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Record W2200490162 · doi:10.3968/7581

Research on Low Density Cementing Technology of Coalbed Methane Well

2015· article· en· W2200490162 on OpenAlexvenueno aff
Chufeng Wang, Ruihe Wang, Huanqiang Yang, Fangxiang Wang

Bibliographic record

VenueAdvances in petroleum exploration and development · 2015
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsRheologySlurryCementCoalbed methaneLost circulationSedimentationMaterials sciencePetroleum engineeringOil wellThickeningCompressive strengthDrilling fluidCoalWaste managementComposite materialDrillingEngineeringGeologyCoal miningMetallurgy

Abstract

fetched live from OpenAlex

The pressure of the coalbed methane (CBM) reservoir is relatively low, there is serious loss circulation, andlow mechanical strength of the coal, which seriously influence the cementing quality. In this paper, according to the characteristics of coal in southern Qinshui basin, an ultra-low density cement slurry system and a foamed cement slurry system are designed and developed to reduce the density of cement slurry by adding lightening admixture and foaming agent through the experimental method. An appropriate bridging type preflush system is also researched and developed. Considering the drilling conditions, the operation technologies are put forward.The ultra-low density cement slurry system has good rheological performance, small API filter loss (≤ 45 mL/ (30 min·6.9 MPa)), excellent sedimentation stability, suitable thickening time and higher early strength. Foamed cement slurry systemhas good rheological performance, excellent sedimentation stability andsuitable thickening time, and can satisfy the compressive strength requirements for the cementing of CBM wells. The fluid loss of the bridging type preflush system is small under the medium or high pressure, withgood rheological performance,andexcellent plugging ability. The ultra-low density cement slurry system and foamed cement slurry system were applied in 14 and 11 CBM wells respectively, and good results have been achieved.

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)0.001
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: none
Teacher disagreement score0.596
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.045
GPT teacher head0.302
Teacher spread0.257 · 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
Published2015
Admission routes1
Has abstractyes

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