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Abstract This study seeks to improve the quantification of greenhouse gas (GHG) emissions from unconventional natural gas production in the Appalachian Basin, covering Carbon Dioxide (CO2), Methane (CH4), and Nitrous Oxide (N2O). The goal is to automate the calculation process for compliance with the US Environmental Protection Agency (EPA), corporate sustainability reporting, and internal monitoring for emissions reduction targets. Traditionally, GHG emissions are manually computed once a year and are often calculated by third-party consultants, reducing transparency in calculation approaches. The development of an on-demand GHG emissions inventory enables real-time identification of emission-intensive processes, guiding strategic decisions for emissions mitigation and methane intensity reductions. In this project, we employed Python and SQL scripts to map activity data related to process and fugitive emissions, including pneumatic devices, drilling and completions combustion, equipment leaks, liquids unloading, dehydrators, and flaring, into US EPA Subpart W formulas. The result is an annualized carbon footprint calculated at the well pad level, providing comprehensive insights into component gasses (CO2, CH4, and N2O), production-based carbon and methane intensities, and emissions contributions by emission source. Automation improved the efficiency and accuracy of the emissions calculation workflow, generating an updated carbon footprint in a runtime under 5 minutes. Additionally, the inclusion of emissions-generating events overlooked due to human error substantially improved the quality of the emissions footprint. Lastly, the employment of data analytics on all calculation inputs and outputs aided in identifying outliers that required modification. The GHG calculation tool enhances accuracy, transparency, and consistency while minimizing the time invested in GHG estimations, allowing teams to redirect their efforts toward value-added activities. Next steps in the project include detailed emissions forecasting, which facilitates determining the most cost-effective emissions reduction strategy. The mapping of activity data into GHG estimation models was completed within six weeks, with additional time spent in result validation against previous manual calculations. After an initial configuration, data mappings do not require ongoing updates, establishing a fully automated GHG calculation process. This advancement enables efficiency and reliability in monitoring and reporting GHG emissions. Additionally, the development of a granular carbon footprint on an emission source basis enables detailed, source-level emissions forecasting to support future decisions around emission reduction projects.
Calad et al. (Mon,) studied this question.