Applied Ultrasonic Technology in Wellbore-Leak Detection and Case Histories in Alaska North Slope Wells
Bibliographic record
Abstract
Summary When operators are faced with well-integrity problems, a variety of methods may be used to detect the source of annular communication. Methods for detecting downhole leak points include spinners, temperature logs, downhole cameras, thermal-decay logs, and noise logs. However, many of these methods are ineffective when dealing with very small leaks and can result in collected data that require a significant amount of logging finesse to interpret. Ultrasonic listening devices have been used for a number of years to detect leak sources effectively in surface production equipment. Ultrasonic energy has some properties that, when compared to audible-frequency energy, make it ideal for accurate leak detection (Beranek 1972; Povey 1997; Evans and Bass 1972). Like audible-frequency energy, ultrasonic energy can pass through steel. However, ultrasonic energy propagates relatively short distances through fluids when compared to equal-energy audible-frequency sound. Thus, when an ultrasonic signal of this nature is detected, the detection tool will be in close proximity to the energy source. On this premise, an ultrasonic leak-detection tool was developed for downhole applications to take advantage of the unique properties of ultrasonic-energy propagation through various media. Data-acquisition equipment and filtering algorithms were developed to allow continuous logging conveyed on standard electric line at common logging speeds. Continuous logging has proved to be significantly more efficient in locating anomalies than static logging techniques commonly used in noise-logging operations. During development, the tool was shown to be effective in locating leaks as small as 0.026 gal/min with an accuracy of 3 ft in production tubing, casing, and other pressure-containing completion equipment. Leaks also have been detected through multiple strings of tubing and casing. The tool has proved to be effective in locating leaks that other diagnostic methods were unable to locate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".