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
Technology Focus I was asked to serve on the JPT Editorial Committee for another 3-year term and happily accepted the offer. In pre-paring for this month’s feature, I revisited my first Technology Focus writeup for the March 2016 issue. I concluded the piece by saying, “Before closing, I would like to bring your attention to two critical points as we experience one of the more severe economic downturns in the oil industry. First, research on technology for heavy-oil recovery must go on one way or another. … Second, cost-effective solutions should be sought and materialized immediately to sustain many ongoing heavy-oil (especially thermal) operations.” What has happened over the last 3 years in relation to these two issues I raised in March 2016? Here are some highlights of my observations. Heavy-oil recovery is a challenge mainly because of the high cost of investment and difficulties and uncertainties in operations; hence, the main issue is the reduction of cost per barrel of oil produced. This reduction to cost can be achieved either by recovery improvement or operational-/capital-expenditure (OPEX/CAPEX) reduction through optimization studies. Recovery improvement requires more research efforts and time-demanding technology development, but the cost reduction is less uncertain and the focus has been on both reduction methods, mainly CAPEX, during the low-oil-price term. OPEX constitutes a greater portion of the total cost than CAPEX. OPEX-reduction efforts have focused on the optimization of artificial lift in producing wells, steam delivery, and monitoring in injectors, as well as tackling problems such as emulsification, sand production, and asphaltene/wax precipitation. Solar steam is an option for OPEX reduction but is still a challenge because it requires high CAPEX and raises sustainability issues. Considering the development of new technologies for efficient recovery improvement, all agree that collaboration is a must, especially in carrying the research results to the field. Yet who (e.g., national oil companies, international oil companies, or service companies) will lead this action is still a question. Government involvement also should be part of this collaboration. Another necessary discussion point is the replacement of costly and environmentally risky steam operations. Nonsteam applications are showing promising results at the laboratory scale (e.g., solvents, electrical heating, and waterflooding with chemicals and nanomaterials), and methods to improve steam efficiency through chemical additives are yet to be tested at the field scale. The cost reduction per barrel of oil produced and the extension of sustainable production life by optimization have been two major areas of focus, but the investments in new technologies and recovery-improvement research have not received sufficient attention during the downturn. Recommended additional reading at nePetro: www.onepetro.org. SPE 189716 Shallow Horizontal-Well Cyclic Steam Stimulation in a Clastic Unconsolidated Unconventional Reservoir in Kuwait: A Case Study by Shaikha Al-Ballam, Kuwait Oil Company, et al. SPE 190111 Laboratory Tests Conducted To Perforate and Displace Viscous Oil From Saturated Formation Core To Help Optimize Steamflood Completion by Dennis J. Haggerty, Halliburton SPE 190770 Visualization of Heavy-Oil Mobilization by Associative Polymer by Tormod Skauge, Centre for Integrated Petroleum Research, 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.
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 | 0,000 | 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