Notice bibliographique
Résumé
Abstract Previous researches on sand production prediction were focusing on when sand will be produced during depletion basing on some mechanics analyses but the amount of sand production was ignored. Recently more and more researches are focusing on the simulation of heavy oil sand production processes. For unconsolidated heavy oil reservoir which employs sand production to enhance production, the amount of sand production is of great importance because too much sand production may cause formation collapse while too less sand production may not maximize well productivity. In view of this, based on both fluid flow modeling and reservoir mechanics concepts, a coupled heavy oil / sand particulate flow/reservoir elasto-plastic deformation model is used to simulate sand production, oil production and reservoir deformation. With this model, we can determine an optimum flow rate which will not cause formation collapse while maximizing well productivity. Introduction Heavy oil sand production as an important production enhancement measure has been used in the primary development of heavy oil reservoirs in Canada for a long time. Because the production of sand may leads to the change of formation flow parameters such as permeability and porosity and mechanical parameters such as cohesion. It also causes near wellbore stress redistribution. So, sand production is a very complicated process involving both fluid flow and geomechanical problems. In order to simulate the effect of sand production and productivity enhancement, simulation of the physical process needs to be done. Because of the long history of cold production, the simulation of cold production is becoming mature and a lot of excellent work has been done by experts in Canada and elsewhere around the world. Wang [1] first developed a model to predict sand production in heavy-oil reservoir in Frog Lake and Lloydminster, Canada. It is believed that reservoir depletion induces stress concentration around the wellbore and large drawdown causes foamy oil zone, in which large drawdown and seepage force are created and causes sand production. A fully coupled geomechanical, foamy oil now model was developed. Sand production is assumed to start when the effective radial stress is equal to the tensile strength. Later Wang [2] also developed a coupled reservoir-geomechanical model to simulate the enhanced production phenomena in both heavy oil reservoirs (Northwestern Canada) and conventional oil reservoirs (North Sea). It is believed that the production enhancement is contributed (I) by the reservoir porosity and permeability improvement after a large amount of sand is produced, and (2) by higher mobility of the nuid due to the movement of the sand particles. Once the reservoir formations yield plastically, loose sand particles can be generated. Sand production has been postulated as a critical condition when the effective radial stress reaches the tensile strength or when the plastic strain reaches the critical plastic strain. Recently, Papamichos et al [3] and Stavropoulou et al [4] also provided similar models to simulate sand production. Later Papamichos et al [5] applied this model to interpret sand production from a North Sea reservoir.
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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».