Preprints
https://doi.org/10.5194/egusphere-2022-771
https://doi.org/10.5194/egusphere-2022-771
 
08 Sep 2022
08 Sep 2022
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Detecting and quantifying methane emissions from oil and gas production: algorithm development with ground-truth calibration based on Sentinel-2 satellite imagery

Zhan Zhang1, Evan D. Sherwin1, Daniel J. Varon2,3, and Adam R. Brandt1 Zhan Zhang et al.
  • 1Department of Energy Resources Engineering, Stanford University, Stanford, California 94305, United States
  • 2School of Engineering and Applied Science, Harvard University, Cambridge, 02138, United States
  • 3GHGSat, Inc., Montréal, H2W 1Y5, Canada

Abstract. Sentinel-2 satellite imagery has been shown by studies to be capable of detecting and quantifying methane emis- sions from oil and gas production. However, current methods lack performance validation by calibration with ground-truth testing. This study developed a multi-band-multi-pass-multi-comparison methane retrieval algorithm that enhances Sentinel-2 sensitivity to methane plumes. The method was calibrated using data from a large-scale controlled release test in Ehrenberg, Arizona in fall 2021, with three algorithm parameters tuned based on the true emission rates. Tuned parameters are the pixel- level concentration upper bound threshold during extreme value removal, the number of comparison dates, and the pixel-level methane concentration percentage threshold when determining the spatial extent of a plume. We found that a low value of the upper bound threshold during extreme value removal can result in false negatives. A high number of comparison dates helps enhance the algorithm sensitivity to the plumes in the target date, but values in excess of 12 days are neither necessary nor computationally efficient. A high percentage threshold when determining the spatial extent of a plume helps enhance the quan- tification accuracy, but it may harm the yes/no detection accuracy. We found that there is a trade-off between quantification accuracy and detection accuracy. In a scenario with the highest quantification accuracy, we achieved the lowest quantification error and had zero false positive detections; however, the algorithm missed 3 true plumes which reduced the yes/no detection accuracy. On the contrary, all the true plumes were detected in the highest detection accuracy scenario, but the emission rate quantification had higher errors. We also illustrated a two-step method that updates the emission rate estimates in an interim step which improves quantification accuracy while keeping high yes/no detection accuracy. We also validated the algorithm’s ability to avoid false positives by applying it to a nearby region with no emissions.

Zhan Zhang et al.

Status: open (until 13 Oct 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-771', Anonymous Referee #1, 19 Sep 2022 reply
    • AC2: 'Reply on RC1', Zhan Zhang, 28 Sep 2022 reply
  • AC1: 'Comment on egusphere-2022-771', Zhan Zhang, 28 Sep 2022 reply
  • RC2: 'Comment on egusphere-2022-771', Anonymous Referee #2, 30 Sep 2022 reply

Zhan Zhang et al.

Zhan Zhang et al.

Viewed

Total article views: 259 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
148 103 8 259 2 3
  • HTML: 148
  • PDF: 103
  • XML: 8
  • Total: 259
  • BibTeX: 2
  • EndNote: 3
Views and downloads (calculated since 08 Sep 2022)
Cumulative views and downloads (calculated since 08 Sep 2022)

Viewed (geographical distribution)

Total article views: 248 (including HTML, PDF, and XML) Thereof 248 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 30 Sep 2022
Download
Short summary
This work developed a multi-band-multi-pass-multi-comparison Sentinel-2 methane retrieval algorithm, and the method was calibrated by data from a controlled release test. To our knowledge, this is the first study that validates the performance of a Sentinel-2 methane detection algorithm by calibration with a ground-truth testing. It illustrates the potential for additional validation with systematic future experiments wherein algorithms can be tuned to meet different detection expectations.