Experimental evaluation of different strategies for improving oil recovery at high-water-cut by quantitative etched-glass micro-models
Bibliographic record
Abstract
In this paper, microscopic visualization devices and image processing techniques were used to conduct microscopic water flooding experiments. A novel classification criterion for residual oil during the water flooding process was established as follows: contiguous type, branched type, oil film type, columnar type, and droplet type. Correlations between the five types of residual oil saturation and water-cut were established. Also, the recovery improving potential for different production strategies have been investigated by water flooding experiments. The relationship between various types of residual oil saturation and injection velocity were obtained, with the former residual oil saturation decreasing to 16.09% during the injection process when the velocity was increased from 0.05 mL/min to 0.5 mL/min. However, with an increase in injection velocity, the saturation of contiguous residual oil type decreased first and then stabilized. The saturation of branched oil type monotonically decreased, but saturation of droplet oil type monotonically increased. Residual oil saturations were 22.62% and 9.69% before and after altering injection direction, respectively, which means residual oil recovery was increased by 12.93%. Some places were considered to be “dead oil regions” before altering injection direction with most of the contiguous and branched type residual oil in these areas been displaced out by altering injection direction. Altering the injection direction is a more effective method of enhancing oil recovery than improving injection velocity when the same volume of water is injected. The proposed method and prediction correlations were presented in this paper to provide a novel means to predict oil recovery and residual oil saturation types for water flooding reservoirs, which have strong plane and weak vertical heterogeneity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".