Past, Present, and Future Trends in Petroleum Research
Notice bibliographique
Résumé
Abstract Previously the large international oil companies set high standards for fundamental petroleum research and used innovation to differentiate their activities and to stay ahead of the competition. Along with national and local government agencies, industry strongly supported university programs to educate the required engineers and scientists. The research and development efforts were extremely successful in filling the increasing energy demand while simultaneously reducing the costs to find, extract, transport and refine oil, and to deliver quality products to consumers. The price of oil and other fossil fuels in aggregate, although sometimes rising and falling sharply over relatively short time periods, remained nearly constant on an inflation-adjusted basis. The availability of inexpensive energy substantially increased the world-wide standard of living. This is an outstanding record of technical, economic and social achievement for the oil companies and is a direct result of the fundamental research and investigative engineering studies those companies supported. The most recent round of painful restructuring, consolidations and layoffs that started in the 1980s caused many research facilities to close and research support to decline or vanish. The prolonged research cutback produced a deficit in the creation of new ideas and out of the box solutions for reducing costs and making technological breakthroughs in our industry. In fact, even value engineering, which has been so successful in other industries, has not really penetrated the oil industry. There are definitely some exceptions, but what is portrayed as fundamental research or investigative engineering is really orthodox design engineering. The desired outcome for the most part is incremental improvements in existing technologies. In this situation, what is the future for petroleum Research and Development? Who will do it? Who will pay for it? Carbon dioxide and other greenhouse gas emissions policies are already changing the oil industry. Oil and gas companies along with utility companies may desire or be called on by governments to provide reduced or zero emissions electricity generation. Will the research and development be done in universities, in government laboratories or in the oil service companies? The large service companies are currently spending more on developmental Research and Development than the major oil companies combined. Should governments play an increased role in encouraging and supporting Research and Development? If so, how should that be done? By way of tax incentives, direct funding, requirements before leases are sold, and etc.? The authors have addressed some of these questions in a previous paper (SPE 69611). In that paper, we presented ideas and opinions on the directions that international oil companies may take in response to the external forces that are reshaping the global economy. In this paper, we address and catalogue a list of important Petroleum Engineering research themes, desirable university curricula changes that are necessary to meet the new economy demands, and then possible funding and collaboration models to support petroleum R&D.
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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 | 0,006 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,005 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 ».