Role of Conventional and Unconventional Hydrocarbons in the 21st Century: Comparison of Resources, Reserves, Recovery Factors and Technologies
Bibliographic record
Abstract
Abstract The paper deals with the availability of natural hydrocarbons until 2100. Starting point of the evaluation is that the global demand will not be met by production of conventional oil and gas. Basis of the discussion is the comparison of available resources and reserves, recovery factors and technologies. The analysis comprised oil shale, tar sand, gas shale, tight sand gas, coalbed methane and gas hydrates. Taking the data of competent organizations into account, obviously the global resources of unconventional oil and gas significantly exceed the availability of conventional natural hydrocarbons in spite of the fact that their recovery efficiency is extremely low. Although the production cost (operation expenditure) of unconventional hydrocarbons is usually much higher than those of the conventional ones, industrial scale production of tar sand oil, tight sand gas and coalbed methane has started over two decades ago and their contribution to total oil and gas production is already substantial in several countries (US, Canada). The authors stated, however, that wider application of sophisticated technology to recover unconventional hydrocarbons needs more extensive and intensive R&D activity and further, new paradigms are necessary in education, research, production, field management, and governmental regulation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".