A Comparison of the Effectiveness of Regulated OGI Leak Detection and Repair (LDAR) Surveys and Aerial Measurements in the Real-World
Notice bibliographique
Résumé
Periodic comprehensive or screening leak detection and repair (LDAR) surveys are a central part of many current regulations, which are intended to reduce unintentional methane emissions caused by leaking infrastructure. In principle, by swiftly identifying and repairing leaks, emissions of methane, a potent greenhouse gas, are reduced. The primary tools used for comprehensive LDAR surveys are Optical Gas Imaging (OGI) cameras. While OGI can be effective for detecting and visualizing methane leaks, its extension to quantitative measurement (QOGI) is notoriously imprecise. Moreover, studies have shown considerable variation in the performance of OGI in practice, where successful use is heavily dependent on the skill of the operator. Manual OGI surveys are also time consuming and labour intensive. Implementing and maintaining an effective LDAR program that involves multiple OGI surveys per facility can be costly, potentially disrupting routine operations while requiring the deployment of trained personnel to each site. Although operators are obligated to address and verify the repair of identified leaks, there is also still potential that significant leaks may be allowed to persist if they are not initially detected. Consequently, despite the substantial costs involved, the full potential of methane reduction benefits may not be realized. By contrast, aerial surveys have the potential to overcome many of the negatives associated with OGI surveys. In particular, aerial surveys can permit large numbers of sites to be surveyed per day at significantly lower cost per site, reducing overall compliance costs, labour requirements, and improving safety through reduced risks. However, there remains no objective way to assess the relative performance of aerial surveys in complementing or replacing LDAR surveys under different scenarios. In the context of emerging regulations, this is an especially important topic.This work seeks to directly compare the effectiveness of conventional OGI surveys and aerial measurement under real-world conditions. At an identical set of approximately 500 operating oil and gas sites in British Columbia, Canada, we compare and contrast detected and quantified sources in regulated LDAR surveys with parallel aerial surveys completed using Bridger Photonics’ Gas Mapping LiDAR (GML) technology. The publicly reported LDAR reports are parsed to analyze patterns in detected emissions on 1 and 3 times per year deployments which are contrasted with aerial measurements at the same set of sites. This direct contrast under real world conditions gives one of the first large scale tests of LDAR and aerial performance in practice, helping to provide quantitative guidance for the design of potential alternative LDAR programs under emerging regulatory scenarios.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».