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Record W2085073224 · doi:10.2118/81718-ms

Inflatable CT Conveyed Selective Well Testing System for Logging Openhole and Horizontal Wellbores: Development and Use

2003· article· en· W2085073224 on OpenAlexaff
C. G. Blount, C. L. Crabtree, C. R. Pierson, S. B. Rennie, G. R. Mackenzie

Bibliographic record

VenueSPE/ICoTA Coiled Tubing Conference and Exhibition · 2003
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsInflatableLoggingPetroleum engineeringCompletion (oil and gas wells)GeologyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Quantitative production logging of openhole and horizontal wellbores presents a challenge for conventional logging tools and techniques. The complicating factors of horizontal logging are primarily related to low fluid velocities and stratified flow. Openhole horizontal completions further the challenge by increasing the opportunity for tool fouling and fill obstruction, creating abrasive running environments, and finally complicating interpretation with unknown hole geometries. To address these challenges, a new diverter logging system based on inflatable packer technology has been developed. The tool is inflated at discrete locations in the horizontal section so that all flow at each stop is diverted through rock-excluding screens and past conventional memory logging tools in a carrier. Pressure drop through the tool is minimal, and the fixed internal diameter carrier allows for high resolution logging over a wide range of rates. Developing a method to allow packer setting and resetting in wire/umbilical-free Coiled Tubing (CT), while constantly maintaining a production or injection path through the tool, called for innovative engineering. The tool has been run in nine wells to date. This paper will discuss the need, development, and field results of the new inflatable packer logging system.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.020
GPT teacher head0.199
Teacher spread0.180 · 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.

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

Citations2
Published2003
Admission routes1
Has abstractyes

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