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Record W4399074524 · doi:10.22488/okstate.24.000034

Reduction of injection flow rate with an underground buffer tank coupled with a standing column well

2024· article· en· W4399074524 on OpenAlexaboutno aff
Mohamed Arbi Ben Aoun, Alain Nguyen, Philippe Pasquier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsColumn (typography)Buffer (optical fiber)Reduction (mathematics)Flow (mathematics)Volumetric flow rateMechanicsPetroleum engineeringComputer scienceGeologyComputer networkPhysicsMathematicsTelecommunications

Abstract

fetched live from OpenAlex

This study proposes a new solution for disposing of bleed water in standing column well systems. A perforated septic tank is used simultaneously as a storage tank for storing large water volumes and as an infiltration basin. By integrating a storage tank with a standing column well, bleed water is stored in the tank and is then drained rather than being returned to an injection well. A 3D coupled thermohydraulic finite element model supports this new bleed method. For this reason, a 30-day simulation examined a case study of a 20 m³ storage tank associated with a standing column well operating in heating mode at an experimental site located in Varennes, Canada. The model considers realistic bleed operation flow rate, temperature, and the geological properties and weather conditions observed at the experimental site. Additionally, the drainage process at the base of the tank was modeled by pairing Navier-Stokes flow with unsaturated and saturated porous media flow. Despite the unfavorable geological conditions of the experimental site, numerical results indicate that infiltration was sufficient for draining the bleed water that accumulated in the storage tank. The latter managed to dispose 121.5 m³ of water throughout the simulation period. Finally, infiltration proved to be a promising alternative to injection wells.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.497
Threshold uncertainty score0.421

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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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
Published2024
Admission routes1
Has abstractyes

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