The Challenge of Predicting Field Performance of Air Injection Projects Based on Laboratory and Numerical Modelling
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Abstract Air injection-based enhanced oil recovery processes are receiving increased interest because of their high recovery potentials and applicability to a wide range of reservoirs. However, most operators require a certain level of confidence in the potential recoveries from these (or any) processes prior to committing resources. This paper addresses the challenges of predicting field performance of air injection projects using laboratory and numerical modelling. Laboratory testing, including combustion tube tests, ramped temperature oxidation and accelerating rate calorimeters can supply data for simple analytical models, as well as providing important insights into potential recovery-related behaviours. These tests are less suited to providing detailed kinetic data for direct and reliable use in numerical simulators. Indeed, the oxidation reactions are sufficiently complex that, regardless of how powerful the thermal reservoir simulator is, its predicting capability will strongly depend on the engineer's understanding of the process and ability to model the most relevant oxidation behaviours of the particular oil reservoir under study. It is proposed that the optimum design cycle for air injection-based processes is to perform laboratory testing that would aid in the understanding of the process and in the design and monitoring of a pilot-scale field operation. Analytical models and simplified, semi-quantitative reservoir simulation models would be employed at this stage. If this evaluation stage is successful, a pilot operation would be initiated and the data gathered during the pilot, as well as laboratory oil property and compositional data, would then be used to history match and tune a model for predictions of the full field operation. Introduction This paper has been written in response to questions which many reservoir engineers express when evaluating the feasibility of air injection as an enhanced oil recovery process for their fields. Questions such as, "What laboratory tests are available? What type of data is provided by each test? How do we use the lab results to predict field performance?" are not uncommon, and, although there are not straightforward answers, a discussion on the usefulness of different lab tests is presented to clarify some of the related concepts. This document has also been written in response to the concerns and comments expressed by many reservoir simulation practitioners when matching combustion tube tests and other supporting oxidation experiments, and trying to predict field performance of an air injection project based on kinetic parameters obtained from such tests. Questions such as, "How do we use the lab data in the reservoir simulator? What are the limitations of thermal reservoir simulation when predicting field performance of air injection projects?" are addressed to provide additional feedback and promote further discussion. Additionally, this manuscript describes some of the combustion behaviours which have been observed by the In Situ Combustion Research Group (ISCRG) at the University of Calgary while performing combustion tube tests and supporting cracking/oxidation experiments, and gives some recommendations to improve the modelling of the combustion process using thermal reservoir simulators.
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.
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 | 0,001 |
| 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 découle