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Record W4318778681 · doi:10.3389/fphy.2023.1141293

Editorial: Multiphase flow behavior in the deep-stratum and deep-water wellbores

2023· editorial· en· W4318778681 on OpenAlexaff
Weiqi Fu, Jianjun Zhu, Huazhou Li, Zhiyuan Wang

Bibliographic record

VenueFrontiers in Physics · 2023
Typeeditorial
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersNational Key Research and Development Program of ChinaState Key Laboratory of Coal Resources and Safe MiningNatural Science Foundation of Jiangsu ProvinceGovernment of Jiangsu ProvinceChina University of Mining and TechnologyNational Natural Science Foundation of China
KeywordsPetroleum engineeringMultiphase flowStratumPorous mediumFlow (mathematics)Two-phase flowHeat transferHeat exchangerGeologyEnvironmental scienceMechanicsPorosityGeotechnical engineeringThermodynamics

Abstract

fetched live from OpenAlex

state, heat/mass transfer between gas and liquid, and fluid hydrodynamics in the wellbore depend highly on the temperature and pressure conditions, the multiphase flow behaviors in deep-stratum and deep-water wellbores become much more complicated. Different from the conventional oil and gas exploitation, the production of oil and gas from the and deep stratum deep-water regions involves multiple complex physical phenomena, such as phase transition (gas-liquid-solid-supercritical), particle deposition (wax, hydrate, water scale, asphaltene, etc.), changes of heat/mass transfer induced by phase change, and so on. It is, therefore, of high importance to discuss the multiphase flow behavior in the deep-stratum and deep-water wellbores. This Research Topic aims to explore recent developments in this area and focused on the following two aspects: 1) Numerical simulations of gas-liquid or liquid-solid two-phase flow in porous media, 2) Experiments and simulations of multiphase flow in the pipelines and wellbores. There are a total of 12 articles under this Research Topic.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.229
Teacher spread0.224 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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
Published2023
Admission routes1
Has abstractyes

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