Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.025 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".