Methane Emissions and Environmental Impacts of Oil and Gas Systems: Post-meter natural gas systems and well sites in northern regions
Notice bibliographique
Résumé
Oil and natural gas (OG) activities are associated with many climate and environmental impacts. Methane (CH4), which is the primary constituent of natural gas is a potent greenhouse gas (GHG) with a short atmospheric lifetime of about 12 years, meaning reductions in CH4 emissions can have a major impact in limiting climate warming in the near-future. Fugitive CH4 emissions arise throughout the supply chain of OG fuels, from production, processing, storage to distribution and end-use, from either intentional (e.g., venting and flaring processes) or unintentional (leaks) gas releases. Here, we identified and investigated two understudied segments of the OG sector, namely the OG production (OG wells) impacts in northern regions and the end-use of natural gas, focusing on leaks from natural gas appliances and piping, as well as CH4 emissions from the incomplete combustion of natural gas. OG wells are a large source of CH4 emissions with underestimated subcategories (inactive or abandoned OG wells). In addition, OG wells are associated with many local environmental impacts, such as vegetation clearing, contamination of the surrounding water and soil and noise pollution, to name a few. This is especially important to consider as many OG reserves are located in remote regions with vulnerable ecosystems. At the end-use of natural gas, CH4 emissions can occur from incomplete combustion of natural gas, considered stationary combustion emissions, or from leaks in natural gas piping connecting the customer natural gas metering device to the natural gas appliance or in natural gas appliances themselves, referred to as post-meter emissions. These latter emissions are currently not consistently included in national GHG inventories. First, we analysed the distribution of OG wells drilled between 1984 and 2018 across the Core Domain of the NASA Arctic-Boreal Vulnerability Experiment (“ABoVE domain”) using public OG well databases. We identified 242,007 OG wells drilled as of 2018 in the ABoVE domain, of which almost two thirds are now inactive or abandoned OG wells. Fugitive CH4 emissions from active and abandoned OG wells drilled in the Canadian portion of the ABoVE domain accounted for approximately 13% of the total anthropogenic CH4 emissions in Canada in 2018. Our analysis identified OG wells as an anthropogenic disturbance in the ABoVE domain with potentially non-negligible consequences to local populations, ecosystems, and the climate system.Second, we investigated natural gas appliance emissions by conducting direct measurements of 34 appliances at an educational facility (Ecole de Technologie Gazière, ETG) and at McGill University student residences. We implemented three different measurement techniques, namely concentration screening (n = 20), high flow sampling (n = 24) and chamber-based measurements (n = 24). We found CH4 leaks around natural gas piping and appliance exhaust vents. This study provides new insight on CH4 emissions occurring at the end-use segment of the natural gas supply chain, such as the location and relative magnitude of emissions or the dependence to appliance ignition and extinguishment. We show how emissions differ based on appliance natural gas consumption and appliance type, highlighting the importance of using technology-specific emission factors when estimating these emissions in national GHG inventories. To conclude, we found that both segments contribute non-negligibly to CH4 emissions and highlighted the importance to consider broader environmental impacts of the OG sector. Additional studies are required on potential interactions between OG wells, permafrost and other Arctic-boreal ecosystem components. Moreover, direct measurements of a wide range of natural gas appliances, are required to improve reporting of these sources in national GHG inventories
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».