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Record W4412475052 · doi:10.1186/s40517-025-00358-1

Modified cyclic SCCO2 injection for energy recovery and thermal breakthrough mitigation in Saskatchewan geothermal reservoirs

2025· article· en· W4412475052 on OpenAlexafffundabout
Runzhi Li, Xue Bai, Na Jia, Gang Zhao, Ezeddin Shirif

Bibliographic record

VenueGeothermal Energy · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Regina
FundersMitacsPetroleum Technology Research CentreUniversity of Regina
KeywordsGeothermal gradientThermalGeothermal energyEnvironmental sciencePetroleum engineeringComputer scienceGeologyMeteorologyGeophysicsGeography

Abstract

fetched live from OpenAlex

Abstract Geothermal energy is a promising solution to meet the increasing global energy demand while mitigate climate change. In recent years, the utilization of carbon dioxide (CO 2 ), especially supercritical CO 2 (SCCO 2 ), for geothermal energy recovery has attracted increasing attention. This study introduces and simulates a modified cyclic SCCO 2 injection method for geothermal energy recovery, marking the first exploration of its kind. We analyzed the SCCO 2 injection process under various well patterns and injection modes, comparing the cumulative energy recovery performance of cyclic and continuous injection across different models. Our findings revealed that the original reservoir dominates the initial energy production until the SCCO 2 breakthrough. After the breakthrough, cyclic injection should be utilized to enhance energy production, with higher heat extraction efficiency and the mitigation of the thermal breakthrough effect. In addition, our findings suggest that an optimal combination of cyclic and continuous injection can leverage the advantages of both strategies. Through further optimization, modified cyclic SCCO 2 injection method enhances energy production, achieving up to a 59% improvement in cumulative energy production (4.155E14J) and a 200% increase in NPV ($600,000) compared to baseline scenarios, with higher heat extraction efficiency and mitigation of thermal breakthrough effects.

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 score0.386
Threshold uncertainty score0.979

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 head0.234
Teacher spread0.226 · 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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