Microscopic Two-Phase Flow Characteristics under Mixed Wetting Conditions of Shale Based on Pore Network Modeling
Bibliographic record
Abstract
Shale oil reservoirs are primarily characterized by the presence of organic matter (OM), which is embedded within an inorganic matrix and exhibits differences in wettability and pore size compared to inorganic matter (IOM). The coexistence of water-wet inorganic pores and oil-wet organic pores results in a mixed wettability condition that complicates the transport behavior of oil and water. The mechanisms governing two-phase oil/water flow in shale media under the mixed wettability condition remain inadequately understood. In this study, pore network models consisting of OM and IOM components were generated based on the pore structure information obtained from actual shale samples. A network-model-based algorithm was proposed for simulating two-phase oil/water flow, incorporating the effects of driving force, capillary force, and viscous force. This algorithm was employed to simulate the water injection process. The distribution of oil and water within both organic and inorganic pores, as well as the mobility of oil within OM, were analyzed in relation to OM wettability, organic pore size, and OM patch size. Results show that the capillary forces within organic pores impede the advancement of the water-flooding front in the water injection process. The mobility of oil within OM is influenced by its microscopic structural properties. A reduction in the hydrophobicity of organic pores or an increase in the radius of organic pore throats can facilitate earlier oil mobilization and enhance displacement efficiency. Moreover, the inlet flow rate is positively correlated with the organic pore throat radius, whereas it exhibits limited sensitivity to the wettability of OM. In addition, larger OM patches are beneficial for water invasion into organic pores under the same OM content. The results of this study provide insight into the behavior of oil and water flow in shale reservoirs with varying OM properties.
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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".