Identifying Viable EOR Thermal Processes in Canadian Tar Sands
Notice bibliographique
Résumé
Abstract Current and forecasted prices for gas and crude oil are generating increased demand for property evaluations utilizing thermal methods. Operators and investors require fast and reliable property evaluations identifying the technical and economic feasibility of implementing thermal processes whether the field is considered as a potential acquisition or for redevelopment. In most cases these evaluations must be performed with limited time and information. Additionally, reservoir complexity, CAPEX, OPEX, and the number of options available for field development make the evaluation more difficult. This paper describes a quick and comprehensive risk management screening methodology, based on prior field experiences, that has been used extensively for thermal processes in Canadian tar sands. The methodology includes geologic evaluation and screening (e.g. estimation of vertical and lateral heterogeneity indexes), conventional and advanced EOR screening, including thermal screening, as well as analytical and numerical simulations coupled with decision-risk and/or economic evaluation models. This methodology enables the identification of potential field analogues, the reduction in uncertainties based on field history, the development of production risk profiles based on reservoir heterogeneities, the identification of optimal recovery processes by area, and the creation of reservoir development plans. Field case evaluations, including CO2 sequestration options (e.g. from bitumen gasification), are also presented. Introduction International field experience shows that thermal methods, such as in-situ combustion and steam injection, continue to be the most technically and economically suitable processes used for the recovery of heavy oil resources. Of particular interest in this paper are Canadian tar sands, which are developed primarily by means of oil mining and in-situ methods. However, if bitumen resources lie deeper than 100 meters, then Cyclic Steam Stimulation (CSS) and Steam-Assisted-Gravity-Drainage (SAGD) represent the most common in-situ oil recovery techniques for recovering these heavy oil resources. All data presented in this paper were changed and do not represent any particular area or property. For all practical purposes, the data and results shown in this paper are presented to illustrate the methodology rather than the results. It is quite commonly assumed that SAGD is the most viable method for in-situ recovery of Canada's tar sands deposits. However, evidence addressing the limits of SAGD applicability and comparing the results of SAGD to CSS is insufficient [1]. Two examples of operators employing CSS recovery methods instead of SAGD in Canadian tar sands are the IOL Cold Lake and Shell Peace River projects. IOL Cold Lake has been operating since the mid 1980's and has recently reported recovery factors ranging from 10 – 40% (with an average of 25% of the OBIP) and Steam-Oil-Ratios (SOR) lower than 3.4 [2]. The other CSS project, Shell Peace River, recently announced the conversion of all SAGD pads to CSS employing different well architectures [3]. To identify the EOR thermal recovery methods most likely to succeed in a specific Canadian tar sand property, a fast screening and evaluation methodology was developed that has now been successfully applied in several property evaluations.
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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 | 0,001 | 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) | 0,001 | 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 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 ».