Study on the Imbibition Characteristics of Different Types of Pore-Throat Based on Nuclear Magnetic Resonance Technology
Bibliographic record
Abstract
“Fracturing network+imbibition oil production” is a new attempt to effectively develop low-permeability tight reservoirs. Fracturing fluid is not only a carrier for sand carrying but also a tool in the process of imbibition. On the basis of imbibition experiments, combined with nuclear magnetic resonance and pseudo-color processing technology, this paper clarified the dominant forces of different types of pore-throat and quantitatively characterized the contribution of different levels of pore-throat to imbibition oil recovery. The results show that gravity is the main controlling force of imbibition for reservoirs with higher permeability. Fluid replacement mainly occurs in the early period of imbibition. Macropores contribute most of the imbibition recovery, mesopores have a weak contribution, and the contribution of micropores and pinholes can be ignored. For the reservoirs with low permeability, capillary force is the main controlling force of imbibition. Fluid replacement mainly occurs in the later period of imbibition. Macropores contribute most of the imbibition recovery rate, mesopores contribute a small part of the imbibition recovery factor, and the contribution of micropores and pinholes can be ignored. This paper clarified that macropores and mesopores are the main sources of the contribution of imbibition recovery efficiency, and oil content and connectivity are key factors for the imbibition recovery efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".