Notice bibliographique
Résumé
Abstract Earlier, we reported results of numerical simulation that, for the low permeability reservoirs of Cold Lake and Peace River, show the Fast-SAGD process to obtain a better performance, lower steam requirement and higher productivity than does the SAGD process. Now, we report results of experiments using a scaled physical model, which represents a bituminous reservoir near Cold Lake being operated at high temperature and pressure. Also, we used an automated process control system. The results of the experiments showed that, for the Fast-SAGD process, the overall cumulative oil production is larger and the end-point CSOR higher than for the SAGD process. We concluded tentatively that for the Fast-SAGD experiment, the boiler did not produce sufficient volume of high quality steam. Results of numerical simulation of these experiments confirmed this conclusion; best history matches showed the steam quality to have been 30% during the SAGD and then 15% after starting the CSS. Moreover, the best history match of the Fast-SAGD experiment showed that the steam chamber was lost and subsequently recovered during the experiment. These preliminary high temperature and pressure experiments have provided important insights into operation of the SAGD and Fast-SAGD processes and suggest a mechanism of the steam chamber's collapse and recovery. Introduction Fast-SAGD, a modification of the steam-assisted gravity drainage (SAGD) process, uses offset wells operated with cyclic steam stimulation (CSS) beside the SAGD well pair in order to accelerate the steam chamber growth sideways (1). Previous numerical studies(2) (3) of a typical Cold Lake-type reservoir have shown that the Fast-SAGD process enhances thermal efficiency, resulting in better production performance as compared to the conventional SAGD process. A numerical study (4) for Fast- SAGD application in the Alberta oil sands areas showed the Fast-SAGD process to have higher net present values (NPV) for the low permeability type reservoirs of Cold Lake and Peace River because of lower steam requirements and higher productivity. In this study, high temperature and high pressure scaled physical model experiments were conducted to investigate the Fast-SAGD process using an automated process control system. From numerical simulations showing the Fast-SAGD process to result in enhanced performance compared to the SAGD process in Cold-Lake-type reservoirs (4),, a suitable permeability of 1.25 Darcy was chosen for the prototype. High pressure and high temperature scaled physical model experiments Conducting high temperature and high pressure model experiments is very difficult as many variables, such as steam quality, injection rate and pressure need to be controlled all at once and in real time. An initial study (5) showed that an automated process control system is capable of controlling and optimizing steam injection processes such as the steam-assisted gravity drainage process. Two-dimensional scaled model A two-dimensional scaled model cannot represent a field reservoir prototype because there are no perfect scaling methods. For example, the heat loss aspect is different. In the field, only over- and under-burden heat losses are considered, whereas in a laboratory, heat losses are not only to the over- and under-burden but also to the sides of the scaled model.
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 | 0,001 |
| 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 | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».