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Enregistrement W1973162337 · doi:10.2118/96418-pa

Chemical Compromise: A Thermodynamic and Low-Dose Hydrate-Inhibitor Solution for Hydrate Control in the Gulf of Mexico

2006· article· en· W1973162337 sur OpenAlexaff
Steve Szymczak, Kenneth Sanders, Marek Pakulski, Timothy D. Higgins

Notice bibliographique

RevueSPE Projects Facilities & Construction · 2006
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueMethane Hydrates and Related Phenomena
Établissements canadiensSeneca Polytechnic
Organismes subventionnairesnon disponible
Mots-clésHydrateClathrate hydrateMethanolDissolutionChemistryProcess engineeringEnvironmental scienceChemical engineeringEngineeringOrganic chemistry

Résumé

récupéré en direct d'OpenAlex

Summary Gas-hydrate inhibition is a serious concern for operators producing at conditions in the hydrate region. As production goes into deeper water, hydrate control grows in importance. Ideally, operators want total hydrate control without the problems associated with thermodynamic inhibitors (THI) and/or low-dose hydrate inhibitors (LDHI). A Gulf of Mexico (GOM) operator experienced hydrate-control problems in an 18,000-ft umbilical line in spite of the addition of methanol as a thermodynamic hydrate inhibitor. In addition to erratic pressure, the high rates of methanol usage created a logistical problem as well as a health, safety, and environmental (HS&E) concern because of the handling issues associated with methanol. Laboratory studies and previous onshore field experience indicated that hydrate-inhibition synergy is gained through the combination of thermodynamic inhibitors and LDHI (Budd et al. 2004). This is termed a hybrid hydrate inhibitor (HHI). Because of the performance, logistical, and cost drivers presented by the use of methanol, any alternative approach must consider those three factors. The performance has to do with hydrate dissolution in the event a hydrate formed during operations. A kinetic inhibitor (KHI) can prevent hydrate formation but cannot dissolve already formed hydrates. Antiagglomerant (AA) inhibitors allow hydrates to form but keep the hydrate particles dispersed in the fluids. The logistics have to do with pump sizing (i.e., conventional LDHI applications require new pumps and configurations). The cost of methanol is far less than specialty LDHI chemistry. Thus, the objective of the study is to provide all the benefits of the existing technology with improved performance, improved logistics, and at a cost not to exceed hydrate prevention with methanol. After a presentation of lab and field studies to the operator, a method of application was approved for use. The differential pressure (Dp) between the wellhead pressure (chemical injection line) and the flowline pressure serves as the key performance metric. There is a significant decrease of Dp after the HHI product is introduced into the system. Initially, the HHI is applied at the same rate (and a much higher equivalent cost) as methanol. After saturation of the system, the inhibitor rate is decreased in a stepwise fashion until the daily costs of treatment fall below the daily cost of methanol. On a cost-performance basis, the new product outperforms the methanol. While the methanol rate is 120 gal/D, the new product controls line pressure at a rate as low as 12 gal/D. The HHI dosage is eventually set at 22 gal/D to compensate for potential flow and pressure/temperature fluctuations. From the logistical standpoint, methanol shipments to the platform decreased five-fold. This decrease meant less cost and handling as well as a reduction in the footprint for product storage. From an HS&E position, the potential for an incident is decreased in line with the reduction in boat trips and crane lifts. The technology described in this paper created a synergy that assuages the concerns of operations, technical, logistics, HS&E, and personnel. After observing that hydrate dissolution is still possible at a lower dosage with less handling and at a comparable cost, the HHI treatment became a permanent hydrate prevention method. The project is a success with possible future expansion. Introduction Gas hydrates form when water molecules crystallize around guest molecules. The water/guest crystallization process has been recognized for several years, is well characterized, and occurs with sufficient combinations of temperature and pressure (Katz 1945). Light hydrocarbons, methane-to-heptanes, nitrogen, carbon dioxide, and hydrogen sulfide are the guest molecules of interest to the natural-gas industry. Depending on the pressure and gas composition, gas hydrates may build up at any place in which water coexists with natural gas at temperatures as high as 80°F [~30°C]. Gas-hydrate formation is a growing problem because producers drill in deeper waters and in cooler waters. The hydrates can form in a wellbore while the fluids go through pressure- and temperature-induced phase changes near the mud line. The hydrates also form in the flowlines from subsea completions to the separation facilities. The problem of finding an effective hydrate control method in a system at hydrate conditions is especially difficult in offshore environments where one has no control over the fluid composition, bottomhole pressure, and temperature. The well operator has only a limited control over the wellhead pressure. The producing formation temperature, Joule-Thomson cooling effect upon gas decompression, and heat loss to the environment are the factors deciding if a particular well or flowlines are at hydrate forming conditions. Hydrates create physical barriers to production and must be inhibited and dissolved if formed for gas production to occur. The operator must maintain the well and production lines free of hydrates at all times.

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.

Comment cette classification a été obtenuedéplier

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,859
Score d'incertitude au seuil0,532

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 technologies0,0000,001
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 Opus0,008
Tête enseignante GPT0,203
Écart entre enseignants0,195 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations19
Publié2006
Routes d'admission1
Résumé présentoui

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