Browsing by Subject "FMM"
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Item Exploration of voltage controlled manganite phase transitions as probed with magnetic force microscopy(2010-05) Ruzicka, Frank Joseph; de Lozanne, Alejandro L.; Tsoi, Maxim; Shih, Chih-Kang; Markert, John T.; Shi, LiLow-temperature magnetic force microscopy was used to study the phase diagram of a La1/3Pr1/3Ca1/3MnO3 thin film grown on a (110) NdGaO3 (NGO) substrate by pulsed laser deposition. Traditionally, one can observe the phase change at the nanoscale level as the sample is cooled from room temperature through the transition temperature to liquid nitrogen temperatures, but in this case a fixed voltage ranging from 0 V to 31 V was applied before each cooling cycle. From in and ex situ transport measurements, it is observed that the temperature of the peak of the transition increases with applied field; however, the MFM images show that the magnetic transition begins at a lower temperature with the same increase in field. Thus, this dissertation shows that a new voltage control exists for the phase transition in certain manganites.Item Exploring an Unstructured Lattice Representation for Carbonate Reservoir Characterization(2014-05-19) Pasumarti, LakshmiCarbonates for flow simulation purposes are typically characterized as grid-blocks of varying permeability, with a finer grid employed where heterogeneity is greatest. However, this manner of representation is more suited to sandstone reservoirs, as transport in carbonate reservoirs is usually far more un-geometric due to the complex types of carbonate rock pore-spaces. Far from simply flow between inter-granular pore-spaces, diagenetic processes produce carbonate reservoirs with permeability heterogeneity mainly within three distinct but yet interacting geologic features - Matrix, Vugs and Fractures ? very often with each feature occurring at various length scales. This project will explore the merits of an unstructured means of representing carbonates via a lattice-network of pore-volumes connected in space in directions and connectivity properties driven by the rock fabric, as opposed to being limited by the rigid geometry of grid-blocks. With this goal in mind, some aspects related to a lattice-based characterization will be studied. Firstly, the geologic context that motivates a non-grid based approach to carbonate reservoir modeling will be discussed in the literature review. Secondly, convective and diffusive calculations on a grid will be compared to their equivalents on a lattice in order to establish the applicability of the lattice-system. Convective time-of-flight on a grid is calculated using Pollock?s method, while an approximation using the average pore-volumes between nodes will be employed on the lattice. Diffusive time-of-flight on a grid is populated using the Fast Marching Method (FMM), whereas Dijkstra?s Algorithm is more appropriate for a lattice. Thirdly, ?-CT-scan data of a rock sample from an outcrop will be used to build an equivalent unstructured lattice-representation of the media at that length scale, and explored for convective and diffusive flow properties. This will be performed by using the AVIZO Suite to first binarize the ?-CT data into pore space and non-pore space, and then skeletonizing it to convert the pore-space into an unstructured set of nodes ? carrying volumes ? and bonds ? each carrying a mean length and radius. These properties will then be used to calculate the transmissibility and diffusive time-of-flight across each bond. Once these are known, convective and diffusive floods can be initiated and the appropriate responses studied to learn about the rock properties. This project is envisaged also as laying the groundwork for a long term goal of unstructured lattice-based carbonate reservoir characterization.Item Fast Marching Methods: Application via Integration with Commercial E&P Software(2014-10-16) Al-Rukabi, MuhammedDevelopment and production of unconventional reservoirs, especially shale, are on the rise and so is the need to better understand drainage volumes, reliably estimate reservoir properties, and forecast well performance. Numerical simulation and analytical techniques, like decline curve analysis and pressure transient analysis, have been applied to unconventional resources. However, analytical methods rely on several simplifications and while numerical simulation can account for complex geological models it is computationally expensive. Fast Marching Methods (FMM), being a semi-analytical calculation, is between the two approaches and retains the simplicity of the analytical approach while achieving the desired generality. The generalization of the concept of depth of investigation to heterogeneous reservoirs utilizes the idea of diffusive time-of-flight and better accounts for the non-uniform pressure fronts that may be distorted due to heterogeneity effects. The pressure front propagation is obtained by solving the Eikonal equation, which is derived from an asymptotic solution of the diffusivity equation. The FMM solves the Eikonal equation very efficiently using a single non-iterative solution, making it very fast. The FMM estimates the drainage volume and the diffusive time of flight can be used as a spatial coordinate to reduce the 3D diffusivity equation into a 1D equation allowing for rapid forecasting of well pressure and rate performance. In this work, the FMM is implemented into an application plug-in and is integrated with a common commercial E&P software platform. The integration of the FMM Plug-in capitalizes on the simplicity, intuitive appeal, power and utility of the approach, like providing the time-evolution of the drainage volume for visualization, and utilizes the software platform features, like state-of-the-art visualization tools. This work also includes a number of applications that demonstrate the capability of FMM Plug-in to calculate the drainage volume and forecast well pressure or rate performance and validate its results against an industry-reference finite difference simulator. Finally, a study on the scalability of calculations runtime demonstrate the speed advantage that FMM has over finite difference simulators.