Economic Co-optimization of Enhanced Oil Recovery and Carbon Sequestration
Notice bibliographique
Résumé
There is a growing consensus in both policy circles and the energy industry that within the next few years, the US Federal government will adopt some form of regulation of CO2 emissions. At the same time, it is widely believed that much of the nation’s energy supply over the coming decades will continue to come from fossil fuels, coal in particular. Many analysts believe the only way to reconcile the anticipated growth in the use of coal with anticipated limits on CO2 emissions is through the development and deployment of carbon capture and geological sequestration (CCS). However, many key players are hesitant to undertake CCS. The recent cancellation of a next-generation power plant in Tampa that would have been CCS-capable speaks to this hesitation. While a number of problems must be resolved before CCS will be widely deployed, a concern of particular importance is the uncertainty that sequestered carbon will escape. One likely way out of this conundrum is expanded use of CO2-based enhanced oil recovery (EOR). This technique, which has been used successfully in a number of oil plays (notably in West Texas, Wyoming, and Alberta), entails injection of CO2 into mature oil fields in a manner that causes the CO2 to mix with some fraction of the oil that still remains underground. Doing so reduces the oil’s viscosity, thereby making it possible to extract additional, otherwise unrecoverable oil. Although some of the CO2 resurfaces with the oil, it can be separated from the output stream, recompressed, and reinjected. Eventually, when the EOR project is terminated, all the injected CO2 is sequestered. Currently, such sequestration yields no economic benefits. In fact, any sequestration over the course of a project is a negative from the point of view of EOR operators, as it results in the need for additional CO2 purchases from some outside source. However, future regulations of CO2 emissions in the context of climate-change policies may generate such benefits, as EOR projects should be able to earn credits for units of CO2 sequestered. Moreover, the enhanced oil revenues that come with EOR make it the economically most attractive sequestration option in the short run, Evaluation of EOR when the potential for CCS is entertained is therefore likely to be of considerable importance in the coming years. Our paper provides such an evaluation. We start by developing a theoretical framework that analyzes the dynamic co-optimization of EOR and CO2 sequestration. This framework explicitly considers both the physical and economic tradeoffs between oil recovery and sequestration. Our model allows for oil extraction via one of two methods: water flooding (so-called secondary extraction) and CO2 flooding (tertiary extraction, or enhanced oil recovery). The decision to commence with tertiary extraction requires payment of a substantial one-time cost, and so firms choose the switching time subject to a transversality condition. Once tertiary extraction has started, some of the injected CO2 displaces the extracted oil; this adds to the amount of sequestered CO2. The decision to cease extraction is also determined by a transversality condition. Given the starting and ending times, and the amount of oil produced during the tertiary phase, an amount of sequestered CO2 can be calculated. Each of the various decisions depends in part on the anticipated price of oil, the cost of the input, and the carbon tax or credit price (which will partially offset the input price). The paper concludes with an example based on an ongoing project. Data from this project are used to predict time paths of extraction under secondary and tertiary production; from these time paths once can estimate the additional volume of oil produced under EOR and the amount of CO2 that is ultimately sequestered.
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.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 | 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) | 0,010 | 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 tête enseignante, 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 ».