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Record W3122538481 · doi:10.1306/m76870c5

The Role of Shale Pore Structure on the Sensitivity of Wire-Line Logs to Overpressure

2001· book-chapter· en· W3122538481 on OpenAlexaff
Glenn L. Bowers, T J Katsube

Bibliographic record

VenueAmerican Association of Petroleum Geologists eBooks · 2001
Typebook-chapter
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsOverpressurePetroleum engineeringOil shaleDrillingGeologyMining engineeringPore water pressureEngineeringGeotechnical engineeringMechanical engineeringPaleontology

Abstract

fetched live from OpenAlex

Abstract Petrophysical characteristics of shales have been analyzed to improve our understanding of wire-line log response to overpressure. Bulk density and neutron porosity logs sometimes mask pore-pressure increases that are clearly evident on sonic and resistivity logs. This may be because sonic and resistivity logs respond to transport properties, whereas neutron and density logs reflect bulk properties. Results of this study indicate that shale pore structure can be characterized by a storage-connecting pore system, with connecting pore sizes on the order of 2–20 nm. Laboratory compaction tests indicate that connecting pores are mechanically more flexible compared to storage pores and are likely to have lower aspect ratios. Consequently, connecting pores are likely to undergo more elastic rebound (widening) compared to storage pores as a result of effective stress reductions caused by overpressure. Essentially, these results provide evidence that suggest sonic and resistivity logs respond to transport properties, whereas neutron and density logs reflect bulk properties, as previously proposed. This suggests that, whereas overpressure resulting from compaction disequilibrium may be detected by all four logs, fluid expansion overpressure may be best detected by sonic and resistivity logs.

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)0.001
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: none
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.183
Teacher spread0.179 · 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

Citations55
Published2001
Admission routes1
Has abstractyes

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