Insights From Unsteady Flow Analysis of Underdamped Slug Tests in Fractured Rock
Bibliographic record
Abstract
Abstract Slug tests generating oscillating (underdamped) responses are common in high‐transmissivity ( T ) zones, and the nature of the response depends on the plumbing of the test equipment and the formation properties. The standard approach for obtaining T is to measure pressure shallow in the riser pipe to obtain an accurate estimate of flow and then predict the formation response from this shallow measurement by accounting for friction and acceleration assuming steady flow conditions (parabolic radial velocity profile). In this study a mathematical solution is developed for unsteady oscillatory laminar flow that shows non‐parabolic radial velocity profiles resulting in larger frictional losses, which are out of phase with the average flow velocity, indicating that errors are introduced when using the standard approach for underdamped slug test analysis. The unsteady flow model produces correction factors that can be used to improve the standard approach for predicting the formation pressure; however, not all errors are eliminated. Consequently, a new procedure is presented and applied to underdamped slug tests observed in fractured rock that avoids errors associated with quantifying inertial and frictional effects along the test equipment. This is achieved through the use of two transducers, where one is placed shallow in the water column to infer flow, and one is placed inside the test interval to represent the formation pressure. Comparison of T estimated by the new procedure to T derived from constant head step tests show better agreement than T obtained when predicting the formation pressure from a shallow pressure measurement.
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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 | 0.000 |
| 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".