Multi-Faceted Approach for Optimizing a Microbial Control Program
Notice bibliographique
Résumé
Abstract Microorganisms are a major challenge for the petroleum industry and can lead to microbiologically influenced corrosion (MIC), reservoir souring, or biofouling. Typically used standard methods for microbial identification (i.e. MPN) have limited success at identifying problematic organisms and providing timely feedback. Consequently, it is important to develop a multi-faceted approach that provides faster and more insightful results on the risk of microbial difficulties. A three-part approach was developed and implemented at Shell's Ursa platform to understand whether recent failures on the production system were the result of MIC and the efficiency of the biocontrol strategy on the recently installed waterflood to prevent system failure and reservoir souring. The approach involved 2nd generation ATP quantification for rapid enumeration of actively-growing microorganisms, as well as microbial enumeration and speciation using molecular methods. Recent failures on the production system were believed to be the result of under-deposit corrosion and microbial attack. However, culture-based methods revealed low numbers of problematic microbes, suggesting that the failures were not from MIC. Using ATP quantification, the presence of active microbes was observed at the manifolds and shown to increase throughout the production system. Speciation and enumeration results confirmed that problematic microorganisms were present and this additional information allowed for identification of optimized treatment locations in the production system to minimize the risk of MIC. For the waterflood, the new microbial analysis approach confirmed that the biocide program is successfully minimizing the risk of fouling, equipment failure and reservoir souring. ATP testing revealed occasional locations of elevated microbial growth and their potential for downstream contamination, allowing for further optimization, but overall demonstrated the biocide regime to be effective. The success of the biocide regime was determined by a reduction in active microorganisms and a population shift to Bacteria that are not likely to contribute to reservoir souring. This audit revealed a clear understanding of the microbial risks at Ursa, including areas that were performing well and those that needed required changes. This process is an excellent tool for microbial risk identification and biocide program optimization.
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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 | 0,000 | 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 ».