Laboratory Investigation of Free Fall Gravity Drainage in Fractured Porous Systems Using Unconsolidated Macromodels
Bibliographic record
Abstract
Naturally fractured reservoirs (NFRs) contain a significant amount of the world’s oil reserves. Oil recovery by gravity drainage in a NFR strongly depends on the capillary height of the porous medium. Capillarity and gravity forces are usually the major driving forces in NFRs. To address this issue, a series of flow visualization experiments were performed using unconsolidated packed models of rectangular geometry with two fractures on the side. Parametric sensitivity analyses were performed considering effects of different system parameters such as fracture aperture, matrix height and permeability, and fluid viscosity on liquid drainage rate. These experiments enabled us to capture some aspects of the flow communication between matrix block and fracture during gravity drainage. Results from this study showed that the rate of liquid flowing from matrix to fracture is proportional to the difference of liquid levels in the matrix and in the fracture ( H f – H m ). In addition, the characteristic rate and the maximum liquid drainage rate from these fractured models are determined for stable gravity-dominated processes. Also, it was concluded that the characteristic rate depends only on the dimensions of the fracture and properties of the test fluid, and not on the properties of the matrix. For a given fracture–matrix system with different initial liquid saturation conditions, it is observed that the production history can be correlated by plotting the fraction of recoverable liquid as a function of time. Furthermore, ultimate recovery factor and capillary threshold height can be correlated using dimensionless numbers such as the Bond number and the porosity ratio.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".