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Record W3019116007 · doi:10.2118/200490-pa

A Tangent-Line Approach for Effective Density Used in the Ideal Mixing Rule: Part II—Evaluation of Mixing Characteristics of Oil/Gas Systems and Application Criteria

2020· article· en· W3019116007 on OpenAlexaff
Zehua Chen, Daoyong Yang

Bibliographic record

VenueSPE Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsMixing (physics)TangentFraction (chemistry)Line (geometry)AsphaltIdeal (ethics)ThermodynamicsChemistryMaterials scienceMathematicsPhysicsGeometryChromatographyQuantum mechanicsComposite material

Abstract

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Summary Although in Part I of this study (Chen and Yang 2020) we developed a tangent-line approach for effective density that is more general, robust, and flexible than the methods proposed by Saryazdi (2012) and Saryazdi et al. (2013), its application is only limited to heavy-oil/bitumen-associated mixtures [i.e., specifically, it has only been applied to bitumen-rich liquid phase (denoted as L2)]. As indicated in Part I, the density of nitrogen (N2)/hydrocarbon mixtures cannot be accurately predicted by using the ideal mixing rule (IM) with either real density or effective density. Not only do we need to explain and evaluate the observed deviations and patterns, but also the density prediction of solvent/Fraction 1 systems [i.e., Fraction 1 of the Athabasca bitumen, which has a molecular weight (MW) of 268.8 g/mol, as reported in Azinfar et al. (2018a, 2018b, 2018c)] needs to be improved for practical use. In this study, we evaluate the mixing characteristics of different molecules in a mixture using the tangent-line approach. By evaluating and comparing performances of the IM with effective density (IM-E) and the IM with real density (IM-R), the observed patterns and deviations together with those calculated from the Westman equation indicate that the oil/gas molecules somewhat behave like solid particles in mixing. Accordingly, we further modify the effective density used in the IM to bridge the gap between the IM-E and the IM-R. The database has been extended to light-oil/gas systems such as black oils, volatile oils, gas condensates, carbon dioxide (CO2) miscible fluids, sour gases, and wet/dry gases. The IM with modified effective density (IM-ME) has also been applied to solvent/Fraction 1 systems and the C2 or C3 or n-C4-extraction L1 phase (bitumen-related mixtures) with better accuracy. Also, we develop new criteria for the uses of the IM-E, IM-ME, and IM-R that can cover the density predictions for almost all types of oil/gas systems in the petroleum industry with high accuracy. The performances of the IM are thoroughly evaluated and compared with the volume-translated (VT) Peng-Robinson equation of state (EOS) (VT PR EOS), from which the deviations provide new insights for accurately quantifying the mixture density in a more robust and reliable manner.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.274
Teacher spread0.244 · 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
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

Citations6
Published2020
Admission routes1
Has abstractyes

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