Notice bibliographique
Résumé
Introduction Because of restructuring and cost cutting during the past few years, most of the national and international petroleum sector companies have closed their research centers. The objective of this article is to show that there is still money to be made by performing R&D studies to reduce the cost of producing oil and gas. For the past 24 years, Hycal Energy Research Laboratories Ltd. has been involved in contract research for companies around the world. From this research, a number of papers have been published in recognized technical journals. Because of client requests, other research has not been published, indicating the value of R&D. This research has given them a competitive advantage over other companies. There are two main types of research: one is "ivory tower," and the other is missionoriented. Ivory tower researchers are often pictured as "mad scientists" in white lab coats pouring chemicals with rising smoke and arcing electricity. They create arcane equations, theories, and laws, which no one but the researchers can understand. It is, however, these equations, theories, and laws which mission-oriented or applied researchers use to develop products, services, and techniques to increase the efficiency and profit of small, medium, and large corporations. This article will discuss research in Canada and upstream research for the petroleum sector. The article will also give examples of how research has increased the productivity of oil and gas wells. Equation(available in full paper) Research in Canada Canada, as a nation, is not known for the amount of money spent on R&D. This problem has been recognized for a number of years by the federal government and programs have been instituted to motivate industry to perform more R&D. Unfortunately, these programs have not resulted in increased levels of money being spent. Figure 1 shows the percentage of gross domestic research and development costs (GERD) compared to the gross national product (GNP). The figure indicates that, of the G7 countries, only Italy is lower than Canada in the amount spent on R&D. Sweden and Israel have been added to the plot because of their high percentage spent on R&D. Equation(available in full paper) A UN development report endeavored to show the social and economic aspects of R&D. Figure 2 shows the number of scientists per million population for several countries. The U.S., Japan, and Sweden have the highest numbers; Canada is about average. Equation(available in full paper) Figure 3 presents the number of patents per million population for several countries. Japan has the highest number of patents issued, while Canada and Italy are extraordinarily low. Figure 4 shows the income realized from licenses and patents in U.S. dollars per million population for a number of countries. While Canada is low, it has performed better than France, Germany, and Italy. Figure 5 shows a correlation between the percentage of GERD to GNP and the parameters plotted in Figures 2 to 4. The data show a positive correlation up until about 3% of GERD to GNP is reached for all the parameters.
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 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 | 0,007 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,008 |
| Études des sciences et des technologies | 0,002 | 0,008 |
| Communication savante | 0,015 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,037 | 0,025 |
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 ».