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Record W3164046494 · doi:10.2118/205499-pa

Simple and Robust Algorithm for Multiphase Equilibrium Computations at Temperature and Volume Specifications

2021· article· en· W3164046494 on OpenAlexaff
Chang Lu, Zhehui Jin, Huazhou Li, Lingfei Xu

Bibliographic record

VenueSPE Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIsochoric processMultiphase flowIsobaric processComputationSIMPLE algorithmRobustness (evolution)AlgorithmIsothermal processEnhanced oil recoveryThermodynamicsComputer scienceVolume (thermodynamics)Applied mathematicsMathematical optimizationMathematicsPetroleum engineeringChemistryEngineeringPhysics

Abstract

fetched live from OpenAlex

Summary Two-phase and three-phase equilibria are frequently encountered in a variety of industrial processes, such as carbon dioxide (CO2) injection for enhanced oil recovery in oil reservoirs, multiphase separation in surface separators, and multiphase flow in wellbores and pipelines. Simulation and engineering design of these processes using isothermal/isochoric (VT) multiphase equilibrium algorithms are sometimes more convenient than that using the conventional isothermal/isobaric (PT) algorithms. This work develops a robust algorithm for VT multiphase equilibrium calculations using a nested approach. The proposed algorithm is simple because a robust PT multiphase equilibrium algorithm is used in the inner loop without any further modifications, while an effective equation-solving method (i.e., Brent’s method; Brent 1971) is applied in the outer loop to solve the pressure corresponding to a given volume/temperature specification. The robustness of the VT algorithm is safeguarded by using a highly efficient trust-region-method-based PT algorithm. We demonstrate the good performance of the newly developed algorithm by applying it to calculate the isochores of fluid mixtures that exhibit both two-phase and three-phaseequilibria.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.743
Threshold uncertainty score0.411

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 head0.021
GPT teacher head0.250
Teacher spread0.229 · 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
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

Citations17
Published2021
Admission routes1
Has abstractyes

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