Transient-Nonisothermal-Multiphase-Wellbore-Model Development With Phase Change and Its Application to Producer Wells
Notice bibliographique
Résumé
Summary This paper focuses on modeling nonisothermal multiphase outflow of high-temperature producer wells in Shell's in-situ-upgrading process (IUP). Subsurface heating and in-situ upgrading of bitumen involves installing heaters into the subsurface and raising reservoir temperatures to higher than 325°C. Consequently, flow conditions at the wellhead and along the tubing for a typical IUP producer well exceed pressure and temperature ratings of conventional equipment, particularly during peak production periods. Thus, the ability to reasonably predict pressure and temperature along the wellbore over the entire production cycle is important for designing IUP production wells and associated production facilities. A nonisothermal multiphase computational model has been developed for predicting the performance of IUP producer wells. Complex multiphase transport phenomena occur inside an IUP producer well during the production of high-temperature, upgraded hydrocarbon products. These include gas/oil/water three-phase flow; turbulent convective heat transfer between the tubing wall and the surrounding formation; pressure drop along the wellbore caused by gravity, friction, and acceleration; and phase changes caused by condensation and evaporation caused by variations in pressure and temperature along the well. These processes are strongly coupled, and accurate analysis demands a coupled modeling approach. Pressure and temperature variations result in changes in mass density and velocity, which have a significant influence on convective-heat-transfer rates. Mass-flow rates in the wellbore vary significantly with time because of production requirements during the life of a producer well (5 to 8 years). Long durations of high production rates can raise the temperature of the wellbore in the overburden and lower overall heat-loss rates. Sustained periods of low or no flow can cause the wellbore to cool and result in different flow and heat-transfer characteristics upon reopening of the well. Therefore, conductive time scales in the near-well formation are important to accurately predict flow tubing temperatures and pressures. An advanced wellbore model is developed for coupling the multiphase flow, heat transfer, and phase change phenomena in a high temperature, unconventional oil producer well. Vapor/liquid/ liquid (VLL) three-phase flash calculations are used to describe phase condensation and evaporation caused by changes in temperature and pressure along the wellbore. The model is formulated by use of k-values that are consistent with the CMG STARS reservoir model (STARS 2007) used for thermal simulation of Shell's IUP process. The drift-flux model is used to describe gas/liquid two-phase flow, and multiple transient energy equations are used for the wellbore, casing strings, and surrounding formation. The overall pressure gradient in the two-phase flow is formulated as the sum of gravitational, friction, and acceleration components. All transport equations are implicitly coupled for stable efficient transient calculations The model is validated with published data and simplified analytical solutions for limiting flow conditions. Computational results are compared with data from an IUP producer well in the oil sands of Alberta, Canada. Reasonable temperature and pressure matches were obtained, demonstrating that the model can predict transient and axial profiles of pressure, temperature, phase volume fraction, phase mass density, and component composition in a high-temperature flowing producer well during the entire production cycle.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| 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 ».