Semianalytical Model for Reservoirs With Forchheimer's Non-Darcy Flow
Bibliographic record
Abstract
Summary This paper presents a semianalytical model to investigate the effect of Forchheimer's non-Darcy flow on the transient pressure behavior of a vertical well in an infinite homogeneous reservoir. The model uses the Forchheimer number to accurately quantify the non-Darcy flow in the reservoir through differentiating it from sandface-flow-rate-dependent skin factor, which is used to model the inertial-factor variation around the wellbore caused by perforation or formation damage/stimulation. Type curves are documented for both drawdown and buildup tests for the first time by use of the semianalytical model proposed. It is observed that when non-Darcy flow in reservoirs and/or across completions is considered, the dimensionless pressure-derivative curves of drawdown tests have a wider transition region with gentler slopes, while those of buildup tests exhibit a shorter transition region with steeper slopes, similar to the observations of Kim and Kang (1994), Spivey et al. (2004) and Camacho-V et al. (1996). In the radial-flow period, compared with the cases of non-Darcy flow only across completions, the cases with non-Darcy flow in reservoirs for drawdown and buildup tests possess dimensionless pressure derivatives moving downward more gradually and smoothly to approach 0.5 at decreasing gradients. In general, the pressure derivatives of drawdown tests are larger than those of buildup tests before they converge to 0.5. With this model, the skin factor for non-Darcy flow across the completion and the dimensionless Forchheimer number for non-Darcy flow in the reservoir can be estimated from a common drawdown or buildup test. Guidelines for interpreting field test data are presented. Several typical cases from the literature are analyzed, and better type-curve matches and more-reliable results are obtained.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".