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Enregistrement W1983481694 · doi:10.2523/iptc-18090-ms

Evaluation of superparamagnetic nanoparticle-based heating for flow assurance in subsea flowlines

2014· article· en· W1983481694 sur OpenAlex

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Notice bibliographique

RevueInternational Petroleum Technology Conference · 2014
Typearticle
Langueen
DomaineEngineering
ThématiqueOffshore Engineering and Technologies
Établissements canadiensUniversity of Calgary
Organismes subventionnairesUniversity of Texas at Austin
Mots-clésFlow assuranceSuperparamagnetismMaterials scienceNanoparticleMagnetic nanoparticlesChemical engineeringNanotechnologyPetroleum engineeringMagnetizationMagnetic fieldChemistryHydrateGeologyOrganic chemistry

Résumé

récupéré en direct d'OpenAlex

Abstract Flow assurance is a critical problem in the oil and gas industry, as an increasing number of wells are drilled in deep water and ultra-deep water environments. High pressures and temperatures as low as 2° C in these environments hinder flow of hydrocarbon-based fluids by formation of methane hydrate and wax. Commonly used methods for flow assurance in flowlines are chemical injection and direct electric heating which face several limitations. In this paper, an application to use superparamagnetic nanoparticle-based heating for flow assurance, in the form of a magnetic nanopaint is presented. Superparamagnetic nanoparticle-based heating has been extensively researched in the biomedical industry for cancer treatment by hyperthermia. Superparamagnetic nanoparticles in dispersions generate heat by application of an oscillating magnetic field as explained by Neel's relaxation theory. In our application, superparamagnetic Fe3O4 nanoparticles are embedded in a thin layer of cured epoxy termed 'nanopaint'. This nanopaint coating on the internal surface of subsea flowlines could generate heat and thus prevent formation of methane hydrates and wax. In this paper, parameters affecting heating performance of superparamagnetic nanoparticles such as particle size, and magnetic field and frequency are discussed. Rigorous characterization of nanoparticles and nanopaint performed using VSM, TEM etc., is used to quantify heating performance and optimize it. Heating performance of two samples of Fe3O4 nanoparticles varying in size distribution is evaluated in batch experiments and compared to Neel's relaxation theory. Performance of nanopaint to heat static/batch fluids and flowing fluids is evaluated. Heating performance of superparamagnetic nanoparticles in dispersions and in nanopaint is found to be similar and so it is concluded that Neel's relaxation theory is applicable to nanopaint. Heating performance of nanopaint is flow experiment is found to be better than in batch experiments by a factor greater than 5. 1. Introduction Flow assurance is the ability to transport hydrocarbon-based fluids economically and safely from the reservoir to production facilities, over the life of the field. With increasing oil and gas production from deep-water and ultra-deep water wells, flow assurance has become a critical problem for the oil and gas industry. Subsea wells are at greater risk of deposit formation due to low temperatures and high pressures in deep water environments. Methane hydrate formation and wax deposition severely limit production rates, pose safety concerns and may also result in the shutdown of the well. Hence various methods are employed for remediation and prevention of flow assurance problems, primarily relying on the principles of temperature increase, pressure reduction or mechanical removal. These methods include use of pigging solutions, chemical additive injection, SGN (nitrogen steam generation) process, direct electric heating, heated pipe-in-pipe (Hpip) solutions and have been previously summarized in [1]. Commonly used methods in the industry are chemical injection and direct electric heating. In chemical injection, a glycol usually methanol is injected into the pipeline to lower the hydrate formation temperature. However, high costs and concentration limits imposed by quality control limit their usage. In direct electric heating, electricity is forced through tracer cables laid along the length of the flowline. Temperature can be controlled by varying the power input to the system and variable heating rates can be obtained. However, there is risk of electricity leakage and component failure due to excessive heating. In this paper, we use superparamagnetic nanoparticle-based heating to address the issue of flow assurance.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
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 enseignants0,075
Score d'incertitude au seuil0,596

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
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)0,0000,000

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 Opus0,019
Tête enseignante GPT0,250
Écart entre enseignants0,231 · 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