Methane emissions from US low production oil and natural gas well sites

Mitigating methane (CH₄) emissions is crucial for achieving climate neutrality goals. In the US, O&G operations contribute nearly 30% of anthropogenic CH₄ emissions, with measurement-based studies consistently revealing higher emissions than EPA inventories. This discrepancy is largely attributed to the O&G production sector, where fugitive emissions from well site equipment and unintentional emissions from storage tanks are significant. The US O&G sector is highly diverse, with over 800,000 active onshore wells in 2019. Methane emissions from production well sites stem from common (e.g., leaking valves) and non-routine sources. While weak correlations exist between CH₄ emissions and production rates, site-level CH₄ emissions follow heavy-tailed distributions, with a small fraction of sites contributing disproportionately to total emissions. This study focuses on low production well sites (≤15 barrels of oil equivalent per day), analyzing their contribution to national CH₄ emissions relative to their O&G production share. The skewed distribution of production and the heavy-tailed nature of emissions warrant a focused assessment of the overall contribution of low-production sites to methane emissions.

Prior research highlights inconsistencies between reported O&G methane emissions and actual measurements. Studies consistently show higher emissions than EPA estimates, primarily due to fugitive emissions from well sites and storage tanks. Previous work has also documented the skewed nature of methane emissions, with a small percentage of sites responsible for a large portion of total emissions. However, the specific contribution of low-production well sites to this overall emission profile has not been thoroughly examined.

The study utilizes well-level and production data from Enverus Prism, a commercial platform aggregating public and proprietary data, to analyze production, age, and operator profiles of low-production well sites. The analysis defines low production sites as having a combined O&G production rate ≤15 barrels of oil equivalent per day (boed). The data are classified into four cohorts based on production rates: >0-2, 2-5.4, 5.4-9.7, and 9.7-15 boed. A subset of these, ultralow production sites (≤2 boed), are also analyzed separately. Regional variations in production characteristics and operator profiles are examined. Previously published data on site-level CH₄ emissions from various US O&G basins (n=240) are compiled and analyzed. Emission rates are assessed based on absolute CH₄ emission rates (kg CH₄/h) and production-normalized CH₄ loss rates (CH₄ emitted relative to CH₄ production). A hybrid nonparametric Bayesian regression and Monte Carlo model is used to estimate national-level CH₄ emissions from low production sites. The model considers high-emitting sites (top 5%), the bottom 95% of sites with detectable emissions, and below-detection-limit sites separately. The uncertainty assessment is performed through random bootstrap resampling and generating 500 simulated distributions for uncertainty analysis. The study also uses a supplementary lognormal statistical model to validate its findings.

In 2019, 565,000 low production well sites (81% of total US sites) accounted for only 5.6% of total US O&G production. However, these sites emitted an estimated 4 Tg (95% CI: 3–6 Tg) of CH₄, representing 54% (95% CI: 37–75%) of total O&G well site CH₄ emissions, which surpasses the total CH₄ emissions from the Permian Basin. The production-normalized CH₄ loss rate for low production sites was estimated to be 13% (95% CI: 8-17%), six to twelve times higher than the mean for all O&G well sites (1.5%). Approximately 50% (95% CI: 20-80%) of low production well site CH₄ emissions originate from the top 5% of highest-emitting sites. The ultralow production cohort (≤2 boed) accounts for 25% (95% CI: 17-49%) of total low production site CH₄ emissions but only 0.7% of US O&G production. The Appalachian region, with a high proportion (90%) of ultralow production sites, was the largest contributor to regional CH₄ emissions (1.2 Tg, 95% CI: 0.8-1.9 Tg), with ultralow sites representing approximately half of the region's total low production CH₄ emissions. A supplementary lognormal model yielded similar results.

The findings underscore the significant contribution of low production well sites to national CH₄ emissions, highlighting a substantial gap between their production output and their emission intensity. The high production-normalized CH₄ loss rates indicate the presence of significant avoidable emissions from these sites. The heavy-tailed distribution of emissions emphasizes the disproportionate impact of a small percentage of high-emitting sites. The results challenge the notion that focusing solely on large-production sites is sufficient for effective CH₄ mitigation. Targeting low-production sites, particularly those in the Appalachian region, is crucial for substantial emission reductions.

Low production oil and gas wells, while comprising the vast majority of US wells, emit a disproportionately high amount of methane relative to their production. Their contribution to national methane emissions is substantial, requiring targeted mitigation strategies. Future research should focus on refining emission estimates for various well site subtypes, investigating the specific sources of emissions at low-production sites, and developing cost-effective mitigation techniques tailored to these sites.

The study's reliance on a compiled dataset of previously published site-level CH₄ emission measurements presents limitations. The sample size, while statistically robust for national-scale estimations, may not fully capture the variability in emissions across all low-production sites. The model's assumptions about the representativeness of the measured sites and the distribution of emissions within the low-production site category could also affect the accuracy of the estimations. The use of proprietary data from Enverus for O&G production also limits the transparency of the analysis.