Methane emissions from the oil and gas supply chain: Characteristics and mitigation
Bibliographic record
Abstract
Abstract Methane emissions are prevalent across all segments of the oil and gas supply chain. As a potent greenhouse gas, methane presents a significant challenge in mitigating climate change and upholding environmental stewardship. In this review, we examine oil and gas methane-emission characteristics through key findings of measurement campaigns, and control technologies across all segments of the oil and gas supply chain, from production to distribution. Methane emissions exhibit complex spatial and temporal patterns, with significant variations across different geographic regions, supply chain sectors, facilities, and timescales. This complexity underlies the persistent discrepancies between measurement methods, with top-down approaches generally indicating higher emissions than bottom-up estimates. Emission distributions are usually skewed or heavy-tailed, with a small number of sources contributing the majority of methane emissions. While emerging technologies offer improved detection capabilities, they face challenges in capturing the full spectrum of emission scenarios across diverse operational contexts, necessitating the integration of multiple technologies. Future research should focus on integrating advanced technologies, such as artificial intelligence and remote sensing, to enhance emission detection and quantification accuracy. Additionally, developing cost-effective real-time monitoring systems, optimizing data analysis algorithms, and fostering interdisciplinary collaborations are crucial for addressing the complex challenges of methane emissions in the evolving energy landscape.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".