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Record W4200015295 · doi:10.3390/w13243526

Are Engineered Geothermal Energy Systems a Viable Solution for Arctic Off-Grid Communities? A Techno-Economic Study

2021· article· en· W4200015295 on OpenAlexafffundabout
Mafalda M. Miranda, Jasmin Raymond, Jonathan Willis-Richards, Chrystel Dezayes

Bibliographic record

VenueWater · 2021
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologies
KeywordsGeothermal energyGeothermal gradientEnvironmental scienceWork (physics)Petroleum engineeringFossil fuelThermal energyProcess engineeringEngineeringGeologyWaste managementMechanical engineeringGeophysics

Abstract

fetched live from OpenAlex

Deep geothermal energy sources harvested by circulating fluids in engineered geothermal energy systems can be a solution for diesel-based northern Canadian communities. However, poor knowledge of relevant geology and thermo-hydro-mechanical data introduces significant uncertainty in numerical simulations. Here, a first-order assessment was undertaken following a “what-if” approach to help design an engineered geothermal energy system for each of the uncertain scenarios. Each possibility meets the thermal energy needs of the community, keeping the water losses, the reservoir flow impedance and the thermal drawdown within predefined targets. Additionally, the levelized cost of energy was evaluated using the Monte Carlo method to deal with the uncertainty of the inputs and assess their influence on the output response. Hydraulically stimulated geothermal reservoirs of potential commercial interest were simulated in this work. In fact, the probability of providing heating energy at a lower cost than the business-as-usual scenario with oil furnaces ranges between 8 and 92%. Although the results of this work are speculative and subject to uncertainty, geothermal energy seems a potentially viable alternative solution to help in the energy transition of remote northern communities.

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 score0.680
Threshold uncertainty score0.941

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)0.0000.000

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 head0.018
GPT teacher head0.214
Teacher spread0.196 · 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

Citations17
Published2021
Admission routes3
Has abstractyes

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