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Record W4294843968 · doi:10.1063/5.0112855

Thermodynamically consistent modeling of immiscible gas–liquid flow in porous media

2022· article· en· W4294843968 on OpenAlexaff
Jisheng Kou, Xiuhua Wang, Amgad Salama, Yunjin Hu

Bibliographic record

VenuePhysics of Fluids · 2022
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsCompressibilityPorous mediumThermodynamicsPhysicsTwo-phase flowEquation of stateHelmholtz free energyFlow (mathematics)Compressibility factorMechanicsPorosityChemistry

Abstract

fetched live from OpenAlex

Modeling of immiscible gas–liquid two-phase flow with gas compressibility in porous media plays an important role in shale gas production, geological sequestration of carbon dioxide, and underground gas storage. The second law of thermodynamics is universally recognized as an essential principle any promising model should obey. The existing models have no proper concept of free energies for such a problem, thereby failing to obey this law. In this paper, we first introduce free energies to account for the liquid–gas capillarity effect and gas compressibility, and then using the second law of thermodynamics, we rigorously derive a thermodynamically consistent model for immiscible gas–liquid two-phase flow in porous media. The proposed free energy that describes the capillarity effect is verified by the laboratory data. For gas flow, we use molar density rather than pressure as the primary variable and take the Helmholtz free energy density determined by a realistic equation of state to characterize the gas compressibility. Numerical simulation results are also presented to demonstrate the thermodynamical consistency of the model and the applicability to simulate the liquid and gas displacement processes.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.403

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 head0.015
GPT teacher head0.212
Teacher spread0.197 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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