Effect of CO2 Impurities on Gas-Injection EOR Processes
Bibliographic record
Abstract
Abstract Carbon dioxide flooding is a promising enhanced oil recovery method both on technical and, if operating costs are properly controlled, economic grounds. Injecting this greenhouse gas also has environmental merits. Flue gas from power plants is a ready source of CO2; however extracting CO2 for enhanced oil recovery from such a source will increase project costs. Furthermore, to reduce both the net CO2 utilization and the cost of purchasing gas, it is usually necessary to recycle the produced CO2 with as little purification as possible. Therefore, understanding the roles of impurities in fluid phase behaviour and miscibility characteristics is necessary for designing a cost-effective CO2 enhanced oil recovery process. Laboratory studies of the effect of CO2 impurities on phase equilibrium and minimum miscibility pressure (MMP) were conducted on two Saskatchewan light oils covering a range of densities from 29.5°API to 38°API. The results indicate that the MMP for these light oils could increase unfavourably as the N2 and/or CH4 concentration increased in the CO2 stream. The MMP changes as the type and concentration of impurities in the injected CO2 stream change. However, coreflood tests showed that the near-miscible CO2 displacement might employ the same mechanisms as miscible CO2 flooding to mobilize and displace oil; thus, good oil recovery can be achieved in the vicinity of the MMP. While laboratory measurements are essential in the evaluation of a gas injection process, an equation of state (EOS) simulation was demonstrated to be a useful tool in analyzing the phase behaviour of various injection gases, reservoir fluids, and the gas-oil interactions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".