Recent efforts to optimize oil recovery from Green River reservoirs, Uinta Basin, have stimulated the need for better understanding of the reservoir connectivity at the scale of the operational unit. This study focuses on Green River reservoirs in the Wells Draw study area where oil production response to implemented waterflood is poor and a better understanding of the reservoir connectivity is required to enhance future secondary oil recovery. Correlating the sand bodies between well locations in the area remains difficult at 40-acre well spacing. Thus, interwell connectivity of the reservoirs is uncertain. Understanding the reservoir connectivity in the Wells Draw study area requires integration of all static and dynamic data for generation of probabilistic models of the reservoir at the interwell locations. The objective of this study is two-fold. The first objective was to determine reservoir connectivity at the interwell scale in the Wells Draw study area. To achieve this goal, I used well log and perforation data in the Wells Draw study area to produce probabilistic models of net-porosity for four producing intervals: (1) Castle Peak, (2) Lower Douglas Creek, (3) Upper Douglas Creek, and (4) Garden Gulch. The second objective was to find readily applicable methods for determining interwell connectivity. To achieve this goal, I used sandstone net thickness and perforation data to evaluate interwell connectivity in the Wells Draw study area. This evaluation was done to: (1) assess and visualize connectivity, (2) provide an assessment of connectivity for validating / calibrating percolation and capacitance based methods, and (3) determine flow barriers for simulation. The probabilistic models encompass the four producing intervals with a gross thickness of 1,900 ft and enable simulation assessments of different development strategies for optimization of oil recovery in the Wells Draw study area. The method developed for determining interwell connectivity in Wells Draw study area is reliable and suited to the four producing intervals. Also, this study shows that the percolation based method is reliable for determining interwell connectivity in the four producing intervals.

The goal of any data assimilation or history matching algorithm is to enable better reservoir management decisions through the construction of reliable reservoir performance models and the assessment of the underlying uncertainties. A considerable body of research work and enhanced computational capabilities have led to an increased application of robust and efficient history matching algorithms to condition reservoir models to dynamic data. Moreover, there has been a shift towards generating multiple plausible reservoir models in recognition of the significance of the associated uncertainties. This provides for uncertainty analysis in reservoir performance forecasts, enabling better management decisions for reservoir development. Additionally, the increased deployment of permanent well sensors and downhole monitors has led to an increasing interest in maintaining 'live' models that are current and consistent with historical observations. One such data assimilation approach that has gained popularity in the recent past is the Ensemble Kalman Filter (EnKF) (Evensen 2003). It is a Monte Carlo approach to generate a suite of plausible subsurface models conditioned to previously obtained measurements. One advantage of the EnKF is its ability to integrate different types of data at different scales thereby allowing for a framework where all available dynamic data is simultaneously or sequentially utilized to improve estimates of the reservoir model parameters. Of particular interest is the use of partitioning tracer data to infer the location and distribution of target un-swept oil. Due to the difficulty in differentiating the relative effects of spatial variations in fractional flow and fluid saturations and partitioning coefficients on the tracer response, interpretation of partitioning tracer responses is particularly challenging in the presence of mobile oil saturations. The purpose of this research is to improve the performance of the EnKF in parameter estimation for reservoir characterization studies without the use of a large ensemble size so as to keep the algorithm efficient and computationally inexpensive for large, field-scale models. To achieve this, we propose the use of streamline-derived information to mitigate problems associated with the use of the EnKF with small sample sizes and non-linear dynamics in non-Gaussian settings. Following this, we present the application of the EnKF for interpretation of partitioning tracer tests specifically to obtain improved estimates of the spatial distribution of target oil.

This thesis integrated geology, geophysics, and petroleum engineering data to build a detailed reservoir characterization models for three gas pay sands in the Grand Isle 33 & 43 fields, offshore Louisiana. The reservoirs are Late Miocene in age and include the upper (PM), middle (QH), and lower (RD) sands. The reservoir models address the stratigraphy of the upper (PM) sand and help delineate the lower (RD) reservoir. In addition, this research addresses the partially depleted QH-2 reservoir compartment. The detailed models were constructed by integrating seismic, well log, and production data. These detailed models can help locate recoverable oil and gas that has been left behind. The upper PM model further delineated that the PM sand has several areas that are shaled-out effectively creating a flow barrier within reservoir compartments. Due to the barrier in the PM-1 reservoir compartment, an area of potentially recoverable hydrocarbons remains. In Grand Isle 33, the middle QH sand was partially depleted in the QH-2 reservoir compartment by a series of development wells. Bottom hole pressure data from wells in Grand Isle 32 & 33 reveal that the two QH fault compartments are in communication across a leaking fault. Production wells in the QH-1 compartment produced reserves from the QH-2 compartment. The lower RD sand model helped further delineate the reservoir in the RD-2 compartment and show that this compartment has been depleted. The RD model also shows the possible presence of remaining recoverable hydrocarbons in the RD-1 compartment. It is estimated that about 6.7 billion cubic feet of gas might remain within this reservoir waiting to be recovered. A seismic amplitude anomaly response from the QH and RD sands is interpreted to be a lithologic indicator rather than the presence of hydrocarbons. Amplitude response from the PM level appears to be below the resolution of the seismic data. A synthetic seismogram model was generated to represent the PM and surrounding sands. This model shows that by increasing the frequency of the seismic data from 20 Hz to a dominant frequency of 30 Hz that the PM and surrounding sands could be seismically resolvable. Also the PM-1 compartment has possible recoverable hydrocarbons of 1.5 billion cubic feet of gas remaining.

The chemical characterization of individual nanoparticles (NPs) </= 100 nm in diameter is one of the current frontiers in analytical chemistry. We present here, a methodology for the characterization of individual NPs by obtaining molecular information from single massive cluster impacts. The clusters used in this secondary ion mass spectrometry (SIMS) technique are Au4004+ and C60+. The ionized ejecta from each impact are recorded individually which allows to identify ions emitted from a surface volume of ~10 nm in diameter and 5-10 nm in depth. The mode of analyzing ejecta individually from each single cluster impact gives insight into surface homogeneity, in our case NPs and their immediate surroundings. We show that when the NPs (50 nm Al) are larger than the size of the volume perturbed by the projectile, the secondary ion emission (SI) resembles that of a bulk surface. However, when the NP (5 nm Ag) is of the size range of the volume perturbed by projectile the SI emission is different from that of a bulk surface. As part of this sub-assay volume study, the influence of neighboring NP on the SI emission was examined by using a mixture of different types of NPs (5 nm Au and 5 nm Ag). The methodology of using cluster SIMS via a sequence of stochastic single impacts yield information on the surface coverage of the NPs, as well as the influence of the chemical environment on the type of SI emission. We also present a case of soft landing NPs for laser desorption ionization mass spectrometry. NPs enhance the SI emission in a manner that maintains the integrity of the spatial distribution of molecular species. The results indicate that the application can be extended to imaging mass spectrometry.

Threat management of water distribution systems is essential for protecting consumers. In a contamination event, different strategies may be implemented to protect public health, including flushing the system through opening hydrants or isolating the contaminant by manipulating valves. To select the most effective options for responding to a contamination threat, the location and loading profile of the source of the contaminant should be considered. These characteristics can be identified by utilizing water quality data from sensors that have been strategically placed in a water distribution system. A simulation-optimization approach is described here to solve the inverse problem of source characterization, by coupling an evolutionary computation-based search with a water distribution system model. The solution of this problem may reveal, however, that a set of non-unique sources exists, where sources with significantly different locations and loading patterns produce similar concentration profiles at sensors. The problem of non-uniqueness should be addressed to prevent the misidentification of a contaminant source and improve response planning. This paper aims to address the problem of non-uniqueness through the use of Niched Co-Evolution Strategies (NCES). NCES is an evolutionary algorithm designed to identify a specified number of alternative solutions that are maximally different in their decision vectors, which are source characteristics for the water distribution problem. NCES is applied to determine the extent of non-uniqueness in source characterization for a virtual city, Mesopolis, with a population of approximately 150,000 residents. Results indicate that NCES successfully identifies non-uniqueness in source characterization and provides alternative sources of contamination. The solutions found by NCES assist in making decisions about response actions. Once alternative sources are identified, each source can be modeled to determine where the vulnerable areas of the system are, indicating the areas where response actions should be implemented.

Ni-Mn-Ga is a ferromagnetic shape memory alloy that can be used for future sensors and actuators. It has been shown that magnetic field can induce phase transformation and consequently large strain in stoichiometric Ni2MnGa. Since then considerable progress has been made in understanding the underlying science of shape memory and ferromagnetic shape memory in bulk materials. Ni-Mn-Ga thin films, however is a relatively under explored area. Ferromagnetic shape memory alloy thin films are conceived as the future MEMS sensor and actuator materials. With a 9.5 percent strain rate reported from magnetic reorientation, Ni-Mn-Ga thin films hold great promise as actuator materials. Thin films come with a number of advantages and challenges as compared to their bulk counterparts. While properties like mechanical strength, uniformity are much better in thin film form, high stress and constraint from the substrate pose a significant challenge for reorientation and shape memory behavior. In either case, it is very important to understand their behavior and examine their properties. This thesis is an effort to contribute to the literature of Ni-Mn-Ga thin films as ferromagnetic shape memory alloys. The focus of this project is to develop a recipe for fabricating NiMnGa thin films with desired composition and microstructure and hence unique properties for future MEMS actuator materials and characterize their properties to aid better understanding of their behavior. In this project NiMnGa thin films have been fabricated using magnetron sputtering on a variety of substrates. Magnetron sputtering technique allows us to tailor the composition of films which is crucial for controlling the phase transformation properties of NiMnGa films. The composition is tailored by varying several deposition parameters. Microstructure of the films has been investigated by X-ray diffraction (XRD) and transmission electron microscopy (TEM) techniques. Mechanical properties of as-deposited films have been probed using nano-indentation technique. The chemistry of sputtered films is determined quantitatively by wavelength dispersive X-ray spectroscopy (WDS). Phase transformation is studied by using a combination of differential scanning calorimetry (DSC), in-situ heating in TEM and in-situ XRD instruments. Magnetic properties of films are examined using superconducting quantum interface device (SQUID).

Zeolite beta has been synthesized using hydrothermal methods. In order to synthesize zeolite beta an aqueous gel having a molar batch composition of 2.2 Na2O&amp / #8729 / Al2O3&amp / #8729 / x SiO2&amp / #8729 / 4.6 (TEA)2O&amp / #8729 / 444 H2O was utilized. The synthesis parameters were SiO2/Al2O3 ratio (20 &amp / #8804 / x &amp / #8804 / 50) and crystallization time (6 &amp / #8804 / t &amp / #8804 / 16 days). Pure zeolite beta was crystallized from the experiments which were performed with the batch composition having SiO2/Al2O3 of 20 and 30 in 6 to 16 days period. For SiO2/Al2O3 of 20 and 30, the highest yield was obtained for 12 days. Therefore, the rest of the experiments, in which SiO2/Al2O3 was 40 and 50, were carried out keeping the synthesis time constant (12 days). Pure zeolite beta was also synthesized for SiO2/Al2O3 of 40 and 50. The highest yield and the most crystalline zeolite beta sample were obtained from the experiment performed at SiO2/Al2O3 of 50 with a synthesis time of 12 days. The morphology and crystal size of the zeolite beta samples were identified by using scanning electron microscope (SEM). It was observed that, zeolite beta samples had spheroidal morphology with the crystal size of about 0.5 &amp / #956 / m. According to the thermogravimetric analyses (TGA), it was found that template molecules and moisture constituted nearly 18 % by weight of the zeolite beta samples. The surface area of the calcined zeolite beta sample was determined by N2 adsorption and was found to be 488 m2/g. Gravimetric sorption analyses yield that, the limiting sorption capacity of Na-Beta for methanol, ethanol, isopropanol and n-butanol at 0&deg / C was about the same with a value of 0.25 cm3/g. For o-xylene, m-xylene and p-xylene that value was 0.21 cm3/g, 0.22 cm3/g and 0.24 cm3/g, respectively.

In this thesis, first we replace the condition X ¡Ø y in Huang and Su (2000) by X ¡Ù y and give necessary and sufficient conditions such that there exists a random variable X satisfying that E(g(X)| X ¡Ø y)=h(y) f(y )/ F(y), " y &#x00CE; CX, where CX is the support of X.Next, we investigate necessary and sufficient conditions such that h(y)=E(g(X) | X ¡Ø y ), for a given function h and extend these results to bivariate case.

This thesis presents the development of a novel atomic force microscope (AFM) based nanofabrication technique termed as thermochemical nanolithography (TCNL). TCNL uses a resistively heated AFM cantilever to thermally activate chemical reactions on a surface with nanometer resolution. This technique can be used for fabrication of functional nanostructures that are appealing for various applications in nanofluidics, nanoelectronics, nanophotonics, and biosensing devices. This thesis research is focused on three main objectives. The first objective is to study the fundamentals of TCNL writing aspects. We have conducted a systematic study of the heat transfer mechanism using finite element analysis modeling, Raman spectroscopy, and local glass transition measurement. In addition, based on thermal kinetics analysis, we have identified several key factors to achieve high resolution fabrication of nanostructures during the TCNL writing process. The second objective is to demonstrate the use of TCNL on a variety of systems and thermochemical reactions. We show that TCNL can be employed to (1) modify the wettability of a polymer surface at the nanoscale, (2) fabricate nanoscale templates on polymer films for assembling nano-objects, such as proteins and DNA, (3) fabricate conjugated polymer semiconducting nanowires, and (4) reduce graphene oxide with nanometer resolution. The last objective is to characterize the TCNL nanostructures using AFM based methods, such as friction force microscopy, phase imaging, electric force microscopy, and conductive AFM. We show that they are useful for in situ characterization of nanostructures, which is particularly challenging for conventional macroscopic analytical tools, such as Raman spectroscopy, IR spectroscopy, and fluorescence microscopy.

An advancement of the two stage growth recipe for the fabrication of CIGS solar cells was developed. The developed advancement was inconsistent in producing samples of similar stoichiometry. This was a huge barrier for up scaling the process as the behavior of devices would be different due to variation in stoichiometry. Samples with reproducible stoichiometry were obtained once the heating rate of elements, selenium in particular was better understood. This is mainly attributed to the exponential increase of selenium flux after its evaporation temperature. Monitoring the selenium flux was vital in getting constant selenium fluxes. Few changes to the growth recipe were induced to optimize the amount of selenium being used. Depositions were done using constant selenium to metal flux ratio of 5. Elemental tradeoffs were observed as a result of the growth recipe change. These tradeoffs are in favor of the two stage growth recipe. The solar cells were fabricated on a soda lime glass substrate with a molybdenum back contact. Improper sample cleaning and storage were found to affect the deposition outcome of the molybdenum back contact. This also had a cascading effect on the absorber layer. Residual precipitates during deposition of CdS were avoided by increasing the spinner speed which increased the reaction rate. This is attributed to the growth of CdS either by cluster-by-cluster growth or by ion-by-ion growth. SEM, EDS were some important tools used to characterize the devices. EDS in particular, was used extensively at different stages throughout the growth process to ensure that we were heading in the right direction. Current-voltage (I-V) measurements were done to study the solar cell performance under light and dark.