Field Data Demonstrate Thermal Effects Important in Gas Well Pressure Buildup Tests
Notice bibliographique
Résumé
Abstract Thermal effects become an important factor in gas well pressure buildup tests with a surface shut-in. Welltest data from Western Canada demonstrate that, due to the PVT relationship, temperature changes of wellbore fluids can cause thermal-storage effects that can be misinterpreted as complex reservoir characteristics by unexplained pressure transient response or nonunique pressure response. Understanding the duration of thermalffects can improve the interpretation of buildup tests to allow enough time for observation of true reservoir pressure transient responses. Temperature effects can be significant and extended, depending on many in situ and imposed factors. Thermal effects can be important, even when pressure recorders are placed at the midpoint of the producing interval, due to Joule-Thompson effects. Field observations demonstrate that Joule-Thompson cooling exists in many gas wells. Cooling effects were observed to extend 50 m in the formation, suggesting that significantly long time periods are required for formation fluids to reach thermal equilibrium after shut-in. Implications of not understanding the thermal effects in a buildup test analysis can result in "heterogeneities" being interpreted when there are none and skin calculations indicating an improved wellbore condition when the well has only been perforated. It is the purpose of this paper to show how temperature data diagnostics can be used to aid the welltest engineer in distinguishing between general wellbore effects and reservoir behaviour in the pressure transient data. In this paper, we have adopted the acronym PTTA for (P)ressure (T)emperature (T)ransient (A)nalysis. Introduction Use of pressure transient tests has become an established practice to determine reservoir parameters, potential reserves and near wellbore conditions. It is widely recognized that wellbore and near wellbore effects can distort and mask true reservoir responses, specially in the early stages of a buildup test. Both analytical and practical efforts have been developed to quantify and evaluate these effects by using concepts of skin, wellbore storage and phase redistribution(1–5). In addition, wellbore dynamics beyond wellbore storage and phase redistribution, such as liquid influx/efflux, wellbore (and near wellbore) clean-up, plugging, recorder effects, etc., have been discussed in the past decade(6–8). However, theoretical and numerical analyses of thermal effects have only been addressed recently(9–12). Implicit assumptions that require well test data to reflect information from an isothermal condition in both wellbore and reservoir are generally made in interpretation models. Such assumptions hold only if the pressure and temperature recorders are placed at the middle of the perforation interval, and neither Joule- Thompson cooling or heating occurs. Although various aspects of heat transfer between a wellbore and the formation have been studied, most of them are steady-state models, which assume that fluid properties and flow rate are not functions of time in theellbore(9). Thermal effects are more pronounced in gas wells because gas properties are strong functions of pressure and temperature. Observations demonstrate that gas well pressure buildup tests often exhibit complex reservoir pressure behaviours. These models, such as narrow channels, double porosity, or multiple nearby boundaries, often did not agree with the actual reservoir system and, consequently, resulted in incorrect interpretation of well and formation parameters(12).
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 | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».