Chemical flooding is one of the most difficult enhanced oil recovery methods and was considered a high-risk process in the past. Some reasons are low and uncertain oil price, high chemical prices, lack of confidence in performance of the chemical flooding process, long project life, and reservoir and process uncertainties. However, with significant improvement in simulation and optimization tools and high oil price, chemical flooding is feasible in terms of economical and carefully implemented design. Optimization of chemical floods requires complex integration of reservoir, chemical, economics properties and also drilling and production strategies. Many of these variables are uncertain parameters and many simulations are required to capture the effect of the uncertain and decision variables. These simulations could become very expensive and may not be feasible to consider all of the required simulation models. The goal of this research is the development of a methodology for optimization and design of chemical flooding of candidate oil reservoirs. We performed a comprehensive sensitivity study of reservoir and fluid properties that have significant influence on the oil production during the chemical flooding by performing a series of reservoir simulation runs. For performing the reservoir simulation runs, this study used the UT_IRSP platform and the multiphase, multicomponent, chemical flooding simulator called UTCHEM. During the study, UT_IRSP and UTCHEM have been modified by adding new modules, functions and variables. For example, a deviated well module was implemented in UTCHEM to study deviated wells. Deviated well module allows the users to introduce deviated wells in reservoir and import the well locations similar to Eclipse or CMG simulators. A time-dependent well schedule module was implemented in the UT_IRSP framework. This enhancement allows the well placement optimization studies to find the best time to add new wells, and change the status of the well for example from a producer to an injector in order to have an optimum development plan. An advanced post processing module was added to UT_IRSP in order to design, screen, and optimize complex cases for chemical enhanced oil recovery processes such as investigating the well patterns, well spacing, and type of the well (horizontal vs. vertical wells). An experimental design and response surface methodology with integrated economic model were utilized in this study to obtain the optimum design under uncertainties and have an optimal combination of the decision variables. This methodology is based on applying multi-regression analysis and ANOVA (analysis of variance) between the objective function (i.e. dependent variable, which is net present value (NPV) in chemical flooding) and other uncertain and process variables (independent variables). The economic analysis model used the discounted cash flow method to calculate net present value at the economic life of process, internal rate of return, and growth rate of return for each simulation case. Also the optimizer, OptQuest, is launched with a goal of maximizing the mean NPV. The range and the risk associated with the optimum design was studied using Monte Carlo simulation of objective function of the response variable and other independent variables. This methodology was applied for complex chemical flood cases such as well placement, change of status of wells as a function of time or well pattern and well spacing to investigate the best well scenario from recovery and economics point of view. / text

published_or_final_version / Mechanical Engineering / Master / Master of Philosophy

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Mutation is a fundamental process in evolution because affects the amount of genetic variation in evolving populations. Molecular-structure models offer significant advantages over traditional population-genetics models for studying mutation, mainly because such models incorporate simple, tractable genotype-to-phenotype maps. Here, I use RNA secondary structure models to study four basic properties of mutation. The first section of this thesis studies the statistical properties of beneficial mutations. According to population genetics theory, the fitness effects of new beneficial mutations will be exponentially distributed. I show that in RNA there is sufficient correlation between a genotype and its point mutant neighbors to produce non-exponential distributions of fitness effects of beneficial mutations. These results suggest that more sophisticated statistical models may be necessary to adequately describe the distribution of fitness effects of new beneficial mutations. The second section of this thesis addresses the dynamics of deleterious mutations in evolving populations. There is a vast body of theoretical work addressing deleterious mutations that almost universally assumes that the fitness effects of deleterious mutations are static. I use an RNA simulation model to show that, at moderately high mutation rates, initially deleterious mutations may ultimately confer beneficial effects to the individuals harboring them. This result suggests that deleterious mutations may play a more important role in evolution than previously thought. The third section of this thesis studies the global patterns of mutations connecting phenotypes in fitness landscapes. I developed a network model to describe global characteristics of the relationship between sequence and structure in RNA fitness landscapes. I show that phenotype abundance varies in a predictable manner and critically influences evolutionary dynamics. A study of naturally occurring functional RNA molecules using a new structural statistic suggests that these molecules are biased towards abundant phenotypes. These results are consistent with an "ascent of the abundant" hypothesis, in which evolution yields abundant phenotypes even when they are not the most fit. The final section of this thesis addresses the evolution of mutation rates infinite asexual populations. I developed an RNA-based simulation model in which each individual's mutation rate is controlled by a neutral modifier locus. Using this model, I show that smaller populations maintain higher mutation rates than larger populations. I also show that genome length and shape of the fitness function do not significantly determine the evolved mutation rate. Lastly, I show that intermediate rates of environmental change favor evolution of the largest mutation rates. / text

This dissertation considers modeling groundwater flow under imprecisely known parameters and managing a plume of contaminant. A new approach has been developed to study the effects of parameters uncertainty on the dependent variable, here the head. The proposed approach is developed based on fuzzy set theory combined with interval analysis. The kind of uncertainty modeled here is the imprecision associated with model parameters as a result of machine or human imprecision or lack of information. In this technique each parameter is described by a membership function. The fuzzy inputs into the model are in the form of intervals so are the outputs. The resulting head interval represents the change in the output due to interval inputs of model parameters. The proposed technique is illustrated using a two dimensional flow problem solved with a finite element technique. Three different cases are studied: homogeneous, mildly heterogeneous and highly heterogeneous transmissivity field. The groundwater flow problem analysis requires interval input values for the parameters, the output may be presented in terms of mean value, upper and lower bounds of the hydraulic head. The width of the resulting head interval can be used as a measure of uncertainty due to imprecise inputs. The degree of uncertainty associated with the predicted hydraulic head is found to increase as the width of the input parameters interval increases. Compared to Monte Carlo simulation approach, the proposed technique requires less computer storage and CPU time, however at this stage autocorrelation and crosscorolation are not configured in the presented formulation. In the plume containment problem two formulations are presented using the hydraulic gradient technique to control the movement of the contaminants. The first one is based on multiobjective analysis and the second, on fuzzy set theory. Multiobjective analysis yields a set of alternative strategies each of which satisfies the multiple objectives to a certain degree. Three different techniques have been used to choose a compromise strategy. Although they follow different principles, the same preferred strategies are selected. It is also noticed that rapid restoration results in a large pumping volumes and high costs. Using a fuzzy formulation for plume containment yields the optimum pumping rates and locations in addition to the membership function at each pumping location. The resulting membership functions at these pumping locations can be used to study the sensitivity of each location to a change in objective function and constraints bounds. Overall, both the fuzzy and multiobjective methodologies, presented in this dissertation, provide new and encouraging approaches to groundwater quality management.

The stresses and the elastic constants of bcc sodium are calculated by molecular dynamics (MD) for temperatures to T = 340 K. The total adiabatic potential of a system of sodium atoms is represented by a pseudopotential model. The resulting expression has two terms: a large, strictly volume-dependent potential, plus a sum over ion pairs of a small, volume-dependent two-body potential. The stresses and the elastic constants are given as strain derivatives of the Helmholtz free energy. The resulting expressions involve canonical ensemble averages (and fluctuation averages) of the position and volume derivatives of the potential. An ensemble correction relates the results to MD equilibrium averages. Evaluation of the potential and its derivatives requires the calculation of integrals with infinite upper limits of integration, and integrand singularities. Methods for calculating these integrals and estimating the effects of integration errors are developed. A method is given for choosing initial conditions that relax quickly to a desired equilibrium state. Statistical methods developed earlier for MD data are extended to evaluate uncertainties in fluctuation averages, and to test for symmetry. The fluctuation averages make a large contribution to the elastic constants, and the uncertainties in these averages are the dominant uncertainties in the elastic constants. The strictly volume-dependent terms are very large. The ensemble correction is small but significant at higher temperatures. Surprisingly, the volume derivatives of the two-body potential make large contributions to the stresses and the elastic constants. The effects of finite potential range and finite system size are discussed, as well as the effects of quantum corrections and electronic excitations. The agreement of theory and experiment is very good for the magnitudes of C₁₁ and C₁₂. The magnitude of C₄₄ is consistently small by ∼9 kbar for finite temperatures. This discrepancy is most likely due to the neglect of three-body contributions to the potential. The agreement of theory and experiment is excellent for the temperature dependences of all three elastic constants. This result illustrates a definite advantage of MD compared to lattice dynamics for conditions where classical statistics are valid. MD methods involve direct calculations of anharmonic effects; no perturbation treatment is necessary.

M. Tech. Electrical Engineering. / Studies the symmetrical six phase induction machine structure, to determine different devices this machine can adapt to and to model these devices mathematically. Laboratory experiments are to be conducted and used to verify theoretical findings. The research objectives are:to determine what devices the symmetrical structure can adapt to ; to develop a mathematical model of the machine operating as different devices and to determine the equivalent circuit of the specific mode of operation ; to investigate both steady state and dynamic behaviour of the machine operating as different devices using Matlab / Simulink simulation software package ; to use a laboratory prototype of the "3 & 6" machine to validate theoretical findings.

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