This thesis is part of a project funded by the Minerals Management Service (MMS) (now Bureau of Ocean Energy Management, Regulation and Enforcement (BOEMRE)) to study the use of epoxy to plug hurricane damaged wells. Some of the wells destroyed by hurricanes are damaged to an extent that vertical intervention from the original wellhead is not possible. These wells have to be plugged to prevent future flows through the well to protect the environment. Cement is usually the preferred plugging material because it is very cheap compared to other materials like epoxy. However, cement can easily get contaminated by sea water or brines present in wells as completion fluids. Therefore, to be able to use cement it has to be placed at the bottom of the well by drilling an offset well all the way to the bottom of the original well. Epoxy, on the other hand, being much more chemically stable can be placed at the very top of the well and let to settle by gravity without fearing contamination. Therefore, in wells described above, epoxy can be much more economical than cement. Placing epoxy at the top of a well and letting it settle by gravity can also be more economical than using cement in other situations such as in a leaking annulus of a well where circulation in that annulus is not possible, or if a well that has been previously plugged starts leaking again after the rig has been removed. Placing epoxy in the manner described can be achieved without using a rig and therefore, would be much more economical than cement. One of the most important factors in this process is to be able to predict the settling velocity of the epoxy to be able to determine the required setting time of the epoxy so that the epoxy does not set prematurely. In addition, it is important to evaluate whether the epoxy can successfully settle to the bottom and how much of it will adhere to the pipe walls while freefalling. This thesis aims to design, build and run an experimental setup that would help study the settling velocity of epoxy. Some experiments were conducted to assess the effect of different parameters that might affect the settling velocity of the epoxy such as the epoxy’s density, the annulus size and the inclination angle. The results show that the settling velocity was proportional to the epoxy’s density. Also the settling speed was almost double in experiments done at an angle compared to experiments done at vertical position. The annulus size did not have any clear effect on the settling speed. The adhesion to the pipe walls was found to be proportional to the epoxy’s viscosity and angle of inclination and was inversely proportional to the annulus size.

This thesis presents a framework design and implementation that enables run-time selection of different remote call mechanisms. In order to implement an extendable and modular system with run-time upgrading facility, a plug-in framework design is used. Since such a design requires enhanced usage of run-time facilities of the programming language that is used to implement the framework, in this study Java is selected because of its reflection and dynamic class loading facilities. A sample usage of this framework is enabling an application to distribute its tasks over a network using a suitable distributed object technology (DOT). In this work, CORBA, RMI and Java Sockets are the sample DOT plug-ins. A series of performance evaluations of these DOTs are presented to establish a baseline for choosing a suitable DOT for the application domain that uses this framework.

The focus of this study is on predicting the subcellular localization of a protein. Subcellular localization information is important for protein function annotation which is a fundamental problem in computational biology. For this problem, a classification system is built that has two main parts: a predictor that is based on a feature mapping technique to extract biologically meaningful information from protein sequences and a client/server architecture for searching and predicting subcellular localizations. In the first part of the thesis, we describe a feature mapping technique based on frequent patterns. In the feature mapping technique we describe, frequent patterns in a protein sequence dataset were identified using a search technique based on a priori property and the distribution of these patterns over a new sample is used as a feature vector for classification. The effect of a number of feature selection methods on the classification performance is investigated and the best one is applied. The method is assessed on the subcellular localization prediction problem with 4 compartments (Endoplasmic reticulum (ER) targeted, cytosolic, mitochondrial, and nuclear) and the dataset is the same used in P2SL. Our method improved the overall accuracy to 91.71% which was originally 81.96% by P2SL. In the second part of the thesis, a client/server architecture is designed and implemented based on Simple Object Access Protocol (SOAP) technology which provides a user-friendly interface for accessing the protein subcellular localization predictions. Client part is in fact a Cytoscape plug-in that is used for functional enrichment of biological networks. Instead of the individual use of subcellular localization information, this plug-in lets biologists to analyze a set of genes/proteins under system view.

No description available.

<p>Currently, security is frequently considered late in software life cycle. It is often bolted on late in development, or even during deployment or maintenance, through activities such as add-on security software and penetration-and-patch maintenance. Even if software developers aim to incorporate security into their products from the beginning of the software life cycle, they face an exhaustive amount of ad hoc unstructured information without any practical guidance on how and why this information should be used and what the costs and benefits of using it are. This is due to a lack of structured methods.</p><p>In this thesis we present a model for secure software development and implementation of a security plug-in that deploys this model in software life cycle. The model is a structured unified process, named S3P (Sustainable Software Security Process) and is designed to be easily adaptable to any software development process. S3P provides the formalism required to identify the causes of vulnerabilities and the mitigation techniques that address these causes to prevent vulnerabilities. We present a prototype of the security plug-in implemented for the OpenUP/Basic development process in Eclipse Process Framework. We also present the results of the evaluation of this plug-in. The work in this thesis is a first step towards a general framework for introducing security into the software life cycle and to support software process improvements to prevent recurrence of software vulnerabilities.</p> / Report code: LiU-Tek-Lic-2008:11.

<p>Abstract</p><p>The program analysis tool, VizzAnalyzer, works good for visualizing the program structure as graphs, but currently it does not allow for a UML conform visualization, which allows a effective communication among software engineers. In this thesis we describe the extension of the VizzAnalyzer to allow the visualization of software systems as UML class diagrams.</p><p>The Eclipse platform provides an open source platform for creating an extensible integrated development environment. We create a plug-in that can be seamlessly integrated in the Eclipse platform through the third party tools in Eclipse to visualize the graph in VizzAnalyzer as UML class diagrams. The third party tools that are used in this thesis are the Graphical Editing Framework (GEF) and Draw2D. They are mainly used to create the UML class diagram view.</p><p>In order to get a clear view on the diagram in the limited screen size, it is necessary to provide an appropriate layout for the diagram and drag and zoom actions on its figures. We provide the necessary means and document altogether in this thesis. We implement our solution as Eclipse plug-in and demonstrate feasibility.</p>

<p>Reverse Engineering of software is a complex task. It is supported by various analyses and the computation of certain software quality metrics. To get an overview of the structure of the analysed software it is often necessary to visualize the relations between different components of software.</p><p>The VizzAnalyzer tool was developed for these purposes and allows visualizing the internal structure of software systems as UML2.0 class diagrams. The existing Eclipse-based visualization plug-in does not allow to automatically lay out the computed graph. Applying the layout manually is not feasible for larger graphs. A proper layout is needed to understand the provided information intuitively and the usefulness of the visualization is, on top of that, strongly connected to its layout.</p><p>In this thesis, we first state requirements for the development and implementation process of a suitable layout algorithm. We further describe the development, design, and implementation of the Sugiyama layout algorithm into the existing Eclipse-based visualization plug-in. We add these new functionalities to be able to automatically compute a layout for given UML2.0 class diagrams achieving a proper Sugiyama layout for this type of diagrams.</p><p>At the end of this thesis, we compare the results achieved by the implemented algorithm with the results achieved by yEd given the same diagrams as input.</p>

In the first part of this thesis, GPS data for a year's worth of travel by 255 Seattle households is used to illuminate how plug-in electric vehicles (PEVs) can match household needs. Data from all vehicles in each of these households were analyzed at a disaggregate level primarily to determine whether each household would be able to adopt various types of PEVs without significant issues in meeting travel needs. The results suggest that a battery-electric vehicle (BEV) with 100 miles of all-electric range (AER) should meet the needs of 50% of Seattle's one-vehicle households and the needs of 80% of the multiple-vehicle households, when households charge just once a day and rely on another vehicle or mode just 4 days a year. Moreover, the average one-vehicle Seattle household uses each vehicle 23 miles per day and should be able to electrify close to 80% of its miles, while meeting all its travel needs, using a plug-in hybrid electric vehicle with 40-mile all-electric-range (PHEV40). Households owning two or more vehicles can electrify 50 to 70% of their total household miles using a PHEV40, depending on how they assign the vehicle across drivers each day. Cost comparisons between the average single-vehicle household owning a Chevrolet Cruze versus a Volt PHEV suggest that, when gas prices are $3.50 per gallon and electricity rates are 11.2 ct per kWh, the Volt will save the household $535 per year in energy/fuel costs. Similarly, the Toyota Prius PHEV will provide an annual savings of $538 per year over the Corolla. The results developed in this research provide valuable insights into the role of AER on PEV adoption feasibility and operating cost differences. The second part of this thesis uses detailed travel data from the Seattle metropolitan area to evaluate the effects of built-environment variables on the use of non-motorized (bike + walk) modes of transport. Several model specifications are used to understand and explain non-motorized travel behavior in terms of household, person and built-environment variables. Land-use measures like land-use mix, density, and accessibility indices were also created and incorporated as covariates to appreciate their marginal effects. The models include a count model for household vehicle ownership levels, a binary choice model for the decision to stay within versus departing one's origin zone (i.e., intra- versus inter-zonal trip-making), discrete choice models for destination choices and mode choices, and a zero-inflated negative binomial model for non-motorized trip counts per household. The mode and destination choice models were estimated separately for interzonal and intrazonal trips and for each of three different trip types (home-based work, home-based non-work, and non-home-based), to recognize the distinct behaviors at play when making shorter versus longer trips and different types of trips. This comprehensive set of models highlights how built-environment variables -- like the number and type of intersections present around one's origin and destination, the number of bus stops available within a certain radius, household and jobs densities, parking prices, land use mixing, and walk-based accessibility -- can significantly shape the pattern of one's non-motorized movement. The results underscore the importance of street connectivity (quantified as the number of 3-way and 4-way intersections in a half-mile radius), higher bus stop density, and greater non-motorized access in promoting lower vehicle ownership levels (after controlling for household size, income, neighborhood density and so forth), higher rates of non-motorized trip generation (per day), and higher likelihoods of non-motorized mode choices. Destination choices are also important for mode choices, and local trips lend themselves to more non-motorized options than more distance trips. Intrazonal trip likelihoods rose with higher street connectivity, transit availability, and land use mixing. For example, the results suggest that an increase in the land-use mix index by 10% would increase the probability of choosing to travel within the zone by 12%. As expected destinations with greater population and job numbers (attraction), located closer (to a trip's origin), offering lower parking prices and greater transit availability, were more popular. Interestingly, those with more dead ends (or cul de sacs) attracted fewer trips. Among all built environment variables tested, street structure offered the greatest predictive benefits, alongside jobs and population (densities and counts). For example, a 1-percent increase in the average number of 4-way intersections within a quarter-mile radius of the sampled households is estimated to increase the average household's non-motorized trip generation by 0.36%. A one-standard-deviation increase in the (mean) number of 4-way intersections at the average trip origin is estimated to increase the probabilities of bike and walk modes for interzonal home-based-work trips by 57% and 30%, respectively. In contrast, increasing the number of dead-ends at the origin by one standard deviation is estimated to decrease the probability of biking for both home-based-work and non-work trips by ~30%. These results underscore the importance of network density and connectivity for promoting non-motorized activity. The regional non-motorized travel (NMT) accessibility index ( derived from the logsum of a destination choice model) also offers strong predictive value, with NMT counts rising by by 7% following a 1% increase in this variable -- if the drive alone accessibility index is held constant (along with all other variables, evaluated at their means). Similarly, household vehicle ownership is expected to fall by 0.36% with each percentage point increase in the NMT accessibility index, and walk probabilities rise by 26.9% following a one standard deviation increase in this index at the destination zone. A traveler's socio-economic attributes also have important impacts on NMT choices, with demographics typically serving as much stronger predictors of NMT choices than the built environment. For example, the elasticity of NMT trip generation with respect to a household's vehicle ownership count is estimated to be -0.52. Males and tose with drivers licenses are estimated to have 17% and 39% lower probabilities, respectively, of staying within their origin zone, relative to women and unlicensed adults (ceteris paribus). Non-motorized model choices also exhibit strong sensitivity to age and gender settings. Several of the regional variables developed in this work, and then used in the predictive models, are highly correlated. For example, bus stop and intersection densities are very high in job- and population-dense areas. For example, the correlation co-efficients between the bus stop density and 4-way intersection density is 0.805, between NMT and SOV AIs is 0.830 and between 4-way intersection density and NMT AI is 0.627. As a result, many variables are proxying for and/or competing with each other, as is common in models with many land use covariates, and it is difficult to quantify the exact impact of each of these variables. Nonetheless the models developed here provide valuable insight into the role of several new variables on non-motorized travel choices. Some final case study applications, moving all households to the downtown area (that has high accessibility indices and density), illustrate to what extent these revealed-data-based models will predict shifts toward and away from non-motorized trip-making. It appears that average household vehicle ownership level reduces to 0.57 from 1.89 (a 70% reduction) and average two-day NMT trip generation increases to 5.92 from 0.83 (an increase of more than 6 times). Such ranges are valuable to have in mind, when communities seek to reduce reliance on motorized travel by defining new built-environment contexts. / text

This thesis evaluates emerging electric vehicle technology and estimates what effect it might have on how an oil company decides on its gas station network. It is conducted using data from South Korea, a country poised for a fast adoption of electric vehicles. The study first reviews the literature to gather reasonable cases of electric vehicle penetration. Also, after researching technology-diffusion theories, the study selects a model that can well explain the literature review data. The scenarios induced by this function are utilized as the main uncertainties confronting an oil company’s network decision model. Based on a probabilistic simulation, the study finds that the effects of technology diffusion alter the priority order of an oil company’s network decision alternatives. Namely, after the overall uncertainty level rises, directly owning gas station, with its heavy initial investment, is not preferred for an oil company’s network strategy. From the result, the study also estimates the scale of the new technology’s effect. Such effect is found to be significant enough to alter a part of an oil company’s retail strategy. Nevertheless, such effect cannot be shown to be so great as to change the current retail oil market structures. / text

Ooid grainstone reservoir architecture remains poorly understood, particularly because of sedimentologic and stratigraphic heterogeneities that are innate to grainstone body development. Understanding of Geospatial relationships and recovery of hydrocarbons from these significant reservoir facies can be improved with access to outcrop analog information from well exposed examples. Object-based models and other modern subsurface reservoir models are considered superior methods for portraying realistic sediment distributions. These models, however, are highly dependent on input data describing sediment-body geometry for faithful template generation. Such input data are notably rare in carbonate systems. Maps generated from modern depositional patterns give a first approximation of areal distribution, but they are not as useful for understanding final preserved stratigraphic thickness and internal facies, sedimentary structure, and grain-type patterns. For this purpose, studies of exceptional outcrops are required. The 18 km long oblique-dip-oriented wall of the Shattuck Escarpment provides such a unique exposure of Permian-age grainstones. The Shattuck Escarpment in the Guadalupe Mountains provides an oblique-dip profile that exposes a near-complete middle Permian Grayburg mixed clastic-carbonate shelf succession of three high-frequency sequences which contain 30 high-frequency cycles. Particularly important for this study are the four cycles that display full updip to downdip extents of ooid grainstone tidal bar and tidal delta objects. The data from the Shattuck wall presented in this paper focusses on the transgressive portion of the upper Grayburg, or G12 high-frequency sequence (HFS), located 5 km landward of the time-equivalent shelf margin. This interval is an analog for productive fields along the northwest shelf of the Delaware Basin and on the eastern flank of the Central Basin Platform. The goal of this project is to understand the sedimentology and facies/cycle architectural variability of tidally influenced shelf crest ooid grainstones of the Grayburg Formation. Comparing this outcrop data to modern grainstone deposits allows the reader to understand the small-scale and large-scale sedimentologic and architectural patterns in analogous subsurface ooid grainstone reservoirs. Spatial analysis of these cycles was carried out using measured sections and GigaPan (high resolution photomosaic) data. Petrophysical (Porosity and Permeability) data was collected from three separate vertical core plug transects approximately 1 km apart with a vertical resolution of 30 cm. Cycle-set-scale grainstone complexes up to 6m thick extend at least 4.25 km along depositional dip and show variations in permeability between 6-400 mD and porosities between 8-20% within the lower portions of the grainstone complex. / text