic impedance property was predicted in the study area. AI estimation first performed using sonic log with GRNN and 88% CC was obtained. AI estimation was repeated using sonic and resistivity logs and the result were improved to 94% CC. In the final part of the study, SGS technique was used with collocated cokriging techniques to estimate NPHI property. Results were varying due to nature of the algorithm. Then, GRNN and RNN algorithms were applied to predict NPHI property. Using optimized GRNN network parameters, NPHI was estimated with high accuracy. Results of the study were showed that ANN provides a powerful solution for reservoir parameter prediction in the study area with its flexibility to find out nonlinear relationships from the existing available data.

This study is designed to provide the composites industry as well as the fire engineering industry baseline data for pyrolysis modelling of common fiber reinforced polymer (FRP) systems. Four resin systems and three glass contents will be considered. This matrix of FRP systems has been carefully fabricated and documented so as to provide“transparency" as to the system compositions. An important and interesting aspect of these FRP systems is that all the resins used are listed by the manufacturers as Class 1 or Class A per ASTM E 84. The FRP systems are being evaluated in bench scale modern fire test apparatuses (FPA, ASTM E 2058, and Cone, ASTM E 1354); detailed information on the FPA is provided. These apparatuses provide a range of measurements such as heat release rate that can be used to calculate engineering“properties" of these FRP systems. The“properties", such as minimum heat flux for proper ignition (found to range from 20 to over 100 kW/m2) and the b flame spread parameter, can then be used to compare the fire performance (flashover potential) of these FRP systems according to resin type and glass content. Additional instrumentation has also been added to the specimens to allow surface and in-depth temperatures to be measured. The additional measurements are used to complete a set of data for pyrolysis modelling and for calculating thermal properties of the composites. The effect of environment oxygen concentration and flaming and non-flaming decomposition are investigated in terms of fundamental pyrolysis behavior of the FRP systems. A general conclusion is that the phenolic composite has better fire engineering“properties" than the polyester composite but the glass is the controlling component of the composite with regards to temperature profile and resulting thermal properties.

Video based remote sensing techniques are well suited to collect spatially resolved wave images in the surf zone with breaking waves and dynamic bathymetric changes. An advanced video-based depth inversion method is developed to remotely survey bathymetry in the surf zone. The present method involves image processing of original wave image sequences, wave property estimation based on linear feature extraction from the processed image sequences, and is combined with a nonlinear depth inversion model. The original wave image sequences are processed through video image frame differencing and directional low-pass filtering schemes to remove wave-breaking-induced foam noise having high frequencies in the surf zone. The features of individual crest trajectories are extracted from the processed and rectified image sequences, i.e. processed image cross-shore timestacks, by tracking pixels of high intensity within an interrogation window of a Radon-transform-based line-detection algorithm. The wave celerity is computed using space-time information of the extracted trajectories of individual wave crests in the cross-shore timestack domain. The presented retrieval of nearshore bathymetry from video image sequences is based on a nonlinear depth inversion using the nonlinear shallow water wave theory. The nonlinear wave amplitude dispersion effects at the breaker points are determined by combining the nonlinear shallow water celerity equation with a wave breaker criterion, thereby computing water depths iteratively from the celerity measured from the video data. The water depths estimated at the breaker points present initial bathymetric anchor points. Bathymetric profiles in the surf zone are inverted by calculating wave heights dissipated after wave breaking with a wave dissipation model and wave heights shoaled before wave breaking with a wave shoaling model. The continuous wave amplitude dispersion effects are subtracted from the measured celerity profiles, resulting in nearshore bathymetric profiles. The nonlinear depth inversion derived bathymetric estimates from nearshore imagery match the measured values with a biased mean depth error of about +0.06m in the depth range of 0.1 to 3m. In addition, the wave height estimates by the depth inversion model are comparable to the in-situ measured wave heights with a biased mean wave height error of about +0.14m. The present depth inversion method based on optical remote-sensing supports coastal management, navigation, and amphibious operations.

Ce travail porte sur le développement d’une technique de calorimétrie sans contact pour des écoulements diphasiques. Ces derniers sont réalisés sur la forme d’un train gouttes dans des tubes de taille millimétriques dans des supports isopériboliques. L’idée principale est de coupler la Thermographie Infrarouge et les outils microfluidiques pour proposer une technique adapté de mesure. L’utilisation de la microfluidique rend possible l’utilisation de très faibles volumes réactionnels limitant ainsi tout risque lié à la dangerosité des réactions réalisées au sein des gouttes, l’outil Infrarouge permet de suivre ces écoulements avec grande précision. Les résultats de ces travaux de thèse montrent que l’outil est capable d’estimer des propriétés thermo-physiques des écoulements non réactifs. Ainsi, que de caractériser de réactions chimiques en termes d’enthalpie et cinétique. Finalement cette dernière caractérisation a été comparée aux techniques classiques pour mettre en évidence la précision et les avantages de l’outil développé / This work concerns the development of a non-contact calorimeter for two-phase flow characterization. The biphasic flow is performed under a droplet configuration inside millimetric tubings which are inserted into the isoperibolic chip. The main idea is to combine the Infrared Thermography and microfluidic tools to propose a suitable technique for accurate measurements. Microfluidics enables the use of small reaction volumes thus limiting any risk of dangerous reactions inside droplets, the Infrared tool enables to monitor the thermal signature of these flows with high accuracy. The results of this thesis show that this tool is able to estimate the thermophysical properties of non-reactive flows. Also , it is possible to characterize chemical reactions in terms of enthalpy and kinetics . Finally the latter characterization was compared to conventional techniques to demonstrate the benefits and the precision of the tool.