Can new mobile technologies enable fugitive methane reductions from the oil and gas industry?
Bibliographic record
Abstract
Abstract New mobile platforms such as vehicles, drones, aircraft, and satellites have emerged to help identify and reduce fugitive methane emissions from the oil and gas sector. When deployed as part of leak detection and repair (LDAR) programs, most of these technologies use multi-visit LDAR (MVL), which consists of four steps: (a) rapidly screen all facilities, (b) triage by emission rate, (c) follow-up with close-range methods at the highest-emitting sites, and (d) conduct repairs. The proposed value of MVL is to identify large leaks soon after they arise. Whether MVL offers an improvement over traditional single-visit LDAR (SVL), which relies on undirected close-range surveys, remains poorly understood. We use the Leak Detection and Repair Simulator (LDAR-Sim) to examine the performance and cost-effectiveness of MVL relative to SVL. Results suggest that facility-scale MVL programs can achieve fugitive emission reductions equivalent to SVL, but that improved cost-effectiveness is not guaranteed. Under a best-case scenario, we find that screening must cost < USD 100 per site for MVL to achieve 30% cost reductions relative to SVL. In scenarios with non-target vented emissions and screening quantification uncertainty, triaging errors force excessive close-range follow-up to achieve emissions reduction equivalence. The viability of MVL as a cost-effective alternative to SVL for reducing fugitive methane emissions hinges on accurate triaging after the screening phase.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".