Low-cost fixed sensor deployments for leak detection in North American upstream oil and gas: Operational analysis and discussion of a prototypical program
Bibliographic record
Abstract
Low-cost fixed sensors are an emerging option to aid in the management and reduction of methane emissions at upstream oil and gas sites. They have been touted as a cost-effective continuous monitoring technology to detect, localize, and quantify fugitive emissions. However, to support emissions management, the efficacy of low-cost fixed sensors must be assessed in the context of the sites, technologies, methods, work practices, action thresholds, and outcomes that constitute a broader program to manage and reduce emissions. Here, we build on technology-focused research and testing by defining a prototypical low-cost fixed sensor program framework and considering the deployment from an operational perspective. We outline potentially large operational cost penalties and risks to industry relative to incumbent programs. Most costs are caused by (i) follow-up callouts, (ii) nontarget emissions, and (iii) maintenance requirements. These represent core areas for improvement. Results highlight a need for careful consideration in regulations, ensuring that alerts protocols are carefully codified and system performance is maintained.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".