Pore-Scale Investigation of Phase Distribution and Residual-Oil Development During Secondary and Tertiary Solvent Injection
Bibliographic record
Abstract
Summary Flow of three phases, of which one is miscible with another, in porous media may commonly be encountered during enhanced-oil-recovery (EOR) applications in oil reservoirs. Typical examples include solvent (miscible-gas) injection alternated with water and coinjection or alternate injection of steam (or hot water) and solvent in heavy-oil/bitumen reservoirs. Oil, water, and solvent flow together under immiscible (water/oil and water/solvent) and miscible (oil/solvent) conditions at the same time, and the distribution of phases and removal of residual oil in those types of processes depend on many parameters. This paper reports microscale experimental investigations on this complex flow process and provides an extensive parametric analysis on the microscopic displacement efficiency of oil recovery using miscible solvent. For this purpose, micromodels created by use of a replica of sandstones were used. Waterflood residual-oil displacement requires contact of the injected solvent with the blocked oil. The efficiency of this process depends on several parameters, including matrix wettability, oil viscosity, initial water saturation, and reservoir heterogeneity. On the other hand, the sequence of injection of solvent and water is considered as another parameter by which oil recovery can be affected significantly. The results of the micromodel-visualization experiments showed that injection of solvent before the introduction of any water to the reservoir can increase the recovery factor significantly. Existence of the water phase in the reservoir creates capillary barriers that prevent oil/solvent contact. The matrix wettability and oil viscosity were observed to be critically important to the amount of oil recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".