Graduate Thesis Or Dissertation


Signatures in the Subsurface - Big & Small Data Approaches for the Spatio-Temporal Analysis of Geologic Properties & Uncertainty Reduction Public Deposited

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  • Despite more than two centuries of exploration, including more than six million deep wellbores with depths exceeding 40,000 feet in some parts of the world, our ability to constrain subsurface processes and properties remains limited. Characteristics of the subsurface vary and can be analyzed on a variety of spatial scales. Characterization and prediction of subsurface properties, such as depth, thickness, porosity, permeability, pressure and temperature, are important for models and interpretations of the subsurface. Subsurface studies contribute to insights and understanding of natural system but also enable predictions and assessments of subsurface resources (water, heat, hydrocarbon, mineral, storage capacity) and support environmental and geohazard assessments. However, the availability of data to characterize these systems as well as the techniques that utilize those data vary significantly. There is a wealth of data and information in structured and unstructured datasets stemming from subsurface characterization and interpretation studies. In addition, the geo-data science landscape is shifting, becoming more open. This affords opportunities to fill knowledge gaps, mine large, interrelated datasets, and develop innovative methods to improve our understanding of the subsurface and the impacts of its exploration. This study demonstrates different approaches, at a range of scales, for evaluating subsurface properties using a combination of “small” and “big” data approaches. In particular, focusing on wellbore data which can be used to investigate questions at the individual well or the global scale. Wellbore related datasets, such as those associated with India’s NGHP-01 Site 17A, are the primary source of direct subsurface measurements. In this work, small-scale analyses from a single wellbore were used to establish that diagenetic mineralization is responsible for anomalous porosity preservation and enhanced permeability in sediments from NGHP-01 Site 17A. This relationship explains and further constrains how geologic history and architecture influences gas hydrate distribution both within the lithostratigraphic record at NGHP-01 Site 17A and in other sedimentary settings worldwide. In addition, collections of wellbore data are increasingly used in spatial statistical analyses to improve prediction of subsurface properties at the field to basin-scale. These analyses typically have disregarded contextual geologic information because of its complex and unstructured format. This results in the loss of valuable information. This study presents a structured, hybrid deductive-probabilistic approach that integrates both contextual geologic information with quantitative analytical tools to improve prediction of subsurface properties and reduce uncertainty. The Subsurface Trend Analysis approach is demonstrated and validated in the prediction of subsurface pressure for the north-central region of the Gulf of Mexico. Finally, this study assembles and presents together information for the global catalog of deep subsurface wells. This global dataset spans over two centuries of drilling and includes more than six million wellbore records. Spatial and temporal analyses performed using this dataset provide insights into the implications of human engineering of the subsurface worldwide. Collectively, these data were used to assess to what degree the subsurface has been perturbed by drilling related activities, and investigated how human changes to deep subsurface systems contrast with the effects of other species and processes on the planet.
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