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Record W4200151287 · doi:10.3390/fluids6120444

Reduction of Energy Consumption for Water Wells Rehabilitation. Technology Optimization

2021· article· en· W4200151287 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFluids · 2021
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsSAIT Polytechnic
FundersKuban Science Foundation
KeywordsCloggingDrawdown (hydrology)Petroleum engineeringNozzleEnvironmental scienceWater wellNatural gasLead (geology)GroundwaterEnvironmental engineeringAquiferEngineeringWaste managementGeologyGeotechnical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Groundwater wells are widely used in the energy sector, including for drinking water supplies and as water source wells in the oil and gas industry to increase production of natural gas and petroleum. Water well clogging, which can happen to any well for various reasons, is a serious problem that can lead to increased power costs due to a higher head to the pump, a reduction in the flow rate and various drawdown issues. If rehabilitation procedures do not take place in time, this can result in permanent loss of the well, and a new well must be drilled, which is not a sustainable approach. Rehabilitation methods for water wells usually include mechanical and chemical treatments, and even though these methods are well established and have been used for many years we can still observe many abandoned wells which could be rehabilitated. In this study, sets of cavitation generators are developed and used in combination with common conic hydrodynamic nozzles. This combination reduces the pressure in the system and makes the cleaning setup much lighter and more mobile. The designed nozzles were successfully used in hydrodynamic cleaning of four water 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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.641
Threshold uncertainty score0.220

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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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