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Record W4396540913 · doi:10.1190/tle43050278.1

Overcoming Gassmann's equation limitations in reservoir rocks

2024· article· en· W4396540913 on OpenAlexaff
Fabien Allo, Lev Vernik

Bibliographic record

VenueThe Leading Edge · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsCanadian Society of Petroleum GeologistsVirtual Materials Group (Canada)
Fundersnot available
KeywordsPetroleum engineeringGeologyEnvironmental science

Abstract

fetched live from OpenAlex

Abstract Velocities of low-frequency seismic waves and, in most rocks, sonic logging waves depend on the compressibility of the undrained rock, which is conventionally computed from the drained rock compressibility using Gassmann's equation. Although more comprehensive and accurate alternatives exist, the simplicity of the equation has made it the preferred fluid substitution model for geoscience applications. In line with recent publications, we show that Gassmann's equation strictly applies only to rocks with a microhomogeneous void space microstructure that is devoid of cracks and microcracks. We use a rock physics model that separates the respective compliance contributions of pores and cracks on dry (drained) moduli and show that Gassmann's model does not apply to rocks with measurable crack density. A fourth independent bulk modulus (in addition to the bulk moduli of the mineral matrix, dry frame, and saturating fluid) is required to take the effect of cracks into account and perform fluid substitution modeling for rocks with pores and cracks more accurately than prescribed by Gassmann's equation. Therefore, we propose combining the Vernik-Kachanov model with Brown-Korringa's equation for more reliable modeling of undrained bulk compressibility for reservoir rocks with measurable crack density. To conclude, a practical quantification of the applicability of Gassmann's equation based on the combined effects of crack density and stress sensitivity is proposed.

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 categoriesnone
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 score0.640
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.001

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 head0.094
GPT teacher head0.273
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2024
Admission routes1
Has abstractyes

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