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Record W4386448494 · doi:10.2118/217451-pa

Constructing Three-Phase Envelopes Using a Trust-Region-Based Algorithm

2023· article· en· W4386448494 on OpenAlexaff
Lingfei Xu, Zhuo Chen, Sirui Li, Huazhou Li

Bibliographic record

VenueSPE Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAlgorithmSolverConstruct (python library)Robustness (evolution)Phase (matter)AsphalteneComputer scienceEnvelope (radar)Mathematical optimizationMathematicsEngineeringChemistry

Abstract

fetched live from OpenAlex

Summary We develop a new trust-region (TR)-based algorithm to construct complete three-phase envelopes for reservoir fluid mixtures. The new algorithm is developed based on a basic algorithm for two-phase envelope constructions (Xu and Li 2023). A state-of-the-art TR method with a realistic exact subproblem solver is implemented in the algorithm, and an integrated strategy is adopted to construct complete phase envelopes with two-phase and three-phase branches. We test the performance of the TR-based algorithm by constructing multiphase envelopes for a total of 15 fluid mixtures that include hydrocarbons-CO2, hydrocarbons-asphaltene, and hydrocarbons-water mixtures. Comparison against the conventional Newton-based algorithm indicates that the TR-based algorithm leads to a much higher computational efficiency and an enhanced robustness.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.984
Threshold uncertainty score0.688

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 head0.029
GPT teacher head0.288
Teacher spread0.259 · 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 designOther design
Domainnot available
GenreMethods

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

Citations3
Published2023
Admission routes1
Has abstractyes

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