In 2007, the Barnett Shale in the Fort Worth basin of Texas produced 1.1 trillion cubic feet (Tcf) gas and ranked second in U.S gas production. Despite its importance, controls on Barnett Shale gas well performance are poorly understood. Regional and vertical variations of reservoir properties and their effects on well performances have not been assessed. Therefore, we conducted a study of Barnett Shale stratigraphy, petrophysics, and production, and we integrated these results to clarify the controls on well performance. Barnett Shale ranges from 50 to 1,100 ft thick; we divided the formation into 4 reservoir units that are significant to engineering decisions. All but Reservoir Unit 1 (the lower reservoir unit) are commonly perforated in gas wells. Reservoir Unit 1 appears to be clay-rich shale and ranges from 10 to 80 ft thick. Reservoir Unit 2 is laminated, siliceous mudstone and marly carbonate zone, 20 to 300 ft thick. Reservoir Unit 3 is composed of multiple, stacked, thin (~15-30 ft thick), upward coarsening sequences of brittle carbonate and siliceous units interbedded with ductile shales; thickness ranges from 0 to 500 ft. Reservoir Unit 4, the upper Barnett Shale is composed dominantly of shale interbedded with upward coarsening, laterally persistent, brittle/ductile sequences ranging from 0 to 100 ft thick. Gas production rates vary directly with Barnett Shale thermal maturity and structural setting. For the following five production regions that encompass most of the producing wells, Peak Monthly gas production from horizontal wells decreases as follows: Tier 1 (median production 60 MMcf) to Core Area to Parker County to Tier 2 West to Oil Zone-Montague County (median production 10 MMcf). The Peak Monthly oil production from horizontal wells is in the inverse order of gas production; median Peak Monthly oil production is 3,000 bbl in the Oil Zone-Montague County and zero in Tier 1. Generally, horizontal wells produce approximately twice as much oil and gas as vertical wells.This research clarifies regional variations of reservoir and geologic properties of the Barnett Shale. Result of these studies should assist operators with optimization of development strategies and gas recovery from the Barnett Shale.

Traditionally, the design of continuous time active filters usually has a trade offbetween low-voltage and high dynamic range. One way to solve this problem is companding technology. There are two methods for companding filters. The first method utilizes the exponential I-V characteristics of BJT in the saturation region. In order to reduce the cost andintegrate the analog and digital circuits, the other method was exploited using CMOS process. In this project, a new first-order low pass log-domain filter based on CMOS parasitic vertical BJTwill be proposed. This filter has higher frequency response than previous circuits. We will first employ Hspice to simulate the log-domain filter to ensure the correctness of the circuit and make it a reliable reference with the circuit layout. After summarizing all the simulations and analyses, the chip will be fabricated with 0.35um CMOS technology.

<p>Queueing models have been used extensively in the design of call centres. In particular, a queueing model will be used to describe a help desk which is a form of a call centre. The design of the queueing model involves modelling the arrival an service processes of the system.</p><p>Conventionally, the arrival process is assumed to be Poisson and service times are assumed to be exponentially distributed. But it has been proposed that practically these are seldom the case. Past research reveals that the log-normal distribution can be used to model the service times in call centres. Also, services may involve stages/tasks before completion. This motivates the use of a phase-type distribution to model the underlying stages of service.</p><p>This research work focuses on developing statistical models for the overall service times and the service times by job types in a particular help desk. The assumption of exponential service times was investigated and a log-normal distribution was fitted to service times of this help desk. Each stage of the service in this help desk was modelled as a phase in the phase-type distribution.</p><p>Results from the analysis carried out in this work confirmed the irrelevance of the assumption of exponential service times to this help desk and it was apparent that log-normal distributions provided a reasonable fit to the service times. A phase-type distribution with three phases fitted the overall service times and the service times of administrative and miscellaneous jobs very well. For the service times of e-mail and network jobs, a phase-type distribution with two phases served as a good model.</p><p>Finally, log-normal models of service times in this help desk were approximated using an order three phase-type distribution.</p>

Large software systems are composed of various different run-time components, partner applications and, processes. When such systems operate they are monitored so that audits can be performed once a failure occurs or when maintenance operations are performed. However, log files are usually sizeable, and require filtering and reduction to be processed efficiently. Furthermore, there is no apparent correspondence of how logged events relate to particular use cases the system may be performing. In this thesis, we have developed a framework that is based on heuristic clustering algorithms to achieve log filtering, log reduction and, log interpretation. More specifically we define the concept of the Event Dependency Graph, and we present event filtering and use case identification techniques, that are based on event clustering. The clustering process groups together all events that relate to a collection of initial significant events that relate to a use case. We refer to these significant events as beacon events. Beacon events can be identified automatically or semiautomatically by examining log event types or event names against event types or event names in the corresponding specification of a use case being considered (e.g. events in sequence diagrams). Furthermore, the user can select other or additional initial clustering conditions based on his or her domain knowledge of the system. The clustering technique can be used in two possible ways. The first is for large logs to be reduced or sliced, with respect to a particular use case so that, operators can better focus their attention to specific events that relate to specific operations. The second is for the determination of active use cases where operators select particular seed events of interest and then examine the resulting reduced logs against events or event types stemming from different alternative known use cases being considered, in order to identify the best match and consequently provide insights on which of these alternative use cases may be running at any given time. The approach has shown very promising results towards the identification of executing use cases among various alternative ones in various runs of the Session Initiation Protocol.

Different universal methods (also called automatic or black-box methods) have been suggested to sample from univariate log-concave distributions. The description of a suitable universal generator for multivariate distributions in arbitrary dimensions has not been published up to now. The new algorithm is based on the method of transformed density rejection. To construct a hat function for the rejection algorithm the multivariate density is tranformed by a proper transformation T into a concave function (in the case of log-concave density T(x) = log(x).) Then it is possible to construct a dominating function by taking the minimum of several tangent hyperplanes which are transformed back by $T^(-1)$ into the original scale. The domains of different pieces of the hat function are polyhedra in the multivariate case. Although this method can be shown to work, it is too slow and complicated in higher dimensions. In this paper we split the $R^n$ into simple cones. The hat function is constructed piecewise on each of the cones by tangent hyperplanes. The resulting function is not continuous any more and the rejection constant is bounded from below but the setup and the generation remains quite fast in higher dimensions, e.g. n=8. The paper describes the details how this main idea can be used to construct algorithm TDRMV that generates random tuples from multivariate log-concave distribution with a computable density. Although the developed algorithm is not a real black box method it is adjustable for a large class of log-concave densities. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

Large software systems are composed of various different run-time components, partner applications and, processes. When such systems operate they are monitored so that audits can be performed once a failure occurs or when maintenance operations are performed. However, log files are usually sizeable, and require filtering and reduction to be processed efficiently. Furthermore, there is no apparent correspondence of how logged events relate to particular use cases the system may be performing. In this thesis, we have developed a framework that is based on heuristic clustering algorithms to achieve log filtering, log reduction and, log interpretation. More specifically we define the concept of the Event Dependency Graph, and we present event filtering and use case identification techniques, that are based on event clustering. The clustering process groups together all events that relate to a collection of initial significant events that relate to a use case. We refer to these significant events as beacon events. Beacon events can be identified automatically or semiautomatically by examining log event types or event names against event types or event names in the corresponding specification of a use case being considered (e.g. events in sequence diagrams). Furthermore, the user can select other or additional initial clustering conditions based on his or her domain knowledge of the system. The clustering technique can be used in two possible ways. The first is for large logs to be reduced or sliced, with respect to a particular use case so that, operators can better focus their attention to specific events that relate to specific operations. The second is for the determination of active use cases where operators select particular seed events of interest and then examine the resulting reduced logs against events or event types stemming from different alternative known use cases being considered, in order to identify the best match and consequently provide insights on which of these alternative use cases may be running at any given time. The approach has shown very promising results towards the identification of executing use cases among various alternative ones in various runs of the Session Initiation Protocol.

<p>Queueing models have been used extensively in the design of call centres. In particular, a queueing model will be used to describe a help desk which is a form of a call centre. The design of the queueing model involves modelling the arrival an service processes of the system.</p><p>Conventionally, the arrival process is assumed to be Poisson and service times are assumed to be exponentially distributed. But it has been proposed that practically these are seldom the case. Past research reveals that the log-normal distribution can be used to model the service times in call centres. Also, services may involve stages/tasks before completion. This motivates the use of a phase-type distribution to model the underlying stages of service.</p><p>This research work focuses on developing statistical models for the overall service times and the service times by job types in a particular help desk. The assumption of exponential service times was investigated and a log-normal distribution was fitted to service times of this help desk. Each stage of the service in this help desk was modelled as a phase in the phase-type distribution.</p><p>Results from the analysis carried out in this work confirmed the irrelevance of the assumption of exponential service times to this help desk and it was apparent that log-normal distributions provided a reasonable fit to the service times. A phase-type distribution with three phases fitted the overall service times and the service times of administrative and miscellaneous jobs very well. For the service times of e-mail and network jobs, a phase-type distribution with two phases served as a good model.</p><p>Finally, log-normal models of service times in this help desk were approximated using an order three phase-type distribution.</p>

With the growth of digital content industry, publishers start to provide online services for ebook search, reading and downloading. Users can access to online resources from anywhere, any place with laptop or mobile devices at any time. Nowadays more and more libraries have purchased ebooks as an important part of the library collection. To access the online resources users can link directly to publisher's ebook portal or via the OPAC system. Compared to the library circulation process, ebooks are more convenient to patrons and improve the utilization of library online resources. There are various kinds of ebooks available in the market, so libraries have to focus their investment on the most valuable online resources. Usage statistics report plays an important role in providing valuable information to libraries. It is usually based on the standard of COUNTER to generate the statistic reports, although it provides when and where users access to specific ebooks, it fails show the general topics and how they change. In this study, we introduce a post process method to weighting the LDA topic model via the usage statistic report to emphasize the changes of topic and compare it to the classification method and subject heading method in the bibliographic, namely LCC and LCSH respectively. The result show that weighted topic model significantly affect the ranking of topics, and the topic model are independent from the classification method and the subject heading method in the bibliographic record.

In 2007, the Barnett Shale in the Fort Worth basin of Texas produced 1.1 trillion cubic feet (Tcf) gas and ranked second in U.S gas production. Despite its importance, controls on Barnett Shale gas well performance are poorly understood. Regional and vertical variations of reservoir properties and their effects on well performances have not been assessed. Therefore, we conducted a study of Barnett Shale stratigraphy, petrophysics, and production, and we integrated these results to clarify the controls on well performance. Barnett Shale ranges from 50 to 1,100 ft thick; we divided the formation into 4 reservoir units that are significant to engineering decisions. All but Reservoir Unit 1 (the lower reservoir unit) are commonly perforated in gas wells. Reservoir Unit 1 appears to be clay-rich shale and ranges from 10 to 80 ft thick. Reservoir Unit 2 is laminated, siliceous mudstone and marly carbonate zone, 20 to 300 ft thick. Reservoir Unit 3 is composed of multiple, stacked, thin (~15-30 ft thick), upward coarsening sequences of brittle carbonate and siliceous units interbedded with ductile shales; thickness ranges from 0 to 500 ft. Reservoir Unit 4, the upper Barnett Shale is composed dominantly of shale interbedded with upward coarsening, laterally persistent, brittle/ductile sequences ranging from 0 to 100 ft thick. Gas production rates vary directly with Barnett Shale thermal maturity and structural setting. For the following five production regions that encompass most of the producing wells, Peak Monthly gas production from horizontal wells decreases as follows: Tier 1 (median production 60 MMcf) to Core Area to Parker County to Tier 2 West to Oil Zone-Montague County (median production 10 MMcf). The Peak Monthly oil production from horizontal wells is in the inverse order of gas production; median Peak Monthly oil production is 3,000 bbl in the Oil Zone-Montague County and zero in Tier 1. Generally, horizontal wells produce approximately twice as much oil and gas as vertical wells.This research clarifies regional variations of reservoir and geologic properties of the Barnett Shale. Result of these studies should assist operators with optimization of development strategies and gas recovery from the Barnett Shale.

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