Characterizing methane emissions on oil and gas sites

Reducing methane emissions from the oil and gas sector is a key component of short-term climate action, and emission reduction efforts in this sector are increasingly driven by measurements at the individual facility scale. This thesis introduces a number of inversion techniques that use methane concentration measurements from point sensor networks to infer emission characteristics on oil and gas sites. We first introduce an inversion method for inferring single-source emission characteristics, such as emission start and end time, source location, and emission rate. We then discuss two applications of this method. The first reconciles top-down measurements from an aerial survey-based technology with bottom-up inventories that are designed to represent long-term emission volumes. The second compares different point sensor networks and isolates differences in the sensor platform (i.e., sensor type and arrangement) from differences in the inversion algorithm. We next introduce a method for more robustly inferring methane emission durations by addressing a key limitation of point sensor networks: "nondetect times," the times when emitted methane is not blown towards any of the sensors in the network. Finally, we generalize our initial single-source inversion method to a fully Bayesian hierarchical model for inferring multi-source emission characteristics. This model makes it possible to use point sensor networks for accurate emission localization and quantification on complex sites where it is common for multiple sources to emit simultaneously. Ultimately, through the methodology presented in this thesis, we provide the tools needed to rapidly detect and quantify methane emissions from the oil and gas sector, a critical step towards climate change mitigation through targeted emissions reductions.

Name thesis:degree_name

Doctor of Philosophy (Ph.D.)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Applied Mathematics and Statistics

Grantor dc:publisher

Colorado School of Mines. Arthur Lakes Library

Year dc:date.issued

2024

Author dc:creator

• Daniels, William S.

Advisor dc:contributor.advisor

• Hammerling, Dorit

Committee members dc:contributor.committeemember

• Nychka, Douglas
• Wu Fung, Samy
• Bazilian, Morgan

• climate change
• greenhouse gases
• inverse modeling
• methane emissions
• oil and gas

Statement dc:rights

• Copyright of the original work is retained by the author.

Language dc:language.iso

eng, English

Dc Identifier Other

T 9914

OAI identifier oai:identifier

oai:repository.mines.edu:11124/180346