New directions for shale oil: path to a secure new oil supply well into this century
Bibliographic record
Abstract
Australia has enjoyed near self-sufficiency in crude oil production since the late 1960s but has declined to 80–90 % in recent years. This is expected to drop to below 50 % of demand by 2019–2020. Southern Pacific Petroleum NL is pioneering the development of breakthrough technology to unlock oil from Australia’s largest oil resource, with 17.3 billion barrels of oil shale held in ten deposits along the coast of Central Queensland, Australia. These deposits could support production of more than one million barrels of oil per day, helping to secure oil self-sufficiency for Australia and meet part of a growing oil demand in Asia. The company spent more than A$150 million on research and develop-ment before embarking on a demonstration plant to produce oil commer-cially. It is the depth of this research and its application that has provided a sound basis for the development of a new industry that can help fuel the growing demand for oil in the transportation sector in a way that is environ-mentally responsible. The company’s initial operations at the Stuart deposit, located 15 km north of Gladstone, Central Queensland, is the start of a multi-stage risk-managed approach to developing a shale oil industry using the first-of-a-kind Alberta Taciuk Processor (ATP) technology. Stage 1, a 4,500-bpsd (barrels per stream day) demonstration-scale plant, is a 75: 1 scale-up of a successful laboratory pilot plant developed to proc-ess oil sands in Canada. The Stage 1 plant was constructed in 1997–1999 and has produced over 500,000 barrels of oil to date. A great deal of operat-ing data has been captured and the plant has been able to run in excess of 52 days continuously at peak rates up to 84 % of capacity. A current capital
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".