Abstract

Methane and CO2 emissions from the natural gas supply chain have been shown to vary widely but there is little understanding about the distribution of emissions across supply chain routes, processes, regions and operational practises. This study defines the distribution of total methane and CO2 emissions from the natural gas supply chain, identifying the contribution from each stage and quantifying the effect of key parameters on emissions. The study uses recent high-resolution emissions measurements with estimates of parameter distributions to build a probabilistic emissions model for a variety of technological supply chain scenarios. The distribution of emissions resembles a log-log-logistic distribution for most supply chain scenarios, indicating an extremely heavy tailed skew: median estimates which represent typical facilities are modest at 18–24 g CO2 eq./MJ HHV, but mean estimates which account for the heavy tail are 22–107 g CO2 eq./MJ HHV. To place these values into context, emissions associated with natural gas combustion (e.g. for heat) are approximately 55 g CO2/MJ HHV. Thus, some supply chain scenarios are major contributors to total greenhouse gas emissions from natural gas. For methane-only emissions, median estimates are 0.8–2.2% of total methane production, with mean emissions of 1.6–5.5%. The heavy tail distribution is the signature of the disproportionately large emitting equipment known as super-emitters, which appear at all stages of the supply chain. The study analyses the impact of different technological options and identifies a set of best technological option (BTO) scenarios. This suggests that emissions-minimising technology can reduce supply chain emissions significantly, with this study estimating median emissions of 0.9% of production. However, even with the emissions-minimising technologies, evidence suggests that the influence of the super-emitters remains. Therefore, emissions-minimising technology is only part of the solution: reducing the impact of super emitters requires more effective detection and rectification, as well as pre-emptive maintenance processes.

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