Methodology for Evaluating Drag Reduction Characteristics of Friction Reducer
Notice bibliographique
Résumé
Abstract Friction reducers have been an integral part of the oil and gas industry for many years. They possess very unique properties of reducing friction pressure associated with the flow of fluid in tubulars. Friction pressure loss or hydraulic characteristics of friction reducers depend greatly on how they are tested and evaluated in the laboratory. Today, there is no standard procedure for their evaluation. American Petroleum Institute (API) oversees the development and publication of industry standard practices for various fluids and materials. Recognizing the need for a standard testing procedure for friction reducers, a Committee made up of members from industry and academia was formed and charged to develop a document outlining the standard procedure. Round-robin tests were conducted by four industry organizations and one academic institution, employing their in-house flow loops and were requested to report the results. Tests were to conduct friction pressure measurements of friction reducers, and to develop and deliver to the industry a standard procedure and method to measure and analyze friction reducers data in straight pipes. The test fluids chosen were two friction reducers: one anionic and the other cationic. Water data were also gathered as base line. Same fluid samples were submitted to all laboratories. The calibration procedure and fluid testing procedure was developed and distributed to all involved in fluid testing. The analysis for data reduction and for reporting results was also developed and distributed to all. It was found that the calibration procedure was more critical than originally thought. The determination of internal diameter of the circular tube is the most important parameter that influences the friction pressure loss results greatly. In this paper, the details of various flow loops, calibration procedure, data analysis procedure, and results obtained with water as base line and two friction reducers are presented and discussed. A standard procedure for testing and evaluating friction reducers for their friction loss properties is outlined. Following this standard procedure and carefully performing the testing with friction reducers will yield very similar results among various laboratories in the industry. This will make it easy when comparing the performance of friction reducers for their friction loss properties from different organizations.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».