A review of close-range and screening technologies for mitigating fugitive methane emissions in upstream oil and gas
Bibliographic record
Abstract
Abstract Fugitive methane emissions from the oil and gas industry are targeted using leak detection and repair (LDAR) programs. Until recently, only a limited number of measurement standards have been permitted by most regulators, with emphasis on close-range methods (e.g. Method-21, optical gas imaging). Although close-range methods are essential for source identification, they can be labor-intensive. To improve LDAR efficiency, there has been a policy shift in Canada and the United States towards incorporating alternative technologies. However, the suitability of these technologies for LDAR remains unclear. In this paper, we systematically review and compare six technology classes for use in LDAR: handheld instruments, fixed sensors, mobile ground labs (MGLs), unmanned aerial vehicles (UAVs), aircraft, and satellites. These technologies encompass broad spatial and temporal scales of measurement. Minimum detection limits for technology classes range from <1 g h−1 for Method 21 instruments to 7.1 × 106 g h−1 for the GOSAT satellite, and uncertainties are poorly constrained. To leverage the diverse capabilities of these technologies, we introduce a hybrid screening-confirmation approach to LDAR called a comprehensive monitoring program. Here, a screening technology is used to rapidly tag high-emitting sites to direct close-range source identification. Currently, fixed sensors, MGLs, UAVs, and aircraft could be used as screening technologies, but their performances must be evaluated under a range of environmental and operational conditions to better constrain detection effectiveness. Methane-sensing satellites are improving rapidly and may soon be ready for facility-scale screening. We conclude with a speculative discussion of the future of LDAR, touching on integration, analytics, incentivization, and regulatory pathways.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".