Evaluation of CO <sub>2</sub> Storage Potential in the Deep Mannville Coals of Alberta
Notice bibliographique
Résumé
Deep unmineable coal seams are a promising geologic sink in which to store CO 2 permanently, providing a potential pathway to achieving a net-zero emission future. Unlike other porous sedimentary formations (i.e., sandstone) where structural trapping is the dominant storage mechanism, the primary storage mechanism in coal is gas adsorption. Importantly, CO 2 adsorbs onto coal to a greater degree than other typical natural gas components, including CH 4 , providing tremendous retention capacity for carbon sequestration. However, a significant challenge to the long-term injection of CO 2 into coal seams is the loss of injectivity and permeability associated with the adsorption-induced matrix swelling effect. In this study, the results of a field demonstration (pilot) of CO 2 sequestration in the deep Mannville coals of Alberta are provided. The pilot consists of a vertical injection well, used to inject water (pre-CO 2 ) and CO 2 into the coals, and a closely spaced observation well, used to evaluate pressure responses (in the coal and bounding strata) and fluid compositions (in the coal) during injection. A reservoir simulation study was performed in order to guide pilot operations, history-match the pilot data, and predict CO 2 storage and potential migration. The numerical model was set up to include both coal and non-coal bounding strata (multi-layer model) in order to simulate CO 2 , natural gas, and water flow within the coal and to evaluate potential migration into bounding strata. The extended Langmuir model was employed to model the competitive adsorption behavior of CO 2 and CH 4 in the coal, and the Palmer–Higgs model was used to simulate the effect of geomechanical anisotropy, effective stress, and volumetric adsorption strain on permeability evolution during CO 2 injection. As a result, coal permeability was dynamically updated as a function of gas composition and pore pressure in the simulation model. Prior to the pilot demonstration, pre-field simulation results suggested that the operator-specified amount of CO 2 (~1,500 tonnes) can be safely injected into the target Mannville coal seam (at 1,500 m) in less than 7 days. The pre-field simulation model, populated with site-specific geologic information, provided critical guidance to operational design. The reservoir model was then calibrated to both pre-CO 2 water injection/falloff data and to CO 2 injection/falloff data obtained from the field pilot. Matching of the water injection/falloff data was used to derive critical reservoir properties, unaffected by CO 2 adsorption, such as permeability as a function stress. Furthermore, permeability anisotropy was quantified. Matching of the subsequent CO 2 injection/falloff data (a total of 1,512 tonnes was actually injected) was used to assess the impact of CO 2 on coal transport properties and to evaluate the CO 2 storage potential of the Mannville coal seam at the pilot site. Through this analysis, it was determined that injection could be conducted without a significant loss of injectivity and that the coal exhibited strong geomechanical anisotropy. This study successfully demonstrates the feasibility of CO 2 sequestration in the deep Mannville coals in the studied area.
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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».