Evaluation of micro-dispersion on oil recovery during low-salinity water-alternating-CO2 processes in sandstone cores: An integrated experimental approach
Bibliographic record
Abstract
Low-salinity water (LSW) and CO 2 could be combined to perform better in a hydrocarbon reservoir due to their synergistic advantages for enhanced oil recovery (EOR); however, its microscopic recovery mechanisms have not been well understood due to the nature of these two fluids and their physical reactions in the presence of reservoir fluids and porous media. In this work, well-designed and integrated experiments have been performed for the first time to characterize the in-situ formation of micro-dispersions and identify their EOR roles during a LSW-alternating-CO 2 (CO 2 -LSWAG) process under various conditions. Firstly, by measuring water concentration and performing the Fourier transform infrared spectroscopy (FT-IR) analysis, the in-situ formation of micro-dispersions induced by polar and acidic materials was identified. Then, displacement experiments combining with nuclear magnetic resonance (NMR) analysis were performed with two crude oil samples, during which wettability, interfacial tension (IFT), CO 2 dissolution, and CO 2 diffusion were quantified. During a CO 2 -LSWAG process, the in-situ formed micro-dispersions dictate the oil recovery, while the presence of clay minerals, electrical double-layer (EDL) expansion and multiple ion exchange (MIE) are found to contribute less. Such formed micro-dispersions are induced by CO 2 via diffusion to mobilize the CO 2 -diluted oil, alter the rock wettability towards more water-wet, and minimize the density contrast between crude oil and water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".