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Record W3189375140 · doi:10.2118/0621-0041-jpt

Technology Focus: Coiled Tubing (June 2021)

2021· article· en· W3189375140 on OpenAlexaboutno aff
Alex Crabtree

Bibliographic record

VenueJournal of Petroleum Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCoiled tubingFocus (optics)EngineeringPetroleum engineeringComputer science

Abstract

fetched live from OpenAlex

Last year, this feature opened, almost inevitably, with comments on the effects the COVID-19 pandemic might have on our industry. Unfortunately, a year later, we probably have all experienced the effects, both personal and work-related. One of these effects is that there has been re-evaluation of what’s important. To understand what is important takes some reflection and evaluation of the past. In previous features, the focus has been on what is new or reimagined. Therefore, I thought that, in selecting papers for this year’s feature, it would be useful to select ones that look at coiled tubing operations performed and that have been evaluated in one way or another. From Bolivia comes a paper that reviews some 25 well interventions performed. Most of these operations are of a type that will be familiar to the reader. Also, some of the challenges that were faced in performing these coiled tubing operations will be familiar. These operations used a range of established types of coiled tubing operations and blended the techniques to meet particular operating conditions, especially location and logistics challenges. All conclusions and best practices that resulted, however, may not be familiar to all readers. During the past few years, many coiled tubing papers have focused on the use of coiled tubing in multizone fracturing operations, especially plug milling. An area of coiled tubing use in fracturing operations that has had less of an audience recently has been the use of coiled tubing in annular fracturing operations. This activity is still routinely being performed, particularly in Canada. The question has long existed about how the pipe is being eroded. In the related paper in this feature, the authors explain how they have tried to answer that question and have shared some of their review insights. In the past decade, coiled tubing size, weight, and grade all have continued to increase. Looking back at this trend and thinking about its effect on pressure control equipment led the developers in the third of this year’s papers to work on solutions for shear rams. This is particularly relevant after having just passed the 11th anniversary of the tragic Macondo disaster. Again, this year, I ask everyone to stay well. Recommended additional reading at OnePetro: www.onepetro.org. OTC 30408 Design and Safety Considerations To Perform Coiled Tubing Operations in Large-Diameter, High-Temperature Geothermal Wells by Ishaan Singh, Schlumberger, et al. SPE 204446 Implementing Business Analytics Software To Optimize Coiled Tubing Operations: A Digital Approach to Operations Efficiency by Xaymaca Bautista Alarcon, Royal Oaks Energy Services, et al. SPE 203272 The Reinvention of a Well-Established Coiled Tubing Intervention Work Flow Creates New Perspectives for Acidizing Openhole Horizontal Tight Carbonate Water Injectors by Sameer Punnapala, ADNOC, et al.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.416
Threshold uncertainty score0.833

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0060.005
Open science0.0010.002
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.4160.267

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 head0.003
GPT teacher head0.184
Teacher spread0.181 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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