MétaCan
Menu
Back to cohort
Record W2940710090 · doi:10.1190/geo2018-0344.1

The longitudinal modulus of bitumen: Pressure and temperature dependencies

2019· article· en· W2940710090 on OpenAlexaff
Arif Rabbani, Douglas R. Schmitt

Bibliographic record

VenueGeophysics · 2019
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBulk modulusShear modulusAttenuationModulusAsphaltMaterials scienceUltrasonic sensorLongitudinal waveShear (geology)Dynamic modulusComposite materialGeologyMineralogyGeotechnical engineeringWave propagationDynamic mechanical analysisAcousticsPhysicsOptics

Abstract

fetched live from OpenAlex

ABSTRACT Bitumen retains significant solid-like behavior even in temperatures in excess of 50°C. Traditional ultrasonic wave-propagation studies have, however, largely ignored the existence of the shear modulus in such materials, and they have mostly assumed that the observed longitudinal (P) wave speeds solely depend on the fluid’s bulk modulus. To further study this, we have measured ultrasonic longitudinal (P) wave transmission speeds through viscous bitumen at different pressures (0.1–15 MPa) and temperatures (7–132°C) using an adapted version of the technique that consists of two piezoelectric receivers placed at unequal lengths from the transmitter. As such, we are able to calculate the P-wave attenuation and velocity that is used to derive the material’s complex longitudinal modulus. Using parallel measurements of the bitumen’s complex shear modulus, we find that the bulk modulus differs from the longitudinal modulus particularly at lower (reservoirs) temperatures. The results, together with the realization that bitumen experiences a sequence of various compositional and thermophysical phase that is primarily temperature-dependent, can be implemented to improve the fluid-substitution analyses of rock-physics studies of bitumen-saturated reservoirs.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.266

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.002
GPT teacher head0.146
Teacher spread0.144 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueGeophysicsSame topicDrilling and Well EngineeringFrench-language works237,207