Characterization of the Dynamic Imbibition Displacement Mechanism in Tight Sandstone Reservoirs Using the NMR Technique
Bibliographic record
Abstract
An experimental technique is developed to investigate the dynamic imbibition displacement mechanism in tight sandstone formations of the Yanchang group of the Ordos basin. By combining the dynamic imbibition core flooding experiments and NMR technique, the effects of the injection volume and rate on displacement efficiency are investigated. Moreover, the displacement efficiency of dynamic imbibition is compared with that of static imbibition. This study gains insights into the micromechanisms of dynamic imbibition in tight sandstone formations. It is found that the relative displacement efficiency of dynamic imbibition increases with the increase of injection volume. But the increment amplitude decreases with the increase of injection volume. With the same injection volume, the core displacement efficiency of dynamic imbibition with high permeability is obviously improved. However, the core displacement efficiency decreases rapidly with the increase of injection volume. Optimal injection volumes are recommended for tight sandstone formations with different permeabilities. With the increase of the displacement rate, the core displacement efficiency of dynamic imbibition shows a trend of first rising and then declining. There exists an optimal displacement rate in dynamic imbibition displacement, and the optimal displacement rate almost linearly increases with the increase of core permeability. The static imbibition displacement efficiency increases with the increase of soaking time, but the increment amplitude slows down obviously. The displacement efficiency of static imbibition in small pores is higher than that of dynamic imbibition. The displacement efficiency of dynamic imbibition in large pores or microcracks is significantly higher than that of static imbibition. This study provides theoretical support for the optimization and improvement of the waterflooding recovery process in tight sandstone reservoirs.
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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) | 0.000 | 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".