Impact of Secondary and Tertiary Floods on Microscopic Residual Oil Distribution in Medium-to-High Permeability Cores with NMR Technique
Bibliographic record
Abstract
In order to explore the impact of various flood schemes and pore throat heterogeneity on oil recovery efficiency in porous media, core-flood experiments and nuclear magnetic resonance (NMR) tests are conducted to quantitatively determine the initial oil distribution and the residual oil distribution in medium-to-high-permeability cores subjected to these various flood schemes. Multiple experimental runs are conducted with four field core samples to cover the various flood schemes: the secondary water flood, CO 2 -foam flood, and water-alternating-CO 2 flood (WAG). Experimental results show that, relatively speaking, at the initial oil saturation condition, the moderate pore throats contain the highest amount of oil. The water flood recovery degree is higher from larger pore throats (average recovery degree of 98.57%) than that from moderate pore throats (average recovery degree of 78.29%). The water flood efficiency in different cores is found to be dependent on the degree of heterogeneity in pore throat distribution. After water flood, the residual oil is mainly located in smaller pore throats. CO 2 -foam flood shows good performance in tapping the residual oil contained in smaller pore throats, while the WAG can recover more oil from larger pore throats. Furthermore, it is found that the combination of CO 2 -foam flood and WAG provides the highest recovery efficiency since it is effective in reducing the oil saturation in pore throats with varied sizes. Based on this investigation on the residual oil saturation in pore throats subjected to secondary and tertiary floods, it is possible to design an optimum flood scheme which suits the microscope pore throat characteristics for a given reservoir.
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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 | 0.000 | 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 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".