Bibliographic record
Abstract
President's column In the last couple of years, we have seen an extraordinary growth of unconventional gas activity that is literally reshaping our industry—notably, tight gas reservoirs and gas shales in North America and CSG/LNG projects in Australia (CSG means coal seam gas, which is another name for coalbed methane). These advances were made feasible by technology, which made possible what was once considered impossible to achieve (production per well, sufficiently low cost per well, etc.), but also by perseverance. We must not forget that it took about 20 years to find the key to unlock the potential of these unconventional gas plays, whose resource had been identified a long while ago. This remark applies also to the development of another unconventional hydrocarbon, the oil sands of Canada; it took 20 years from the concept of steam-assisted gravity drainage (SAGD), and the first patent by Roger Butler, to significant production using this technology As recently as 5 years ago, almost nobody was forecasting what is happening now with gas shales. Our current success has been made possible by a few technological breakthroughs, or step changes, which are by essence difficult to predict. I think that will be the case for all unconventional hydrocarbons, such as oil shales and, perhaps someday, gas hydrates. It is almost impossible to make a reliable guess about when gas hydrates will come onstream with significance, but this will happen at some time. It is also interesting to compare oil shales and gas shales—oil-shale production still faces a lot of technological barriers, and the economics look quite uncertain, which is exactly the situation gas shales were in 20 years ago. Unconventional hydrocarbons are interesting for two reasons: (1) the resources are huge, and may cover our needs for more than a century; and (2) the geographic distribution of those resources, which is mostly in non-OPEC countries. The gas resource is important in North America, central Europe, and Australia. The oil-shales resource, as known today, is mostly in North America and “new” countries such as Jordan, Lithuania, Morocco, and a few others. A very important point is that, in addition to “traditional” technology challenges, the development of all types of unconventional hydrocarbons is facing similar environmental issues: One is land use—in all cases, footprint is important, mostly due to the large number of wells. The other is water issues—production of dirty water (to be treated adequately), or the risk or fear of contamination of aquifers when fracturing the pay zone.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.060 | 0.022 |
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".