Empirical Modeling of Gravity Drainage in Fractured Porous Media
Bibliographic record
Abstract
Gravity drainage is considered to be the main mechanism in primary oil production from naturally fractured reservoirs, but mathematical models to adequately predict the oil recovery and flux rate between the matrix and fracture network under gravity drainage are rarely described in the literature. To address this lacuna, gas−oil contact movement and oil recovery rates in a thin glass-bead-packed simulator were measured, allowing for the capture of information about the matrix−fracture fluid-transfer process. A two-dimensional mathematical model was developed to numerically simulate the process under the same conditions as the experiments, and then empirical models were proposed for oil production in such fractured systems because the final liquid recovery was found to be correlated to dimensionless groups, such as the Bond number. The empirical model approach was then extended to predict the matrix−fracture liquid-transfer rate during the free-fall gravity drainage process. On the basis of experimental data and empirical correlations, the matrix−fracture liquid flux rate appears to be proportional to the liquid level difference in the matrix and fracture. These correlations were tested against numerical simulation results and actual field data of oil production by free-fall gravity drainage. The empirical models have been judged to perform acceptably in the prediction of the oil production and fluid-transfer rate in the oil−gas gravity drainage cases studied.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".