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Record W4401480632 · doi:10.56952/arma-2024-0379

Thermal Measurements and Geothermal Gradient from Deep Boreholes in the Revell Batholith, Northwest Ontario

2024· article· en· W4401480632 on OpenAlexaffabout
Hossein A. Kasani, Aaron DesRoches, E. Sykes, Andrew Parmenter, Mohammad Sadegh Khorshidi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsNuclear Waste Management Organization
Fundersnot available
KeywordsBatholithGeothermal gradientBoreholeGeologyRemote sensingGeophysicsSeismologyGeotechnical engineeringTectonics

Abstract

fetched live from OpenAlex

ABSTRACT: The Revell Site, in northwestern Ontario, within the Canadian Shield, is presently being studied as one of the sites for hosting a deep geological repository (DGR) in Canada. Preliminary site investigation included the drilling and coring of six 1-km long boreholes. The thermal characterization involved obtaining temperature logs from multiple sources in boreholes and laboratory thermal properties testing of different rock types. After drilling, coring, and flushing of the borehole, downhole temperature data were collected in this order: (1) continuous logs from geophysical dynamic fluid temperature probe, (2) discrete temperature data measured during hydraulic packer testing, and (3) discrete temperature data collected post installation of a Westbay monitoring system. The hydraulic testing and Westbay data provided the most reliable estimates of in-situ temperature profile with an average geothermal gradient of 9.9°C/km. This gradient is within the lower range of the data from other study sites in the Canadian Shield. The low gradient is consistent with low concentrations of natural uranium, thorium, and potassium in the tested core samples, which results in a low radiogenic heat generation rate. This paper presents a summary of the thermal data collection in the first three boreholes and development of a thermal model for the site, which provides boundary conditions for thermal simulations of the DGR. This initial thermal property dataset and site model also provides a prediction against which data from the latter three boreholes can be expected. 1. INTRODUCTION Deep geological disposal is an internationally accepted method for the safe and long-term management of used nuclear fuel. Safe long-term performance of a Deep Geological Repository (DGR) spans over the construction, operation, and post-closure phases which includes geological processes such as earthquakes and glacial advance and retreat. The Nuclear Waste Management Organization (NWMO) is implementing Adaptive Phased Management (APM), the approach approved by the Government of Canada in 2007 for the long-term management of used nuclear fuel. The NWMO site selection process is described in NWMO (2010). As part of APM, the NWMO is conducting preliminary site assessment activities in potential siting areas in: i) crystalline rocks in the northern portion of the Revell batholith within the Canadian Shield, near the Township of Ignace in the traditional territory of Wabigoon Lake Ojibway Nation (WLON) in northwest Ontario (referred to as the Revell Site), and ii) in sedimentary rocks in the Municipality of South Bruce in the traditional territory of Saugeen Ojibway Nation (SON) in southern Ontario (referred to as the South Bruce Site).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.044
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.020
GPT teacher head0.207
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes2
Has abstractyes

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