Dynamic Simulation to Predict Self-Restart Potential of Acid Stimulated Wells by Bullhead Treatment in Deepwater Environment
Notice bibliographique
Résumé
Abstract Matrix stimulation by acid is a technique used to enhance production from underperforming wells. It involves injection of acid at pressures lower than fracture pressure, with the aim of dissolving (in sandstones) or bypassing (in carbonates) the damage in the near-wellbore region, thereby clearing/improving the rock pore-throats and improving flow of hydrocarbons. Dynamic modelling of the acid stimulation process is essential to optimise the process by understanding conditions that will oppose self-restart of the wells treated by fluid bullhead and also to formulate operational guidelines. In particular, for the production systems discussed in this paper, it was imperative to determine whether the well(s) could self-restart (i.e., self-unload the intervention liquid volumes left in the well and nearwellbore zone) without intervention (e.g., nitrogen kickoff) after the acidizing treatment is completed. Dynamics in the various system components—the pipeline, wellbore, and near-wellbore reservoir area—affect each other and also the overall feasibility of attaining a self-restart (liquid unloading) after stimulation. Evidently, for an operation such as this, changes in saturations and effective permeability of the fluid phases in the near-wellbore region are of great importance. It follows that integration of the transient multiphase well model with a near-wellbore reservoir model becomes necessary to capture the full dynamics of the system. The integration of the well model to a near-wellbore reservoir model is, in this paper, discussed as a coupled model. By contrast, a typical dynamic standalone well model would use an inflow performance relationship (IPR) to represent the reservoir performance, which would simply have no history of fluids injected into the reservoir and their distribution in the near-wellbore area of the reservoir rock. As a result, there would be a lesser degree of confidence in the predictive capability of such standalone well model. Using coupled models, two gas wells were tested for their self-restart feasibility following acid stimulation. Furthermore, two methods of fluid injection were simulated to compare their effectiveness in aiding the self-restart of the wells. One approach involves sequential injection of fluids, followed by wellbore displacement with nitrogen to squeeze the treatment fluids (liquid) away from the near-wellbore region, making self-restart more likely. The second approach is to simultaneously inject the treatment fluids and nitrogen to lower the effective density of the treatment fluid mixture and also to energize the injected stimulation fluids, with the aim of facilitating self-restart. The fluids sequence of this second approach also ends with the wellbore displacement by nitrogen. This paper presents the results of the modelling and simulations carried out for gas wells and their fluids injection sequence. Important differences for the design of the operation were found when the stand alone and the coupled models were compared. The findings of this study have since been supported by observations in the field.
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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».