Quantifying the reimbibition effect on the performance of gas‐oil gravity drainage in fractured reservoirs: Mathematical modelling
Bibliographic record
Abstract
Abstract As a major oil production mechanism, gravity drainage in fractured oil reservoirs is heavily affected by the reimbibition phenomenon. In this work, a stack of oil‐saturated two matrix blocks surrounded by oil‐saturated fractures are considered as a synthetic fractured reservoir. A mathematical computer program is developed to numerically simulate the fluids’ flow through the matrixes and fractures employing the finely gridded single porosity concept. Simulation results are validated by checking the continuity of the gas volume through the reservoir. The percentage of oil recovered by gravity drainage is determined employing the new material balance equation approach. A new procedure is employed for deactivating the reimbibition phenomenon, introducing the zero transmissibility for the oil phase in the case of oil transfer from the fracture to matrix region. According to the simulation results and considering the proposed model, under the constraints of constant gas injection and oil production rates, fully or partially deactivating the reimbibition phenomenon causes a faster gas movement through vertical fractures. The reason for this is that some of the oil leaving the upper matrix block goes to vertical fractures and causes a faster gas breakthrough in them due to reduced available pore volume to gas. In this case the oil recovered by gravity drainage from the system is decreased. Inactivating this phenomenon in the simulated case study causes a 40 % reduction in oil production by gravity drainage from the matrix blocks until the breakthrough time.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".