Investigation of an Unexpected Flow Event for a Duvernay Artificial Lift System and Optimization for Future Installation
Notice bibliographique
Résumé
Abstract This paper summarizes the work of a client project investigating an "unexpected flow phenomenon" observed for an operational downhole jet pump installed in a deep, high-pressure, light hydrocarbon-condensate well in the Duvernay region of Western Alberta. The jet pump appeared to allow the well to produce multiphase condensate, water, and Non-Condensable Gases (NCGs) without the injection of power fluid. Due to a lack of understanding of the cause of this phenomenon, the well production rate could not be predicted for future installations. The objective of this work was to understand the underlying mechanisms causing this flow occurrence, and subsequently use the findings to optimize the artificial lift pump without the use of the injection system. This study was structured into three tasks: creating a custom fluid model for the Duvernay well; importing the custom model into Computational Fluid Dynamics (CFD) simulations to model the flow through the jet pump; and verifying the accuracy of the simulations with a coupled wellbore analysis of the Duvernay system. The fluid model was created with an in-depth Pressure-Volume-Temperature (PVT) analysis using the chemical composition of the production fluid determined from field samples. The Duvernay fluid model consisted of density, viscosity, and specific heat relationships as a function of the local temperature and pressure. The fluid model was implemented into three-dimensional (3D) multiphase flow CFD simulations of the existing jet pump to characterize the flow. The results showed that the jet pump nozzle created a sharp pressure drop triggering the hydrocarbon mixture to flash from a supercritical fluid phase to a gas-liquid mixture resulting in a gas-lift effect that produced flow to surface. A one-dimensional (1D) radial wellbore analysis was conducted for a large range of production flow rates at the current field wellhead pressure to generate a well outflow curve. The discharge pressure of the CFD results was compared to the wellbore pressure at the pump depth to verify the results of the simulations. Using further CFD simulations, the pump was optimized by changing the nozzle and diffuser designs to reduce downstream turbulence and improve discharge pressure recovery. Lastly, a parametric study was conducted using CFD for multiple mass flow rates and nozzle diameters to create a semi–empirical model to predict production rates for any given pump size. This model was used for a second case study to test feasibility in future Montney region wells, in which optimal pump specifications were sized based on the region's downhole properties. The results of this case study showed that this new optimized pump has the capability to produce flow rates in wells that do not naturally produce condensate, with a predictive model having been developed to choose the ideal pump geometry specifications to maximize the outflow.
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 | 0,001 | 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 | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».