Carbonated water injection: Effects of silica nanoparticles and operating pressure
Bibliographic record
Abstract
Carbonated water injection (CWI) is the process of injecting CO2‐saturated water into hydrocarbon reservoirs as a displacing fluid. As CO2 is dissolved in and transported by the flood water, CO2 is more evenly distributed within the reservoir, improving sweep efficiency. This is beneficial to watered‐out oil reservoirs, where high water saturation can adversely affect the performance of conventional CO2 injection. In this study, the effects of increasing CO2 concentration in water using silica nanoparticles, and of pressure on the CWI process were investigated through a number of high‐pressure coreflooding experiments. The experiments were performed in a highly water‐wet core, using normal decane as the oil phase. The results showed an increase in ultimate oil recovery as the level of CO2 concentration in water increased. It was also observed that in the application of nanoparticles, an optimized concentration of nanoparticles must be used to obtain the maximum oil recovery factor. CWI showed a higher recovery factor both in the secondary and tertiary modes at higher pressures, owing to the increased solubility of CO2 in water at high pressures. The results of this study suggest that secondary CWI performs better than tertiary recovery, due to the high probability of contact between the oil and the CO2 gas and a growing concentration of CO2 in the water.
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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) | 0.000 | 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) | 0.001 | 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".