Bibliographic record
Abstract
President's column This is a great time to be an engineer. Global workforce changes and other challenges today present tremendous opportunities for personal growth and career advancement. We E&P professionals are witnessing an extraordinary convergence of factors that are reshaping the energy workforce and will profoundly impact our industry and our careers. I would like to share with you my thoughts on this situation and suggest how SPE can help members maximize their opportunities for professional growth. According to the latest Schlumberger Business Consulting (SBC) HR Benchmark survey,* four global workforce realities are significantly shrinking the availability of technical talent in our industry: the “big crew change,” the shortage of mid-career professionals, the shift to more unconventional oil and gas resources, and post-Macondo regulatory challenges. The big crew change, long talked about, is now in full swing. An entire generation of Baby Boomers, who entered the work force in the late 1960s and early 1970s, is moving on to other activities. The SBC survey found that about 25% of the current experienced workforce of petroleum engineers and geoscientists is over age 50, with the majority likely to retire in the next 3 to 5 years. This amounts to about 22,000 experienced professionals expected to leave the industry by 2014. The SBC survey also found that graduate schools are expected to replace only about 17,000 of those in that same time period, leaving the industry with a technical talent gap of 5,000 experienced professionals in the next several years. At the same time, to meet the growing demand for energy, the industry must use ever more complex and sophisticated technologies. That will require that our E&P professionals stay current on the latest technologies and their applications. How can SPE help you respond to these challenges and better enable you to ply your engineering skills in an energy-thirsty world? SPE does many things extremely well, such as meetings and publications. But there are some areas, such as training and education, where we can focus more attention. That is one area where SPE is already making great efforts. Today, SPE offers more than 80 training courses** to help you solve real-world problems. You can find our courses across the globe, held in conjunction with SPE conferences and workshops as well as at our regional training centers in Calgary, Houston, and Dubai. Courses are also offered in other venues to meet member needs. ** “2010 SBC Oil & Gas HR Benchmark, Schlumberger Business Consulting, March 2011 (www.sbc.slb.com/SBCInstitute). ** See www.spe.org/training/.
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 | 0.000 |
| 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".