An event-based framework for estimating, tracking, and managing annual methane emissions from upstream oil and gas sites

Mozhou Gao, Zahra Ashena, Steve H.L. Liang, Sina Kiaei, Saeedi Sara

Accurate reporting of annual site-level methane emissions is increasingly required under emerging regulatory and voluntary frameworks in the oil and gas (O&G) sector. In this study, we present an event-based framework for estimating and tracking annual methane emissions from upstream O&G operations. The framework applies the Emission Event Data Model (EEDM) to spatiotemporally group multi-scale emissions data into discrete events using the concept of Allen’s interval algebra and spatial proximity. Following event creation, emissions are categorized into three groups—resolved (known emission rate and duration), partially resolved (known emission rate but unknown duration), and unresolved (unknown emission rate and duration)—to facilitate different management and emissions estimation approaches. Three Monte Carlo-based approaches are developed under the framework. They include (1) estimating durations for partially resolved events using null detection, leak generation, and natural repair processes; (2) estimating emissions from unresolved events based on the minimum detection limit of deployed technologies; and (3) estimating emissions from unresolved events using probabilistic occurrence and best-fit distributions. The methodology enables emissions to be reported and verified at the group level rather than individual observation. To demonstrate estimating emissions using this framework, we created two scenarios and performed emissions estimation using synthetic emission observations based on real emissions data for an upstream O&G site. The proposed framework can be implemented in voluntary initiatives such as Veritas 2.0 and the Oil & Gas Methane Partnership (OGMP) 2.0 and applied as a data management framework for the Measurement, Monitoring, Reporting and Verification (MMRV) framework.