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Record W2357137337

A New High Resolution Dual Laterolog Logging Method

2007· article· en· W2357137337 on OpenAlexaff
Jun Zhu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeoscience and Mining Technology
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsBoreholeLoggingDual (grammatical number)High resolutionWell loggingResolution (logic)Dual purposePetroleum engineeringRemote sensingEngineeringGeologyComputer scienceGeotechnical engineeringMechanical engineeringArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

The widely used conventional dual laterolog tools have their disadvantages of poor vertical resolution and too long electrode sonde which is not convenient for combination logging.To overcome these disadvantages,a new High Resolution Dual Laterolog(HRDL) tool has been suggested,developed,and put into oilfield application on a large scale.Its electrode sonde configuration and operation principle are presented.Its vertical resolution,depth of investigation and environmental effects including shoulder,invasion and borehole effects are discussed by numerical results.It shows that although the HRDL tool is much shorter than conventional dual laterolog tools and has more complex borehole effects,it can still obtain both high resolution and deep measurement,and can replace the extensively used conventional dual laterolog tools in most cases.HRDL tool is one of very important downhole tools of EIlog-05 logging system,this!paper can help logging engineers and analysts to understand its characteristics of investigation and provides references in logging data processing and interpretation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.250
Teacher spread0.238 · 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 designBench or experimental
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

Citations3
Published2007
Admission routes1
Has abstractyes

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