A Case Study: Profiling Gas Production in the Tubing/Casing Annulus, Using Noise/Temperature Logging Techniques
Why this work is in the frame
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Bibliographic record
Abstract
Abstract To reduce liquid loading on multizone unconventional gas wells, tubing can be run to a depth below the lowest perforation interval. Gas flows down the tubing/casing annulus and flows up the tubing, eliminating liquid buildup. For production surveillance, this wellbore configuration is not conducive to obtaining conventional production logs. Conventional production profiling techniques involve repositioning the tubing string or removing it altogether. If the tubing remains in place during logging, the costs associated with pulling the tubing are eliminated; production is not suspended; and the risks associated with well control are reduced. Also by not modifying the wellbore configuration, fluid velocities are not affected and the log results more closely represent the actual production profile. Ideally the well should be logged without manipulating the tubing to provide a representative production profile. Noise/temperature logging has been used for many years to assist in locating sources of fluid flow behind casing. The use of this technique to obtain a pseudo or qualitative production-flow profile in the tubing/casing annulus was explored to enable the tubing to remain in the wellbore and obtain a measurement of the flow behind pipe. In the 1970s, tests where conducted to quantify wellbore inflow using noise logs. The research was recently used to evaluate numerous wells in western Canada with favorable results. This paper discusses the logging method and presents comparisons to profiling results from conventional production-logging techniques with emphasis on the cost savings to the operator.
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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 | 0.001 | 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 | 0.000 |
| 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 it