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Record W2905418419 · doi:10.1190/geo2018-0311.1

Extended poroelastic impedance

2018· article· en· W2905418419 on OpenAlexaff
Brian Russell, Ken Hedlin

Bibliographic record

VenueGeophysics · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsHusky Energy (Canada)
Fundersnot available
KeywordsPoromechanicsMathematical analysisAmplitudeMathematicsAmplitude versus offsetFunction (biology)Electrical impedanceGeometryOpticsPhysicsGeologyPorous mediumPorosity

Abstract

fetched live from OpenAlex

ABSTRACT Linearized approximations to the P-wave reflectivity as a function of the incidence angle (called amplitude variation with offset) involve the extraction of band-limited reflectivity terms that are a function of changes in the elastic constants of the earth across each lithologic interface. The most common of these extracted reflectivities are the intercept and gradient, usually labeled A and B, respectively. The extended elastic impedance (EEI) method uses a rotation angle χ to map A and B into a new reflectivity corresponding to a particular elastic parameter. The success of EEI depends on finding an optimum value for the angle χ. This value is usually calculated by correlating the EEI result over a range of χ angles with various elastic parameters and then finding the best correlation coefficient. We have developed a new approach for the interpretation of the EEI method, which incorporates the Biot-Gassmann poroelastic theory and attaches a physical meaning to the χ angle. We call this method extended poroelastic impedance (EPI). The main advantage of the EPI method is that the χ angle is now interpreted as a parameter that is dependent on the dry-rock properties of the reservoir, rather than a parameter whose value is estimated empirically. The method is evaluated by numerical and synthetic seismic examples and by application to field data from a gas sand reservoir.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations22
Published2018
Admission routes1
Has abstractyes

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