Evaluating Technologies and Methods for Measuring Methane Emissions from the Upstream Oil and Gas Sector
Bibliographic record
Abstract
Methane, a potent greenhouse gas, is commonly emitted during the production, processing, transmission, and storage of oil and natural gas (O&G). The O&G industry is the leading source of anthropogenic methane in Canada and the US, but methane measurement and mitigation strategies remain underdeveloped. In recent years, an increasing number of O&G producing jurisdictions have introduced regulations mandating methane leak detection and repair (LDAR) programs in order to reduce fugitive emissions. Meanwhile, innovation in methane measurement has exploded, with companies emerging that promise to reduce methane emissions using drones, aircraft, satellites, fixed installations, handheld instruments, and other vehicle systems. These new solutions are not well understood, and how they might contribute to reducing methane emissions is unclear. This thesis seeks to improve understanding of emerging methane-sensing technology performance and participation in the O&G industry. Specifically, it seeks to reveal whether current and emerging technologies are technically capable of reducing emissions, can meet regulatory requirements for approval, and offer cost savings relative to established methods. This thesis presents four chapters of research with the following main results: (1) Screening is a way for mobile technologies to rapidly search for large leaks. Most emerging technologies and methods use screening and can detect methane in some capacity, but much more testing is needed to understand performance metrics, precise limitations, how to direct follow-up, and mitigation potential; (2) Policies are evolving to enable adoption of new systems, but careful work will be required to properly evaluate suitability through controlled testing, simulation modeling, and piloting; (3) LDAR-Sim and similar tools can support the development of LDAR programs with new technologies but modeling results are highly sensitive to technology performance assumptions, empirical inputs, and environmental conditions; (4) Screening technologies that require follow up may struggle to be cost-effective due to high quantification errors and the confounding presence of vented and combustion emissions at most facilities. Impressive progress has been made in developing, enabling, and implementing new LDAR technologies, but regulators, industry, and researchers should continue to work together to ensure credible and defensive emissions reductions are achieved through implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".