Life-Cycle Analysis of CO<sub>2</sub> EOR on EOR and Geological Storage through Economic Optimization and Sensitivity Analysis Using the Weyburn Unit as a Case Study
Bibliographic record
Abstract
At the global, national, and subnational levels, many policies have been created or are in the process of development to deal with greenhouse gas (GHG) emissions, particularly CO 2 . CO 2 enhanced oil recovery (EOR) is an option available to governments and industry to help meet emission reduction levels. In addition to increasing the production of oil, the CO 2 can be stored in the oil reservoir for a very long period of time. However, CO 2 capture and CO 2 EOR operation result in significant costs and energy penalties, for example, CO 2 capture from a point source, transportation to the site of use, and recycling produced CO 2 . This article evaluates the life cycle of CO 2 storage from delivery to the oil field through the production, transportation, and refining of the oil and identifies opportunities for optimization. Information from the IEA GHG Weyburn Monitoring and Storage Project is used to provide baseline information for the storage of CO 2 . The value of this life-cycle study lies in the development of an understanding of the “carbon” economics of the EOR process and the impact on net storage of changes to the value of different components in the chain. These results provide a mechanism whereby environmental consequences can be evaluated within economic decision-making.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".