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Record W3155996114 · doi:10.2118/102815-pa

Applied Ultrasonic Technology in Wellbore-Leak Detection and Case Histories in Alaska North Slope Wells

2009· article· en· W3155996114 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSPE Production & Operations · 2009
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsConocoPhillips (Canada)
FundersConocoPhillips
KeywordsUltrasonic sensorSonic loggingLeakAcousticsLoggingUltrasonic testingEnergy (signal processing)Logging while drillingEngineeringWell loggingPetroleum engineeringDrillingMechanical engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.180
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it