On the Long-Term Temporal Variations in Methane Emissions from an Unconventional Natural Gas Well Site

Understanding methane emissions from the natural gas supply chain continues to be of interest. Previous studies identified that measurements are skewed due to super-emitters, and recently, researchers identified temporal variability as another contributor to discrepancies among studies. We focused on the latter by performing 17 methane audits at a single production site over 4 years, from 2016 to 2020. Source detection was similar to Method 21 but augmented with accurate methane mass rate quantification. Audit results varied from similar to 78 g/h to over 43 kg/h with a mean emissions rate of 4.2 kg/h and a geometric mean of 821 g/h. Such high variability sheds light that even quarterly measurement programs will likely yield highly variable results. Total emissions were typically dominated by those from the produced water storage tank. Of 213 sources quantified, a single tank measurement represented 60% of the cumulative emission rate. Measurements were separated into four categories: wellheads (n = 78), tank (n = 17), enclosed gas process units (n = 31), and others (n = 97). Each subgroup of measurements was skewed and fat-tailed, with the skewness ranging from 2.4 to 5.7 and kurtosis values ranging from 6.5 to 33.7. Analyses found no significant correlations between methane emissions and temperature, whole gas production, or water production. Since measurement results were highly variable and daily production values were known, we completed a Monte Carlo analysis to estimate average throughput-normalized methane emissions which yielded an estimate of 0.093 +/- 0.013%.

Automated summary

Understanding methane emissions from the natural gas supply chain remains a topic of interest. A study focused on temporal variability by conducting 17 methane audits at a single production site, finding high variability in emissions and a significant contribution from produced water storage tanks. Measurements were categorized into different groups, each displaying skewness and fat-tailed distributions.