Understanding the Generation of Dissolved Silica in Thermal Projects: Theoretical Progress
Notice bibliographique
Résumé
Abstract The production of silica in thermal petroleum recovery projects is a well known phenomenon, and considerable efforts at its control are a common feature of facilities engineering in such projects. Recent work related to the generation of silica in SAGD projects has shown that the largest effect on silica production in SAGD is the steam zone pressure. Silica levels in produced water tend to increase with temperature. However, a significant suppressive effect of silica dissolution from quartz sands is provided by the presence of carbon dioxide dissolved in steam condensate. Lesser effects of silica concentration are due to ionic strength of the condensate. The carbon dioxide concentration in the steam condensate is amenable to theoretical prediction, available from recent progress in gas dissolution thermodynamics, and this allows the estimation of one of the major factors responsible for the suppression of produced water silica concentration. Other effects may be due to pH and fluid alkalinity, which are not independent of CO2 concentration. This paper is intended to provide an overview of the progress in obtaining a predictive capability, and to highlight the issues related to appropriate sampling methods that hinder simple correlations to date. Introduction The effect of temperature on silica dissolution from quartz has been known for a long time. Cowan and Weintritt1 in their well-known treatise on water formed scale deposits, cite Kennedy's work2 and provide convenient tabulations and graphs of silica solubility and temperature. The experimental data for the solubility of silica from quartz are shown in Figure 1. By comparison, the data generated by the SOLMINEQ program of the Alberta Research Council are quite similar (Figure 2), with slightly increased solubilities at lower temperatures, and a slight reduction at higher temperatures. Data such as these usually make it mandatory that silica concentrations be reduced in produced water treatment in SAGD schemes, whether the water is recycled for steam generation or injected into a disposal well. Common methods in use are warm or hot lime softeners, MagOx units, or acidification schemes. One would expect that apart from temperature, some aspects of produced water chemistry, such as dissolved acid gas content, pH, alkalinity and ionic strength, play a part in controlling the silica content of produced water from SAGD operations, so that the simple solubility curves published by Cowan and Weintritt cannot be used to correctly predict silica concentrations in any given scheme. Temperature Effect The magnitude of the temperature effect has already been shown. The use of the SOLMINEQ program produces results consistent with original experimental data. The use of this program to generate silica solubilities for any given produced water composition is a simple trial and error procedure, as follows:For a given water analysis, if silica at some higher temperature is required, enter an estimated temperature, and run the program.Check the quartz scaling index calculated at the temperature of interest.Select a somewhat higher or lower silica estimate, and again run the program and obtain the quartz scaling index.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».