Study on the Residual Oil Utilization Mechanism of Micron Polymer Microspheres Based on Microfluidic Technology
Bibliographic record
Abstract
Polymer microspheres have shown significant potential in enhancing deep reservoir conformance through their unique synergistic mechanism involving deformation, migration, and temporary plugging. To better understand the mechanisms of enhanced oil recovery and their effects on the distribution of residual oil in reservoirs, this study develops a dual-strip pore-throat heterogeneous model for low-permeability reservoirs. This model is constructed by integrating microscopic pore structure analysis of core cast thin sections with micronano lithography, simulating dynamic displacement processes in multistage seepage channels. Using a microfluidic real-time monitoring system, the study systematically reveals the evolution of residual oil morphologies (sheet-like, column-like, and lump-like) in different seepage zones during waterflooding and microsphere flooding stages. It also quantitatively characterizes the differential impacts of the microsphere concentration on the mobilization efficiency of various residual oil types. The results show that after waterflooding, residual oil primarily exists as continuous sheets, with some distribution of column-like and lump-like forms. However, after microsphere flooding, the residual oil predominantly adopts column-like and lump-like morphologies, with sheet-like forms becoming secondary. The mobilization efficiency of microspheres is positively correlated with concentration and negatively correlated with migration distance, with overall mobilization rates ranging from 16.39% to 25.58%. At low concentrations, microspheres primarily mobilize residual oil through dynamic temporary plugging and flow diversion, achieving a mobilization rate of 9.36%. Continuous sheet-like residual oil is consistently the primary target for mobilization by microspheres. As concentration increases, the mobilization pattern shifts from the initial transition of sheet-like residual oil to column-like and lump-like forms to a modified pattern where both sheet-like and column-like residual oil transition primarily into lump-like forms.
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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".