Research and Development of an Empirical Method for Predicting Oil Recovery During EOR Studies
Notice bibliographique
Résumé
The coefficient of oil recovery (RE) depends on both connate water saturation as well as the residue oil after water, gas, or chemical flooding. In turn, the respective initial and residual fluids saturation depends on petrophysical and reservoir conditions like temperature, reservoir pressure, and overburden pressure. The current study is aimed at developing an empirical model for predicting the coefficient of oil recovery based on the above mentioned parameters, as measured in commercial laboratories during routine and special core analysis studies. The preliminary studies, based on a 100 sandstone- and carbonates-samples database, yielded an excellent diagnostic correlation with R2 > 0.95. The correlation is characteristic for each given reservoir, and hence the ultimate goal of this study is to develop oil recovery coefficient signature charts for each reservoir. The signature charts will be useful in the design of water, gas, and chemical flooding in improved oil recovery projects. Klinkenburg and Forchheimer corrections have been applied, in order to extend the correlation application to different reservoir fluids and formations. The correlation provides a tool for determining a reservoir's RE, where data is scanty.Literature Review. The knowledge of absolute and effective permeability is very useful in drilling and completion design, as well as in designing the stimulation and production method. During the completion, the only data available is mainly routine core petrophysical data. There is no reliable empirical correlation for predicting the core's permeability, initial water saturation, and porosity at the net overburden pressure(1 –3). The existing correlations do not show any generalized results which could be applied in reservoirs in a regional scale. This research is based on core data from different oil and gas pools in Alberta, Canada. The formation types and the number of samples pertinent to each formation are shown in Table 1.Mathematical DevelopmentThe Concept of Storetransmission Coefficient. Consider a unit volume of a fractured core as shown in Figure 1. The fluids in this fractured core are stored and transmitted through the pores and fractures, respectively. Therefore the sumof matrix (porosity x permeability) plus fracture (porosity x permeability) is equal to the total (porosity x permeability) storage and transmission capacity: i.e., storetransimission capacity. Mathematically this relationship can be expressed as per Equation (1).Equation (Available In Full Paper)Equation (5) is used to develop empirical correlations for describing the relationships of permeability, porosity, reservoir net overburden pressure, and connate water saturation in both fractured and nonfractured reservoirs.From laboratory measurements, it is evident that absolute gas permeability at net overburden pressure (KN) is directly proportional to both routine gas permeability (Kgr) and porosity (φr). However it is inversely proportional to reservoir net overburden pressure (PN) In mathematical notation, this can be expressed as follows:Equation (Available In Full Paper)Equation (4) can be changed into an equation by multiplying its right-hand side by a coefficient of propotionality C1. Equation Available In Full PaperEquation (10) can be modified to include connate water saturation.
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 | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 ».