<scp>CO<sub>2</sub></scp> mobility control by small molecule thickeners during secondary and tertiary enhanced oil recovery
Bibliographic record
Abstract
Abstract Recently, polymer thickeners have been considered for CO2 mobility control during enhanced oil recovery (EOR) processes. Despite that, the requirement of co‐solvents is a controversial challenge for the solution of high‐molecular weight thickeners in gases. This study is focused on small molecule thickeners for carbon dioxide EOR without adding co‐solvents. Polydimethylsiloxane (PDMS) was used as a CO2‐philic thickener in different low molecular weights. Cloud‐point pressure, relative viscosity, and interfacial tension (IFT) between intermediate crude oil and pure/thickened CO2 were measured at reservoir conditions. Also, the impact of PDMS‐CO2 thickener on gas mobility control was evaluated during the coreflooding experiments in secondary and tertiary modes. The experimental results show that PDMS caused an increase in relative viscosity up to 4.7‐fold and successfully thickened CO2. In addition, the minimum miscibility pressure of PDMS‐thickened CO2 was lower than that of pure CO2, and miscible PDMS‐CO2 thickener occurred at higher PDMS molecular weights. However, the gas breakthrough time can be considerably delayed if the PDMS‐thickened CO2 was flooded directly, which increased the oil recovery factor between 6% to 15% during tertiary recovery.
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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 | 0.000 |
| 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) | 0.001 | 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".