Mathematical model of dynamic imbibition in nanoporous reservoirs
Bibliographic record
Abstract
The oil-water imbibition equation in the nano-scale pores considering the dynamic contact angle effect, nanoconfinement effect, inertia effect, and inlet end effect was established, and the relation between the friction coefficient of solid-oil-water three-phase contact line and the fluid viscosity in the interface zone was derived. In combination with the capillary bundle model and the lognormal distribution theory, the imbibition model of tight core was obtained and key parameters affecting imbibition dynamics were analyzed. The study shows that in the process of nanopore imbibition, the dynamic contact angle effect has the most significant impact on the imbibition, followed by nanoconfinement effect (multilayer sticking effect and slippage effect), and the inertia effect and inlet end effect have the least impact; in the initial stage of imbibition, the effect of inertial force decreases, and the effect of contact line friction increases, so the dynamic contact angle gradually increases from the initial equilibrium contact angle to the maximum and then remains basically stable; in the later stage of imbibition, the effect of contact line friction decreases, and the contact angle gradually decreases from the maximum dynamic contact angle and approaches the initial equilibrium contact angle; as the pore radius decreases, the dynamic contact angle effect increases in the initial stage of imbibition and decreases in the later stage of imbibition; as the oil-water interfacial tension increases, the imbibition power increases, and the dynamic contact angle effect increases; there is a critical value for the influence of interfacial tension on the imbibition dynamics. In improving oil recovery by imbibition in tight oil reservoir, interfacial tension too low cannot achieve good imbibition effect, and the best interfacial tension needs to be obtained through optimization.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".