Comparison Study of Two Different Methods on the Localised Enkf on SAGD Processes
Notice bibliographique
Résumé
Abstract The localization on the automatic history matching of SAGD processes has not been studied. Also the distance-based localization is not applicable to SAGD processes. That is because in SAGD processes oil is produced mainly from the transition zone instead of regions centered producer. Distance cannot be used as the localization scale for SAGD processes. As the transition zone could be defined by temperature distribution, a new localization function with temperature as localization scale was developed. The temperature-based localization function was obtained through modifying distance-based localization function. The localization regions were determined through covariance analysis by using a large ensemble. Based on the covariance analysis, the temperature range of the transition zone is determined. The localization function is 1.0 for the regions within this temperature range. Beyond or below this range, the localization function reduces from 1.0 and at the critical or steam temperature the localization function reduces to zero. The localization is applied to covariance of data with permeability, saturation and temperature, as well as the covariance of data with data. The general localization function was developed by sensitivity analysis with synthetic cases. It could be applied to any SAGD cases with different steam temperatures and reservoir parameters. In the numerical simulation process, the history matching results showed that without localization, the variability in the ensemble collapsed very quickly and lost the ability to assimilate later data with a small ensemble (50 ensemble members) while the prediction was far from the reference with data mismatch remaining at a high level. The temperature-based localization is able to avoid the collapse of the ensemble variability with a smaller ensemble (10 ensemble members) which saves computation time and gives better history-match and prediction results. The computation time was analyzed in the work. With the temperature-based localization the computation time is greatly decreased by 75%. And another approach (localised EnKF with oil saturation) is also applied in this study to compare the calculation rate and accuracy. For this two methods, the localization regions should be the regions that have true correlations with production data. Dynamic data-temperature and oil saturation is used as localization scale in the localization function. And the temperature range and oil-saturation range is defined at which oil is flowing. And critical temperature is defined at which oil begins to get mobile. The localization function is 1 for regions in the temperature and oil saturation range, and decreases to 0 when the temperature reaches the steam temperature or critical temperature, or oil saturation reached initial oil saturation or residual oil saturation. To get the best history matching and prediction results, the localization function is applied to the Kalman gain and covariance matrices separately.
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 | 0,002 | 0,005 |
| 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 | 0,001 | 0,000 |
| 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) | 0,001 | 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 ».