Bibliographic record
Abstract
Abstract This paper presents an analytical study of transient flow into multiple vertical wells producing from a porous media containing randomly distributed discrete fractures. The model may be used to analyze the production and well test data from tight gas sands and Austin chalk type reservoirs. Both vertical openholes and hydraulically fractured vertical wells are considered. Wells and fractures are randomly distributed. The model dynamically couples the multiple fracture flow models with an analytical reservoir flow model. The analytical model simulates pressure and pressure derivative characteristics of wells and flow distribution along and through both the natural and hydraulic fractures. The study shows that single or multiple isolated fractures yield negative pseudoskin factors in vertical wells near isolated fractures. The negative pseudoskin factor in unstimulated wells has also been observed in field tests. The negative pseudoskin factor is a function of fracture conductivity, fracture density, length, distance from the wellbore, and azimuth. Using the model, we demonstrate that the shape of pressure derivative is related to fracture distribution. However, the wellbore pressure derivative response is controlled by the fractures in the near wellbore region. The result of this study indicate that the conventional analysis, based on the double porosity model such as the Warren and Root model, to predict the storativity ratio of a naturally fractured system is not reliable. Also, the displacement between two semilog straight lines is not necessarily a good indicator of the storativity ratio.
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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 | 0.001 | 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".