Bibliographic record
Abstract
Technology Focus The future of “unconventionals” is upon us! The 21st century will see the growth of clean renewable-energy sources. Natural gas will play a prominent role. Currently, a large portion of the natural gas comes from unconventional sources (e.g., shale gas, tight gas, coalbed methane, and, soon, gas hydrates). Shale and tight gas exploitation are widely commercialized, with constant improvement of fracturing techniques to increase yield and decrease costs. North America is leading the way in unconventional-resources recovery and technology development. Transfer of these technologies to up-and-coming players has become essential. This has to be managed carefully to minimize environmental, societal, and political effects. Nations that are deficient in natural resources are turning to importing necessary fuels or wisely gathering renewable resources. For example: What will Japan do after the accident at the Fukushima nuclear plant? Will it still invest as much as it used to in nuclear power to supply its energy needs? Will Japan increase its imports of nonrenewable energies, such as liquefied natural gas, or rely on renewable energies such as wind power? What will be the nature of its main demand for energy in the near future and in the long term, and how will it strike a delicate balance between these options? China has emerged as the second world power behind the USA. It also has immense nonrenewable resources that are being exploited. East/West relationships are changing with regard to energy resources. Other players besides China (i.e., India, Australia, and Europe) are starting to exploit their unconventional resources by taking advantage of the dissemination of new technologies. Inevitably, cards must be redealt in light of this new reality. What are the new challenges of this century? Who will be the new energy partners of the western world, and what kind of relationships will be built? How will this new reality influence the choices of economic and resource development? Future opportunities are great, and unconventional resources clearly will play a key role in our future energy landscape. Unconventional Resources additional reading available at OnePetro: www.onepetro.org SPE 137667 • “Natural-Gas Hydrates: Development and Test of Innovative Methods for Gas Production From Hydrate-Bearing Sediments” by J.M. Schicks, Helmholtz Centre, Potsdam German Research Centre for Geosciences, et al. SPE 140864 • “Managing Environmental Risks From Shale-Gas Exploration—Applying Lessons Learned in the US to New Ventures in Poland” by F.V. Jones, SPE, ERM Southwest, et al. SPE 139723 • “Enhanced Gas Recovery and CO2 Storage in Coalbed-Methane Reservoirs: Optimized Injected-Gas Composition for Mature Basins of Various Coal Rank” by Karine Schepers, SPE, Advanced Resources International, et al. SPE 144093 • “Application of Propane (LPG)-Based Hydraulic Fracturing in the McCully Gas Field, New Brunswick, Canada” by Don LeBlanc, SPE, Eastex Petroleum Consultants, et al.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.195 | 0.113 |
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".