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Record W2971561432 · doi:10.1002/aic.16783

Investigation of the dynamic capillary pressure during displacement process in fractured tight rocks

2019· article· en· W2971561432 on OpenAlexaff
Ying Li, Haitao Li, Shengnan Chen, Hongwen Luo, Chang Liu

Bibliographic record

VenueAIChE Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
FundersSouthwest Petroleum UniversityChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsCapillary pressureSaturation (graph theory)Capillary actionPorous mediumDisplacement (psychology)MechanicsGeologyPermeability (electromagnetism)Multiphase flowMatrix (chemical analysis)Relative permeabilityTight gasPetroleum engineeringGeotechnical engineeringMineralogyMaterials sciencePorosityChemistryComposite materialHydraulic fracturingMathematicsPhysics

Abstract

fetched live from OpenAlex

Abstract This work investigates the dynamic capillary pressure during the displacement process in fractured tight rocks, through specially designed experiments on fractured and intact core samples. The dynamic capillarity coefficient of matrix and the multiphase flow behaviors are also obtained. Results have shown that the dynamic capillary pressure of the matrix becomes around 5–20% higher after the fracturing treatment. The lower and less variable values of dynamic capillarity coefficient of matrix illustrate a weakened dynamic effect and a more uniform displacement front. Moreover, time derivative of water saturation is increased significantly with fractures. Finally, oil relative permeability after fracturing is lower than its value of the intact core at high water saturations. A dynamic capillarity coefficient model for matrix, which includes the influence of fractures, is derived and verified with the average R 2 more than 0.95. This wok helps to understand and predict multiphase flow in fractured tight porous media.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.279

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 head0.003
GPT teacher head0.195
Teacher spread0.192 · 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

Citations31
Published2019
Admission routes1
Has abstractyes

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