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Record W1964520990 · doi:10.2118/154290-ms

Optimizing Coil Tubing Extended-Reach Capabilities Through the Application of Downhole Friction Reduction Tools

2012· article· en· W1964520990 on OpenAlexaffabout
Scott Hilling, G.. Ayling, John Yeung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsDirectional drillingPetroleum engineeringLimitingCoiled tubingElectromagnetic coilDrillingReduction (mathematics)EngineeringPetroleum industryMechanical engineeringWorkoverRange (aeronautics)Fossil fuelComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

Abstract The demand for drilling and completing horizontal wells in both conventional and unconventional oil and gas plays has drastically increased in recent years. A growing economic and environmental appetite for ever increasing horizontal lengths continues to apply pressure on drilling and completion technologies. Although well engineering programs have improved and adapted to solve some of these issues, coil tubing reach capabilities can, at times, become the limiting design factor for horizontal wells. To tackle this issue, the industry has developed and adopted the use of downhole friction reduction tools to optimize and extend the range of coil tubing. Using case history from Shell Canada’s Groundbirch shale gas development, this paper will compare and analyse the effectiveness of three different downhole friction reduction tools currently available to industry. While this paper will discuss the use of Tubing Force Analysis (TFA) modeling software to obtain representative values for comparison, the focus of the study is to compare the overall effectiveness of the three friction reduction tools using actual field data.

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 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: none
Teacher disagreement score0.877
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.218
Teacher spread0.204 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations17
Published2012
Admission routes2
Has abstractyes

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