Ice Formation During Gas Hydrate Decomposition
Notice bibliographique
Résumé
Abstract A number of numerical simulation studies of gas hydrate reservoirs have indicated that the pressure reduction method known as depressurization is a promising technique to produce gas from hydrate reservoirs. In some cases, severe ice formation has been observed, leading to plugging and termination of gas production. Some researchers have suggested that if the flowing bottomhole pressure is not lowered beyond the equilibrium pressure corresponding to the freezing point of water, then ice formation may be avoided. This argument is based on the premise that the lowest temperature would occur at the wellbore. If temperature can be controlled to above zero by controlling the bottomhole pressure, then freezing should not occur. The objective of this work is to explore under what conditions ice particles form. Various cooling mechanism (cooling because of decomposition, gas expansion, etc) are studied in detail. For this purpose, a 3D mathematical model for gas production from hydrate reservoirs is introduced which incorporates energy balance, fluid flow and kinetics of the hydrate decomposition along with the ability to predict the formation of ice particles. This model is developed by modifying the GPRS (General Purpose Reservoir Simulator) platform to account for a number of mechanisms including hydrate decomposition and ice formation. GPRS is an object oriented reservoir simulator code developed at Stanford University. We will then apply this simulator to model largescale hydrate decomposition process in porous media, and demonstrate the effect of ice formation on gas production behavior. Through some case studies we investigate the conditions in which ice forms and becomes an issue. The learning for these studied are then used to suggest practical ways of avoiding ice formation. Introduction Hydrate particles are made up of natural gas molecules trapped in water molecule structures, and are considered as a potential resource for clean energy. Enormous quantities of methane gas exist in the form of hydrate in the permafrost and offshore environments. Large resources of hydrate have been explored worldwide including the North West Territories of Canada, Siberia, Alaska and Japan. In the last two decades much interest and research has been devoted towards the mathematical modelling of gas production from hydrate reservoirs. Three general techniques have been suggested to recover gas from hydrate reservoirs which are all based on breaking the stability conditions of hydrate leading to generation of gas; Depressurization, Thermal Stimulation and Inhibitor Injection. While depressurization does not require an external source of energy and is based on propagation of pressure drop from the wellbore to the hydrate decomposition zone, the thermal stimulation technique needs an external source of energy, not unlike those applied in the thermal recovery of heavy oils. Efficiency and economics of these techniques is the subject of numerous investigations. The first attempts to model hydrate formation and decomposition go back to the works done in the first decades of 1900's which aimed at preventing hydrate formation in the gas transportation pipes. Exploration of hydrate reservoirs and their potential as a new resource for energy has resulted in more research activities in the last two decades.
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) | 0,006 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».