MétaCan
Menu
Retour à la cohorte
Enregistrement W2038162464 · doi:10.2118/08-10-14

A New Method Using Wellhead Measurement to Approximate Unsteady- State Gas-Water Two-Phase Flow in Wellbore to Calculate Inflow Performance

2008· article· en· W2038162464 sur OpenAlexaff
Qing Lin, L. Zhang, Yu‐Pin Lin, Nan Xie

Notice bibliographique

RevueJournal of Canadian Petroleum Technology · 2008
Typearticle
Langueen
DomaineEngineering
ThématiqueReservoir Engineering and Simulation Methods
Établissements canadiensApache (Canada)
Organismes subventionnairesnon disponible
Mots-clésWellheadArtificial liftInflowPetroleum engineeringGas liftMechanicsLift (data mining)SlippagePressure dropFlow (mathematics)Volumetric flow rateWellboreGeologyEngineeringComputer sciencePhysics

Résumé

récupéré en direct d'OpenAlex

Abstract An accurate analysis of the pressure and water inflow at the bottomhole of a gas well with free water influx is important for determining an optimized artificial lift method. The commonly used two-phase flow correlations based on steady-state flow have been successfully applied to gas wells in homogeneous reservoirs. However, for reservoirs with a high degree of heterogeneity, such as fractures, the existing techniques often fail due to the unsteady-state flow behaviour from reservoir to wellbore. This paper presents a new method that is derived based on the unsteady-state two-phase flow phenomena and the conservation of mass, taking into consideration the slippage effect of the two phases. In this method, the fluid flow is divided into a number of segments at which pressure drop and gas-liquid distribution under unsteady-state flow conditions can be calculated utilizing the measured data at the wellhead. In this case, more representative bottomhole pressures and water inflow rate changes with time can be calculated, permitting the selection of an optimum artificial lift method. The new method has been successfully tested. A field case is presented involving a gas well in the Sichuan Field in China. The results of the new calculation method and the steady-state calculations were compared with field measurements to determine the accuracy of the method. Introduction The total amount of gas that can be produced from a gas well with free water influx largely depends on its ability to lift water. Sometimes, it is necessary to engage artificial lift to dewater the well. Accurate forecasting of gas well deliverability requires accurate predictions of pressure loss and gas and liquid inflow performance to select and design the appropriate artificial lift method. Most often, two-phase flow correlations are employed to determine flow rate and pressure loss in gas wells. Those methods are generally adequate for wells completed in homogeneous reservoirs that exhibit steady-state flow. But these correlations typically fail in reservoirs with a high degree of heterogeneity, such as fractures, due to the unsteady-state flow behaviour from the reservoir to the wellbore. The modelling calculation presented in this paper outlines a method using wellhead measurements to calculate sandface pressure and flow rate in order to design artificial lift systems to manage water loading problems and improve production and ultimate recovery. An example of the application of this method for a gas lift design is included in this paper. Theoretical Development Gas flows intermittently with water from a fractured formation with free water influx. The intermittent flow is maintained in the reservoir into the wellbore. By studying gas-water unsteady-state flow in the tubing, it is possible to estimate fluctuations along the entire flow path from the sandface to the wellhead. The production of gas and water at the wellhead is not synchronized with the sandface. This is primarily due to wellbore storage effects. The slippage between water and gas under unsteady-state flow conditions causes time dependent flow rate changes to the gas and liquid phases as the gas arrives at the wellhead.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,039
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0050,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,027
Tête enseignante GPT0,283
Écart entre enseignants0,256 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2008
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Canadian Petroleum TechnologyMême sujetReservoir Engineering and Simulation MethodsTravaux en français237 207