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Spatio-Temporal Analysis to Constrain Uncertainty in Wellbore Datasets: An Adaptable Analytical Approach in Support of Science-Based Decision Making

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As the use of subsurface reservoirs for energy extraction, wastewater disposal, and geologic storage of CO2 grows, so do concerns about the potential for unwanted interactions between subsurface and surface receptors. Wellbores are engineered fluid flow pathways designed to facilitate the production and injection of fluids in a controlled manner. Because of the coupling between wellbore age and integrity, regions with prior drilling histories may be particularly susceptible to unwanted fluid migration if disturbed by contemporary subsurface activities. Until recently, wellbore drilling records were prone to a range of error sources. In addition, record coverage and quality varies based on local, state, tribal, and federal jurisdictions. As a result, there is a significant amount of spatial and temporal variation in reporting standards, and consequent data content, preservation, and access to information. The recent development of subsurface reservoirs has accelerated the need for a standardized methodology to characterize and capture uncertainty in these records. This study develops and demonstrates a flexible approach for the meso-scale collection, analysis, and communication of uncertainty in wellbore datasets. It is shown that incorporating publicly available data from a variety of digital and non-digital sources can inform characterization and uncertainty of the locations of wellbores.
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Keywords: BIG DATA; SPATIO-TEMPORAL UNCERTAINTY ANALYSIS; WELLBORE INTEGRITY

Document Type: Research Article

Publication date: 2016-06-01

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