Bibliographic record
Abstract
In May, JPT surveyed SPE members to get their assessment of this magazine and their suggestions for ways to improve it and make it more relevant to them. The online survey was sent to a random sample of members worldwide. Of the respondents, one-third were from the U.S. and two-thirds outside the U.S.; half were engineers, and a quarter were managers; and 32% work for service companies and manufacturers, while 21% work for international oil companies, 19% for independents, and 7% for national oil companies. To the more than 2,000 members who responded, thank you for taking the trouble to complete the survey. It will help us do a better job of giving you the information you need in your work and in your careers. Now, to the findings. The overall content of JPT was rated “good” to “excellent” by 95% of the survey respondents. This is slightly higher than the feedback we received in our most recent reader survey 2 years ago. JPT is considered to be “useful in their jobs” by 88% of readers; again, a little higher ranking than the previous survey. Employees at national oil companies find the information most useful. The most popular features in the magazine, in order, are Technology Update, Technology Applications, the summaries of the technical papers, and the quarterly Case Study feature. The Case Study article was added after the 2003 survey found that our readers wanted more case study and practical application information. The most popular technical topics, in order, are: field development; high-pressure/high-temperature challenges; multilateral/extended reach; mature field revitalization; well stimulation; horizontal and complex trajectory wells; reservoir performance and monitoring; formation evaluation; production operations; and well testing. The most favored nontechnical topics include career development articles, compensation and salary surveys, project management information, industry software reviews, and oil and gas industry politics.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".