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Record W2905853691 · doi:10.32920/ryerson.14665845.v1

Modelling of heat recovery from suspended oil wells

2021· preprint· en· W2905853691 on OpenAlexaffabout
Steven James England

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPetroleum engineeringHeat exchangerGeothermal gradientGeothermal energyOil wellEnvironmental scienceHeat transferGeothermal heatingEngineeringVolume (thermodynamics)MechanicsMechanical engineeringGeologyThermodynamics

Abstract

fetched live from OpenAlex

A geothermal system for suspended oil wells is designed to produce 1.25MW of heat. After a review of literature, a concentric double pipe heat exchanger was the system chosen. A control volume model was created to calculate the heat transfer characteristics of the system. After completing the model a suspended oil well near Hinton, Alberta was selected as a candidate well for a geothermal system. The well contains 33 perforated sections which need to be closed off. Three designs to accomplish this are proposed. The model predicted that the best design uses cement squeezes to close off the perforations and produces a net energy of 2.4MW at its optimum operating condition, with a mass flow rate of 8.18kg/s. When producing 1.25MW of heat, the design has a net energy of 1.2MW. It is found that internal heat generation plays a large roll in these systems.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.036
GPT teacher head0.230
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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes2
Has abstractyes

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