Determination of Real-Time Dynamic Fluid Levels by Analysis of the Dynamometer Card
Notice bibliographique
Résumé
Abstract Although the beam pumping is the dominant pumping system in the onshore oilfields, many issues pertaining to its hydraulic performance have not been well understood. This is mainly ascribed to the fact that it is difficult to determine the downhole pump performance data, such as the dynamic fluid levels. In practice, the dynamic fluid levels are traditionally measured on-site by using the acoustic method. This method, however, finds its limitations in determining the real-time dynamic fluid level. Recently, the automation technology has been widely used in the oilfields, it is essential to measure the real-time dynamic liquid levels for optimum reservoir management. In this paper, a method is developed to determine the real-time dynamic liquid levels by analyzing the measured dynamometer card. Theoretically, a mathematical model is developed to determine dynamic fluid levels by using the average polished-rod loads (PRLs) for the two turning points at the end of the upstroke and downstroke, respectively. In practice, the average PRLs can be directly obtained from the dynamometer cards and then used to determine the dynamic fluid level. It has been found from field applications that this newly developed method provides accurate determination of the dynamic fluid levels which will not be affected by the existence of noise, dogleg, tubing collars, and annular gas. In addition to determination of the real-time dynamic fluid level, this method greatly reduces the production maintenance cost. A PC-based software for determining the dynamic liquid levels is also developed. Introduction The dynamic fluid level, defined as the distance from the wellhead to the fluid level in the annulus, is a key pumping parameter to be measured on a regular basis. It has been well accepted that fluid level survey is considered as the primary diagnostic tool for monitoring downhole status and confirming the problem[1]. In general, the dynamic fluid level can be used to determine the production strategies, analyze the reservoir performance, and to estimate the reservoir pressure at the various stages of field development since it is constrained by the formation pressure and the production rate[2, 3]. In addition, the dynamic fluid level together with other pumping parameters can be used not only to determine the downhole pumping status, but also to perform well diagnosis and maintenance[4]. At present, numerous techniques have been developed to measure the dynamic fluid level, among which the acoustic method is the simple and widely used one. More specifically, an acoustic wave is first generated at the surface, transmitted to the dynamic fluid level in the annulus, and then reflected back to the surface. The time interval is recorded from the emission of the acoustic wave to the return of its reflection to the surface. Then the dynamic fluid level in the annulus is determined as the velocity of the acoustic wave in the air is known[5]. In practice, it is difficult to use this technique for measuring the real-time dynamic fluid levels where the automatic monitoring system is installed due mainly to its limitation of continuously generating the acoustic wave[6].
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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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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; 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 ».