An Illustration of the Information that can be Obtained from Pressure Transient Analysis of Wireline Formation Test Data
Bibliographic record
Abstract
Abstract The theoretical analysis of pressure response associated with Wireline Formation Test (WFT) data was first introduced in 1962 by Moran and Finklea1 but was generally not widely used due to its radius of investigation being very small comparable to that of conventional drill stem testing, and due to gauge resolution limit2. Analytical techniques using pressure derivative curves introduced by Bourdet et. al. (1983)3 have subsequently had a significant impact on the increased use of pressure transient analysis (PTA) techniques. This paper aims to illustrate the wide range of information that can be obtained from WFT data using an advanced well test analysis technique to analyze the WFT pressure response, and is illustrated using field examples from the Asia Pacific Region. A single well model numerical simulation for a wireline formation tester deploying a single probe is used to verify the PTA results presented. An analytical solution in well test analysis software is also used to generate pressure transient response to confirm results from actual field examples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".