INCORPORATING GRAVITY DRAINAGE AND REIMBIBITION MECHANISMS IN TRADITIONAL MATERIAL BALANCE EQUATION FOR FRACTURED OIL RESERVOIRS: MATHEMATICAL MODELING AND SIMULATION ANALYSIS
Bibliographic record
Abstract
The contribution of gravity drainage as a main production mechanism in fractured reservoirs has not previously been considered in the General Material Balance Equation (MBE). Although the traditional MBE for conventional oil and gas reservoirs has been modified and applied to fractured reservoirs, none of them explicitly incorporated the Gravity Drainage (GRD) mechanism. In this study, the MBE is reconstructed for fractured reservoirs to consider the effects of gravity drainage and reimbibition phenomena in the equation. This is realized using historical records of gas-oil contact level data in vertical fractures. The gravity drainage is numerically modeled in a series of synthetic cases (single matrix block and stacks of two and three matrix blocks), employing the finely gridded single porosity concept. A numerical computer program is developed to simulate the fluids' flow through the matrix and fracture system. Historical records of gas-oil contact in the vertical fractures, reservoir pressure, and production data are analyzed with the developed material balance equation to quantify the contribution of gravity drainage and all other active production mechanisms. Over 97% agreement is observed between the calculated oil production by gravity drainage based on our developed MBE and simulation results.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".