Notice bibliographique
Résumé
Technology Focus Simulation is about building a mathematical model of the reservoir and then using it to predict, for the purpose of making decisions. That may be restating the obvious, but there are a few points that bear a little re-emphasis. First, it is only a model. Second, if the model does not predict adequately, it does not serve its purpose. There is a growing tendency in some quarters to use very simple models—mainly because they are faster. As my colleague Ed May recently put it, “I know everyone pushes for faster, faster, but doing it right has definite advantages.” Following my recent move into well testing, I have been rereading the late Laurie Dake’s The Practice of Reservoir Engineering. Dake often is regarded as hostile to simulation, but what he really was about was understanding your assumptions, and using the most appropriate model for the decision to be made. At the 2011 SPE Reservoir Simulation Symposium, many papers focused on re-examining original basic assumptions. Correcting or removing those assumptions (improving the model) often led not only to better predictions, but to faster simulation. For example: Is the most recent solution of the well model the best starting point for the next solution? As the industry develops more-complex reservoirs (e.g., shales, naturally fractured, and heavy oil), we are called on to apply our simulation tools to new environments, and we must check constantly that the assumptions that are implicit in those tools are still valid. For example: Do assumptions for light-oil flow in homogeneous sandstone also apply to gas flow in shale? The need to check assumptions applies as much to the data as to the tools. In simulation support, we often see data sets in which hundreds of warning messages about bad data are ignored or even suppressed, and the complaint is about the final failure to converge. Is that discontinuity in the pressure/volume/temperature data really a property of the fluid, or just a measurement artifact? A good way to consistently challenge your assumptions is to read the best of the best technical articles: JPT is here for you! Reservoir Simulation additional reading available at OnePetro: www.onepetro.org SPE 144401 • “Design and Examination of Requirements for a Rigorous Shale-Gas-Reservoir Simulator Compared to Current Shale-Gas Simulators” by Juan Andrade, SPE, University of Oklahoma, et al. SPE 140882 • “Multilateral Complex-Well Optimization” by P.I. Crumpton, Schlumberger, et al. SPE 137507 • “Reservoir Characterization and Flow Simulation of a Low-Permeability Gas Reservoir: An Integrated Approach for Modeling Tommy Lakes Gas Field” by H. Deng, University of Calgary, et al. (See Journal of Canadian Petroleum Technology, May 2011, page 32.) SPE 141935 • “An Adaptive Newton’s Method for Reservoir Simulation” by Pengbo Lu, ExxonMobil, et al. SPE 142093 • “A New Method To Model Relative Permeability in Compositional Simulators To Avoid Discontinuous Changes Caused by Phase-Identification Problems” by Chengwu Yuan, SPE, University of Texas at Austin, 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 | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 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 ».