Simulation of Bacterial Souring Control in an Alberta Heavy-Oil Reservoir
Notice bibliographique
Résumé
Abstract This paper presents the development of a simulation model describing the bacterial-induced souring of an Alberta heavy-oil producing field and its remediation via injection of nitrate. An area of the field with extensive bacterial activity was selected for the simulation study. The primary production and water-flood injection phases were history matched with basic reservoir maps and injection/production operating conditions adjusted via an automatic history match algorithm. Extensive chemical and microbiological compositional data for injected and produced waters were collected and analyzed at the University of Calgary and a mathematical model of the souring process and its remediation treatment was developed based on the information collected. Simulation indicated the volumetric distribution of the hydrogen sulfide over time and how the injected nitrate was distributed and acted to achieve souring control. Predicted individual production well responses were shown to be consistent with field observations, and issues regarding improved monitoring and design of laboratory experiments for future field operations are highlighted. Hence, simulation can be a useful tool in understanding and designing remedial treatments to bacterial souring in the Western Canada Sedimentary Basin. Introduction The field of interest is a shallow glauconitic sandstone in the Medicine Hat area of the Western Canada Sedimentary Basin which is marginal marine to fluvial incised valley infill. The lower channel has good intergranular porosity and high permeability while the upper channel has a mixed composition that can lead to porosity and permeability variations and flow barriers. The area has been under primary production since the early 1980's with the use of horizontal producers by the mid- 1990's. Water injection started in early 2000's to recapture drive energy but souring began to appear after several years of produced water re-injection in this previously sweet field. As the reservoir is close to a high population area, remediation action was deemed necessary. The operator contacted Baker Hughes Incorporated to design a treatment scheme and the University of Calgary to monitor the process and to conduct laboratory experiments. Treatment with nitrate began in May 2007 and consisted of continuous injection of 2.4 mM nitrate over a period of more than one year. Thereafter it was decided to augment this treatment with high concentration "squeeze" treatments which also had a beneficial effect(1). Reservoir simulation was also deemed useful to quantify the reservoir flow paths and resultant bacterial activities. This paper describes the implementation and insight provided by the simulation study to the understanding of the process effectiveness. Reservoir Characteristics and Study Area General characteristics of this reservoir are given in Table 1. Discussions with the field operator lead to the decision to focus the simulation modelling in an area of particular interest, centered around an injector/producer pattern showing interesting response characteristics of souring and the remediation treatments. Figure 1 shows the pattern area of interest which consists of two vertical injectors internal to the pattern plus five boundary injectors. Internal to this region are six horizontal producers.
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 | 0,000 | 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 ».