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Record W2607981028 · doi:10.3968/9415

Experimental study on loss-prevention performance of oil-based drilling fluids

2017· article· en· W2607981028 on OpenAlexvenueno aff
Jinpeng Chai, Junyi Liu, Zhengsong Qiu

Bibliographic record

VenueAdvances in petroleum exploration and development · 2017
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsLost circulationDrilling fluidPetroleum engineeringDrillingRheologyWellboreFiltration (mathematics)Materials scienceMechanical engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

Compared to the water-based drilling fluids, lost circulation is more prone to occur when drilling with oil-based drilling fluids and it is difficult to deal with this problem on the drilling site for lack of high-efficiency lost circulation materials. In order to solve this technical problem, the principles of pressure resistance plugging technology of oil-based drilling fluids was firstly investigated according to the force-chain principle of granular matter mechanics, and rigid bridging particles, elastic packing particles and micro fibers could synergistically form fracture tight sealing zones with strong force-chain network to strengthen the lost circulation prevention ability of oil-based drilling fluids. Based on the principle above, novel loss-prevention materials were finally developed for oil-based drilling fluids by optimizing different plugging agents. Experimental results show that novel loss-prevention materials could be compatible with other components of oil-based drilling fluids. The oil-based drilling fluids with high sealing capacity could be optimized by adding novel loss-prevention materials, and it exhibits good rheological behavior with a low PPT filtration of 11.4 mL and excellent lost circulation prevention ability to strengthen the wellbore while drilling.

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 score0.216
Threshold uncertainty score0.543

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 communication0.0000.001
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 head0.021
GPT teacher head0.271
Teacher spread0.250 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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