Automatic Optimisation of Oilfield Scale Inhibitor Squeeze Treatments Delivered by DSV
Notice bibliographique
Résumé
Abstract Oilfield scale deposition is one of the important flow assurance challenges facing the oil industry. There are a number of methods to mitigate oilfield scale such as sulphate reduction of the injected brine, flow modification to reduce water flow, damage removal by dissolvers or physically by milling or reperforating, and finally, inhibition, particularly recommended if a severe risk of sulphate scale deposition is present. Inhibition consists of the injection of a chemical which prevents the deposition of scale, either by stopping nucleation or retarding crystal growth. The inhibiting chemicals are either injected in a dedicated continuous line, or bull-headed as a batch treatment into the formation, commonly known as a scale squeeze treatment. Generally, scale squeeze treatments consists of the following stages: preflush, to condition the formation or act as a buffer to displace tubing fluids; main treatment, where the main pill of chemical is injected; overflush, to displace the chemical deep into the reservoir; followed by a shut-in stage to allow further chemical retention; finally, the well is put back in production. The well will be protected as long as the concentration of chemical in the produced brine is above a certain threshold, commonly known as minimum inhibitor concentration (MIC), usually this value is between 1 and 20 ppm. The most important factor in a squeeze treatment design is the squeeze lifetime, which is determined by the volume of water or days of production where the chemical return concentration is above MIC. The main purpose of this paper is to describe the automatic optimisation of squeeze treatment designs using an optimisation algorithm, in particular, using particle swarm optimisation (PSO). The algorithm provides the optimum design, which strictly speaking in terms of squeeze treatment designs, it provides the longest squeeze lifetime, although, it might not be the most efficient. To determine the most efficient design, an optimisation algorithm is used to provide an optimum design based on the following objectives: operational deployment costs, chemical cost, total injected water volume and squeeze treatment lifetime. Operational deployment costs include support vessel, pump and tank hire. There might not be a single design optimising all objectives, thus the problem becomes a multi-objective optimisation. The algorithm is capable of analysing a great number of designs, making it easy to identify the designs that are non-dominated, which provides the right amount of information to identify the most cost effective squeeze treatment design, and therefore cutting total treatment costs.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| 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 | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 tête enseignante, 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 ».