Importance of conformance control in reinforcing synergy of CO2 EOR and sequestration
Bibliographic record
Abstract
Injecting CO2 into hydrocarbon reservoirs can enhance the recovery of hydrocarbon resources, and simultaneously, CO2 can be stored in the reservoirs, reducing considerable amount of carbon emissions in the atmosphere. However, injected CO2 tends to go through fractures, high-permeability channels and streaks present in reservoirs, resulting in inefficient hydrocarbon recovery coupled with low CO2 storage performance. Conformance treatments with CO2-resistant crosslinked polymer gels were performed in this study to mitigate the CO2 channeling issue and promote the synergy between enhanced oil recovery (EOR) and subsurface sequestration of CO2. Based on a typical low-permeability CO2-flooding reservoir in China, studies were performed to investigate the EOR and CO2 storage performance with and without conformance treatment. The effect of permeability contrast between the channels and reservoir matrices, treatment size, and plugging strength on the efficiency of oil recovery and CO2 storage was systematically investigated. The results indicated that after conformance treatments, the CO2 channeling problem was mitigated during CO2 flooding and storage. The injected CO2 was more effectively utilized to recover the hydrocarbons, and entered wider spectrum of pore spaces. Consequently, more CO2 was trapped underground. Pronounced factors on the synergy of EOR and CO2 storage were figured out. Compared with the treatment size, the CO2 storage efficiency was more sensitive to the plugging strength of the conformance treatment materials. This observation was important for conformance treatment design in CCUS-EOR projects. According to this study, the materials should reduce the channel permeability to make the channel/matrix permeability ratio below 30. The results demonstrate the importance of conformance treatment in maximizing the performance of CCUS-EOR process to achieve both oil recovery improvement and efficient carbon storage. This study provides guidelines for successful field applications of CO2 transport control in CO2 geo-utilization and storage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".