The search for new oil and CO2 storage resources: residual oil zones in Australia
Bibliographic record
Abstract
Residual oil zones (ROZs) could present a new, potentially large and commercially viable oil resource for Australia and provide an avenue for geological storage of carbon dioxide (CO2) through CO2 enhanced oil recovery (CO2-EOR). These reservoirs, which can contain a moderate amount of residual oil and resemble water-flooded oil fields, can be associated with conventional fields (brownfields) or occur with no associated main pay zone (greenfields). Both types of ROZ are currently produced commercially through CO2-EOR in the Permian Basin, USA, and are of growing interest internationally, but our understanding of the occurrence and economic viability of oil production from ROZs in Australia is limited. We are employing geological and petrophysical methods to identify, map and quantify the potential oil resources of ROZs, initially in central Australian basins. Complementing this, we are conducting a series of CO2 core-flooding experiments combined with reservoir modelling to investigate the techno-economic feasibility of producing oil and storing CO2 in these formations. We aim to establish and test a workflow for characterising and evaluating ROZs in Australia. ROZs could prove to be good targets for CO2-EOR+, potentially even producing carbon-neutral or carbon-negative oil by using CO2 from anthropogenic sources, such as from blue hydrogen 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".