Quantification of non‐Darcian flow observed during packer testing in fractured sedimentary rock
Bibliographic record
Abstract
High‐precision straddle packer tests were conducted in boreholes in a fractured dolostone aquifer using constant rate injection (Q) step tests to identify the conditions of change from Darcian to non‐Darcian flow on the basis of Q versus the applied head above ambient (dH), where the ambient head represents static conditions. The linear portion, representing Darcian flow, passes through the origin, but after the onset of non‐Darcian flow, there is proportionally less Q per unit dH, and the transmissivity (T) calculated for the test interval using Darcy's law‐based models can be substantially underestimated. Onset of nonlinear flow depends on the test interval length and permeability, typically beginning at injection rates less than 0.5 L min−1 for a relatively transmissive (2 × 10−5 m2 s−1) 1.5 m test interval. In studies of nonlinear flow during pumping tests, the Forchheimer equation is commonly used to describe nonlinear flow near the well using a Q2 versus dH relationship. However, for packer tests in fractured rock, we propose the Darcy‐Missbach equation, which relates Qn to dH, as an alternative equation. While both equations accurately predict the observed dH within the range of flows used, the Darcy‐Missbach exponent (n) describes the degree of deviation from the linear regime; moreover, all calculated exponents were less than 2, implying that the flow is nonlinear but not quadratic in nature. This quantification of the linear to nonlinear flow relations provides for a more accurate identification of the Darcian range, resulting in better T estimates.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".