Effects of Non-Darcy Flow and Penetrating Ratio on Performance of Horizontal Wells With Multiple Fractures in a Tight Formation
Bibliographic record
Abstract
A novel slab source function has been formulated and successfully applied to examine effects of non-Darcy flow and penetrating ratio on performance of a horizontal well with multiple fractures in a tight formation. The Barree–Conway model is incorporated in the mathematical model to analyze non-Darcy flow behavior in the hydraulic fractures, while the pressure response under non-Darcy flow is determined by two dimensionless numbers (i.e., relative minimum permeability (kmr) and non-Darcy number (FND)). A semi-analytical method is then applied to solve the newly formulated mathematical model by discretizing the fracture into small segments. The newly developed function has been validated with numerical solution obtained from a reservoir simulator. Non-Darcy effect becomes more evident at a smaller relative minimum permeability (kmr < 0.05) and a larger non-Darcy number (FND > 10). The non-Darcy number is found to be more sensitive than the relative minimum permeability, resulting in a larger pressure drop even at a larger kmr. In addition, the non-Darcy flow is found to impose a significant impact on the early-stage bilinear/linear flow regime, resulting in an additional pressure drop that is similar to lowering the fracture conductivity. The pressure response can be classified into two categories by a penetrating ratio of 0.5. When the penetrating ratio is decreased, the early bilinear/linear flow regime occurs, followed by an early radial flow regime.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".