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Enregistrement W2007967062 · doi:10.4043/21402-ms

Changes of Operating Procedures and Chemical Application of A Mature Deepwater Tie-back -Aspen Field Case Study

2011· article· en· W2007967062 sur OpenAlexaff
Weihong Meng, Paul Ogea, M. D. King

Notice bibliographique

RevueOffshore Technology Conference · 2011
Typearticle
Langueen
DomaineEngineering
ThématiqueOffshore Engineering and Technologies
Établissements canadiensNexen (Canada)
Organismes subventionnairesnon disponible
Mots-clésProduced waterWater injection (oil production)Environmental scienceAsphaltenePetroleum engineeringFlow assuranceWaste managementEngineeringHydrateEnvironmental engineeringChemistryChemical engineering

Résumé

récupéré en direct d'OpenAlex

Abstract The Aspen field is located in about 3050 ft water in the Gulf of Mexico on GC243 block and tied back to Bullwinkle production facilities on GC65 block via dual 16-mile wet-insulated pipelines. Its first production began in December 2002. The project was a fast-track, marginal field with an assumption of quick reservoir pressure decline. Hydrate is managed primarily by MeOH injection during startup and shutdowns. The reservoir fluids are complex. Downhole sample tests showed asphaltene onset pressure was 6500 psig (~500 psi above bubble point). An asphaltene inhibitor was continuously injected downhole as well as scale inhibitor since the field was suspected with scale and asphaltene issues in the earlier production life. The produced water has a total dissolved salt of 230,000 mg/l. The oil production has declined significantly from initial production while the water cut reaches up to 80%. The characteristics of field production have been changed significantly since the initial production date. A review was conducted by flow assurance team working with its chemical supplier. During the study period a large amount of fluid tests were conducted and chemical application program were reviewed by chemical supplier's experts along with the operator's engineering team. Planning to field execution took more than one year, resulting in termination of downhole injection of asphaltene inhibitor and scale inhibitor. Additionally there was a reduction in methanol usage and other topside chemicals, and the operating guidelines were revised. The financial benefits include cost reduction, as well as quicker startup and shutdown. This paper will present the methodologies, organization, tests, recommendations, field execution plan and results after implementation of the recommendations. The case study also demonstrates how the operation team and engineering support team of an operator can work with suppliers, utilizing the industry experience, to achieve best results. Introduction In deepwater water asset operations, understanding the multiphase flow system and associated chemical applications are two major components to properly formulate operating procedures. During the exploration and development phase of a project, design criteria usually tend to be conservative. After a field starts production, periodic reviews will confirm the validity of design promises and improve operational efficiency. In the petroleum industry, wax, asphaltene, hydrates, scale, internal corrosion, sand production were always the major problems in production operation, great deal efforts were made to understand the issues and develop chemicals to ease these issues, especially for deepwater developments. Significant progress has been made on the chemistry aspects through university and industry consortia [1-5]. Petroleum production operation should be based on and treated as a system[6] operation. Systematic approach is more important to the success of the deepwater project due to the long subsea pipelines and cold environment as well as high costs associated with subsea intervention. Phase-equilibrium (chemistry, including inhibitors) and multiphase flow behaviors (modeling) are the two key subjects (components) to understand a production system, and are related to each other. A systematic approach with a focus on fluid characterization was presented by Jamaludin et al.[7] As more reserves are discovered in deeper waters; more attention is paid to the flow assurance issues.

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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,024
Score d'incertitude au seuil0,049

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

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 Opus0,016
Tête enseignante GPT0,220
Écart entre enseignants0,204 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
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

Citations4
Publié2011
Routes d'admission1
Résumé présentoui

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