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Record W4409258299 · doi:10.1016/j.geoen.2025.213876

Geothermal potential of low enthalpy reservoirs in the Western Canada Sedimentary Basin

2025· article· en· W4409258299 on OpenAlexafffundabout
Makram Hedhli, Wanju Yuan, Stephen E. Grasby, Andy Mort

Bibliographic record

VenueGeoenergy Science and Engineering · 2025
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsGeological Survey of Canada
FundersNatural Resources Canada
KeywordsGeothermal gradientGeologyStructural basinSedimentary rockGeochemistrySedimentary basinEnthalpyEarth scienceGeomorphologyPaleontologyThermodynamics

Abstract

fetched live from OpenAlex

Here we investigate Mesozoic and Paleozoic porous aquifer systems with different grades of temperature reservoirs to meet growing heat demand and sustain government infrastructure overlying The Western Canada Sedimentary Basin, Canada, where winters are cold (average daily temperature below −4 °C) and direct heat is an essential energy demand. Two stratigraphic intervals were modeled and simulated for geothermal heat production systems: conventional and closed loop. We estimated that 2.93 MWth and 6.9 MWth heat energy can be generated over 30 years of operation in the Mesozoic (300–500 m depth; 100 m thickness, 16 °C reservoir temperature, 1 D permeability, 0.3 porosity, 180 m 3 /h flow rate, 800 m well spacing) and Paleozoic (1400-1200 m depth, 100 m thickness, 35 °C reservoir temperature, 10 mD permeability, 0.1 porosity, a 180 m 3 /h flow rate, 400 m spacing) respectively. This study highlights the geothermal potential of the WCSB as a viable opportunity. • Stratigraphic horizons with geothermal potential near communities were identified in the Western Canada Sedimentary Basin. • Geothermal simulations used regional data to model doublet well and closed-loop systems based on formation lithology. • Up to 6.9 MWth over 30 years can be produced by either method, depending on reservoir depth and total wellbore length.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.825

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)0.0000.000

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 head0.004
GPT teacher head0.185
Teacher spread0.181 · 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 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

Citations0
Published2025
Admission routes3
Has abstractyes

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