Effects of Aquifer Parameters on Long-Term Storage of Carbon Dioxide in Saline Aquifers
Notice bibliographique
Résumé
Abstract Geological storage of carbon dioxide has been recognized as one of the most effective options for mitigation of industrial emissions. Deep saline formations, otherwise called saline aquifers, are among the potential sequestration targets. To enhance the confidence regarding some of the key issues, such as site selection, planning, injection itself and long term monitoring of sequestration site, management of uncertainties is an essential step. This paper consists of two main parts. In the first part, CO2 storage in Mt. Simon sandstone in Ohio State, USA, is modeled using two compositional simulators - TOUGH2-ECO2N and CMG-GEM, which results provide an initial assessment for storage capacity of this site and discuss possible safety issues. In the second part, objective is reached using combination of experimental design and response surface methodology. Experimental Design (DOE) is an unbiased, rapid approach for obtaining probabilistic results. The purpose of Response Surface Methodology (RSM) is to fit simulation results to a response surface using analytical or numerical functions. In this study, DOE and RS methodologies were jointly applied to investigate the effect of uncertainties of key saline aquifer parameters on long-term CO2 storage in the form of solubility trapping and on the total storage capacity. The selected parameters in this study are: absolute permeability, global porosity, end point saturations, irreducible liquid saturation, temperature, aqueous phase salinity, vertical to horizontal permeability ratio, diffusion coefficient of CO2 in brine and relative depth of perforation interval. Mt. Simon is expected to be a safe, secure CO2 storage formation within selected site due to several factors such as regionally extensive caprock and seals including Eau Claire Formation (Cambrian) and Knox Dolomite (Cambo-Ordovician) and high CO2 storage capacity with favorable reservoir properties. This conclusion is supported by the results of modeling performed using both TOUGH2 and CMG GEM simulators. It is expected that 15–17 Mt of CO2 could be safely injected into Mt. Simon formation during 25 years via one vertical injection well while staying below fracturing pressure. It was demonstrated that combination of DOE and RSM techniques could be successfully applied for research into CO2 sequestration. INTRODUCTION The continuous rising of industrial emissions worldwide at unprecedented scale drives humanity to search for mitigation measures. One of the proposed mitigation measures is sequestration of carbon dioxide. CO2 GS (carbon dioxide geological storage) is one of the most talked about but least understood topics among the populace. CO2 GS involves capturing carbon dioxide from industrial emitters and sequestering it underground, which "sequestration" term defines a permanent safe confinement of gas in underground strata, where it would be injected by using conventional oil and gas industrial practices such as via vertical and/or horizontal injection wells. Canada has the same environmental issues as other developed and developing countries. The problem of the greenhouse gas (GHG) emissions increase and meeting the Kyoto emission goals is the most critical issue in the Canadian environmental policy development today. All GHG emissions could be categorized into two main components: the emissions caused by the activities of individuals and industries. An underlying principle is that without demand by individuals, the industry would not exist. Total emissions caused by individuals (residential plus transportation sector) accounted roughly for 35% in Canada as of 2004. In an attempt to make a forecast for the future we also should take into consideration the expected growth of the Canadian population to about 50 million in 2050 [1]. The population growth will drive demand for food and energy, goods and services, which, in turn, will cause the significant increase of GHG emissions from this sector.
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 | 0,002 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,000 | 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 ».