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Deformation Analysis and Fracture Mechanism of Steel Casing in Oil Wells

2013· article· en· W2112154962 on OpenAlexaff
Qing Zhu, Jing Chen

Bibliographic record

VenueApplied Mechanics and Materials · 2013
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsLa Cité Collégiale
Fundersnot available
KeywordsCasingCreepDisplacement (psychology)Finite element methodDeformation (meteorology)Coupling (piping)Materials scienceFracture (geology)Porous mediumGeotechnical engineeringStructural engineeringPorosityEngineeringPetroleum engineeringComposite material

Abstract

fetched live from OpenAlex

Steel casing damage in oil wells is the primary hazards of oil production in China. There are two main reasons for the steel casing damage, one is the displacement loads on the external interface which primary come from rock creep, and another reason is temperature load on the internal surface which comes from steam injection. In this article, rock creep under fluid-solid interaction is calculated, and the displacement loads on external interface of steel casing is worked out. Then, steel casing deformation and crack propagation with temperature load is investigated. Underground rocks can be treated as porous media and rock creep is controlled by fluid-solid interaction for porous media. The method of fluid-solid interaction for finite element analysis is the calculation of two-way fluid-solid coupling for porous media; it means fully coupling between the solid and fluid solution variables. This method is analyzed and the displacement load is worked out as an example application. Finite element model is constructed; deformation and crack propagation of steel casing under multiple actions of outer loads and inner temperature load are calculated. Finally, fracture mechanism of steel casing in oil wells is analyzed, and some advice is proposed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.430

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 head0.003
GPT teacher head0.160
Teacher spread0.157 · 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 designBench or experimental
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
Published2013
Admission routes1
Has abstractyes

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