A Reservoir Screening Methodology for SAGD Applications
Notice bibliographique
Résumé
Abstract A new methodology to determine the potential SAGD, "Steam Assisted Gravity Drainage", application for Venezuelan reservoirs, with °API gravity less than 20, is proposed as a first phase to select the most proper candidates. A systematic ranking of all the possible reservoir candidates using screening criteria supported by statistical methods is performed. Based on the evaluation of several analytical models a more representative analytical tool was generated to evaluate and predict the performance of SAGD process. Also, this analytical tool was used to define the critical parameters and the statistical analysis was used to estimate the relative influence of each one. Ranking method was based on a similarity approach. By defining a reference reservoir from published field tests and comparing statistical distances from it thirty-five candidates were pre-selected from a total of 1067 Venezuelan reservoirs. The best candidates should be evaluated with more details through numerical simulations. The resulting technical efficiency and economic balance will be taken as a decision criterion for a field application. This methodology will facilitate the decisions related to the application of SAGD process to increase the recovery factor in Venezuelan heavy oil reservoirs and upon its potential massive application. Introduction Steam Assisted Gravity Drainage (SAGD) is visualized as the technology with the greatest potential to increase the recovery factor in heavy and extra-heavy oil reservoirs. This technology is based on the steam continuous injection process using two horizontal wells, one injector and one producer, which allows the maximum sweep efficiency due to the predominant effect of the gravity drainage production mechanism. The study and consequently the understanding of this process has been based on laboratory evaluations, analytical and numerical model development and pilot tests showing significant large recovery factor, greater than 65 %. SAGD concept was proposed by Dr. Butler from University of Calgary, Canada in 19811. Although the first field test was performed in 1987 at Underground Test Facility (UTF), Athabasca, Canada2. In Venezuela this technology was proved in 1997 through a successful field test in Lagunillas area, at Eastern coast of Maracaibo Lake3. This test showed a 62 % recovery factor after 3 years of injection, and allowed the validation of the technology potential. However, the full understanding of the process mechanisms and its behavior for the Venezuelan reservoir characteristics is not yet achieved. In consequence, it is very important to generate proper methodologies for evaluation, selection, and ranking reservoir candidates for SAGD applications in Venezuela. Due to the complexity involved in the SAGD process, the identification of the most influencing variables on the successful application of this process is required. DEVELOPMENT OF ANALYTICAL TOOL Presently there are commercial analytical tools for the SAGD process evaluation, which have been developed and validated with information from Canadian reservoirs. The application of these tools is limited to pre-established ranges for those reservoirs. For this reasons, and aimed at the evaluation of the SAGD process in Venezuelan reservoirs, a new analytical tool was generated to evaluate the performance of the SAGD process.
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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 | 0,000 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».