Measurement-Informed Methane Emission Inventories Must Account for the Impact of Repair Interventions

Accurate methane emissions estimation from the oil and gas sector has become a cornerstone of regulatory actions and voluntary initiatives. Methane emissions in the oil and gas sector follow a heavy-tailed distribution, where underestimating the frequency of short-duration large emitters leads to inaccurate emission inventories. However, even high-frequency surveys may miss short-lived, high-rate emission events. Furthermore, operator-led surveys are routinely followed by repairs of addressable emissions, affecting inventory estimates and introducing uncertainty in measurement-informed inventories (MII). In this study, we developed a stochastic uncertainty estimation model to quantify uncertainties in MII resulting from variation in emitter duration, survey frequency, sample size, technology characteristics, and the effect of repair interventions. We find that quantification accuracy is mainly driven by survey frequency, while precision is driven by sample size. Thus, there exists an optimal combination of survey frequency and sample size that balances operational costs with developing accurate MII. For applications that require an unbiased estimate of emissions to conduct reconciliation (e.g., OGMP 2.0), a higher survey frequency is preferable to larger sample sizes. We conclude with key recommendations for planning measurement campaigns to comply with requirements in regulatory programs and voluntary initiatives.