Lab Study of High WAT Wax Deposition Reduction with Wax Inhibitors and Dispersants
Notice bibliographique
Résumé
Abstract Canadian crude oil and pigged wax from the Montney formation show high wax appearance temperatures (WAT) and experience severe deposition issues during production and transportation. Several commercial wax inhibitors and wax dispersants were studied in the crude oil and reconstituted oils (pigged wax added back to the crude oil and dodecane model system), to minimize the wax deposition by a systematic lab screening protocol. Suitable wax inhibitors (WI) and dispersants were selected and formulated at optimized dosage to efficiently reduce the wax deposition at close to field condition. The crude oil and reconstituted oils were utilized to study the high WAT wax performance with different types of wax inhibitors and dispersants. This included ethylene vinyl acetate (EVA), alkylphenol formaldehyde resin (AFR), acrylic copolymer (AC), α-olefin maleic anhydride copolymer (AOMAC) and several surfactant-based wax dispersants (WDs). A pour point tester was employed as the initial screening tool to determine the pour point and detected wax appearance temperature (DWAT). Multiple Light Scattering (MLS) was used to evaluate the dispersions of wax in the oil. Dynamic wax deposition tests by capillary flow through (CFT) and dynamic flow loop (DFL) systems were used to verify the wax deposition reduction efficiency, and to study the effect of the test parameters on wax deposition. The reconstituted oils had higher WAT (>55 °C) than produced oil. The screening tests showed that EVA significantly reduced the DWAT and pour point of the crude oil but was not very efficient in the reconstituted oil. Both AFR and AC reduced the DWAT and pour point but were not as efficient as AOMAC. AOMAC provided the lowest DWAT in the reconstituted oil. It was interesting to find that surfactant-based dispersants also reduced the DWAT of the reconstituted model oil. The top performing WIs and dispersants were then tested by CFT wax deposition system at a flowrate of 1.5 cm3/hr. For the crude oil at 10 °C, 225 ppm AOMAC WI was needed to efficiently reduce the wax deposition in the CFT system. A lower dosage was required in the DFL system. It was also found that wax inhibitor and dispersant together further reduced the reconstituted model oil wax deposition in the CFT system. MLS and bottle tests showed that the WDs helped to disperse the wax in both oil and aqueous phases. From this systematic WI study on kinetic and dynamic behaviors of high WAT wax deposition, a synergy was observed between wax inhibitors and dispersants. Further investigation is needed to understand how they work together. The specially designed laboratory screening protocol helped to understand the structure and performance relation, efficiently formulate the WIs/dispersants, and optimize the treatment dosages. The inclusion of surfactants/dispersants with WIs could further mitigate wax deposition and be a more cost-effective approach.
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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 | 0,000 | 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) | 0,001 | 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 ».