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Record W2065756318 · doi:10.2118/166833-ms

The Borehole Gravity Meter: Development and Results

2013· article· en· W2065756318 on OpenAlexaboutno aff
C. Nind, Jeffrey D. MacQueen, Roman Wasylechko

Bibliographic record

VenueSPE Arctic and Extreme Environments Technical Conference and Exhibition · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsnot available
FundersBattelle
KeywordsBoreholeGeologyGravimeterInversion (geology)Well loggingData processingVolume (thermodynamics)GeophysicsGeotechnical engineeringPetroleum engineeringSeismologyComputer sciencePhysics

Abstract

fetched live from OpenAlex

Abstract Borehole Gravity (BHGM) measurements are passive observations of the gravity field beneath the surface. The BHGM responds to the bulk density of a large rock volume surrounding the borehole, which is orders of magnitude larger than the volume sampled by nuclear logs or cores. A summary of the corrections applied to BHGM measurements during processing as well as simplistic rules-of-thumb for interpreting BHGM data in a uniform host rock are presented. However, processing and interpretation of BHGM data are complicated by changes in the density of formations intersected by the borehole, and modeling and inversion yield more sophisticated solutions. The first generation borehole gravity instrument was introduced in the 1960s and was suitable for large diameter petroleum wells. A second generation BHGM probe for mining and geotechnical applications was introduced in 2011. Using this recently developed probe several examples of BHGM data acquired in Canada and USA for mining exploration and CO2 sequestration are presented.

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.029
GPT teacher head0.218
Teacher spread0.189 · 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
GenreMethods

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

Citations5
Published2013
Admission routes1
Has abstractyes

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