Development of Coalbed Methane in Australia: Unique Approaches and Tools
Bibliographic record
Abstract
Abstract Coalbed Methane (CBM), also known as Coal Seam Gas (CSG) or Coal Seam Methane (CSM) in Australia is fast becoming a significant contributor to the country's energy needs. The potential coal seams for methane production in Australia are found in coal zones that are Jurassic to Permian in age with coal ranks ranging from sub-bituminous to low volatile bituminous coals. Many Australian coal seams contain high volumes of methane gas upto 25 m3 per tonne. Australia began producing CBM in 1988 but it was not until 1996 when the commercial CBM production started in the state of Queensland. Australia has total CBM reserves of about 300 to 500 Tcf (8.6 to 14.3 trillion m3). With the total amount of CBM in-place reserves worldwide estimated to be between 3,500 and 95,000 Tcf (100 and 272 trillion m3), CBM is considered one of the world's largest sources of fossil fuel. In the United States, the total CBM in-place reserves are estimated at 749 Tcf (21.4 trillion m3), and CBM now represents almost 10% of its domestic natural gas production. Canada has just begun producing gas from CBM reservoirs and its estimated in-place CBM reserves are about 1,300 Tcf (37 trillion m3). In Australia, as CBM is seen as a clean and pipeline-quality energy, it is rapidly developing. Along with the large CBM resource, the main drivers for this move are the continuously reducing cost of coal seam gas production, and the depleting conventional energy resources. A number of sophisticated CBM reservoir simulation and exploitation tools have been developed by the University of New South Wales and CSIRO to simulate the conventional CBM production as well as the CBM recovery using multi-component gases. In the field, Australia looks toward enhancing CBM recovery by injecting nitrogen and or carbon dioxide to increase CBM extraction. This paper will focus on how Australia is maximizing its CBM production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".