National Energy Technology Laboratory , 626 Cochrans Mill Road , P.O. Box 10940, Pittsburgh , Pennsylvania 15236 , United States.
NOAA Earth System Research Laboratory , 325 Broadway , Boulder , Colorado 80305 , United States.
Environ Sci Technol. 2019 Apr 16;53(8):4619-4629. doi: 10.1021/acs.est.8b05546. Epub 2019 Mar 29.
A "bottom-up" probabilistic model was developed using engineering first-principles to quantify annualized throughput normalized methane emissions (TNME) from natural gas liquid unloading activities for 18 basins in the United States in 2016. For each basin, six discrete liquid-unloading scenarios are considered, consisting of combinations of well types (conventional and unconventional) and liquid-unloading systems (nonplunger, manual plunger lift, and automatic plunger lift). Analysis reveals that methane emissions from liquids unloading are highly variable, with mean TNMEs ranging from 0.0093% to 0.38% across basins. Automatic plunger-lift systems are found to have significantly higher per-well methane emissions rates relative to manual plunger-lift or non-plunger systems and on average constitute 28% of annual methane emissions from liquids unloading over all basins despite representing only ∼0.43% of total natural gas well count. While previous work has advocated that operational malfunctions and abnormal process conditions explain the existence of super-emitters in the natural gas supply chain, this work finds that super-emitters can arise naturally due to variability in underlying component processes. Additionally, average cumulative methane emissions from liquids unloading, attributed to the natural gas supply chain, across all basins are ∼4.8 times higher than those inferred from the 2016 Greenhouse Gas Reporting Program (GHGRP). Our new model highlights the importance of technological disaggregation, uncertainty quantification, and regionalization in estimating episodic methane emissions from liquids unloading. These insights can help reconcile discrepancies between "top-down" (regional or atmospheric studies) and "bottom-up" (component or facility-level) studies.
采用基于工程第一原理的“自下而上”概率模型，量化了 2016 年美国 18 个盆地天然气液体卸载活动的归一化甲烷年排放量（TNME）。对于每个盆地，考虑了六种离散的液体卸载情景，由井型（常规和非常规）和液体卸载系统（非柱塞、手动柱塞提升和自动柱塞提升）的组合组成。分析表明，液体卸载产生的甲烷排放量变化很大，平均 TNME 范围为 0.0093%至 0.38%，具体取决于盆地。与手动柱塞提升或非柱塞系统相比，自动柱塞提升系统每口井的甲烷排放量明显更高，平均占所有盆地液体卸载年甲烷排放量的 28%，尽管仅占总天然气井数的 0.43%左右。虽然之前的工作主张运营故障和异常工艺条件解释了天然气供应链中超级排放源的存在，但这项工作发现，由于基础组件工艺的变化，超级排放源可能自然存在。此外，所有盆地液体卸载归因于天然气供应链的累积甲烷排放量平均比 2016 年温室气体报告计划（GHGRP）推断的高出约 4.8 倍。我们的新模型强调了在估算液体卸载间歇性甲烷排放时进行技术分解、不确定性量化和区域化的重要性。这些见解可以帮助调和“自上而下”（区域或大气研究）和“自下而上”（组件或设施层面）研究之间的差异。
