{"id":{"repo_id":"colo-mines","oai_identifier":"oai:repository.mines.edu:11124/180346"},"canonical_url":"https://search.dev.ndltd.org/etd/colo-mines/oai:repository.mines.edu:11124/180346","repository":{"repo_id":"colo-mines","name":"Colorado School of Mines","base_url":"https://repository.mines.edu/server/oai/request"},"display":{"title":"Characterizing methane emissions on oil and gas sites","abstract":"Reducing methane emissions from the oil and gas sector is a key component of short-term climate action, and emission reduction efforts in this sector are increasingly driven by measurements at the individual facility scale. This thesis introduces a number of inversion techniques that use methane concentration measurements from point sensor networks to infer emission characteristics on oil and gas sites. We first introduce an inversion method for inferring single-source emission characteristics, such as emission start and end time, source location, and emission rate. We then discuss two applications of this method. The first reconciles top-down measurements from an aerial survey-based technology with bottom-up inventories that are designed to represent long-term emission volumes. The second compares different point sensor networks and isolates differences in the sensor platform (i.e., sensor type and arrangement) from differences in the inversion algorithm. We next introduce a method for more robustly inferring methane emission durations by addressing a key limitation of point sensor networks: \"nondetect times,\" the times when emitted methane is not blown towards any of the sensors in the network. Finally, we generalize our initial single-source inversion method to a fully Bayesian hierarchical model for inferring multi-source emission characteristics. This model makes it possible to use point sensor networks for accurate emission localization and quantification on complex sites where it is common for multiple sources to emit simultaneously. Ultimately, through the methodology presented in this thesis, we provide the tools needed to rapidly detect and quantify methane emissions from the oil and gas sector, a critical step towards climate change mitigation through targeted emissions reductions.","abstract_html":"Reducing methane emissions from the oil and gas sector is a key component of short-term climate action, and emission reduction efforts in this sector are increasingly driven by measurements at the individual facility scale. This thesis introduces a number of inversion techniques that use methane concentration measurements from point sensor networks to infer emission characteristics on oil and gas sites. We first introduce an inversion method for inferring single-source emission characteristics, such as emission start and end time, source location, and emission rate. We then discuss two applications of this method. The first reconciles top-down measurements from an aerial survey-based technology with bottom-up inventories that are designed to represent long-term emission volumes. The second compares different point sensor networks and isolates differences in the sensor platform (i.e., sensor type and arrangement) from differences in the inversion algorithm. We next introduce a method for more robustly inferring methane emission durations by addressing a key limitation of point sensor networks: "nondetect times," the times when emitted methane is not blown towards any of the sensors in the network. Finally, we generalize our initial single-source inversion method to a fully Bayesian hierarchical model for inferring multi-source emission characteristics. This model makes it possible to use point sensor networks for accurate emission localization and quantification on complex sites where it is common for multiple sources to emit simultaneously. 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