Modeling of Water Coning Phenomena in a Fractured Reservoir and Design a Simulator
Bibliographic record
Abstract
Abstract The problem of water production is one of the major technical, environmental, and economical issues associated with oil and gas production. It is the general accepted approach to put the well on production below a critical rate without the risk of coning. The main goal of this study is to prepare a reservoir numerical simulator with emphasis on water coning. Present work mostly involves numerical simulation of water coning and includes proposed correlations in the literature. The computer program included four distinct modules to calculate: critical or maximum allowable oil rate, water breakthrough time, well performance after water coning take palaces and water coning simulation. Flow equations of water and oil were discretized and numerically solved for two-dimensional coordinates. The implicit scheme was used to calculate unknown pressures of any grid block. For calculation of water saturation, explicit scheme was used. Real field data of a well in southwest of Iran was put into the program and critical rate, water breakthrough time, well performance after water coning and water coning simulation of reservoir were determined. We found that the results of correlations are very far from the reality. On the other hand, numerical simulation shows good agreement with real production data. In addition, it was observed that the current production rate of this well would result in rapid water coning. The critical oil rate for water-free production is important in several categories, including limiting the productive life of the oil and gas wells, separation costs, corrosion of tubular, fines migration, and hydrostatic loading.
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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 | 0.000 |
| 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) | 0.002 | 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".