Technical and Economical Screening of Chemical EOR Methods for the Offshore
Notice bibliographique
Résumé
Abstract Enhanced oil recovery (EOR) and maximizing recovery from declining production fields remain a challenge in the offshore industry. The challenge is to find an EOR method that is both technically and economically feasible considering the high capital and operating costs in the offshore environment. The goal of this work was to conduct a simple cost-benefit analysis based on a technical, facility, and economical screening of chemical EOR methods applicable to the offshore. Offshore Newfoundland, Canada is used as a base case as it represents a challenging geographical environment. The reservoir properties are good, based on volumetrics and characteristics, but the fields are located over 300 km offshore in a harsh environment where operational costs are high. A data mining approach was used for the EOR screening process. Data from over one thousand core flooding experiments investigating various chemical EOR methods, including surfactant, polymer, alkaline-surfactant (AS), alkaline-surfactant-polymer (ASP), nanoparticle, and low salinity water injection (LSWI), were collected. Factors with the greatest influence on the performance of a given EOR method were statistically examined and discretized. The ranges of recovery factor, rock type, chemical concentrations, and the most commonly used chemicals are presented in this review paper. Economic factors examined included capital expenditures (CAPEX) and the operating cost of production (OPEX). Benefits are found to be strongly related to oil production and Brent crude oil forecasts. Sensitivity studies of the recovery factor ranges with the different chemical concentrations, net present values (NPV), and the influence of the inflation were all taken into consideration. Two different injection plans were considered: injection from day one of production, and injection after secondary production. The highest CAPEX and OPEX were calculated for the ASP method, whereas LSWI resulted in the lowest. The results indicate that most of the chemical EOR methods could be economically successful, however, the timing of implementation will affect the potential benefits. If high recovery and low chemical concentrations are considered, ASP flooding is the most successful chemical EOR method when injecting from day one. However, if the EOR method starts after a decline in production, surfactant flooding proves more beneficial, regardless of the scenario considered. This paper presents a systematic approach to chemical EOR screening, combining available technical data using a data analytics approach with economic and technical uncertainty.
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 | 0,001 |
| 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) | 0,000 | 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 ».