Life Cycle Analysis (LCA) and Carbon Intensity of In-Situ Reflux (ISR) Process Using Renewable Solvents
Notice bibliographique
Résumé
Abstract In-Situ Reflex (ISR) is a novel solvent-based process that utilizes resistive electric heaters to vaporize solvent and recycle mobilized water downhole. ISR promises a significant reduction in greenhouse gases emissions through the elimination of steam generation and water handling facilities at the surface as well as effectively vaporizes the injected fluid along a wellbore. However, the economic viability of this process is highly dependent on the in-situ refluxing of the solvent and the clean fuel regulations which requires an in-depth understanding of the process and associated challenges environmentally and economically. In-situ recovery processes are mainly known as greenhouse gas emission intensive under SAGD operation, while there is an excessive bitumen reserves that can be only recovered by In-situ method. Solvent co-injection with steam alternative processes, including ISR, are potential solution to reduce the greenhouse gas emission of In-situ recovery processes, however, Life Cycle Analysis (LCA) hasn't been carried out in the same level as the effort to prove the technical feasibility on these alternative processes. The performance of the process didn't alter as results of switching from propane to renewable propane however, the CI associated with two scenarios was improved by almost 35% when renewable propane was used. The GHG model indicated power to heaters, source of the power generation units, solve to water ratio and solvent type are determining factors in calculating the carbon intensity of the ISR. Close approximate of the operating area to the refining infrastructure has impacted the CI score of the ISR process which puts Alberta in advantage to other provinces. The proposed model brings an insight into GHG intensity of the ISR process with the aim of increasing the understanding of clean fuel regulation, along with identifying the advantages and limitations of using the bottom-hole resistive heater technology. This will lead to a higher predictability of successful field implementation, lower upfront capital cost taking advantage of future carbon credits, higher energy efficiency, and environmentally sustainable development Recently, by taking advantage of the opportunity, the majority of municipalities across the nation are already in a very strong position to address a portion of their GHG emission concerns by converting readily available biogas into functional RNG. Methane can be separated or upgraded from other naturally existing main ingredients like water and CO2 to produce biogas, which typically comprises between 50% and 60% methane. The resulting cleaned-up gas, also known as biomethane or RNG, has a chemical makeup that makes it appropriate for injection directly into the local gas utility distribution system without the need for any additional adjustments. Any gas device that is currently in use (such as furnaces, hot water heaters, boilers, CNG automobiles, etc.) can be used to burn RNG. It has value as a physical good (gas for consumption like traditional natural gas) as well as a carrier of environmental qualities. Because of this, the value of RNG is higher than the value of conventional natural gas.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».