Bibliographic record
Abstract
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.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".