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Record W2784658044 · doi:10.3968/9885

The Research and Application of Low Density Cement Slurry System at Low Temperature in Daqing Oilfield

2017· article· en· W2784658044 on OpenAlexvenueno aff
Dongnian Yu

Bibliographic record

VenueAdvances in petroleum exploration and development · 2017
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCementSlurryMaterials sciencePetroleum engineeringComposite materialPolymerPermeability (electromagnetism)Drop (telecommunication)Geotechnical engineeringGeologyChemistryEngineering

Abstract

fetched live from OpenAlex

During long sealing cementing of low temperature shallow gas well in Daqing oilfield, for the low density cement slurry at low temperature, the setting time is longer, gelling strength development is slower, filter loss is greater and the anti-channeling ability is weak. It would make the happening of annular gas channeling and fluid emitting, affect the cement job quality. Low density low temperature anti-channeling cement slurry system was studied with compound early strength agent, polyacrylate polymer latex drop loss of water, dispersed polymer powder anti-channeling agent, improve the comprehensive performance low density cement slurry. Laboratory experiments showed that setting time shortened by 50%, early strength increased by 46%, permeability decreased by 50%, interfacial bond strength increased by 47%, compared with the low density cement and the class G well cement. The application tests in the field were carried out in 18 wells. High-quality rate of well cementing increased by 11.1 percentage points. The incidence rate of fluid emitting is decreased by 1.6%. This cement slurry system can satisfy the requirements of cementing operation. It will improve the cementing quality of long sealing section in a shallow layer in Daqing Oilfield.

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: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.284

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 head0.016
GPT teacher head0.262
Teacher spread0.245 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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