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Record W2026020575 · doi:10.2118/2002-049

Using a Wireless Coiled-Tubing Collar Locator Tool on Coiled-Tubing Fracturing Operations

2002· article· en· W2026020575 on OpenAlexaboutno aff
Michael L. Connell, Robert Howard

Bibliographic record

VenueCanadian International Petroleum Conference · 2002
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCitationComputer scienceCollarGeologyEngineeringLibrary scienceMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Fracturing operations through coiled tubing represent one of the newest and best uses of coiled tubing. Large-diameter coiled tubing is used to convey straddle packers that are used to isolate particular zones to be fractured. Thousands of coiled-tubing fracturing operations have been performed in North America. Most of these jobs were performed on wells with depths shallower than 3,000 feet. One reason why deeper jobs are not more common is because poor depth control is inherent with coiled tubing. A new tool has been developed that helps solve the problem of straddle packer placement. Based on a proven wireless telemetry design, the new tool is designed specifically to fit the needs of coiled-tubing fracturing. The tool gives the precise location of casing collars in real time, and a surface computer package allows the collars to be plotted on a standard log format that can readily be correlated to existing well logs. Once the collar logs have been correlated, and the coiled-tubing measuring-counter depths have been corrected, the tool can be switched from the logging mode to the fracturing mode by simply increasing the flowrate through the tool. At a predetermined rate, a fullbore flowpath through the tool is opened to allow unobstructed fracturing operations to proceed. This paper discusses the development of the new tool and presents case histories of its use. Introduction Fracturing through coiled tubing is especially well suited to shallow wells with multiple thin zones.1 With coiled tubing, these wells can be fractured in a much shorter time than with conventional methods. Often, the fracturing job can be completed in one day. In fact, in some shallow fields in Canada, it is not uncommon to fracture two wells in the same day using the same equipment and crew. This is possible because the coiled tubing can reposition the packers quickly from one zone to the next and can do this in an underbalanced condition. Conventional fracturing operations with jointed pipe on a rig require that the well be in an overbalanced state before moving the tools to the next job. Coiled-tubing fracturing (CTF) operations have been carried out in Canada for several years, and in fact, most of the wells stimulated using this process have been in Canada. However, CTF operations have now been performed in several areas of the United States, most notably in Colorado, Texas, Alabama, and Virginia. In the United Kingdom, CTF operations have been performed in England and Ireland. Virtually all of these jobs have been performed on shallow, multizone onshore wells. The techniques used in CTF are similar regardless of the field. A large-diameter coiled-tubing string is needed to achieve sufficient flow rates to properly fracture the zones. 2The most common coiled-tubing strings are 2 ⅜- or 2 ⅞- in. in diameter. Most of the coiled-tubing units (CTU) have an integral mast or derrick to support the injector head and the lubricator. A lubricator is used so that the tools can be retrieved from the well in an underbalanced condition.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.212
Teacher spread0.188 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2002
Admission routes1
Has abstractyes

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