Notice bibliographique
Résumé
Abstract Attention in CO2 flooding for incremental oil recovery and greenhouse gas (GHG) sequestration has prompted the need for screening and ranking Alberta oil pools for this EOR process. In a previous paper by the same authors, over eight thousands of Alberta oil pools were ranked for CO2 EOR suitability using a new parametricranking software which utilized six essential reservoir properties: oil density, residual oil saturation, minimum miscibility pressure (MMP), reservoir temperature, net pay thickness, and porosity. This continuation paper describes the results of using an advanced method to estimate production forecasts for numerous candidate pools in Alberta. A Microsoft Excel program with VBA based on the modified Koval method (1963) by Claridge (1972) has been developed to predict CO2 flooding performance using the Alberta reserves database. The program estimates live oil and CO2 viscosities at reservoir conditions, oil MMP and reservoir heterogeneity based on the rock type, to predict oil recovery at any specific pore volume of CO2 throughput. Over 8,000 Alberta pools were first screened for CO2- flood suitability, and pertinent reservoir properties were used for the remaining 4,729 pools to calculate oil recovery. The predicted recoveries for all pools ranged from 1.2-13.9%, 6.3-18.7% and 11.8-27.1% at breakthrough and 0.25 and 0.5 hydrocarbon pore volume (HCPV) injection respectively. These values compared well to an average of 13% incremental oil recovery from the field experience of CO2 floods. More importantly, the results clearly identify the most suitable Alberta pools for CO2 flooding. Introduction The recent high oil price and interest in reducing GHG (Greenhouse Gas) emissions in response to global warming may have created new business opportunities to realize incremental value from depleted oil pools through CO2 flooding. Not all oil pools in Alberta are suitable for CO2 flooding. Thus, in a previous paper by the same Authors1, about 8,800 Alberta oil pools were ranked by using a newly-developed VBA program capable of retrieving pertinent information from the digitized Alberta reserve database and perform parametric technical rankings. Six parameters with different assigned weightings were used in the technical ranking. These include API gravity of oil, residual oil saturation, ratio between reservoir pressure and predicted minimum miscibility pressure (P/MMP), reservoir temperature, net pay thickness, and porosity. However, the screening software is not capable of providing production forecasts of CO2 flooding, which is the motivation of this study. Numerous active CO2 flooding projects in the United States and Canada have provided valuable theoretical and practical information on the technology. Desktop engineering prediction tools such as US DOE "CO2 Prophet" 2 have been developed for quick technical and economic assessment. These tools are based on sophisticated analytical equations derived from theoretical calculations, numerical simulation and field experience. However, we are not aware of any tools that are capable of evaluating the performance of large numbers of oil pools as reported in this paper. CO2 FLOODING PREDICTIVE MODELS The recovery efficiency prediction of CO2 flooding can be used to provide useful estimates of financial viability of the project.
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 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) | 0,012 | 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 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 ».