Critical success factors of CBM development - Implications of two strategies to global development
Bibliographic record
Abstract
The US currently gets almost 30% of its gas from unconventional resources and CBM makes up 10% of this, with projections of strong future ramp up. One strong indicator is drilling activity for CBM, which is rapidly growing in the U.S and Canada. In 2007, it is estimated that there was 20% increase of CBM completions in the North America. This helps make the US one of the leaders in CBM and a model for other countries hoping to develop CBM. The North American model exists due to the extensive infrastructure; strong gas prices, strong demand and a declining conventional resource base. Outside of the North America, another key region for CBM is Australia. The country contains about 30 coal-bearing basins, mostly Permian and Mesozoic in age. Based on IHS data, proven reserves in Australia have been estimated at about 10 tcf of gas. Adequate exploration efforts can potentially increase this number to 100 tcf level. However, the two locations are very different from a business model or strategy stand point. In North America, the CBM business is run by traditional oil and gas companies. To monetize the production, CBM producers utilize exiting gas transportation systems and distribution networks. They compete with other sources of supply. While in Australia, the power market and lack of alternatives drive the need from CBM. A typical Australian CBM project is led by a power generation company who moves upstream only to get fuel for power and energy. This tends to be an integrated effort with the company involved in the CBM well site, water management facilities, a 100 km gas transmission pipeline, and the power generation plant. A review of the global database of potential and other active CBM plays indicates that these two strategies/models are applicable in other areas and could also provide some guidance into development of new areas.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".