Critical Oil Rates in Naturally Fractured Reservoirs to Minimize Gas and Water Coning: Case History of a Mexican Carbonate Reservoir
Bibliographic record
Abstract
Abstract This study extends Birks (1963) coning method by defining various options for calculation of the critical oil rate in naturally fractured reservoirs through an explicit use of the radial pressure gradient in the fracture, ∂P/∂R. The study aims at minimizing gas and water coning, and introduces methodologies for estimating fixed and variable radius of drainage, as well as an iterative procedure for calculating the critical oil rate and the drainage radius within the fracture. The methodology has been used with good practical results, allowing optimization of oil production, in various naturally fractured carbonate reservoirs in the southeast of Mexico. Determining a critical or maximum efficient oil rate in naturally fractured reservoirs is very important in those cases where oil productivity can decrease significantly due to increases in water/oil and/or gas/oil ratios. Imposing a critical oil rate is also related to the capacity of fluids handling in surface installations and reservoir management when gas and/or water injection projects are being conducted in the reservoir. It is concluded that the methodology, which is not developed with the idea of replacing detailed coning simulation studies, can be used quickly, easily and with a good level of certainty in naturally fractured reservoirs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".