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Record W4291718120 · doi:10.1190/image2022-3749247.1

ERT and crosswell EM imaging of CO2: Examples from a shallow injection experiment at the Carbon Management Canada CaMI FRS in Southeast Alberta, Canada

2022· article· en· W4291718120 on OpenAlexafffundabout
David Alumbaugh, Michael Wilt, Edward Nichols, Evan Schankee Um, Marie Macquet, Don C. Lawton, Dennis Rippe, Kerry Key, David Myer

Bibliographic record

VenueSecond International Meeting for Applied Geoscience & Energy · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsCarbon Management Canada
FundersLawrence Berkeley National LaboratoryNational Energy Technology LaboratoryGovernment of CanadaU.S. Department of EnergyUniversity of CalgaryCenovus EnergyCanada First Research Excellence Fund
KeywordsGeologySeismologyRemote sensingMining engineering

Abstract

fetched live from OpenAlex

Electromagnetic (EM) geophysical techniques offer the possibility of monitoring subsurface CO2 in saline reservoirs due to the fact that CO2 has high electrical resistivity compared to the surrounding geologic materials. In this paper we first discuss the underlying physics of two different borehole-based EM monitoring techniques; electrical resistivity tomography (ERT) and crosswell EM. This discussion is followed by the description of an experiment at the Carbon Management Canada CaMI FRS test site where time-lapse single well ERT and crosswell EM data have been acquired to image CO2 injection into a shallow aquifer. Resistivity imaging results from inversion of the two data types separately and jointly will be compared and contrasted, and interpretation of the extent of the injected CO2 provided.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.668

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)0.0010.000

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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
Published2022
Admission routes3
Has abstractyes

Explore more

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