Worldwide Comparison of CO₂-EOR Conditions: Comparison of fiscal and industrial conditions in seven global regions where CO₂-EOR is active or under consideration
Bibliographic record
Abstract
Previous work within the Scottish Carbon Capture & Storage (SCCS) joint industry project (JIP) on carbon dioxide enhanced oil recovery (CO2-EOR) which looked at financial incentives for CO2-EOR in the United Kingdom (UK) suggested that development of an EOR project in the UK continental shelf area was most likely only to be considered by a super-major or multinational oil company (Durusut and Pershad, 2014). For such a project to be initiated the overall conditions for CO2-EOR - financial, policy, industrial - would need to be equivalent or favourable compared to other oil-producing regions, otherwise investments would likely be made elsewhere. The purpose of this work package was to compare such conditions between seven major oil- producing regions that either are already, or are considering using CO2-EOR to increase oil outputs. The regions chosen were: • United States of America (USA) onshore • USA Gulf of Mexico • Canada • Malaysia • China • Norway • UK This report covers initial, desk-based research to compare regional conditions for CO2-EOR developments focussing in particular on tax regimes and also covering CO2 supply availability and CO2 transport infrastructure. Other areas of comparison - energy policies, regulatory conditions and government support - are not covered in this report but may be included in further studies.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".