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Record W2074454228 · doi:10.3997/2214-4609.20146712

Analysis of Scattered Signal to Estimate Reservoir Fracture Parameters

2007· article· en· W2074454228 on OpenAlexaboutno aff
S. Grandi, Mark E. Willis, Daniel R. Burns, M. Nafi Toksöz

Bibliographic record

VenueProceedings · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsnot available
FundersU.S. Department of Energy
KeywordsCodaGeologyFracture (geology)Stack (abstract data type)SeismologyScatteringOrientation (vector space)Frequency domainSIGNAL (programming language)Time domainGeometryPhysicsOpticsGeotechnical engineeringMathematicsComputer science

Abstract

fetched live from OpenAlex

We detect fracture corridors and determine their orientation and average spacing based on an analysis of seismic coda in the frequency-wave number (f-k) domain. Fracture corridors have dimensions similar to seismic wavelengths which causes scattering. The distribution of energy in shot records in the f-k domain depends upon its orientation to the fracture strike. In the direction normal to fractures, energy reflected and transmitted at the reservoir level mostly propagates as scattered waves with slower apparent velocities than waves propagating along the fracture channels. The associated f-k spectral differences allow the identification of the preferred fracture orientation and spacing. We apply our technique to a fractured reservoir in the Lynx field, in the Canadian foothills. The estimated preferential fracture orientation is about N40°E, which agrees with regional stress measurements. The average fracture spacing is 75 m on the West side of the survey, while the fractures are more sparse on the East side. We also applied the Scattering Index methodology (Willis et al., 2006) to the same data, post-stack and pre-stack. This technique has higher resolution to map fracture distribution, intensity and orientation, and therefore complements the spectral method in providing an integrated description of reservoir fractures.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.269
Teacher spread0.252 · 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 designObservational
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

Citations1
Published2007
Admission routes1
Has abstractyes

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