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Record W2014517567 · doi:10.2118/60690-ms

Fracturing Through Coiled Tubing - Recent Developments and Case Histories

2000· article· en· W2014517567 on OpenAlexaboutno aff
W. Gavin

Bibliographic record

VenueSPE/ICoTA Coiled Tubing Roundtable · 2000
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCoiled tubingPetroleum engineeringWell stimulationAbrasion (mechanical)Hydraulic fracturingWork (physics)AbrasiveFracturing fluidGeologyReservoir engineeringEngineeringMechanical engineeringPetroleum

Abstract

fetched live from OpenAlex

Abstract Fracturing through coiled tubing has progressed considerably since the first job done in 1993. In southeastern Alberta large numbers of wells are being selectively fractured through coiled tubing with mechanical isolation tools. Conventional fracturing techniques may result in small lenses that have the potential to contribute to production being either bypassed, or ineffectively treated. By utilizing coiled tubing and selective fracturing, all contributing zones can be fractured and the full potential of the well realized. Up to eight zones are being treated per well. This paper will discuss work currently being undertaken with Carbon Dioxide and Nitrogen energized water based fracturing fluids. A summary of work done to date and case histories will be presented. Current and future developments in isolation tools and fracturing fluids will be discussed and the issues regarding geographical technology transfer examined. The results of pumping fluids containing abrasive particulates at high pump rates through the coiled tubing are discussed with emphasis on abrasion of the pipe, fatigue and pressure limitations. Specific additional safety considerations are outlined and discussed.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
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 head0.012
GPT teacher head0.203
Teacher spread0.191 · 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 designCase report
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

Citations16
Published2000
Admission routes1
Has abstractyes

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