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Record W7162314859 · doi:10.3997/2214-4609.202510624

Clay Volume Calculation from Acoustic and Density Curves using the Gyllenhammar Equation

2025· article· W7162314859 on OpenAlex
T. Nekrasova, V. Kalashnikova, A. Finogenova, C.F. Gyllenhammar, V. Sopivnik

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Language
FieldChemistry
TopicElectrostatics and Colloid Interactions
Canadian institutionsGeoscience BC
Fundersnot available
KeywordsVolume (thermodynamics)Wave equationDifferential equation

Abstract

fetched live from OpenAlex

Summary This case study demonstrates the calculation of clay volume in rock using density and acoustic logging, specifically in the northern oil and gas fields of the North Sea. The methodology is based on the Gyllenhammar formula and involves selecting appropriate coefficients for each analyzed interval. First, the clay volume should be calculated using gamma gay and/or a combination of neutron and density logs. Next, a cross-plot of density and acoustic logging is created, and the coefficients in the equation are determined from this plot. Finally, both clay volumes are compared, and if necessary, the coefficients in the equation are further calibrated. We can derive acoustic impedance from seismic data, calculated as the product of rock density and velocity. In this study, we modified the Gyllenhammar equation to create a formula for estimating clay content based on velocity and rock density. This approach demonstrates the ability to accurately calculate one of the essential parameters, such as clay volume, in the inter-well space using seismic 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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.280
Teacher spread0.263 · 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

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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