Technical Challenges in the Conversion of CO2-EOR Projects to CO2 Storage Projects
Bibliographic record
Abstract
Abstract While enhanced oil recovery using carbon dioxide (CO2-EOR) is a mature technology and known to concurrently store large volumes of CO2, it is not currently viewed by industry as a CO2 storage process. Application of CO2-EOR for CO2 storage to reduce anthropogenic CO2 emissions 1) enables carbon capture and storage (CCS) technology improvement and cost reduction; 2) improves the business case for CCS demonstration and early movers; 3) supports the development of CO2 transportation networks; 4) may provide significant CO2 storage capacity in the short-to-medium-term, particularly if residual oil zones (ROZ) are produced and hybrid CO2-EOR/CCS operations are considered; 5) enables knowledge transfer; and 6) it helps gaining public and policy-makers acceptance. Although there are a number of commonalities between CO2-EOR and pure CO2 storage operations, currently there are a significant number of differences between the two types of operations that can be grouped in five broad categories: 1) operational; 2) objectives and economics, including CO2 supply, demand and purity; 3) legal and regulatory; 4) long term CO2 monitoring requirements; and 5) industry's experience. There are no specific technological barriers or challenges per se in adapting or converting a pure CO2-EOR operation into a CO2 concurrent or exclusive storage operation. The main differences between the two types of operations stem from legal, regulatory and economic differences between the two. The legal and regulatory framework for CO2 storage is being refined and is still evolving and it is clear that CO2 storage operations will likely require more monitoring and reporting. Because of this, CO2 storage will impose additional costs on the operator. A challenge for existing CO2-EOR operations which may, in the future, adapt to concurrent or exclusive CO2 storage operations is the lack of baseline data for monitoring, except for wellhead and production monitoring for which there is a wealth of data. Thus, in order to facilitate the transition of a pure CO2-EOR operation to concurrent or exclusive CO2 storage, operators and policy makers have to address a series of legal, regulatory and economic issues in the absence of which this transition cannot take place.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".