Notice bibliographique
Résumé
In last year's overview of Reservoir Simulation and Visualization, the focus was on technology designed to handle the ever-increasing volumes of data that are readily available for use by reservoir engineers. This trend has continued and has resulted in some extremely innovative adaptations of technology toward reservoir management. Large volumes of data produce large models, which in turn produce large amounts of simulation results. This large volume of simulation results is difficult to analyze by conventional means. Significant improvements have been made by extending the methods of analysis of reservoir-simulation results to use the senses of hearing and touch, in addition to sight, to better understand the mechanisms at work in large full-field models. Research has been done with methods of artificial intelligence to predict reservoir performance, such as the training of neural networks. It is essential that research continues in these innovative areas as the industry strives to maximize production to meet market demands. On the traditional front, important research has continued in the area of grid coarsening and scaleup to reduce model sizes to more-manageable levels. Significant contributions have been made by both industry and academia, with improved methods for selecting layers for grouping and with new algorithms for including additional reservoir flow characteristics in the scaleup calculations. As computing power continues to increase, the inclusion of compositional fluids and geomechanical effects in full-field reservoir models is becoming more feasible. Workflows continue to be invaluable to reservoir management. With the ambitious goal of simulating the geological model, significant work remains to enable seamless transition of geological models through to numerical simulation. As the industry continues to move closer to this goal, we will also move closer to the ultimate goal of managing risk and uncertainty better. Ongoing work to provide the tools to understand both the input and the output of these large models will largely determine the success of this endeavor. Reservoir Simulation and Visualization additional reading available at the SPE eLibrary: www.spe.org SPE 96410 "Reservoir Simulation and Reserves Classifications—Guidelines for Reviewing Model History Matches To Help Bridge the Gap Between Evaluators and Simulation Specialists," by D. Rietz, SPE, Ryder Scott Co., et al. SPE 94319 "Improved Coarse-Grid Generation Using Vorticity," by H. Mahani, SPE, Imperial College London, et al. SPE 97155 "How To Approximate Effects of Geomechanics in Conventional Reservoir Simulation," by A.T. Settari, SPE, U. of Calgary, et al. SPE 96416 "Tuning an Equation of State—The Critical Importance of Correctly Grouping Composition Into Pseudocomponents," by A.A. Al-Meshari, SPE, Saudi Aramco, et al.
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 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) | 0,000 | 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 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 ».