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Record W2330113349 · doi:10.3968/7977

The Impact of Casing Damage Critical Condition on Marker Bed With Differential Internal and External Pressure in Daqing Oilfield

2016· article· en· W2330113349 on OpenAlexvenueno aff
Chaoyang Hu, Jian Hua Gao, Yazhen Liu

Bibliographic record

VenueAdvances in petroleum exploration and development · 2016
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCasingSlippageInternal pressureGeotechnical engineeringGeologyPetroleum engineeringMaterials scienceEngineeringStructural engineeringComposite material

Abstract

fetched live from OpenAlex

During long-term development of Daqing oilfield of marker bed has been affected sliding between layers, causing a large area of casing damage. The marker bed casing damage mechanism has been basically clear at present, but the influencing factors of casing damage are not yet consummate. There is fluid impact of pressure on the casing on the marker bed, and fluid pressure on the critical conditions of casing damage has not yet been carried out in research. To research the critical conditions, taking into account the mechanics characteristics of the formation and elastic-plastic the finite element model has been established, and different sets of pressure differential as critical condition impact on casing damage were studied in this paper. The results showed that the internal and external casing liquid pressure difference has little effect on the formation of critical slippage. High internal casing pressure wells, with small casing damage critical slippage, are more likely to damage.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.249
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2016
Admission routes1
Has abstractyes

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