OU - Dissertations

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Browsing OU - Dissertations by Subject "3D modeling"

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    Integrated characterization of tight siliciclastic reservoirs: examples from the Cretaceous Burro Canyon Formation, Colorado, and Mississippian Meramec Strata, Oklahoma
    (2021-05-14) Tellez, Jerson; Pranter, Matthew; Bedle, Heather; Devegowda, Deepak; Pigott, John; Cole, Rex
    Integration of multiscale data sources for reservoir characterization becomes problematic and challenging due to the collected information variable resolution. Core and well data provide high vertical resolution to evaluate stratigraphic variability but fails to assess lateral variability and connectivity. In contrast, outcrop exposures and seismic data are used to extract dimensional statistical measurements and three-dimensional spatial distribution of reservoirs, respectively. It is time-consuming to characterize large outcrops, and rock exposures are often discontinuous. Seismic data offers great horizontal coverage with a low resolution, making it hard to evaluate small lateral variability and spatial distribution of reservoirs. This research explores workflows using new emerging techniques and methods for reservoir characterization such as petrophysics, seismic interpretation, and stratigraphic analysis to effectively address the intrinsic uncertainty in predicting large- and small-scale heterogeneity caused by lateral and vertical facies changes and their petrophysical properties. This detailed characterization reduces the risk associated with exploring and developing potential reservoirs for fluid storage or hydrocarbon production. The applied techniques detailed in this study are used to collect and integrate multiple data sources for reservoir characterization of unconventional reservoirs, mainly fluvial tight sandstones and mixed siliciclastic-calcareous deep marine platform deposits. I present three case studies using workflows that include new techniques for reservoir characterization at different scales. The research starts using UAS (drones) for three-dimensional outcrop reconstruction models for 50 miles of rock exposure combined with fieldwork to define the sequence stratigraphy and architecture of a fluvial reservoir. Then, I present a workflow to integrate information that includes thin-sections, core description, petrophysical data, well logs, and seismic data through machine learning techniques to define the sequence stratigraphic variability and structural configuration of a mixed siliciclastic system. The last case study shows a workflow to predict, map, and analyze the mechanical stratigraphy of the Meramecian STACK play in Oklahoma and its impact on hydrocarbon production. The illustrated workflows allowed collecting and integrating data from diverse sources to build robust geological models and better constrain reservoir models to reduce uncertainty in reservoir prediction and volumetric estimation.
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    ItemOpen Access
    Real-time 3-D Scene Reconstruction
    (2016-05) Petrich, Erik; Sluss, James Jr; Tull, Monte; Runolfsson, Thordur; Havlicek, Joseph; Özaydin, Murad
    This dissertation describes a complete system that captures image data from multiple stereoscopic camera pairs and reconstructs a 3-D model of the imaged scene in real-time. To achieve real-time rates, the system is organized in a distributed hierarchical fashion to maximize parallelism and uses algorithms that, in many instances, are suitable for direct implementation in digital hardware rather than software on a general purpose computer. At the lowest level of the hierarchy, image data is acquired from a single camera and processed to compensate for lens distortion and to apply rectification in preparation for stereo image processing. At the next level, data from pairs of cameras is matched to compute a dense stereoscopic disparity map from which 3-D surfaces are inferred and a mesh model is constructed. Finally, at the top level all of the individual 3-D mesh models are merged into a single 3-D model. If desired, the camera image data can be applied to the resultant 3-D model as a texture and the model re-rendered from a virtual camera viewpoint. Previous 3-D research focuses on individual steps in this process (lens distortion correction, image rectification, stereoscopic disparity computation, and model building). This dissertation considers them instead in the context of a complete end-to-end system. Traditional approaches to model building begin with an unstructured "point cloud" that is neutral with respect to how the data was acquired; this allows model building to be studied independent of data acquisition but may miss some opportunities available in a more tightly coupled interface. By taking a broader view of the problems faced by the entire system, a novel algorithm for 3-D model building has been developed that takes advantage of the organization in the dense stereoscopic disparity map to efficiently build its model. The core of this novel algorithm is a method of evaluating linear regression error to fit a series of line segments to data points in a way that can be efficiently implemented directly in hardware.