Intermittency of Large Methane Emitters in the Permian Basin

Abstract

The Permian Basin is the largest and fastest growing oil and gas (O&G) producing region in the United States. We conducted an extensive airborne campaign across the majority of the Permian in September−November, 2019 with imaging spectrometers to quantify strong methane (CH 4 ) point source emissions at facility-scales, including high frequency sampling to evaluate intermittency. We identified 1100 unique and heavy-tailed distributed sources that were sampled at least 3 times (average 8 times), showing 26% average persistence. Sources that were routinely persistent (50−100%) make up only 11% of high emitting infrastructure but 29% of quantified emissions from this population, potentially indicative of leaking equipment that merits repair. Sector attribution of plumes shows that 50% of detected emissions result from O&G production, 38% from gathering and boosting, and 12% from processing. This suggests a 20% relative shift from upstream to midstream compared to other US O&G basins for large emitters. Simultaneous spectroscopic identification of flares found that 12% of detected Permian CH 4 plume emissions were associated with either active or inactive flares. Frequent, high-resolution monitoring is necessary to accurately understand intermittent methane superemitters across large, heterogeneous O&G basins and efficiently pinpoint persistent leaks for mitigation.

Methane Super-Emitters in California’s Oil Fields

“…The non-persistent emitters (or highly intermittent sources 37 ) aside from the equipment category were significant contributors to CH4 emissions (77% of 255 sources). Some super-emitters could be due to abnormal processes that could be repeated but not necessarily from the same equipment or location.…”

Mapping methane plumes at very high spatial resolution with the WorldView-3 satellite

“…The Algeria site covers a largely homogeneous area similarly to Turkmenistan although it contains some cirrus/cloud shadows at the right half of the selected subset that alter the radiance in SWIR B7. 235 Finally, it is important to remark that the sites selected for the simulations are realistic insofar as they are well-known areas of O&G and coal production facilities with known methane emissions (Irakulis-Loitxate et al, 2021a;2021b;Varon et al, 2021;Cusworth et al, 2021).…”

Remote sensing of methane plumes: instrument tradeoff analysis for detecting and quantifying local sources at global scale

“…For instance, just the question whether or not the leak rate in the natural gas extraction system is 1 or 2% is equivalent to a 100% uncertainty in methane emissions. At the same time, leak rate outliers (Frankenberg et al, 2016;Duren et al, 2019;Cusworth et al, 2021) are often local in nature and easily fixable, representing a win-win scenario if faulty equipment or practices can be readily detected and then efficiently mitigated. As CH 4 reduction plays a significant role in climate mitigation efforts, one key step in emission reduction is determining where these emissions are coming from.…”

“…While individual lines are hard to resolve, the strong methane band in this range causes enough fine-structure in terms of bulk absorptions by a multitude of methane lines within the instrument resolution. Previous studies by Frankenberg et al (2016), Duren et al (2019) and Cusworth et al (2021) have utilized AVIRIS-NG to conduct field campaigns in California and the Four-Corner region where they could create a map of methane enhancements in the area and detected several hundreds of individual methane sources, which followed a heavy-tail flux distribution. The concept of this airborne spectrometer provides a signal of opportunity for local source detection and quantification.…”

“…Even though most strong plumes can be observed, the uncertainties in the overall detection and quantification at the regional level can present persistent problems and often involve human judgement to isolate plumes from artefacts. In fact, during each of the California survey, the Four-corner study, and the Permian survey (Duren et al (2019), Frankenberg et al (2016), Cusworth et al (2021)), human analysts were involved in a manual process to look through each flight line to classify true emission sources from false positives. Similarly, in previous space-based studies to locate and approximate a large emission such as a blowout event, prior information about the location of the source is usually already known, making it much easier to find a true methane source from space-based measurements over the area after the fact.…”