Bibliographic record
Abstract
Despite the commonly used term ‘unconventional gas’, the gas itself is not unconventional but rather methane and other hydrocarbon compounds, the same as are found in conventional reservoirs. The gas is however trapped in an unconventional manner such as by capillarity or adsorption where the gas occurs as a continuous phase over large areas independent of trap geometry. There are a number of categories of unconventional gas we consider, including coal bed methane, shale gas, tight gas, basin-centred gas and gas hydrates. Initially, the commercial development of unconventional gas was motivated by a desire to improve the safety of coal mining through degassing, coupled with the OPEC (Organization of the Petroleum Exporting Countries) oil crisis of the mid-1970s raising concerns of energy security. This drove the U.S. government to provide tax incentives and invest in the adaptation of horizontal drilling technology and micro-seismic monitoring that ultimately unlocked the shale gas potential in North America. The very nature of unconventional gas resources being widely distributed but with low technical recovery rates makes resource and reserve estimation difficult to define and subject to high uncertainty. Recovery is fundamentally technology dependent and thus as advances occur the resulting available commercially viable reserves could vary widely. The International Energy Agency (IEA) estimates total global technically recoverable gas resources (conventional and unconventional) to be >30 million PJ (>30,000 tcf) of which roughly half is unconventional gas. Despite the rapid development of unconventional gas resources in the United States, closely followed in Canada, and later in Australia; and despite the impact on reducing the cost of energy, reducing greenhouse gas (GHG) emissions and bringing the United States towards energy self-sufficiency, the shift to unconventional gas has not been without its challenges. There have been concerns about the long-term impact of gas development on the environment (particularly groundwater and surface water resources), there has been speculation that methane emissions may result in higher life cycle emissions for power generation, and the historical surface footprint of unconventional gas operations has led to local community concern. Research has focused on addressing these challenges. The IEA predict that between 2010 and 2035 world energy demand will increase between 16% and 47% depending on the policy scenario, and various parts of the energy mix will grow or shrink to various degrees to match this demand. In 2010, the global primary energy consumption consisted of oil (32%), coal (27%), gas (21%) and all other energy sources combined (20%). For electricity generation the mix is coal (40%), gas (22%), renewables (19%, mainly hydro), nuclear (12%) and other sources combined (7%). In all three IEA forward scenarios the demand for gas is expected to grow. This is because of transport flexibility (pipeline by land or liquefied natural gas by sea), a geographically widely dispersed resource base, the cost competitiveness of gas as a fuel, GHG emissions advantage over coal and the flexibility of gas fired power to rapidly match the variable output of distributed renewable energy sources (i.e. achieves grid stability). Public pressure to solve air quality issues in China may also drive down the utilisation of coal fired power aiding the uptake of gas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".