Microfluidic study of surfactant flooding of heavy oil in layered porous media containing fractures
Bibliographic record
Abstract
Abstract Surfactant injection is a promising method for enhanced oil recovery (EOR) due to its effective micro‐displacement mechanisms. However, understanding the interaction of a surfactant solution with heavy oil in porous media is neither straightforward nor well understood, particularly in heterogeneous systems. By enabling in‐situ real‐time monitoring of flow transport, microfluidic studies have provided novel insights into the underlying multiphase physics of flow at the pore scale. This paper examines the two‐phase displacement efficiency of a new surfactant in layered–fractured porous microfluidic patterns, a topic seldom discussed in the literature. To evaluate the performance of the proposed surfactant, we considered several heterogeneous media with varying layer and fracture geometrical characteristics, quantifying displacement efficiency for each case. Based on the analysis of pore‐scale snapshots, it was inferred that the primary mechanisms responsible for EOR during surfactant flooding into heavy oil include pore wall transportation, emulsifications, the deformation of residual oil, inter‐pore or intra‐pore bridging, and wettability alteration. Macroscopic displacement experiments revealed that the width of the swept area from surfactant injection significantly exceeded that of water injection, resulting in a substantially higher oil recovery. Furthermore, it was demonstrated that the direction of fluid flow in relation to fracture orientation plays a critical role in the dynamics of surfactant solution movement and, consequently, the ultimate oil production.
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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".