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Enregistrement W2033934847 · doi:10.2118/2000-087

Experimental and Mathematical Modelling of Ultrasonic Treatments for Breaking Oil-Water Emulsions

2000· article· en· W2033934847 sur OpenAlex

Pourquoi ce travail est dans la base

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affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.
fundUn bailleur canadien est enregistré sur le travail.

Notice bibliographique

RevueCanadian International Petroleum Conference · 2000
Typearticle
Langueen
DomaineEngineering
ThématiqueCoal Combustion and Slurry Processing
Établissements canadiensUniversity of Regina
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésUltrasonic sensorPetroleum engineeringEnvironmental scienceComputer scienceMaterials scienceProcess engineeringAcousticsEngineeringPhysics

Résumé

récupéré en direct d'OpenAlex

Abstract Stable oil-in-water emulsions are very difficult to break and constitute one of the most difficult problems encountered during oil production. The emulsion viscosity is much higher than that of the segregated phases and, therefore, accounts of high pressure drop in the wellbore as well as low recovery in the reservoir. A laboratory investigation on the use of ultrasonic energy for enhancing the separation of suspended oil phase from aqueous media was carried out in this study. This paper investigates the effect of ultrasonic energy on separation of oil and water from a stable oil-in-water emulsion. It was found that concentration of oil phase, composition of oil phase, ultrasonic intensity, and temperature are key factors in emulsion coalescence, which occurs after a relatively short time upon exposure to ultrasonic treatment. Also, clumping of oil droplets with higher oil phase compositions (10%, 35%), a possible explanation for reduced residual oil observed in past research efforts. Numerous microphotographs of the dynamic process of the coalescence were taken and changes in average droplet sizes noted. This led to the development of a mathematical model for the coalescence rate as a function of ultrasonic frequency, oil phase concentration, and other variables. These models are theoretically sound and easy to use. A comparison between mathematical model predictions and experimental results provided excellent agreement. It was observed that, for the cases studied, ultrasonic treatment at the optimal energy level outperforms some chemical flocculants in terms of enhancing liquid-liquid separation. This technology could also be applied to postproduction emulsion separation as well as in situ wellbore treatment. Introduction Oil-in-water emulsions are important in various phases of drilling, completion and production of petroleum wells. Oil-water emulsions are present whether it be in the oil reservoir itself or is formed as a result of the extraction process. These emulsions add considerably to the cost of transportation and refining and, in fact, to wellbore and reservoir problems. Emulsions are conventionally thought of as an irreversible yet integral stage of oil production. The most important properties of these emulsions include particle size and distribution, viscosity, density, concentration, oil and carbon content, sound velocity, pH, zeta-potential, and surface charge, etc. Depending on the values of these parameters, emulsions can form or break. In most cases in petroleum engineering applications, however, these oilin- water emulsions need to be broken. USE OF ULTRASOUND ENERGY AND EMULSIONS The breaking of emulsions, which is the demulsification process, involves the coalescence of small oil droplets to form larger ones. For accelerating the coalescence process for the enhancement of oil recovery, some additional forces are needed. There exists several techniques for enhancing demulsification. The typical ones found in the literature are the addition of chemical demulsifiers, pH adjustments, gravity or centrifugal settling, filtration, heat treatment and electrostatic demulsification1. The ultrasonic technique has the ability to rapidly break emulsions. This technique can actually be used to analyse emulsions non-destructively2.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,173
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,029
Tête enseignante GPT0,243
Écart entre enseignants0,215 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle