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Enregistrement W4400193574 · doi:10.2118/0724-0102-jpt

Study Examines Limitations of CCS and Their Effect on Oil and Gas Production

2024· article· en· W4400193574 sur OpenAlexaboutno aff
Chris Carpenter

Notice bibliographique

RevueJournal of Petroleum Technology · 2024
Typearticle
Langueen
DomaineEngineering
ThématiqueReservoir Engineering and Simulation Methods
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCarbon capture and storage (timeline)Production (economics)SubsidyTerminologyEnhanced oil recoveryFossil fuelNatural resource economicsEnvironmental economicsEnvironmental scienceBusinessWaste managementEngineeringEconomicsPolitical scienceClimate changeLaw

Résumé

récupéré en direct d'OpenAlex

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 214950, “Limitations and Fallacies of Carbon Capture and Storage and Impact on Oil and Gas Production,” by S.M. Farouq Ali, SPE, and Mohamed Y. Soliman, SPE, University of Houston. The paper has not been peer reviewed. _ In the complete paper, the authors write that, while carbon capture and storage (CCS) initiatives are affecting oil and gas operations profoundly, such efforts have had little perceptible effect on atmospheric CO2, which continues to increase. The paper aims to show that current CCS regimens have serious technical and fiscal constraints and questionable validity, stating that, globally, CCS has not increased beyond approximately 0.1% of global CO2 emissions in the past 20 years. The paper offers partial solutions and concludes that, while oil and gas will continue to be important energy sources beyond the foreseeable future, oil companies will accomplish the needed CCS. Introduction The authors write that, while CCS efforts have been pursued for 4 decades, little has been achieved. For the past 20 years, the percentage of CO2 captured and stored is less than 0.1% of the CO2 emitted worldwide, if one considers CO2 enhanced oil recovery (EOR) projects to be CSS—which, the authors write, is a fallacy. They emphasize that CCS means injection with no production. The key to CCS success, they write, is major governmental subsidization, by whatever terminology it is known, and that means taxpayer money. Sweeping decisions that have a profound effect on oil and gas production and petroleum engineering education are being made based on predictions of an increase in CO2 concentration in the atmosphere in various time frames. Magnitude of the Problem The problem of world CO2 emissions capture is gigantic. To appreciate the magnitude of the problem, imagine that 1 year’s CO2 emissions (40 billion tonnes) are captured, compressed and liquified, and injected into a reservoir the size of the Ghawar oil field, the largest reservoir in the world, with the entire pore space (approximately 0.5 Tcf) available for storage. In this hypothetical, nine such reservoirs would be required every year. Presumably, such storage space can be found, but collecting the CO2 and bringing it to a storage site is a highly complex task. For example, in a sequestration effort in a building complex in New York, the CO2 is separated, liquified, and trucked to a storage site to be injected underground, which is impractical. Often, the example of the Nordic countries (mainly Denmark, Sweden, and Norway) is cited as evidence of successful emissions reduction. But the total population of these countries is approximately the same as that of metropolitan Mumbai in India. Definitions Carbon capture use and storage (CCUS) implies that the CO2 produced by various processes is captured and used for EOR. This accounts for approximately 30% of the 230 mtpa of CO2 captured globally. CCS means that any CO2 produced by the process is captured and injected into a suitable geological formation for storage for thousands of years. Net-zero emissions (NZE) implies that all the CO2 produced is captured and stored. In Canada, “absolute zero emissions” is discussed, meaning that no CO2 is produced in the first place, pointing to total fossil-fuel phaseout.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,405
Score d'incertitude au seuil0,265

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,023
Tête enseignante GPT0,266
Écart entre enseignants0,244 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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