Inertial Effect on Spontaneous Oil-Water Imbibition by Molecular Kinetic Theory
Bibliographic record
Abstract
Abstract Imbibition is one of the most common physical phenomena in nature, and it plays an important role in enhanced oil recovery, hydrology, and environmental engineering. For the tight reservoirs, the imbibition method has an obvious advantage in fracturing, shut-in, and huff-puff development. Although the current imbibition studies focus on oil recovery, and the inertial effect in imbibition is neglected and its mechanism is also unclear. In this paper, the inertial effect on spontaneous oil-water imbibition at micron-scale is studied by molecular kinetic theory (MKT). The frictional coefficient in the model is a fitted parameter to match the experimental data during the total imbibition process. Then, the simulation of the initial imbibition stage is conducted and the inertial effect on imbibition is identified by the difference between the model considering the inertial effect (CI) and the model neglecting the inertial effect (NI), or by the proportion of inertial force to the total resistance. Results show that (i) with an increase in the water phase viscosity, the inertial effect time shortens, maximum imbibition height and rate decrease, and thus the inertial effect on imbibition weakens; (ii) with an increase in the oil phase viscosity, the inertial effect time changes little, the maximum imbibition height and rate decrease slightly, namely, the inertial effect depends slightly on the oil phase. (iii) with an increase in the capillary wettability (hydrophilicity), the inertial effect time shortens, the maximum imbibition rate first increases and then decreases, and the inertial effect on imbibition weakens. This work sheds light on the inertial effect on oil-water imbibition by MKT, considering the effects of dynamic contact angle, water phase viscosity, oil phase viscosity, and wettabilities, which is helpful to understand the role of inertia in the oil-water or oil-fracturing fluid imbibition process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".