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Record W4362568805 · doi:10.1093/gji/ggad146

On method-of-moments modelling of electromagnetic sources connected to metallic well casings

2023· article· en· W4362568805 on OpenAlexaff
Rita Streich, Andrei Swidinsky

Bibliographic record

VenueGeophysical Journal International · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCasingDiscretizationMethod of moments (probability theory)Sensitivity (control systems)Position (finance)Connection (principal bundle)MechanicsGeologyAcousticsPhysicsMechanical engineeringEngineeringGeophysicsElectronic engineeringMathematical analysisMathematics

Abstract

fetched live from OpenAlex

SUMMARY Metallic well casings strongly impact electromagnetic fields due to their high electrical conductivity. We can take advantage of their presence and increase the sensitivity of EM signals to deeper subsurface structure by driving electric currents down through existing casings, or using the metal as a wave guide for EM telemetry. Interpretation of such well-casing-enhanced measurements requires accurate simulation of these setups. The Method of Moments (MoM) can be used for modelling well casings without having to discretize them finely as part of the subsurface model. Extending the MoM for EM sources directly connected to well casings is straightforward in principle. However, we find that the accuracy of MoM results for such configurations depends strongly on details of the computational model definition, such as the exact position of the connection point and the discretization of the source wire. We explain those important details and provide strategies for the most accurate MoM modelling of electric currents injected into well casings.

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 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 score0.402
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
Bibliometrics0.0000.001
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 head0.030
GPT teacher head0.284
Teacher spread0.254 · 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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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