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The increase in energy demand throughout the world has forced the oil industry to develop and expand on current technologies to optimize well productivity. Distributed temperature sensing has become a current and fairly inexpensive way to monitor performance in hydraulic fractured wells in real time by the aid of fiber optic. However, no applications have yet been attempted to describe or estimate the fracture parameters using distributed temperature sensing as the observation parameter. The Ensemble Kalman Filter, a recursive filter, has proved to be an effective tool in the application of inverse problems to determine parameters of non-linear models. Even though large amounts of data are acquired as the information used to apply an estimation, the Ensemble Kalman Filter effectively minimizes the time of operation by only using “snapshots” of the ensembles collected by various simulations where the estimation is updated continuously to be calibrated by comparing it to a reference model. A reservoir model using ECLIPSE is constructed that measures temperature throughout the wellbore. This model is a hybrid representation of what distributed temperature sensing measures in real-time throughout the wellbore. Reservoir and fracture parameters are selected in this model with similar properties and values to an unconventional well. However, certain parameters such as fracture width are manipulated to significantly diminish the computation time. A sensitivity study is performed for all the reservoir and fracture parameters in order to understand which parameters require more or less data to allow the Ensemble Kalman Filter to arrive to an acceptable estimation. Two fracture parameters are selected based on their low sensitivity and importance in fracture design to perform the Ensemble Kalman Filter on various simulations. Fracture permeability has very low sensitivity. However, when applying the estimation the Ensemble Kalman Filter arrives to an acceptable estimation. Similarly fracture halflength, with medium sensitivity, arrives to an acceptable estimation around the same number of integration steps. The true effectiveness of the Ensemble Kalman Filter is presented when both parameters are estimated jointly and arrive to an acceptable estimation without being computationally expensive. The effectiveness of the Ensemble Kalman Filter is directly connected to the quantity of data acquired. The more data available to run simulations, the better and faster the filter performs.

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Master of Science / Department of Computing and Information Sciences / Gurdip Singh / Distributed applications that operate on networks of sensors to gather data are important in real world. TinyOS is an operating system designed to support wireless sensor networks. . It has interfaces and components, which provide functionalities for sensing parameters in the environment, packet communication and computation. These sensors have multiple purposes such as gathering different kinds of data and can be deployed in distributed networks to gather important information. NesC is a language which is used to write sensor applications for TinyOS which are deployment on the sensors. TinyViz is an application which simulates the NesC applications on a computer so that the applications can be tested first in the simulation environment and then can be tested on the sensors and deployed. However, TinyViz by default represents a static and closed environment where the conditions simulated may not be realistic. This project aims at providing real-world scenarios on the platform TinyViz, by communicating with TinyViz using Tython, a script language for this specific purpose. In terms of sensor network applications, events are classified into categories, which can be mapped to tangible parameters. This project takes as input the real-world parameters as input by the developer of the NesC applications in the form of a configuration file and converts them into implementable threads that run in parallel with TinyViz and keep sending instructions to the TinyViz which then simulates real-world environment. Thus, it helps simulate NesC applications in a realistic environment even before the real deployment. This is packaged as an Eclipse plug-in for portability and ease of implementation, using which developers of NesC applications can give as input configuration and obtain the files required for simulation. The implementation is done in java, using ‘Tython’.

With the rapidly advancing technologies, training has been vital to keep companies competitive. Web-based training grows rapidly and attracts more attention for its most flexible manner. Virtual classroom is a form of synchronous web-based training. It provides real-time interactivity in learning process. I have developed a virtual classroom that uses Web services to control the audio/video transmission, chat box, whiteboard, and synchronous HTML presentation. Compared to an early implementation of the virtual classroom based on the Jini network, my Web-service based implementation has a significantly different control structure. My implementation has better interoperability.

A robot should be able to autonomously modify and utilize its environment to assist its task completion. While mobile manipulators and humanoid robots have both locomotion and manipulation capabilities, planning systems typically just consider one or the other. In traditional motion planning the planner attempts to find a collision free path from the robot's current configuration to some goal configuration. In general, this process entirely ignores the fact that the robot has manipulation capabilities. This is in contrast to how humans naturally act - utilizing their manipulation capabilities to modify the environment to assist locomotion. If necessary, humans do not hesitate to move objects, such as chairs, out of their way or even place an object, such as a board, on the ground to reach an otherwise unreachable goal. We argue that robots should demonstrate similar behavior. Robots should use their manipulation capabilities to move or even use environment objects. This thesis aims at bringing robots closer to such capabilities. There are two primary challenges in developing practical systems that allow a real robotic system to tightly couple its manipulation and locomotion capabilities: the inevitable inaccuracies in perception as well as actuation that occur on physical systems, and the exponential size of the search space. To address these challenges, this thesis first extends the previously introduced domain of Navigation Among Movable Obstacles (NAMO), which allows a robot to move obstacles out of its way. We extend the NAMO domain to handle the underlying issue of uncertainty. In fact, this thesis introduces the first NAMO framework that allows a real robotic systems to consider sensing and action uncertainties while reasoning about moving objects out of the way. However, the NAMO domain itself has the shortcoming that it only considers a robot's manipulation capabilities in the context of clearing a path. This thesis therefore also generalizes the NAMO domain itself to the Navigation Using Manipulable Obstacles (NUMO) domain. The NUMO domain enables a robot to more generally consider the coupling between manipulation and locomotion capabilities and supports reasoning about using objects in the environment. This thesis shows the relationship between the NAMO and NUMO domain, both in terms of complexity as well as solution approaches, and presents multiple realizations of the NUMO domain. The first NUMO realization enables a robot to use its manipulation capabilities to assist its locomotion by changing the geometry of the environment for scenarios in which obstructions can be overcome through the usage of a single object. The system led a real humanoid robot to autonomously build itself a bridge to cross a gap and a stair step to get on a platform. A second NUMO realization then introduces reasoning about force constraints using knowledge about the mechanical advantages of a lever and battering ram. The discussed system allows a robot to consider increasing its effective force though the use of objects, such as utilizing a rod as a lever. Finally this thesis extends the NUMO framework for geometric constraints to scenarios in which the robot is faced with a substantial lack of initial state information and only has access to onboard sensing. In summary, this thesis enables robots to autonomously modify their environment to achieve task completion in the presence of lack of support for mobility, the need to increase force capabilities and partial knowledge.