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Record W2792654865 · doi:10.15273/ijge.2018.01.004

A Formula of Potassium Polymer Sulfonated Drilling Fluid Developed Based on Rock Microscopic Analysis

2018· article· en· W2792654865 on OpenAlexvenueno aff
Sheng Wang, Jinyong Zhu, Jingfei Wang, Liyi Chen, Tongde Zhang, Chuan Zhang, Chaopeng Yuan

Bibliographic record

VenueInternational Journal of Georesources and Environment · 2018
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
FundersState Key Laboratory of Geohazard Prevention and Geoenvironment Protection
KeywordsDrilling fluidDrillingCoringPetroleum engineeringGeologyPolymerFault (geology)Materials scienceComposite materialMetallurgy

Abstract

fetched live from OpenAlex

During the rapid coring drilling in the Wenchuan earthquake Fault Scientific Drilling project, the deep fault zone is characterized with extremely broken strata and strong creeping, which made the drilling tasks very difficult. The key technology to drilling success is a suitable drilling fluid system. To design such a drilling fluid system with desired properties, the mineral composition of rock samples from deep fault zone was analyzed using X-Ray Diffraction (XRD) and Fourier Transform Infrared (FTIR) absorption spectrum. Based on the results, a potassium-based polymer sulfonated drilling fluid was developed. This drilling fluid system possesses a variety of advantages including good quality of filter cake, strong anti-pollution ability, anti-collapse ability and inhibition ability, and great compatibility with treating agent. Also, the composition of this drilling fluid is simple, easy to prepare and at a relatively low cost. The study of potassium sulfonated drilling fluid system is of great significance for the rapid core drilling in deep fault zone.

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 score0.094
Threshold uncertainty score0.415

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 head0.005
GPT teacher head0.191
Teacher spread0.187 · 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
Published2018
Admission routes1
Has abstractyes

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